stroop experiment procedure

Stroop Effect Experiment in Psychology

Charlotte Ruhl

Research Assistant & Psychology Graduate

BA (Hons) Psychology, Harvard University

Charlotte Ruhl, a psychology graduate from Harvard College, boasts over six years of research experience in clinical and social psychology. During her tenure at Harvard, she contributed to the Decision Science Lab, administering numerous studies in behavioral economics and social psychology.

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

The Stroop effect is a psychological phenomenon demonstrating interference in reaction time of a task. It occurs when the name of a color is printed in a color not denoted by the name, making it difficult for participants to identify the color of the word quickly and accurately.

Take-home Messages

• In psychology, the Stroop effect is the delay in reaction time between automatic and controlled processing of information, in which the names of words interfere with the ability to name the color of ink used to print the words.
• The Stroop test requires individuals to view a list of words printed in a different color than the word’s meaning. Participants are tasked with naming the color of the word, not the word itself, as fast as they can.
• For example, when presented with the word “green” written in red ink, it is much easier to name the word that is spelled instead of the color ink in which the word is written.
• The interference, or the delay in response time, is measured by comparing results from the conflict condition (word and color mismatch) to a neutral condition (e.g., a block of color or a color word with matching ink). Subtracting the results from these two conditions helps to eliminate the influence of general motor responses.
• Reading, a more powerful automatic process, takes some precedence over color naming, which requires higher cognitive demands.
• Since psychologist John Ridley Stroop first developed this paradigm back in 1935, the Stroop task has since been modified to help understand additional brain mechanisms and expanded to aid in brain damage and psychopathology research.

What Is The Stroop Effect?

The Stroop effect refers to a delay in reaction times between congruent and incongruent stimuli (MacLeod, 1991).

Congruency, or agreement, occurs when a word’s meaning and font color are the same. For example, if the word “green” is printed in green.

Incongruent stimuli are just the opposite. That is the word’s meaning and the color in which it is written do not align. For example, the word “green” might be printed in red ink.

The Stroop task asks individuals to name the color of the word instead of reading the word itself.

The delay in reaction time reveals that it is much harder to name the color of a word when the word itself spells another color (the incongruent stimuli) than it is to name the color of the word when the word itself spells that same color (the congruent stimuli).

The First Stroop Experiment

The Stroop effect was first published in 1935 by American psychologist John Ridley Stroop, although discoveries of this phenomenon date back to the nineteenth century (Stroop, 1935).

Building off previous research, Stroop had two main aims in his groundbreaking paper:

• To examine how incongruency between the color of the word and the word’s content will impair the ability to name the color.
• To measure what effect practicing reacting to color stimuli in the presence of conflicting word stimuli would have upon the reaction times.

To empirically study these two major aims, Stroop ran three different experiments:

1) Experiment 1 :

Participants (70 college undergraduates) were tasked with reading the word aloud, irrespective of its color. In other words, participants must read aloud the word “green” even if written in a different color.

2) Experiment 2 :

The second experiment was the opposite of the first. Participants (100 college students) were first asked to name the color of individual squares (instead of the color of words) as a training mechanism for the subsequent task. Afterward, participants had to say the color of the word, regardless of its meaning – the opposite of the experiment 1 procedure.

3) Experiment 3 :

The third and final experiment integrated all of the previously mentioned tests with an undergraduate population of 32 participants.

The independent variable (IV) was the congruency of the font name and color.

• Congruent (word name and font color are the same)
• Incongruent (word name and font color are different)

The dependent variable (DV) was reaction time (ms) in reporting the letter color.

After running the three experiments, Stroop drew two main conclusions:

• The interference of conflicting word stimuli upon the time for naming colors caused an increase of 47.0 seconds or 74.3 percent of the normal time for naming colors printed in just squares.
• The interference of conflicting color stimuli upon the time for reading words caused an increase of only 2.3 seconds or 5.6 percent over the normal time for reading the same words printed in black.

These tests demonstrate a disparity in the speed of naming colors and reading the names of colors, which may be explained by a difference in training in the two activities.

The word stimulus has been associated with the specific response “to read,” while the color stimulus has been associated with various responses: “to admire,” “to name,” etc.

The observed results might reflect the fact that people have more experience consciously reading words than consciously labeling colors, illustrating a difference in the mechanisms that control these two processes.

How the Stroop Effect Works

Why does the Stroop effect occur? We can tell our brain to do lots of things – store memories, sleep, think, etc. – so why can’t we tell it to do something as easy as naming a color? Isn’t that something we learn to do at a very young age?

Researchers have analyzed this question and come up with multiple different theories that seek to explain the occurrence of the Stroop effect (Sahinoglu & Dogan, 2016).

Speed of processing theory:

The processing speed theory claims that people can read words much faster than they can name colors (i.e., word processing is much faster than color processing).

When we look at the incongruent stimuli (the word “green” printed in red, for example), our brain first reads the word, making it much more difficult to then have to name the color.

As a result, a delay occurs when trying to name the color because doing so is not our brain’s first instinct (McMahon, 2013).

Selective attention theory:

The theory of selective attention holds that recognizing colors, compared to reading words, requires more attention.

Because of this, the brain needs to use more attention when attempting to name a color, making this process take slightly longer (McMahon, 2013).

Automaticity:

A prevalent explanation for the Stroop effect is the automatic nature of reading. When we see a word, its meaning is almost instantly recognized. Thus, when presented with a conflicting color, there’s interference between the automatic reading process and the task of naming the ink color.

This theory argues that recognizing colors is not an automatic process , and thus there is a slight hesitancy when carrying out this action.

Automatic processing is processed in the mind that is relatively fast and requires few cognitive resources.

This type of information processing generally occurs outside of conscious awareness and is common when undertaking familiar and highly practiced tasks.

However, the brain is able to automatically understand the meaning of a word as a result of habitual reading (think back to Stroop’s initial study in 1935 – this theory explains why he wanted to test the effects of practice on the ability to name colors).

Word reading, being more automatic and faster than color naming, results in involuntary intrusions during the color-naming task. Conversely, reading isn’t affected by the conflicting print color.

Researchers in support of this theory posit that automatic reading does not need controlled attention but still uses enough of the brain’s attentional resources to reduce the amount left for color processing (Monahan, 2001).

In a way, this parallels the brain’s dueling modes of thinking – that of “System 1” and “System 2.” Whereas the former is more automatic and instinctive, the latter is slower and more controlled (Kahneman, 2011).

This is similar to the Stroop effect, in which we see a more automatic process trying to dominate over a more deliberative one. The interference occurs when we try to use System 2 to override System 1, thus producing that delay in reaction time.

Parallel distributed processing:

The fourth and final theory proposes that unique pathways are developed when the brain completes different tasks. Some of these pathways, such as reading words, are stronger than others, such as naming colors (Cohen et al., 1990).

Thus, interference is not an issue of processing speed, attention, or automaticity but rather a battle between the stronger and weaker neural pathways.

Additional Research

John Ridley Stroop helped lay the groundwork for future research in this field.

Numerous studies have tried to identify the specific brain regions responsible for this phenomenon, identifying two key regions: the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLFPC).

Both MRI and fMRI scans show activity in the ACC and DLPFC while completing the Stroop test or related tasks (Milham et al., 2003).

The DLPFC assists with memory and executive functioning, and its role during the task are to activate color perception and inhibit word encoding. The ACC is responsible for selecting the appropriate response and properly allocating attentional resources (Banich et al., 2000).

Countless studies that repeatedly test the Stroop effect reveal a few key recurring findings (van Maanen et al., 2009):

• Semantic interference : Naming the ink color of neutral stimuli (where the color is only shown in blocks, not as a written word) is faster than incongruent stimuli (where the word differs from its printed color).
• Semantic facilitation : Naming the ink of congruent stimuli (where the word and its printed color are in agreement) is faster than for neutral stimuli.
• Stroop asynchrony : The previous two findings disappear when reading the word, not naming the color, is the task at hand – supporting the claim that it is much more automatic to read words than to name colors.

Other experiments have slightly modified the original Stroop test paradigm to provide additional findings.

One study found that participants were slower to name the color of emotion words as opposed to neutral words (Larsen et al., 2006).

Another experiment examined the differences between participants with panic disorder and OCD. Even with using threat words as stimuli, they found that there was no difference among panic disorder, OCD, and neutral participants’ ability to process colors (Kampman et al., 2002).

A third experiment investigated the relationship between duration and numerosity processing instead of word and color processing.

Participants were shown two series of dots in succession and asked either (1) which series contained more dots or (2) which series lasted longer from the appearance of the first to the last dots of the series.

The incongruency occurred when fewer dots were shown on the screen for longer, and a congruent series was marked by a series with more dots that lasted longer.

The researchers found that numerical cues interfered with duration processing. That is, when fewer dots were shown for longer, it was harder for participants to figure out which set of dots appeared on the screen for longer (Dormal et al., 2006).

Thus, there is a difference between the processing of numerosity and duration. Together, these experiments illustrate not only all of the doors of research that Stroop’s initial work opened but also shed light on all of the intricate processing associations that occur in our brains.

Other Uses and Versions

The purpose of the Stroop task is to measure interference that occurs in the brain. The initial paradigm has since been adopted in several different ways to measure other forms of interference (such as duration and numerosity, as mentioned earlier).

Additional variations measure interference between picture and word processing, direction and word processing, digit and numerosity processing, and central vs. peripheral letter identification (MacLeod, 2015).

The below figure provides illustrations for these four variations:

The Stroop task is also used as a mechanism for measuring selective attention, processing speed, and cognitive flexibility (Howieson et al., 2004).

The Stroop task has also been utilized to study populations with brain damage or mental disorders, such as dementia, depression, or ADHD (Lansbergen et al., 2007; Spreen & Strauss, 1998).

For individuals with depression, an emotional Stroop task (where negative words, such as “grief,” “violence,” and “pain,” are used in conjunction with more neutral words, such as “clock,” “door,” and “shoe”) has been developed.

Research reveals that individuals who struggle with depression are more likely to say the color of a negative word slower than that of a neutral word (Frings et al., 2010).

The versatility of the Stroop task paradigm lends itself to be useful in a wide variety of fields within psychology. What was once a test that only examined the relationship between word and color processing has since been expanded to investigate additional processing interferences and to contribute to the fields of psychopathology and brain damage.

The development of the Stroop task not only provides novel insights into the ways in which our brain mechanisms operate but also sheds light on the power of psychology to expand and build on past research methods as we continue to uncover more and more about ourselves.

Critical Evaluation

Dishon-Berkovits and Algom (2000) argue that the Stroop effect is not a result of automatic processes but is due to incidental correlations between the word and its color across stimuli.

They suggest that participants unconsciously recognize these correlations, using word cues to anticipate the correct color hue they should name.

When testing with word-word stimuli, Dishon-Berkovits and Algom created positive, negative, and zero correlations.

They observed that zero correlations nearly eliminated Stroop effects, implying that the effects might be more about the way stimuli are presented rather than true indicators of automaticity or attention.

However, their methodology raised concerns:

• They had difficulty creating zero correlations with color-hue situations.
• Their study didn’t include a neutral condition, which means interference and facilitation were not examined.
• There’s a general finding that facilitation effects are smaller than interference effects, which their findings don’t necessarily support

Despite these considerations, the correlational approach does not invalidate Stroop’s original paradigm or the many studies based on it.

Stroop-based findings have been instrumental in understanding various clinical conditions like anxiety, schizophrenia, ADHD, dyslexia, PTSD, racial attributions, and others.

The takeaway is that while the theory proposed by Dishon-Berkovits and Algom introduces a fresh perspective, it does not negate the established findings and implications of the Stroop effect.

Instead, it encourages a deeper examination of how automaticity and attention might be influenced by certain environmental factors and correlations.

Describe why the Stroop test is challenging for us.

The Stroop test is challenging due to the cognitive conflict it creates between two mental processes: reading and color recognition. Reading is a well-learned, automatic process, whereas color recognition requires more cognitive effort.

When the word’s color and its semantic meaning don’t match, our brain’s automatic response to reading the word interferes with naming the color, causing a delay in response time and an increase in mistakes. This is known as the Stroop effect.

Banich, M. T., Milham, M. P., Atchley, R., Cohen, N. J., Webb, A., Wszalek, T., … & Magin, R. (2000). fMRI studies of Stroop tasks reveal unique roles of anterior and posterior brain systems in attentional selection . Journal of cognitive neuroscience, 12 (6), 988-1000.

Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: a parallel distributed processing account of the Stroop effect . Psychological Review, 97 (3), 332.

Dishon-Berkovits, M., & Algom, D. (2000). The Stroop effect: It is not the robust phenomenon that you have thought it to be .  Memory & Cognition ,  28 , 1437-1449.

Dormal, V., Seron, X., & Pesenti, M. (2006). Numerosity-duration interference: A Stroop experiment . Acta psychologica, 121 (2), 109-124.

Frings, C., Englert, J., Wentura, D., & Bermeitinger, C. (2010). Decomposing the emotional Stroop effect . Quarterly journal of experimental psychology, 63 (1), 42-49.

Howieson, D. B., Lezak, M. D., & Loring, D. W. (2004). Orientation and attention. Neuropsychological assessment , 365-367.

Kahneman, D. (2011). Thinking, fast and slow . Macmillan.

Kampman, M., Keijsers, G. P., Verbraak, M. J., Näring, G., & Hoogduin, C. A. (2002). The emotional Stroop: a comparison of panic disorder patients, obsessive–compulsive patients, and normal controls, in two experiments. Journal of anxiety disorders, 16 (4), 425-441.

Lansbergen, M. M., Kenemans, J. L., & Van Engeland, H. (2007). Stroop interference and attention-deficit/hyperactivity disorder: a review and meta-analysis . Neuropsychology, 21 (2), 251.

Larsen, R. J., Mercer, K. A., & Balota, D. A. (2006). Lexical characteristics of words used in emotional Stroop experiments . Emotion, 6 (1), 62.

MacLeod, C. M. (1991). Half a century of research on the Stroop effect: an integrative review . Psychological bulletin, 109 (2), 163.

MacLeod, C. M. (2015). The stroop effect. Encyclopedia of Color Science and Technology.

McMahon, M. (2013). What Is the Stroop Effect. Retrieved November, 11 .

Milham, M. P., Banich, M. T., Claus, E. D., & Cohen, N. J. (2003). Practice-related effects demonstrate complementary roles of anterior cingulate and prefrontal cortices in attentional control . Neuroimage, 18 (2), 483-493.

Monahan, J. S. (2001). Coloring single Stroop elements: Reducing automaticity or slowing color processing? . The Journal of general psychology, 128 (1), 98-112.

Sahinoglu B, Dogan G. (2016). Event-Related Potentials and the Stroop Effect. Eurasian J Med , 48(1), 53‐57.

Spreen, O., & Strauss, E. (1998). A compendium of neuropsychological tests: Administration, norms, and commentary . Oxford University Press.

Stroop, J. R. (1935). Studies of interference in serial verbal reactions . Journal of experimental psychology, 18 (6), 643.

van Maanen, L., van Rijn, H., & Borst, J. P. (2009). Stroop and picture—word interference are two sides of the same coin . Psychonomic bulletin & review, 16 (6), 987-999.

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What the stroop effect reveals about our minds.

The Stroop effect is a simple phenomenon that reveals a lot about how the how the brain processes information.

The Stroop effect is a simple phenomenon that reveals a lot about how the how the brain processes information. First described in the 1930s by psychologist John Ridley Stroop, the Stroop effect is our tendency to experience difficulty naming a physical color when it is used to spell the name of a different color. This simple finding plays a huge role in psychological research and clinical psychology.

The Original Stroop Experiments

In Stroop’s original study, he used three elements: names of colors printed in black ink, names of colors printed in different ink than the color named, and squares of each given color. He then conducted his experiment in two parts:

• In his first experiment, he asked participants to simply read the color printed in black ink. He then asked them to read the words printed, regardless of the color they were printed in.
• For his second experiment, he asked participants to name the ink color instead of the word written. For example, “red” might have been printed in green and participants were asked to identify the color green instead of reading the word “red.” In this segment, participants were also asked to identify the color of the squares.

Stroop found that subjects took longer to complete the task of naming the ink colors of words in experiment two than they took to identify the color of the squares. Subjects also took significantly longer to identify ink colors in experiment two than they had to simply read the printed word in experiment one. He identified this effect as an interference causing a delay in identifying a color when it is incongruent with the word printed.

The Stroop Test

The discovery of the Stroop effect led to the development of the Stroop test. According to an article in Frontiers in Psychology, the Stroop test is used in both experimental and clinical psychology to “assess the ability to inhibit cognitive interference that occurs when processing of a specific stimulus feature impedes the simultaneous processing of a second stimulus attribute.”

In short, the Stroop test, a simplified version of the original experiment, presents incongruent information to subjects by having the color of a word differ from the word printed. The Stroop test can be used to measure a person’s selective attention capacity and skills, processing speed, and alongside other tests to evaluate overall executive processing abilities.

Explanations for the Stroop Effect

A few theories have emerged about why the Stroop effect exists, though there is not widespread agreement about the cause of the phenomenon. Some reasons proposed for the Stroop effect include:

• Selective Attention Theory: According to the second edition of the “Handbook of Psychology,” selective attention chooses “which information will be granted access to further processing and awareness and which will be ignored.” In relation to the Stroop effect, identifying the color of the words takes more attention than simply reading the text. Therefore, this theory suggests that our brains process the written information instead of the colors themselves.
• Automaticity Theory: Our two types of cognitive processing include automatic and controlled thinking. In relation to the Stroop effect, the brain likely reads the word because reading is more of an automated process than recognizing colors.
• Speed of Processing Theory : Simply stated, this theory for the cause of the Stroop effect posits we can process written words faster than we can process colors. Thus, it is difficult to identify the color once we’ve already read the word.
• Parallel Distributed Processing: This theory suggests the brain creates different pathways for different tasks. Therefore, it’s the strength of the pathway that plays an important role in which is easier to name, the color or the text.

Psychologists continue to research the Stroop effect to find the underlying cause for the phenomenon, although many factors have been identified that affect results. For example, some variations in the severity of the Stroop effect are found in women and men. Stroop himself first noted that women experience shorter interruptions than men. Studies have also typically found that older people show longer delays than younger people.

The Impact of the Stroop Effect

It may seem as though the Stroop effect is just a fascinating experiment with no real effect on human psychology. In truth, it illustrates a lot about the way we process information and helps us assess our ability to override our instinctual fast thinking. A study published in the Psychological Review stated, “The effects observed in the Stroop task provide a clear illustration of people’s capacity for selective attention and the ability of some stimuli to escape attentional control.”

The Journal of Experimental Psychology reported that Stroop’s article introducing this phenomenon was among the most cited of the articles they’ve published in their first 100 years. In 2002 as part of its centennial issue , it stated “More than 700 studies have sought to explain some nuance of the Stroop effect; thousands of others have been directly or indirectly influenced by Stroop’s article.”

While the Stroop test is interesting, it also has incredible uses in the world of psychology and the study of the brain. According to a study published on the National Center for Biotechnology Information, the Stroop test is valuable when assessing interference control and task-set coordinating in adults with ADHD . Also, a study published in 1976 found that it was 88.9 percent accurate in distinguishing between clients who had suffered brain damage and those who had not. Later studies confirmed these findings, and the Stroop test is often used to assess selective attention in traumatic brain injury patients .

Multiple studies, including the original experiments by Stroop, suggest that practice can decrease Stroop inference. This has implications for our learning skills, ability to multitask, and how we form habits. Psychologist and economist, Daniel Kahneman explored this concept in his book “Thinking, Fast and Slow.” Our fast thinking, what he refers to as System 1, is our initial, automatic reaction to things we encounter.

Kahneman wrote, “When System 1 runs into difficulty, it calls on System 2 to support more detailed and specific processing that may solve the problem of the moment.” When it comes to the Stroop effect, System 1 (our automatic, fast thinking) seeks to find the quickest pattern available. Kahneman believes by understanding how our brains make connections, we can overcome them to reach more logical conclusions by calling on System 2, our controlled thinking, quicker.

Exploring the Stroop effect continues to play a role in studies and experiments involving automatic and controlled thinking, selective attention, our cognitive processing, and more. Even though the Stroop effect has never been definitively explained, it provides a tried and true benchmark for psychologists and scientists that has been referred to for many years.

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How the Stroop Effect Works

Naming a Color but Not the Word

Performing Your Own Stroop Test

Terms and key questions, frequently asked questions.

The Stroop effect is a phenomenon that occurs when the name of a color doesn't match the color in which it's printed (e.g., the word "red" appears in blue text rather than red). In such a color test (aka a Stroop test or task), you'd likely take longer to name the color (and be more likely to get it wrong) than if the color of the ink matched the word.

Although it might sound simple, the Stroop effect refers to the delayed reaction times when the color of the word doesn't match the name of the word. It's easier to say the color of a word if it matches the semantic meaning of the word.

For example, if someone asked you to say the color of the word "black" that was also printed in black ink, it would be much easier to say the correct color than if it were printed in green ink.

The task demonstrates the effect that interference can have when it comes to reaction time. It was first described during the 1930s by American psychologist John Ridley Stroop for whom the phenomenon is named. His original paper describing the effect has become one of the most famous, as well as one of the most frequently cited, in the history of psychology. The effect has been replicated hundreds of times by other researchers.

For students of psychology looking for a relatively easy and interesting experiment to try on their own, replicating the Stroop effect can be a great option.

Theories of the Stroop Effect

Researchers don't yet know why words interfere with naming a color in this way, but researchers have proposed several theories:

• Selective attention theory : According to this theory, naming the actual color of the words requires much more attention than simply reading the text.
• Speed of processing theory : This theory states that people can read words much faster than they can name colors. The speed at which we read makes it much more difficult to name the color of the word after we've read the word.
• Automaticity :   This theory proposes that automatic reading doesn't require focused attention . Instead, the brain simply engages in it automatically. Recognizing colors, on the other hand, may be less of an automated process. While the brain registers written meaning automatically, it does require a certain amount of attentional resources to process color, making it more difficult to process color information and therefore slowing down reaction times.
• Parallel Distributed Processing : Word recognition is an unconscious process that's better described as "contextually controlled" rather than automatic.

Other Uses of the Stroop Test

Over time, researchers have altered the Stroop test to help study populations with brain damage and mental disorders such as dementia, depression, and attention-deficit/ hyperactivity disorder (ADHD).

For example, in studying people with depression, researchers present negative words such as "grief" and pain" along with neutral words such as "paper" and "window." Typically, these people speak the color of a negative word more slowly than they do a neutral word.

The original Stroop test included two parts. In the first, the written color name is printed in a different color of ink, and the participant is asked to speak the written word. In the second, the participant is asked to name the ink color.

There are a number of different approaches you could take in conducting your own Stroop effect experiment.

• Compare reaction times among different groups of participants. Have a control group say the colors of words that match their written meaning. Black would be written in black, blue written in blue, etc. Then, have another group say the colors of words that differ from their written meaning. Finally, ask a third group of participants to say the colors of random words that don't relate to colors. Then, compare your results.
• Try the experiment with a young child who has not yet learned to read. How does the child's reaction time compare to that of an older child who has learned to read?
• Try the experiment with uncommon color names, such as lavender or chartreuse. How do the results differ from those who were shown the standard color names?

Before you begin your experiment, you should understand these concepts:

• Selective attention : This is the way we focus on a particular item for a selected period of time.
• Control group : In an experiment, the control group doesn't receive the experimental treatment. This group is extremely important when comparing it to the experimental group to see how or if they differ. 
• Independent variable : This is the part of an experiment that's changed. In a Stroop effect experiment, this would be the colors of the words. 
• Dependent variable : The part of an experiment that's measured. In a Stroop effect experiment, it would be reaction times.
• Other variables :   Consider what other variables might impact reaction times and experiment with those.

The Stroop test helps researchers evaluate the level of your attention capacity and abilities, and how fast you can apply them. It's particularly helpful in assessing attention-deficit/hyperactivity disorder (ADHD) and executive functioning in people with traumatic brain injuries (TBIs).

The Stroop test helps researchers measure the part of the brain that handles planning, decision-making, and dealing with distraction.

There are many possible combinations of scores on the first and second tasks. They might indicate speech problems, reading skill deficits, brain injury. color blindness, emotional upset, or low intelligence. Likewise, they might mean that your brain is able to handle conflicting information well and has adequate cognitive adaptability and skills.

Stroop JR.  Studies of interference in serial verbal reactions . J. Exp. Psychol. Gen. 1935;18;643-662. doi:10.1037/h0054651

Sahinoglu B, Dogan G. Event-Related Potentials and the Stroop Effect .  Eurasian J Med . 2016;48(1):53‐57. doi:10.5152/eurasianjmed.2016.16012

Besner D, Stolz JA. Unconsciously controlled processing: the stroop effect reconsidered .  Psychonomic Bulletin & Review . 1999;6(3):449-455. doi:10.3758/BF03210834

Frings C, Englert J, Wentura D, Bermeitinger C. Decomposing the emotional Stroop effect .  Quarterly Journal of Experimental Psychology . 2010;63(1):42-49. doi:10.1080/17470210903156594

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

The Stroop Effect

You might have seen this exercise before in a workbook or museum. You see a list of colors, but each word is also a different color. For example, the word “red” might be written in blue font, or the word “yellow” might be written in purple font. The exercise says you must go through the list and say the font color, not the word written before you.

This isn’t easy! Most people experience a delayed reaction while trying to complete this activity. They are also more likely to get answers wrong than if they had to read the words aloud.

This test is called “The Stroop Test.” The paper describing the test, as well as The Stroop Effect, is one of the most famous papers in psychology.

What is the Stroop Effect?

The Stroop Effect is a phenomenon that describes delayed reaction time that occurs when the brain is faced with two different types of stimuli. Reading the word and recognizing the color “race” through the brain helps us complete the task.

​ Unfortunately, in the original Stroop test, it is believed that our minds read words faster than they recognize colors. We may flub or accidentally say the word on the page rather than the color of the word because we read the word first.

This phenomenon is named after John Ridley Stroop , although some experts say he is not the man who discovered it. Stroop conducted experiments with participants, including the Stroop Test I mentioned earlier, and shared his findings in 1935. After his experiments showed that participants spent a longer time recognizing color names when they didn’t match up with the words on the screen, psychologists created different versions of the experiment and theorized why this phenomenon exists.

Reasons Behind the Stroop Effect

There are a few theories as to why we have a hard time recognizing the colors in the Stroop Test.

One common theory is that our brains process words faster out of habit. This theory is that of the speed of processing theory. It just takes longer to recognize and name colors.

Another theory has looked at the possibility of parallel distributed processing. When we learn different information, we create pathways in our brains. Some of these pathways are stronger than others. Psychologists believe that the pathways we’ve created to process the meaning of words may be stronger than the ones we’ve created to identify colors.

Other psychologists say that the delayed response comes from the fact that our brains automatically start to process the meaning of the words before us but don’t do the same with the font color. This is the automaticity theory. When we see words in front of us, we automatically read them. We don’t do the same when we see colors in front of us. Wouldn’t it be exhausting to look around the room and process and name all of the colors you see in front of you? You don't look at the grass and think "green", however, when you open a book, you say the words in your head.

Do you think someone who did not know how to read (or did not know how to read in English) would be able to name the colors of the fonts faster?

Yet another theory goes back to a phenomenon that we described in an earlier video. The selective attention theory describes how our brain decides what information is important to pay attention to. When we take in stimuli, we “filter” or “turn down the volume” out the stimuli that do not need our attention. But what happens when we filter out the wrong information and have to go back and look at the stimuli again?

Utilizing the Stroop Effect in Neurorehabilitation

The Stroop Effect, beyond its significance in cognitive psychology and its demonstration of the interplay between attention and automaticity, has therapeutic potential, especially in neurorehabilitation. Here's how the Stroop Effect can be utilized to assist stroke patients and those with brain injuries:

• Cognitive Assessment: The Stroop Test can be employed as a diagnostic tool to assess cognitive function and identify potential processing issues in brain injury patients. A patient's performance on the test can highlight impairments in selective attention, cognitive flexibility, and processing speed.
• Mapping Recovery: By administering the Stroop Test periodically, clinicians can track the progress of a patient's cognitive recovery, adjusting therapeutic strategies accordingly.
• Stimulating the Brain: The Stroop Test inherently challenges the brain by presenting conflicting stimuli. This stimulates brain regions responsible for processing visual and linguistic information, which can help promote neural regeneration and functional recovery.
• Adaptive Difficulty: The test can be modified by increasing the presentation speed, adding more color-word discrepancies, or introducing other variations. This allows therapists to customize the challenge based on the patient's cognitive capabilities, ensuring optimal stimulation without overwhelming them.
• Training Selective Attention: Repeated exposure to the Stroop task can help patients improve their ability to focus on specific stimuli (color) while ignoring distractors (word meaning).
• Enhancing Cognitive Flexibility: By frequently switching the required task (e.g., from naming the color in one round to reading the word in the next), patients can enhance their ability to shift between different tasks or mental sets, a skill often impaired in brain injuries.
• Immediate Feedback: The Stroop Test provides instant feedback. Errors during the test offer immediate insights into processing lapses, allowing patients and therapists to address issues in real-time.
• Progress Tracking: Seeing improvement over time, as tasks become easier or reaction times decrease, can serve as a motivational tool for patients, encouraging them to participate in their rehabilitation journey actively.
• Integrated with Virtual Reality (VR): With technological advances, the Stroop Test can be incorporated into VR platforms. This not only provides an immersive experience for the patient but also allows for diverse and complex environments that can further challenge and stimulate cognitive functions.
• Group Therapy: The collaborative nature of group sessions can make the Stroop Test more engaging. Patients can work in teams, fostering a sense of community and collective achievement.

While the Stroop Effect is an intriguing cognitive phenomenon, its implications far beyond mere academic interest. By harnessing the principles underlying the Stroop Effect, clinicians can devise innovative therapeutic strategies to aid in the recovery and rehabilitation of stroke and brain injury patients. As with all therapeutic tools, the effectiveness of the Stroop Test in rehabilitation should be continually assessed and tailored to individual patient needs.

Variations of the Stroop Effect

The Stroop Effect continues to be one of the more fascinating and fun phenomena for psychologists, young and old. Many psychology students have tweaked the original experiment to show how the brain might get confused or work more slowly when faced with similar challenges.

One of my favorite variations of this experiment is to change the list of words to words that aren’t colors. Examples are the word “microphone” in red font or the word “suitcase” in blue font. The directions are the same: list the font colors rather than the word.

This can be even more frustrating than the original Stroop test!

Get creative and make some of your versions of the Stroop test. Use different font colors, images, font sizes, or other types of stimuli to trick the brain and stump participants (if only for a moment.) Who knows, maybe you’ll uncover a different side to the Stroop effect that hasn’t been introduced to psychology before!

Related posts:

• The Psychology of Long Distance Relationships
• Operant Conditioning (Examples + Research)
• Beck’s Depression Inventory (BDI Test)
• Attention (Psychology Theories)
• Variable Interval Reinforcement Schedule (Examples)

Reference this article:

About The Author

• Selective Attention Theories
• Invisible Gorilla Experiment
• Cocktail Party Effect
• Stroop Effect
• Multitasking
• Inattentional Blindness

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Stroop effect

The Stroop task

The stroop effect, in pictures, why is the effect interesting, do it yourself, ideas for home work, test if you understood this lesson, reading material, introduction.

The Stroop effect is one of the best known phenomena in cognitive psychology. The Stroop effect occurs when people do the Stroop task, which is explained and demonstrated in detail in this lesson. The Stroop effect is related to selective attention , which is the ability to respond to certain environmental stimuli while ignoring others.

In the Stroop task, people simply look at color words, such as the words "blue", "red", or "green". The interesting thing is that the task is to name the color of the ink the words are printed in, while fully ignoring the actual word meaning. It turns out that this is quite difficult, and you can find out exactly how difficult this is below.

It is very easy to name the color of the word "black" when it is printed in black (most text is written in black ink). It is also very easy to name the color of the word "red" printed in red ink color.

It is difficult, though, when the word and the ink color are different! This extent of this difficulty is what we call the Stroop effect .

As you now understand, the Stroop effect is the degree of difficulty people have with naming the color of the ink rather than the word itself. In Stroop’s words, there is so-called "interference" between the color of the ink and the word meaning. This interference occurs no matter how hard you try, which means that it is uncontrollable with the best conscious effort. It implies that at least part of our information processing occurs automatically. It happens, whether you want it or not! Do you think this is true? If you think it is not true, how can you test this? Could you argue that if you train yourself long enough, you would no longer show the Stroop effect?

In the original study by Stroop, people were shown a list of words printed in different colors. They were asked to name the ink color, and to ignore the meaning of the word. It turned out that people were slower and made more mistakes when there was a clash between the word meaning and the ink color (e.g., the word "green" in red ink color).

This effect is quite surprising. The task is surpringly more difficult than you would think when you just read about the Stroop task. Something that is surprising is interesting, because it forces you to think: Hey, why is this happening? It is not as easy as I had expected!

One of the explanations for the difficulty is that we are so used to processing word meaning while ignoring the physical features of words, that it is a learned response. The Stroop task requires us to do something which we have never learned and which is opposite what we normally do. MacLeod’s 1991 paper is still an excellent overview of about the Stroop task (although already more than 2 decades old).

Here is an image on how I recommend to put your fingers on the keyboard:

Click here to run a demo of the Stroop task

Which colors did Stroop use in his experiments? Why?

Read the description of the original experiments and describe how they differed from the current experiment.

Give at least three examples of automatic visual processing in daily life.

Do you get better at the task with training? Does your Stroop effect get smaller? Can you get rid of it altogether with training?

What would happen if the task is carried out by someone who does not know any English?

If you can answer the questions below, you have a good grasp of the lessons.

Easy questions:

Question: What is the Stroop effect?

Question: Why is it called the "Stroop" effect?

More difficult questions:

Question: What is "interference"?

Question: In what sort of units is the Stroop effect measured?

Question: Does it matter what colors are used in the Stroop task?

Question: A German with no knowledge does the English Stroop task, what would happen?

Very difficult questions:

Question: Why do we use the same stimuli over and over?

Question: Would it be possible to overcome the Stroop effect with enough training?

Stroop, J.R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18 , 643-662. Read this original paper online.

MacLeod, C. M. (1991). Half a century of research on the Stroop: An integrative review. Psychological Bulletin, 109 , 163-203.

What is the Stroop Effect and how does it impact cognitive processing?

The Stroop Effect is a phenomenon in psychology that demonstrates the interference between automatic and controlled cognitive processes. It was first described by John Ridley Stroop in 1935 and has since been widely studied and replicated. The effect occurs when individuals are presented with conflicting information, such as a word printed in a color that does not match the meaning of the word (e.g., the word blue printed in red ink). This creates a conflict between the automatic process of reading the word and the controlled process of identifying the color, resulting in delayed reaction times and errors. The Stroop Effect has significant implications for our understanding of cognitive processing and has been applied in various fields, including psychology, neuroscience, and education. In this essay, we will explore the Stroop Effect and its impact on cognitive processing.

Green Red Blue Purple Red Purple Mouse Top Face Monkey Top Monkey

Naming the font color of a printed word is an easier and quicker task if word meaning and font color are not incongruent. If both are printed in red, the average time to say “RED” in response to the word ‘Green’ is greater than the time to say “RED” in response to the word ‘Mouse’.

In psychology, the Stroop effect is a demonstration of interference in the reaction time of a task. When the name of a color (e.g., “blue”, “green”, or “red”) is printed in a color that is not denoted by the name (e.g., the word “red” printed in blue ink instead of red ink), naming the color of the word takes longer and is more prone to errors than when the color of the ink matches the name of the color. The effect is named after John Ridley Stroop, who first published the effect in English in 1935. The effect had previously been published in Germany in 1929. The original paper has been one of the most cited papers in the history of experimental psychology, leading to more than 700 replications. The effect has been used to create a psychological test (Stroop test) that is widely used in clinical practice and investigation.

Stimulus 1: Purple Brown Red Blue Green Stimulus 2: Brown Green Blue Green Stimulus 3: ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ ▀ Examples of the three stimuli and colors used for each of the activities of the original Stroop article.

Figure 1 from Experiment 2 of the original description of the Stroop Effect (1935). 1 is the time that it takes to name the color of the dots while 2 is the time that it takes to say the color when there is a conflict with the written word.

The effect was named after John Ridley Stroop, who published the effect in English in 1935 in an article in the Journal of Experimental Psychology entitled “Studies of interference in serial verbal reactions” that includes three different experiments. However, the effect was first published in 1929 in Germany by Erich Rudolf Jaensch, and its roots can be followed back to works of James McKeen Cattell and Wilhelm Maximilian Wundt in the nineteenth century.

In his experiments, Stroop administered several variations of the same test for which three different kinds of stimuli were created: Names of colors appeared in black ink: Names of colors in a different ink than the color named; and Squares of a given color.

In the first experiment, words and conflict-words were used (see first figure). The task required the participants to read the written color names of the words independently of the color of the ink (for example, they would have to read “purple” no matter what the color of the font). In experiment 2, stimulus conflict-words and color patches were used, and participants were required to say the ink-color of the letters independently of the written word with the second kind of stimulus and also name the color of the patches. If the word “purple” was written in red font, they would have to say “red”, rather than “purple”. When the squares were shown, the participant spoke the name of the color. Stroop, in the third experiment, tested his participants at different stages of practice at the tasks and stimuli used in the first and second experiments, examining learning effects.

Unlike researchers now using the test for psychological evaluation, Stroop used only the three basic scores, rather than more complex derivative scoring procedures. Stroop noted that participants took significantly longer to complete the color reading in the second task than they had taken to name the colors of the squares in Experiment 2. This delay had not appeared in the first experiment. Such interference were explained by the automation of reading, where the mind automatically determines the semantic meaning of the word (it reads the word “red” and thinks of the color “red”), and then must intentionally check itself and identify instead the color of the word (the ink is a color other than red), a process that is not automated.

Experimental Findings

Stimuli in Stroop paradigms can be divided into 3 groups: neutral, congruent and incongruent. Neutral stimuli are those stimuli in which only the text (similarly to stimuli 1 of Stroop’s experiment), or color (similarly to stimuli 3 of Stroop’s experiment) are displayed. Congruent stimuli are those in which the ink color and the word refer to the same color (for example the word “pink” written in pink). Incongruent stimuli are those in which ink color and word differ. Three experimental findings are recurrently found in Stroop experiments. A first finding is semantic interference, which states that naming the ink color of neutral stimuli (e.g. when the ink color and word do not interfere with each other) is faster than in incongruent conditions. It is called semantic interference since it is usually accepted that the relationship in meaning between ink color and word is at the root of the interference. The second finding, semantic facilitation, explains the finding that naming the ink of congruent stimuli is faster (e.g. when the ink color and the word match) than when neutral stimuli are present (e.g. stimulus 3; when only a coloured square is shown). The third finding is that both semantic interference and facilitation disappear when the task consists of reading the word instead of naming the ink. It has been sometimes called Stroop asynchrony, and has been explained by a reduced automatization when naming colors compared to reading words.

In the study of interference theory, the most commonly used procedure has been similar to Stroop’s second experiment, in which subjects were tested on naming colors of incompatible words and of control patches. The first experiment in Stroop’s study (reading words in black versus incongruent colors) has been discussed less. In both cases, the interference score is expressed as the difference between the times needed to read each of the two types of cards. Instead of naming stimuli, subjects have also been asked to sort stimuli into categories. Different characteristics of the stimulus such as ink colors or direction of words have also been systematically varied. None of all these modifications eliminates the effect of interference.

Neuroanatomy

Brain imaging techniques including magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) have shown that there are two main areas in the brain that are involved in the processing of the Stroop task. They are the anterior cingulate cortex, and the dorsolateral prefrontal cortex. More specifically, while both are activated when resolving conflicts and catching errors, the dorsolateral prefrontal cortex assists in memory and other executive functions, while the anterior cingulate cortex is used to select an appropriate response and allocate attentional resources.

The posterior dorsolateral prefrontal cortex creates the appropriate rules for the brain to accomplish the current goal. For the Stroop effect, this involves activating the areas of the brain involved in color perception, but not those involved in word encoding. It counteracts biases and irrelevant information, for instance, the fact that the semantic perception of the word is more striking than the color in which it is printed. Next, the mid-dorsolateral prefrontal cortex selects the representation that will fulfil the goal. The relevant information must be separated from irrelevant information in the task; thus, the focus is placed on the ink color and not the word. Furthermore, research has suggested that left dorsolateral prefrontal cortex activation during a Stroop task is related to an individual’s’ expectation regarding the conflicting nature of the upcoming trial, and not so much on the conflict itself. Conversely, the right dorsolateral prefrontal cortex aims to reduce the attentional conflict and is activated after the conflict is over.

Moreoever, the posterior dorsal anterior cingulate cortex is responsible for what decision is made (i.e. whether you will say the incorrect answer [written word] or the correct answer [ink color]). Following the response, the anterior dorsal anterior cingulate cortex is involved in response evaluation—deciding whether the answer is correct or incorrect. Activity in this region increases when the probability of an error is higher.

There are several theories used to explain the Stroop effect and are commonly known as ‘race models’. This is based on the underlying notion that both relevant and irrelevant information are processed in parallel, but “race” to enter the single central processor during response selection. They are:

Processing Speed

This theory suggests there is a lag in the brain’s ability to recognize the color of the word since the brain reads words faster than it recognizes colors. This is based on the idea that word processing is significantly faster than color processing. In a condition where there is a conflict regarding words and colors (e.g., Stroop test), if the task is to report the color, the word information arrives at the decision-making stage before the color information which presents processing confusion. Conversely, if the task is to report the word, because color information lags after word information, a decision can be made ahead of the conflicting information.

Selective Attention

The Selective Attention Theory that color recognition as opposed to reading a word, requires more attention, the brain needs to use more attention to recognize a color than to word encoding, so it takes a little longer. The responses lend much to the interference noted in the Stroop task. This may be a result of either an allocation of attention to the responses or to a greater inhibition of distractors that are not appropriate responses.

This theory is the most common theory of the Stroop effect. It suggests that since recognizing colors is not an “automatic process” there is hesitancy to respond; whereas, the brain automatically understands the meaning of words as a result of habitual reading. This idea is based on the premise that automatic reading does not need controlled attention, but still uses enough attentional resources to reduce the amount of attention accessible for color information processing. Stirling (1979) introduced the concept of response automaticity. He demonstrated that changing the responses from colored words to letters that were not part of the colored words increased reaction time while reducing Stroop interference.

Parallel Distributed Processing

This theory suggests that as the brain analyzes information, different and specific pathways are developed for different tasks. Some pathways, such as reading, are stronger than others, therefore, it is the strength of the pathway and not the speed of the pathway that is important. In addition, automaticity is a function of the strength of each pathway, hence, when two pathways are activated simultaneously in the Stroop effect, interference occurs between the stronger (word reading) path and the weaker (color naming) path, more specifically when the pathway that leads to the response is the weaker pathway.

Cognitive Development

In the neo-Piagetian theories of cognitive development, several variations of the Stroop task have been used to study the relations between speed of processing and executive functions with working memory and cognitive development in various domains. This research shows that reaction time to Stroop tasks decreases systematically from early childhood through early adulthood. These changes suggest that speed of processing increases with age and that cognitive control becomes increasingly efficient. Moreover, this research strongly suggests that changes in these processes with age are very closely associated with development in working memory and various aspects of thought. The stroop task also shows the ability to control behavior. If asked to state the color of the ink rather than the word, the participant must overcome the initial and stronger stimuli to read the word. This inhibitions show the ability for the brain to regulate behavior.

The Stroop effect has been widely used in psychology. Among the most important uses is the creation of validated psychological tests based on the Stroop effect permit to measure a person’s selective attention capacity and skills, as well as their processing speed ability. It is also used in conjunction with other neuropsychological assessments to examine a person’s executive processing abilities, and can help in the diagnosis and characterization of different psychiatric and neurological disorders.

Researchers also use the Stroop effect during brain imaging studies to investigate regions of the brain that are involved in planning, decision-making, and managing real-world interference (e.g., texting and driving).

Stroop Test

The Stroop effect has been used to investigate a person’s psychological capacities; since its discovery during the twentieth century, it has become a popular neuropsychological test.

There are different test variants commonly used in clinical settings, with differences between them in the number of subtasks, type and number of stimulus, times for the task, or scoring procedures. All versions have at least two numbers of subtasks. In the first trial, the written color name differs from the color ink it is printed in, and the participant must say the written word. In the second trial, the participant must name the ink color instead. However, there can be up to four different subtasks, adding in some cases stimuli consisting of groups of letters “X” or dots printed in a given color with the participant having to say the color of the ink; or names of colors printed in black ink that have to be read. The number of stimuli varies between fewer than twenty items to more than 150, being closely related to the scoring system used. While in some test variants the score is the number of items from a subtask read in a given time, in others it is the time that it took to complete each of the trials. The number of errors and different derived punctuations are also taken into account in some versions.

This test is considered to measure selective attention, cognitive flexibility and processing speed, and it is used as a tool in the evaluation of executive functions. An increased interference effect is found in disorders such as brain damage, dementias and other neurodegenerative diseases, attention-deficit hyperactivity disorder, or a variety of mental disorders such as schizophrenia, addictions, and depression.

The Stroop test has additionally been modified to include other sensory modalities and variables, to study the effect of bilingualism, or to investigate the effect of emotions on interference.

Warped Words

For example, the warped words Stroop effect produces the same findings similar to the original Stroop effect. Much like the Stroop task, the printed word’s color is different from the ink color of the word; however, the words are printed in such a way that it is more difficult to read (typically curved-shaped). The idea here is the way the words are printed slows down both the brain’s reaction and processing time, making it harder to complete the task.

The emotional Stroop effect serves as an information processing approach to emotions. In an emotional Stroop task, an individual is given negative emotional words like “grief,” “violence,” and “pain” mixed in with more neutral words like “clock,” “door,” and “shoe”. Just like in the original Stroop task, the words are colored and the individual is supposed to name the color. Research has revealed that individuals that are depressed are more likely to say the color of a negative word slower than the color of a neutral word. While both the emotional Stroop and the classic Stroop involve the need to suppress irrelevant or distracting information, there are differences between the two. The emotional Stroop effect emphasizes the conflict between the emotional relevance to the individual and the word; whereas, the classic Stroop effect examines the conflict between the incongruent color and word.

The spatial Stroop effect demonstrates interference between the stimulus location with the location information in the stimuli. In one version of the spatial Stroop task, an up or down-pointing arrow appears randomly above or below a central point. Despite being asked to discriminate the direction of the arrow while ignoring its location, individuals typically make faster and more accurate responses to congruent stimuli (i.e., an down-pointing arrow located below the fixation sign) than to incongruent ones (i.e., a up-pointing arrow located below the fixation sign). A similar effect, the Simon effect, uses non-spatial stimuli.

The Numerical Stroop effect demonstrates the close relationship between numerical values and physical sizes. Digits symbolize numerical values but they also have physical sizes. A digit can be presented as big or small (e.g., 5 vs. 5), irrespective of its numerical value. Comparing digits in incongruent trials (e.g., 3 5) is slower than comparing digits in congruent trials (e.g., 5 3) and the difference in reaction time is termed the numerical Stroop effect. The effect of irrelevant numerical values on physical comparisons (similar to the effect of irrelevant color words on responding to colors) suggests that numerical values are processed automatically (i.e., even when they are irrelevant to the task).

Another variant of the classic Stroop effect is the reverse Stroop effect. It occurs during a pointing task. In a reverse Stroop task, individuals are shown a page with a black square with an incongruent colored word in the middle — for instance, the word “red” written in the color green — with four smaller colored squares in the corners. One square would be colored green, one square would be red, and the two remaining squares would be other colors. Studies show that if the individual is asked to point to the color square of the written color (in this case, red) they would present a delay. Thus, incongruently-colored words significantly interfere with pointing to the appropriate square. However, some research has shown there is very little interference from incongruent color words when the objective is to match the color of the word.

In Popular Culture

The Brain Age: Train Your Brain in Minutes a Day! software program, produced by Ryūta Kawashima for the Nintendo DS portable video game system, contains an automated Stroop Test administrator module, translated into game form.

MythBusters used the Stroop effect test to see if males and females are cognitively impaired by having an attractive person of the opposite sex in the room. The myth was busted.

A Nova episode used the Stroop Effect to illustrate the subtle changes of the mental flexibility of Mount Everest climbers in relation to altitude.

REVIEW article

The stroop color and word test.

• 1 “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Turin, Italy
• 2 IRCCS Istituto Auxologico Italiano, Ospedale San Giuseppe, Piancavallo, Italy
• 3 CiMeC Center for the Mind/Brain Sciences, University of Trento, Rovereto, Italy

The Stroop Color and Word Test (SCWT) is a neuropsychological test extensively used to assess the ability to inhibit cognitive interference that occurs when the processing of a specific stimulus feature impedes the simultaneous processing of a second stimulus attribute, well-known as the Stroop Effect. The aim of the present work is to verify the theoretical adequacy of the various scoring methods used to measure the Stroop effect. We present a systematic review of studies that have provided normative data for the SCWT. We referred to both electronic databases (i.e., PubMed, Scopus, Google Scholar) and citations. Our findings show that while several scoring methods have been reported in literature, none of the reviewed methods enables us to fully assess the Stroop effect. Furthermore, we discuss several normative scoring methods from the Italian panorama as reported in literature. We claim for an alternative scoring method which takes into consideration both speed and accuracy of the response. Finally, we underline the importance of assessing the performance in all Stroop Test conditions (word reading, color naming, named color-word).

Introduction

The Stroop Color and Word Test (SCWT) is a neuropsychological test extensively used for both experimental and clinical purposes. It assesses the ability to inhibit cognitive interference, which occurs when the processing of a stimulus feature affects the simultaneous processing of another attribute of the same stimulus ( Stroop, 1935 ). In the most common version of the SCWT, which was originally proposed by Stroop in the 1935, subjects are required to read three different tables as fast as possible. Two of them represent the “congruous condition” in which participants are required to read names of colors (henceforth referred to as color-words) printed in black ink (W) and name different color patches (C). Conversely, in the third table, named color-word (CW) condition, color-words are printed in an inconsistent color ink (for instance the word “red” is printed in green ink). Thus, in this incongruent condition, participants are required to name the color of the ink instead of reading the word. In other words, the participants are required to perform a less automated task (i.e., naming ink color) while inhibiting the interference arising from a more automated task (i.e., reading the word; MacLeod and Dunbar, 1988 ; Ivnik et al., 1996 ). This difficulty in inhibiting the more automated process is called the Stroop effect ( Stroop, 1935 ). While the SCWT is widely used to measure the ability to inhibit cognitive interference; previous literature also reports its application to measure other cognitive functions such as attention, processing speed, cognitive flexibility ( Jensen and Rohwer, 1966 ), and working memory ( Kane and Engle, 2003 ). Thus, it may be possible to use the SCWT to measure multiple cognitive functions.

In the present article, we present a systematic review of the SCWT literature in order to assess the theoretical adequacy of the different scoring methods proposed to measure the Stroop effect ( Stroop, 1935 ). We focus on Italian literature, which reports the use of several versions of the SCWT that vary in in terms of stimuli, administration protocol, and scoring methods. Finally, we attempt to indicate a score method that allows measuring the ability to inhibit cognitive interference in reference to the subjects' performance in SCWT.

We looked for normative studies of the SCWT. All studies included a healthy adult population. Since our aim was to understand the various available scoring methods, no studies were excluded on the basis of age, gender, and/or education of participants, or the specific version of SCWT used (e.g., short or long, computerized or paper). Studies were identified using electronic databases and citations from a selection of relevant articles. The electronic databases searched included PubMed (All years), Scopus (All years) and Google Scholar (All years). The last search was run on the 22nd February, 2017, using the following search terms: “Stroop; test; normative.” All studies written in English and Italian were included.

Two independent reviewers screened the papers according to their titles and abstracts; no disagreements about suitability of the studies was recorded. Thereafter, a summary chart was prepared to highlight mandatory information that had to be extracted from each report (see Table 1 ).

Table 1. Summary of data extracted from reviewed articles; those related to the Italian normative data are in bold .

One Author extracted data from papers while the second author provided further supervision. No disagreements about extracted data emerged. We did not seek additional information from the original reports, except for Caffarra et al. (2002) , whose full text was not available: relevant information have been extracted from Barletta-Rodolfi et al. (2011) .

We extracted the following information from each article:

• Year of publication.

• Indexes whose normative data were provided.

Eventually, as regards the variables of interest, we focused on those scores used in the reviewed studies to assess the performance at the SCWT.

We identified 44 articles from our electronic search and screening process. Eleven of them were judged inadequate for our purpose and excluded. Four papers were excluded as they were written in languages other than English or Italian ( Bast-Pettersen, 2006 ; Duncan, 2006 ; Lopez et al., 2013 ; Rognoni et al., 2013 ); two were excluded as they included children ( Oliveira et al., 2016 ) and a clinical population ( Venneri et al., 1992 ). Lastly, we excluded six Stroop Test manuals, since not entirely procurable ( Trenerry et al., 1989 ; Artiola and Fortuny, 1999 ; Delis et al., 2001 ; Golden and Freshwater, 2002 ; Mitrushina et al., 2005 ; Strauss et al., 2006a ). At the end of the selection process we had 32 articles suitable for review (Figure 1 ).

Figure 1. Flow diagram of studies selection process .

From the systematic review, we extracted five studies with Italian normative data. Details are reported in Table 1 . Of the remaining 27 studies that provide normative data for non-Italian populations, 16 studies ( Ivnik et al., 1996 ; Ingraham et al., 1988 ; Rosselli et al., 2002 ; Moering et al., 2004 ; Lucas et al., 2005 ; Steinberg et al., 2005 ; Seo et al., 2008 ; Peña-Casanova et al., 2009 ; Al-Ghatani et al., 2011 ; Norman et al., 2011 ; Andrews et al., 2012 ; Llinàs-Reglà et al., 2013 ; Morrow, 2013 ; Lubrini et al., 2014 ; Rivera et al., 2015 ; Waldrop-Valverde et al., 2015 ) adopted the scoring method proposed by Golden (1978) . In this method, the number of items correctly named in 45 s in each conditions is calculated (i.e., W, C, CW). Then the predicted CW score (Pcw) is calculated using the following formula:

equivalent to:

Then, the Pcw value is subtracted from the actual number of items correctly named in the incongruous condition (CW) (i.e., IG = CW − Pcw): this procedure allows to obtain an interference score (IG) based on the performance in both W and C conditions. Thus, a negative IG value represents a pathological ability to inhibit interference, where a lower score means greater difficulty in inhibiting interference.

Six articles ( Troyer et al., 2006 ; Bayard et al., 2011 ; Campanholo et al., 2014 ; Bezdicek et al., 2015 ; Hankee et al., 2016 ; Tremblay et al., 2016 ) adopted the Victoria Stroop Test. In this version, three conditions are assessed: the C and the CW correspond to the equivalent conditions of the original version of the test ( Stroop, 1935 ), while the W condition includes common words which do not refer to colors. This condition represents an intermediate inhibition condition, as the interference effect between the written word and the color name is not present. In this SCWT form ( Strauss et al., 2006b ), for each condition, the completion time and the number of errors (corrected, non-corrected, and total errors) are recorded and two interference scores are computed:

Five studies ( Strickland et al., 1997 ; Van der Elst et al., 2006 ; Zalonis et al., 2009 ; Kang et al., 2013 ; Zimmermann et al., 2015 ) adopted different SCWT versions. Three of them ( Strickland et al., 1997 ; Van der Elst et al., 2006 ; Kang et al., 2013 ) computed, independently, the completion time and the number of errors for each condition. Additionally, Van der Elst et al. (2006) , computed an interference score based on the speed performance only:

where WT, CT, and CWT represent the time to complete the W, C, and CW table, respectively. Zalonis et al. (2009) recorded: (i) the time; (ii) the number of errors and (iii) the number of self-corrections in the CW. Moreover, they computed an interference score subtracting the number of errors in the CW conditions from the number of items properly named in 120 s in the same table. Lastly, Zimmermann et al. (2015) computed the number of errors and the number of correct answers given in 45 s in each conditions. Additionally, they calculated an interference score derived by the original scoring method provided by Stroop (1935) .

Of the five studies ( Barbarotto et al., 1998 ; Caffarra et al., 2002 ; Amato et al., 2006 ; Valgimigli et al., 2010 ; Brugnolo et al., 2015 ) that provide normative data for the Italian population, two are originally written in Italian ( Caffarra et al., 2002 ; Valgimigli et al., 2010 ), while the others are written in English ( Barbarotto et al., 1998 ; Amato et al., 2006 ; Brugnolo et al., 2015 ). An English translation of the title and abstract of Caffarra et al. (2002) is available. Three of the studies consider the performance only on the SCWT ( Caffarra et al., 2002 ; Valgimigli et al., 2010 ; Brugnolo et al., 2015 ) while the others also include other neuropsychological tests in the experimental assessment ( Barbarotto et al., 1998 ; Amato et al., 2006 ). The studies are heterogeneous in that they differ in terms of administered conditions, scoring procedures, number of items, and colors used. Three studies adopted a 100-items version of the SCWT ( Amato et al., 2006 ; Valgimigli et al., 2010 ; Brugnolo et al., 2015 ) which is similar to the original version proposed by Stroop (1935) . In this version, in every condition (i.e., W, C, CW), items are arranged in a matrix of 10 × 10 columns and rows; the colors are red, green, blue, brown, and purple. However, while two of these studies administered the W, C, and CW conditions once ( Amato et al., 2006 ; Valgimigli et al., 2010 ), Barbarotto et al. (1998) administered the CW table twice, requiring participants to read the word during the first administration and then to name the ink color during the consecutive administration. Additionally, they also administered a computerized version of the SCWT in which 40 stimuli are presented in each condition; red, blue, green, and yellow are used. Valgimigli et al. (2010) and Caffarra et al. (2002) administered shorter paper versions of the SCWT including only three colors (i.e., red, blue, green). More specifically, the former administered only the C and CW conditions including 60 items each, arranged in six columns of 10 items. The latter employed a version of 30 items for each condition (i.e., W, C, CW), arranged in three columns of 10 items each.

Only two of the five studies assessed and provided normative data for all the conditions of the SCWT (i.e., W, C, CW; Caffarra et al., 2002 ; Brugnolo et al., 2015 ), while others provide only partial results. Valgimigli et al. (2010) provided normative data only for the C and CW condition, while Amato et al. (2006) and Barbarotto et al. (1998) administered all the SCWT conditions (i.e., W, C, CW) but provide normative data only for the CW condition, and the C and CW condition respectively.

These studies use different methods to compute subjects' performance. Some studies record the time needed, independently in each condition, to read all ( Amato et al., 2006 ) or a fixed number ( Valgimigli et al., 2010 ) of presented stimuli. Others consider the number of correct answers produced in a fixed time (30 s; Amato et al., 2006 ; Brugnolo et al., 2015 ). Caffarra et al. (2002) and Valgimigli et al. (2010) provide a more complex interference index that relates the subject's performance in the incongruous condition with the performance in the others. In Caffarra et al. (2002) , two interference indexes based on reading speed and accuracy, respectively, are computed using the following formula:

Furthermore, in Valgimigli et al. (2010) an interference score is computed using the formula:

where DC represents the correct answers produced in 20 s in naming colors and DI corresponds to the correct answers achieved in 20 s in the interference condition. However, they do not take into account the performance on the word reading condition.

According to the present review, multiple SCWT scoring methods are available in literature, with Golden's (1978) version being the most widely used. In the Italian literature, the heterogeneity in SCWT scoring methods increases dramatically. The parameters of speed and accuracy of the performance, essential for proper detection of the Stroop Effect, are scored differently between studies, thus highlighting methodological inconsistencies. Some of the reviewed studies score solely the speed of the performance ( Amato et al., 2006 ; Valgimigli et al., 2010 ). Others measure both the accuracy and speed of performance ( Barbarotto et al., 1998 ; Brugnolo et al., 2015 ); however, they provide no comparisons between subjects' performance on the different SCWT conditions. On the other hand, Caffarra et al. (2002) compared performance in the W, C, and CW conditions; however, they computed speed and accuracy independently. Only Valgimigli et al. (2010) present a scoring method in which an index merging speed and accuracy is computed for the performance in all the conditions; however, the Authors assessed solely the performance in the C and the CW conditions, neglecting the subject's performance in the W condition.

In our opinion, the reported scoring methods impede an exhaustive description of the performance on the SCWT, as suggested by clinical practice. For instance, if only the reading time is scored, while accuracy is not computed ( Amato et al., 2006 ) or is computed independently ( Caffarra et al., 2002 ), the consequences of possible inhibition difficulties on the processing speed cannot be assessed. Indeed, patients would report a non-pathological reading speed in the incongruous condition, despite extremely poor performance, even if they do not apply the rule “naming ink color,” simply reading the word (e.g., in CW condition, when the stimulus is the word/red/printed in green ink, patient says “Red” instead of “Green”). Such behaviors provide an indication of the failure to maintain consistent activation of the intended response in the incongruent Stroop condition, even if the participants properly understand the task. Such scenarios are often reported in different clinical populations. For example, in the incongruous condition, patients with frontal lesions ( Vendrell et al., 1995 ; Stuss et al., 2001 ; Swick and Jovanovic, 2002 ) as well as patients affected by Parkinson's Disease ( Fera et al., 2007 ; Djamshidian et al., 2011 ) reported significant impairments in terms of accuracy, but not in terms of processing speed. Counting the number of correct answers in a fixed time ( Amato et al., 2006 ; Valgimigli et al., 2010 ; Brugnolo et al., 2015 ) may be a plausible solution.

Moreover, it must be noted that error rate (and not the speed) is an index of inhibitory control ( McDowd et al., 1995 ) or an index of ability to maintain the tasks goal temporarily in a highly retrievable state ( Kane and Engle, 2003 ). Nevertheless, computing exclusively the error rate (i.e., the accuracy in the performance), without measuring the speed of performance, would be insufficient for an extensive evaluation of the performance in the SCWT. In fact, the behavior in the incongruous condition (i.e., CW) may be affected by difficulties that are not directly related to an impaired ability to suppress the interference process, which may lead to misinterpretation of the patient's performance. People affected by color-blindness or dyslexia would represent the extreme case. Nonetheless, and more ordinarily, slowness, due to clinical circumstances like dysarthria, mood disorders such as depression, or collateral medication effect, may irremediably affect the performance in the SCWT. In Parkinson's Disease, ideomotor slowness ( Gardner et al., 1959 ; Jankovic et al., 1990 ) impacts the processing speed in all SCWT conditions, determining a global difficulty in the response execution rather than a specific impairment in the CW condition ( Stacy and Jankovic, 1992 ; Hsieh et al., 2008 ). Consequently, it seems necessary to relate the performance in the incongruous condition to word reading and color naming abilities, when inhibition capability has to be assessed, as proposed by Caffarra et al. (2002) . In this method the W score and C score were subtracted from CW score. However, as previously mentioned, the scoring method suggested by Caffarra et al. (2002) computes errors and speed separately. Thus, so far, none of the proposed Italian normative scoring methods seem adequate to assess patients' performance in the SCWT properly and informatively.

Examples of more suitable interference scores can be found in non-Italian literature. Stroop (1935) proposed that the ability to inhibit cognitive interference can be measured in the SCWT using the formula:

where, total time is the overall time for reading; mean time per word is the overall time for reading divided by the number of items; and the number of uncorrected errors is the number of errors not spontaneously corrected. Gardner et al. (1959) also propose a similar formula:

where 100 refers to the number of stimuli used in this version of the SCWT. When speed and errors are computed together, the correct recognition of patients who show difficulties in inhibiting interference despite a non-pathological reading time, increases. However, both the mentioned scores ( Stroop, 1935 ; Mitrushina et al., 2005 ) may be susceptible to criticism ( Jensen and Rohwer, 1966 ). In fact, even though accuracy and speed are merged into a global score in these studies ( Stroop, 1935 ; Mitrushina et al., 2005 ), they are not computed independently. In Gardner et al. (1959) the number of errors are computed in relation to the mean time per item and then added to the total time, which may be redundant and lead to a miscomputation.

The most adopted scoring method in the international panorama is Golden (1978) . Lansbergen et al. (2007) point out that the index IG might not be adequately corrected for inter-individual differences in the reading ability, despite its effective adjustment for color naming. The Authors highlight that the reading process is more automated in expert readers, and, consequently, they may be more susceptible to interference ( Lansbergen et al., 2007 ), thus, requiring that the score is weighted according to individual reading ability. However, experimental data suggests that the increased reading practice does not affect the susceptibility to interference in SCWT ( Jensen and Rohwer, 1966 ). Chafetz and Matthews (2004) 's article might be useful for a deeper understanding of the relationship between reading words and naming colors, but the debate about the role of reading ability on the inhibition process is still open. The issue about the role of reading ability on the SCWT performance cannot be adequately satisfied even if the Victoria Stroop Test scoring method ( Strauss et al., 2006b ) is adopted, since the absence of the standard W condition.

In the light of the previous considerations, we recommend that a scoring method for the SCWT should fulfill two main requirements. First, both accuracy and speed must be computed for all SCWT conditions. And secondly, a global index must be calculated to relate the performance in the incongruous condition to reading words and color naming abilities. The first requirement can be achieved by counting the number of correct answers in each condition in within a fixed time ( Amato et al., 2006 ; Valgimigli et al., 2010 ; Brugnolo et al., 2015 ). The second requirement can be achieved by subtracting the W score and C score from CW score, as suggested by Caffarra et al. (2002) . None of the studies reviewed satisfies both these requirements.

According to the review, the studies with Italian normative data present different theoretical interpretations of the SCWT scores. Amato et al. (2006) and Caffarra et al. (2002) describe the SCWT score as a measure of the fronto-executive functioning, while others use it as an index of the attentional functioning ( Barbarotto et al., 1998 ; Valgimigli et al., 2010 ) or of general cognitive efficiency ( Brugnolo et al., 2015 ). Slowing to a response conflict would be due to a failure of selective attention or a lack in the cognitive efficiency instead of a failure of response inhibition ( Chafetz and Matthews, 2004 ); however, the performance in the SCWT is not exclusively related to concentration, attention or cognitive effectiveness, but it relies to a more specific executive-frontal domain. Indeed, subjects have to process selectively a specific visual feature blocking out continuously the automatic processing of reading ( Zajano and Gorman, 1986 ; Shum et al., 1990 ), in order to solve correctly the task. The specific involvement of executive processes is supported by clinical data. Patients with anterior frontal lesions, and not with posterior cerebral damages, report significant difficulties in maintaining a consistent activation of the intended response ( Valgimigli et al., 2010 ). Furthermore, Parkinson's Disease patients, characterized by executive dysfunction due to the disruption of dopaminergic pathway ( Fera et al., 2007 ), reported difficulties in SCWT despite unimpaired attentional abilities ( Fera et al., 2007 ; Djamshidian et al., 2011 ).

According to the present review, the heterogeneity in the SCWT scoring methods in international literature, and most dramatically in Italian literature, seems to require an innovative, alternative and unanimous scoring system to achieve a more proper interpretation of the performance in the SCWT. We propose to adopt a scoring method in which (i) the number of correct answers in a fixed time in each SCWT condition (W, C, CW) and (ii) a global index relative to the CW performance minus reading and/or colors naming abilities, are computed. Further studies are required to collect normative data for this scoring method and to study its applicability in clinical settings.

Author Contributions

Conception of the work: FS. Acquisition of data: ST. Analysis and interpretation of data for the work: FS and ST. Writing: ST, and revising the work: FS. Final approval of the version to be published and agreement to be accountable for all aspects of the work: FS and ST.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The Authors thank Prerana Sabnis for her careful proofreading of the manuscript.

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Valgimigli, S., Padovani, R., Budriesi, C., Leone, M. E., Lugli, D., and Nichelli, P. (2010). The Stroop test: a normative Italian study on a paper version for clinical use. G. Ital. Psicol. 37, 945–956. doi: 10.1421/33435

Van der Elst, W., Van Boxtel, M. P., Van Breukelen, G. J., and Jolles, J. (2006). The Stroop Color-Word Test influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment 13, 62–79. doi: 10.1177/1073191105283427

Vendrell, P., Junqué, C., Pujol, J., Jurado, M. A., Molet, J., and Grafman, J. (1995). The role of prefrontal regions in the Stroop task. Neuropsychologia 33, 341–352. doi: 10.1016/0028-3932(94)00116-7

Venneri, A., Molinari, M. A., Pentore, R., Cotticelli, B., Nichelli, P., and Caffarra, P. (1992). Shortened Stroop color-word test: its application in normal aging and Alzheimer's disease. Neurobiol. Aging 13, S3–S4. doi: 10.1016/0197-4580(92)90135-K

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Zajano, M. J., and Gorman, A. (1986). Stroop interference as a function of percentage of congruent items. Percept. Mot. Skills 63, 1087–1096. doi: 10.2466/pms.1986.63.3.1087

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Keywords: stroop color and word test, neuropsychological assessment, inhibition, executive functions, systematic review

Citation: Scarpina F and Tagini S (2017) The Stroop Color and Word Test Front. Psychol. 8:557. doi: 10.3389/fpsyg.2017.00557

Received: 10 November 2016; Accepted: 27 March 2017; Published: 12 April 2017.

Reviewed by:

Copyright © 2017 Scarpina and Tagini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Federica Scarpina, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

The Stroop Test

Here’s a chance to get involved in Psychology to see how stimulating it can be.

How much do you know about psychology?

The stroop test.

Here is a classic experiment which you can try for yourself. It was first carried out by Stroop in 1935 and shows us that reading words is something we do automatically and can’t stop from doing even if we want to.

Instructions

• Time yourself reading this list of words: list 1
• Now time yourself reading this different list of words: list 2

How did you do? Psychologists would expect that your two times didn’t differ very much at all as you are just reading each list.

Now do something a bit different. Timing yourself, go through each list again as quickly as you can, but this time say the colour of the ink and do not read the word . Be as quick and accurate as you can.

How was that? Did one of the lists seem much harder and take much longer than the other? Stroop showed that the list with the conflicting colours (list 2) takes much longer because you cannot stop yourself from reading the words which are not, of course, the same colour as the ink. List 1, where the ink colour and the word are the same, should have been both easier and quicker.

This is a classic experiment from the area of cognitive psychology which has told psychologists much about both how we read and how processes we do over and over again become automatic. If you would like more information on this, then click on the following useful link: http://psychclassics.yorku.ca/Stroop/ (external link) .

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The Stroop Effect – How it Works and Why

Bryn Farnsworth

To see and interact with the world, we first need to understand it. Visual processing is one way we do this, and is composed of many parts. When we see an object , we don’t just see its physical attributes, we also comprehend the meaning behind them. We know that a chair needs legs because the seat needs to be raised, we know that the wood comes from trees, we know we could sit in it, and so on. There is information that we process about the things we see without even being aware of that processing.

So when John Ridley Stroop asked people to read words on a sheet of paper in 1929, he knew that their automatic processing would come into play, and could offer a breakthrough insight into brain function. Research from as early as 1894  had shown that associations of even nonsense syllables would become embedded into a person’s understanding, and could interfere with how they processed and recalled these syllables, despite no real meaning being attached to them. It was therefore clear, even in the beginnings of contemporary psychological research, that associations are powerful and pervasive.

Table of Contents

The history and origin of the stroop effect.

The Stroop Effect [ 1 ] represents a fascinating phenomenon in the psychology of perception and cognition, showcasing the complex interplay between automatic and controlled processing mechanisms within the human brain. Its discovery is credited to John Ridley Stroop, who first documented the effect in his Ph.D. dissertation at George Peabody College in 1935, later published as “Studies of interference in serial verbal reactions” in the Journal of Experimental Psychology [ 2 ] .

The foundational experiment that Stroop conducted involved presenting subjects with lists of words. These words were colors printed in an ink that either matched or conflicted with the color name (e.g., the word “red” printed in red versus the word “red” printed in blue). Stroop observed that participants took longer to name the color of the ink when the color of the ink and the word itself were incongruent. This delay, now known as the Stroop effect, highlighted the cognitive conflict between the more automatic process of reading the word and the more controlled process of recognizing the color of the ink.

Stroop’s work built upon earlier research into color naming and reading speed, but his experiments uniquely quantified how automatic reading could interfere with a task that required control over this automatic process. The significance of the Stroop effect lies not just in its demonstration of the interference phenomenon but also in its insight into the architecture of the human mind, suggesting that certain cognitive processes are more automatic and thus more difficult to suppress or modify.

Over the decades, the Stroop effect has been extensively replicated and has served as a foundation for numerous psychological studies. It has been used to explore attention, processing speed, cognitive control, and the neural mechanisms underlying these processes. The simplicity of the Stroop task, combined with its robustness, has made it a cornerstone in cognitive psychology and neuroscience [ 3 ] , providing insights into the workings of the human brain and the nature of human cognition [ 4 ] .

What is the Stroop Effect?

Stroop’s innovation was to show, clearly and definitively, that our embedded knowledge about our environment impacts how we interact with it. His research method is now one of the most famous and well-known examples of a psychological test, and is elegant in its simplicity.

First, the participant reads a list of words for colors, but the words are printed in a color different to the word itself. For example, the word “orange” would be listed as text, but printed in green. The participant’s reading time of the words on the list is then recorded. Next, the participant has to repeat the test with a new list of words, but should name the colors that the words are printed in. So, when the word “orange” is printed in green, the participant should say “green” and move on to the next word.

The Stroop Test

Below is a brief example of the Stroop test, try it out!

First, time yourself while you read the following text, ignoring the colors the words are printed in.

BLUE          ORANGE          YELLOW           RED           PURPLE

PINK           BLUE           BLACK           PURPLE        GREEN

ORANGE           BLACK           YELLOW           PINK           RED

BLUE           PINK          ORANGE           BLACK            BLUE

Now time yourself while you state the colors of the following words, ignoring the actual text (as best as you can!).

YELLOW           RED           PINK           BLUE             GREEN

PURPLE            YELLOW          BLUE          BLACK            PINK

BLUE           RED           GREEN           ORANGE           PINK

BLACK            RED           YELLOW          PURPLE            BLUE

In most cases, it takes longer to state the colors of the words, rather than to read the text they are printed in, despite the incongruence being essentially the same across both lists (i.e. both show words in the wrong color). It appears we are more influenced by the physical text than than the text color.

The Stroop test is difficult to perform because it creates a conflict between the automatic processing of reading words and the intentional processing of identifying colors. This conflict leads to interference, resulting in longer response times and increased errors. Our brain’s tendency to prioritize word reading over color naming makes it challenging to accurately and quickly identify the colors of the words.

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Why does this happen?

What this reveals is that the brain can’t help but read. As habitual readers, we encounter and comprehend words on such a persistent basis that the reading occurs almost effortlessly, whereas declaration of a color requires more cognitive effort. When there is a conflict between these two sources of information, our cognitive load is increased, and our brains have to work harder to resolve the required difference. Performing these tasks (preventing reading, processing word color, and resolving information conflict) ultimately slows down our responses, and makes the task take longer.

There are a few theories that slightly differ in their definitions of the Stroop Effect, yet their differences mostly lie in which part that they emphasize. For example, one theory emphasizes that the automaticity of reading as the principal cause of Stroop interference, while another emphasizes the mental prioritizing which we perform when reading, as compared to defining colors. While differences in theories may therefore exist, all essentially converge on the central premise that reading is a simpler and more automatic task than stating colors, and that a conflict between the two will increase the time needed for processing.

There are several theories regarding the Stroop Effect, each with its own emphasis. One theory highlights the automaticity of reading as the main cause of interference [ 9 ] , which is the interpretation that John Ridley Stroop also supported, while another focuses on the mental prioritization involved in reading versus identifying colors [ 10 ] . Despite these variations, all theories agree that the conflict between reading and color identification leads to slower processing and increased task duration.

The Neural Mechanisms Underlying the Stroop Effect

The Stroop effect intricately illustrates the neural complexity of cognitive processes, such as attention, memory, and control mechanisms within the human brain. The neural basis of the Stroop [ 5 ] effect has been extensively studied using various neuroimaging techniques, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), which have helped identify the brain regions involved in the conflict resolution and cognitive control required to overcome the Stroop interference.

Central to the neural mechanisms of the Stroop effect is the prefrontal cortex (PFC), particularly the anterior cingulate cortex (ACC) and the dorsolateral prefrontal cortex (DLPFC). The ACC is believed to play a pivotal role in detecting cognitive conflict—such as the mismatch between a word’s color and its meaning—and signaling this information to other brain regions to recruit additional cognitive control [ 6 ] . This process helps prioritize the task of naming the ink color over the automatic process of reading the word.

The DLPFC, on the other hand, is associated with the implementation of cognitive control and the management of attentional resources. When faced with a Stroop task, the DLPFC is thought to facilitate the suppression of the automatic reading response, enabling the individual to focus on the color-naming task. This region of the brain is crucial for modulating attention and working memory processes, allowing for the successful resolution of the interference caused by the conflicting stimuli.

Moreover, other brain areas are also implicated in the Stroop effect. The parietal cortex, for instance, is involved in processing visual attention and may contribute to the selection of relevant sensory information (i.e., the color of the ink) over irrelevant information (i.e., the word itself) [ 7 ] . Additionally, subcortical structures like the basal ganglia may play a role in the suppression of habitual responses [ 8 ] , further facilitating the ability to choose the color-naming response over the more automatic word-reading response.

Functional connectivity analyses have also highlighted the importance of the interactions between these regions. The dynamic interplay between the ACC, DLPFC, and other areas reflects the complexity of cognitive control mechanisms engaged to overcome the Stroop interference, demonstrating how the brain coordinates across different regions to manage conflict and prioritize tasks.

In summary, the neural mechanisms underlying the Stroop effect involve a sophisticated network of cortical and subcortical regions that detect conflict, engage cognitive control, and allocate attentional resources. These findings underscore the complexity of human cognition and the sophisticated neurobiological systems that support our ability to navigate competing demands on our attention and behavior.

What can we use it for?

Using this paradigm, we can assess an individual’s cognitive processing speed, their attentional capacity, and their level of cognitive control (otherwise known as their executive function). These skills and facets are implicit in so many ways in which we interact with the world, suggesting that this test reveals a brief – yet incisive – view into human thought and behavior.

The test is also used in a variety of different ways to the original, in an effort to exploit the experimental setup to reveal more about a clinical population, for example. Even neurodevelopmental disorders such as schizophrenia and autism have been examined with the Stroop test.

Furthermore, there are several variations and differing implementations of the test available, allowing different aspects of cognition to be honed in on. One of these variations is the “emotional Stroop test” in which participants complete both the original Stroop, and a version which has both neutral and emotionally charged words. The resulting text features words such as “pain” or “joy” amongst everyday words. Research has shown that anxious people were likely to experience more interference (i.e. more time spent declaring word color) with emotionally charged words, suggesting a preponderance of the emotional word content.

Experimental designs like this allow researchers to target and observe cognitive processes that underlie explicit thought. The test reveals the working of non-conscious brain function and reduces some of the biases that can otherwise emerge in testing.

Other experimental setups utilize the lessons of the Stroop Effect – that incongruent information will require more mental resources to resolve correctly – with numbers, rather than words. Termed the “ Numerical Stroop Effect ”, this experiment has shown that presenting numbers of incongruent sizes next to each other will slow down reading and comprehension. For an example, see the image below:

This experiment shows that, with all else being controlled for, incongruence in numerical size will cause the greatest interference, increasing the delay in comprehension. An interesting feature with the Numerical Stroop is that the interference is found for both types of incongruence – when the numbers are incongruent with size, then a delay is shown for reporting the size, as well as for reporting the numbers. This effect reveals that the automatic processing is not just limited to words, suggesting that the brain looks for normal patterns in a variety of presented stimuli, as it appears to struggle when this doesn’t occur.

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The Applications and Implications of the Stroop Effect

The Stroop effect has far-reaching applications and implications across various fields, from cognitive psychology and neuroscience to clinical psychology and beyond. Its ability to elucidate the mechanisms of attention, cognitive control, and the processing speed of conflicting information has made it a valuable tool in both research and practical settings.

In Cognitive Psychology and Neuroscience : The Stroop task is widely used as a research tool to investigate the principles of cognitive control, attention, and information processing. It helps in understanding how the brain manages conflicting information and the efficiency of cognitive control mechanisms across different age groups and cognitive states. This has implications for understanding the development of cognitive control abilities in children, the impact of aging, and the neurodegenerative changes associated with cognitive decline in older adults.

Clinical Applications : The Stroop effect has been applied in the clinical domain to assess and understand various psychiatric and neurological conditions. For instance, individuals with attention deficit hyperactivity disorder (ADHD) or schizophrenia may exhibit increased Stroop interference, indicating difficulties with cognitive control and selective attention. Similarly, the Stroop task can be used to assess executive function in conditions like Alzheimer’s disease and other forms of dementia, providing valuable insights into the cognitive impairments associated with these disorders.

Educational Implications : Understanding the Stroop effect can also have implications for educational strategies, particularly in developing interventions to improve reading skills and attention in children. By understanding the mechanisms of interference and cognitive control, educators can devise methods to enhance cognitive flexibility and attention management in students, potentially improving learning outcomes and academic performance.

Neurorehabilitation : In the field of neurorehabilitation, the Stroop effect is used as a therapeutic tool to help individuals recover from brain injuries or strokes. Cognitive training programs that include tasks similar to the Stroop task can aid in improving cognitive control, attentional capacities, and executive functioning in affected individuals, facilitating their rehabilitation process.

Decision Making and Behavioral Economics : The Stroop effect also has implications for understanding decision-making processes and the influence of cognitive biases. It highlights how conflicting information can impact our ability to make decisions under pressure or when faced with complex information, relevant for fields like behavioral economics and consumer psychology.

Mindfulness and Stress Management : Recent research has explored the use of the Stroop effect in evaluating the impact of mindfulness and stress on cognitive functioning. The performance on Stroop tasks can reflect an individual’s ability to maintain cognitive control under stress, providing insights into stress management techniques and the benefits of mindfulness practices on cognitive health.

In summary, the applications and implications of the Stroop effect are diverse, touching upon various aspects of human cognition and behavior. By providing a window into the intricate workings of the human mind, the Stroop effect continues to inform and inspire research and practice across disciplines, contributing to our understanding of cognitive processes and their impact on our daily lives and wellbeing.

How can the Stroop test be used in biometric research?

The Stroop test can be simply administered with a basic experimental setup. At its most fundamental, all you need is an image of the Stroop test words, a stopwatch, and someone to record the time and answers (and a willing participant!). However, if you want to gain more insights from the data, there are plenty of ways to take the test further. With iMotions you can simply set up and present the Stroop test, while also expanding the data collection possibilities. Using the survey function, the test can be quickly and simply added. This can be done with either the built-in iMotions survey tool, or with the Qualtrics survey tool, which allows even more metrics to be taken into account.

The ability to record from various synchronized biosensors opens up new avenues for research. For example, with an eye-tracking tool, you can examine exactly how long each participant looks at each word, and their precise speed of comprehension. Using areas of interest (AOIs) can be of particular use as this allows you to analyze specific parts of the scene in isolation, or compared to the data for the scene as a whole, or even with other AOIs. It’s then possible to determine which words demanded the most visual attention, allowing you to accurately dissect the data in fine detail.

Below are a few examples of that idea in practice, each of which took only minutes to set up and start.

First, we’ve added an image of a Stroop test to the survey function – one version is essentially the same as the original, while another has neutral words mixed with food related words. This version of the Stroop test would require that the participant verbally declare the color of each word – audio recording could help in accurately measuring participant responses. We have also included an example using a multiple choice paradigm that is detailed below, and using the Qualtrics survey function below that.

After we’ve set up eye-tracking and added a participant list, we can add AOIs to the words, so that we can view and analyze data for each. Below is an image of how this looks:

After running through a few participants, we can start to visualize and analyze their data, producing both detailed AOI data, and heatmaps showing overview data. Below are examples of what this data could look like. Of course, more detailed data is available to export and analyze, if desired.

Alternatively, we can insert each word of the Stroop test within the survey setup, and use the keyboard input function for the participant to answer each word color. This would also allow us to investigate the error rate in a more systematic manner. This is shown across the two images below.

Within this paradigm, eye movements can also be measured, providing information about the amount of time taken to process the information. The approach may take longer for each participant, and remembering the keyboard-color combinations may encumber their cognitive processing (although this shouldn’t present a problem if this approach is used with the correct controls), however it does allow a finer dissection of eye movement for each word, and also informs us about the error rate from incorrect answers.

Using Qualtrics

Finally, we can see how this test is implemented in iMotions using the Qualtrics survey function. This is easily implemented, and appears in a similar way to the above surveys that are built by iMotions. One of the advantages of using Qualtrics is that feedback to participant answers can be immediately provided, should this be desired. The following image shows how the stimulus presentation appears on screen.

The participant can then click on the corresponding color to answer the question. If an incorrect answer is chosen, the response would be shown as below.

The participant can then proceed to complete other questions, and their answers will be recorded, allowing later analysis and visualization of the results.

With all of the information completed and data analyzed, we can now start to discern which words showed the greatest amount of Stroop interference (the latency produced when naming the color that the word is printed in). Having several paradigms with different colors, words, and with only blocks of colors will provide more baseline information and control for experimental error. Ultimately this gives a good basis for the participant data to be normalized, and compared with more validity.

We can now test if there is any difference with the words of interest and potentially start to draw conclusions about the implicit thoughts of participants (with the example above, it could be that participants who are hungrier would spend a longer duration in naming the colors of the words, suggesting those words are more salient to them).

Frequently Asked Question

The basic stroop task.

The basic Stroop task is a psychological test that illustrates the interference in reaction times between conflicting information. Participants are presented with color names printed in incongruent ink colors. For example, the word “red” might be printed in blue ink. The task requires individuals to name the ink color while ignoring the word itself. This creates a cognitive conflict, as the brain’s automatic tendency to read the word interferes with the task of naming the ink color.

Time Limit for Stroop Task

Traditionally, the Stroop task does not have a fixed time limit for responses. The emphasis is rather on accuracy and reaction time. Participants are typically instructed to respond as quickly and accurately as possible. The reaction time – the duration between stimulus presentation and the participant’s response – is the critical measurement. This time is recorded to evaluate the cognitive processing speed and the degree of interference experienced by the individual.

Who is Most Affected by the Stroop Test

The Stroop effect, generally, tends to be universal, but its intensity can vary among individuals. It is most pronounced in people with higher cognitive control and stronger automatic reading abilities. Children, older adults, and individuals with certain neurological or psychiatric conditions (like ADHD or schizophrenia) may exhibit greater Stroop interference. Variations in performance can also be influenced by factors such as language proficiency, fatigue, and attentional capacity.

The Limitations and Challenges of the Stroop Effect

While the Stroop effect is a powerful tool for understanding cognitive processing and control, it is not without its limitations and challenges. These limitations pertain to the interpretation of Stroop data, the generalizability of findings, and the complexity of isolating cognitive processes.

Interpretation of Results : One of the primary challenges associated with the Stroop effect is the complexity involved in interpreting the results. The Stroop task measures several cognitive processes simultaneously, including attention, perception, and response selection. Disentangling these processes to isolate the source of Stroop interference can be challenging, making it difficult to attribute performance changes to specific cognitive mechanisms.

Generalizability : The extent to which Stroop effect findings can be generalized across different populations and settings is another concern. Most Stroop research has been conducted in laboratory environments with controlled conditions, which may not accurately reflect real-world scenarios. Additionally, cultural differences in language processing and color perception can affect Stroop task performance, raising questions about the universality of the effect.

Variability in Task Design : There is considerable variability in how Stroop tasks are designed and administered, including differences in the words used, the colors chosen, and the method of response (verbal vs. manual). This variability can lead to inconsistencies in findings across studies and complicate comparisons between different research projects. Standardization of the task could help mitigate these issues but might also limit the flexibility needed to explore specific research questions.

Individual Differences : Individual differences in cognitive abilities, personality traits, and neurological organization can significantly affect performance on the Stroop task. For example, individuals with high cognitive control or working memory capacity may show less Stroop interference. These differences highlight the challenge of using the Stroop effect as a one-size-fits-all measure and underscore the importance of considering individual variability in cognitive research.

Use in Clinical Populations : While the Stroop task has been applied in clinical settings to assess cognitive impairments, its sensitivity and specificity are not always sufficient for diagnostic purposes. The Stroop effect can indicate general issues with executive function, but it does not pinpoint specific neurological or psychiatric conditions. Therefore, it should be used as part of a broader battery of tests rather than a standalone diagnostic tool.

Neuroimaging Limitations : Although neuroimaging studies have shed light on the neural mechanisms underlying the Stroop effect, interpreting these findings can be complex. The brain areas involved in the Stroop task are also implicated in a wide range of other cognitive processes. Thus, attributing activation patterns solely to the Stroop effect without considering the broader cognitive context can be misleading.

In summary, while the Stroop effect remains a valuable tool in cognitive psychology and neuroscience, it is essential to acknowledge its limitations and challenges. Researchers must carefully design studies, interpret findings within the appropriate context, and consider individual and cultural differences when applying the Stroop task. By addressing these challenges, the Stroop effect can continue to provide valuable insights into the complexities of human cognition.

The Stroop test is a widely-used, well established methodology that reveals various brain functions, and implicit cognitive workings. The original article has now been cited over 13,000 times and that number will surely continue to rise well into the future. With iMotions, it’s easy to start asking questions with the Stroop Task and to get to the answers quickly. To see how the Stroop effect can be set up and used with iMotions, explore the recent article from researchers at Wrocław University .

If this article has piqued your interest, contact us and hear how we can help with your research needs and questions.

[1] https://en.wikipedia.org/wiki/Stroop_effect

[2] https://psycnet.apa.org/doiLanding?doi=10.1037%2Fh0054651

[3] https://link.springer.com/referenceworkentry/10.1007/978-3-319-56782-2_1910-2

[4] https://link.springer.com/article/10.1007/s00426-021-01554-x

[5] https://www.simplypsychology.org/stroop-effect.html

[6] https://link.springer.com/article/10.1007/s10548-014-0367-5

[7] https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256003

[8] https://link.springer.com/article/10.1007/s00426-021-01554-x

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Stroop Experiment

The famous Stroop experiment is named after John Ridley Stroop who first demonstrated the difference in reaction times between automatic and non-automatic cognitive processing.

How hard did you have to think to read the sentence above?

If you’re like most literate adults, reading is a skill so familiar and well-practiced that it may have felt like second nature to you. You may have barely noticed the cognitive effort your brain made to read and comprehend the words.

But what would happen if your brain was asked to do a task that it wasn’t as familiar with? This question is behind the elegant design of the Stroop experiment.

A participant is presented with three different kinds of stimuli. The first is “neutral” and consists of printed words much like what you’re reading right now. In this case, the words are for colors.

Green   Blue   Red   Yellow

The second kind of stimulus is to again present words for colors, but this time, the color of the word varies. These words are “incongruent”, meaning the word “green” may be printed in yellow, or the word “blue” may be printed in red etc.

Green     Blue     Red     Yellow

The third kind is merely to present blocks of color.

As you can see, there are two kinds of information here: the stimulus of color, and the stimulus of the word for a color. Color can be presented alone, words can be presented alone, or they can be presented together.

The Experiment

Stroop conducted two main experiments. The first was to have people read the neutral stimulus – the words printed in black ink – and then read the words printed in colored ink. The challenge was that they were asked to say aloud the words they saw and not state the color they were printed in.

The second experiment was similar. Participants were first asked to state the color of the color blocks they saw and then later, when shown the incongruent words, they had to say the color of the word, regardless of what the word was.

Stroop experiments like this are some of the most frequently replicated studies in cognitive psychology. In fact, you can try it out yourself right now.

The Results

You most likely found that you were quicker to read the neutral stimuli (words in black ink) than the incongruent stimuli (“red” written in blue ink for example). Stroop called this effect semantic interference . Because reading is so automatic, the brain immediately leaps in to think of the color red when presented with the word “red.”

It then has to quickly correct itself and deliberately focus attention on the color instead, a task much less automated. The interference between these two tasks – one automatic and the other more deliberate – is what makes your reaction times for the incongruent stimuli just a little longer.

Though this is the effect most people are familiar with when they think of the Stroop effect, there were other results. Stroop discovered that naming congruent stimuli (when the ink and word match) was faster than when presented with the colored square alone. He called this effect semantic facilitation , as it again suggested that our familiarity with written words was behind the fast reaction times. After all, it’s not very often that we’re asked to name the colors we see!

The Explanation

Question: Now that you’ve tried the experiment for yourself, imagine that you are presented with the results and have to explain the discrepancy in reaction times. What do you think is behind the Stroop effect?

• The brain reads faster than it can recognize colors. In a race between the two, the word information “wins” with the color information lagging behind.
• It simply takes more attention from your brain to recognize a color than it takes to read a word, and this takes more time.
• Recognizing colors is not something that most people have done enough to have it become automatic. We are in the habit of jumping in to do the familiar task even when told to do a less familiar one. This interferes with us doing the task well.
• The neural pathway in the brain that recognizes words is stronger than the pathway that recognizes colors. It’s not that we recognize words more quickly, only that this function is stronger in us than less familiar functions.

In fact, all of the above explanations have been offered to explain the effects we see in Stroop tests.

What’s Happening in the Brain?

When you did the test, did you find yourself thinking, “don’t read the words, just look at the colors” and force yourself to ignore the word in front of you? If so, you were likely using your posterior dorsolateral prefrontal cortex to do so!

In brain scans, this part of the brain has been shown to be active during Stroop tests, and is loosely understood as helping us create heuristics or “rules” for processing what’s in front of us. You may have experienced this as you deliberately shut off the parts of your brain that wanted to perceive the written word and refocused your attention on color recognition.

The anterior cingulate cortex is also involved in the Stroop task, and comes into play with memory, executive function, problem solving and making decisions on how to allocate mental resources.

The Stroop Test in Everyday Life

The Stroop test allowed researchers to not only theorize about what went on inside the brain and how, but to test it experimentally. Today, this test can be used to measure selective attention capacity and processing speed – which is useful in the diagnosis of a range of disorders such as ADHD, schizophrenia, dementia and brain damage.

The test can also be modified to measure the effect that a third variable (such as tiredness or intoxication) can have on processing times. The logic behind the test has undoubtedly inspired other research in cognitive psychology. Measuring the very small differences in processing times gives us some insight into the way the brain orders multiple tasks. Observing how we deal with tasks that are difficult or unusual gives insight into how the brain works in ideal situations.

Lastly, the Stroop test needn’t involve only words and colors. Experimenters can use numerical information, words that are more or less emotive (“chair” versus “murder” for example), or even manipulate the location or font of words.

• Psychology 101
• Flags and Countries
• Capitals and Countries

Lyndsay T Wilson (Nov 16, 2017). Stroop Experiment. Retrieved Aug 23, 2024 from Explorable.com: https://explorable.com/stroop-experiment

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How to make a Stroop task in PsychoPy ¶

A great starter exeriment ¶.

The Stroop task is a great starting point for learning to use PsychoPy, it is why we use it in the textbook ! So let’s talk through the basics of making a Stroop in PsychoPy.

Setting up our experiment ¶

OK step 1 of any experiment is to consider “what makes the main stimuli in my trial”. In the Stroop task, this is simple - a word, and a keyboard response (and maybe a fixation cross to start of the trial!).

Setting up our conditions file ¶

The next thing to do is to think about what information changes trial-by-trial. In a Stroop task the written word can either represent the same color, or a different color to the ink it is written in. Here we have made 2 basic “congruent” and 2 “incongruent” trials. We have also added a column to code the correct answer, in this case we want participants to press the left key if the word says red, and press the right key if the word says blue.

We aren’t going to use the column with the header “conditions” in our experiment. But this info will be saved to our data file, so in general it is good to be kind to future us, and think about what data you might want later when it comes to analysis.

Feeding trial info into PsychoPy ¶

Once we have our conditions file set up and saved in the same location as our experiment we need to give this info to our experiment. Add a loop around your trial routine and give the path to your conditions file in the Conditions field. We want to use the information from our conditions file to set a) the presented word (in the Text field of our word write $thisWord ) and b) the color of that word (in the Appearance tab of our word component write $thisColor in the Foreground Color field) - in both of these fields make sure to set every repeat this is because these are parameters that are going to change on each iteration of our trials loop.

Collecting responses ¶

In this experiment we want the participant to make a response on every trial, so we will leave the duration field of our keyboard component blank and make sure to check the Force end of Routine box (indicating that this routine will end and move on when a key press is made. We only want to watch the ‘left’ and ‘right’ keys, so make sure to only list those in the Allowed keys field. Finally, under the data tab in our keyboard component we then need to select the Store correct option and feed in our column header to the Correct answer field $corrAns

And there you have it! a very simple stroop task!

Exercise (15 mins) ¶

Add some instructions and a thanks message.

Add more colors combinations to the task

add a neutral condition.

Add a routine for participants to practice Hint: you can use the same routine several times in an experiment, which can really save work in the long run!

Youtube tutorial ¶

Building a Stroop Task

.

When you click on the link below, you will be presented with the experiment setup screen.  On this screen will be the variables you can set to define your condition.  Here is a list of the variables and their settings

After you have finished making your settings, press the Done button at the bottom of the screen.  The Stroop experiment screen will then be presented.  Press the button at the top of the page or the space bar to begin the experiment.  First a fixation mark in the middle of the screen will be presented.  It will be removed if the words are presented in the center.  When the word or string of X'x is presented, indicate the color of the stimulus.  You can do this three ways:

• press the relevant button at the bottom of the screen.
• press the following keys: r for red, g for green, b for blue, y for yellow
• press the following keys: a for red, f for green, j for blue and ; for yellow.

The stimuli are randomly selected so you might see the same stimulus twice in a row - there is no break so you might not see it reappear.  Just press the proper key again.

At the end of the experiment, your results will be presented, both average reaction time and your accuracy.  You can also get your trial by trial results.  Closing your results window(s) will take you back to the setup window so you can run another experiment.

Click here to start the experiment.

Acknowledgements:

• The priming flash is the idea of Dana Newton, Becky Nixon, and Sarah Pollom of Hanover College.
• The idea to display words backwards comes from Cole Wyatt, Alexis Rose and Matt Moore of Hanover College.
• The idea to use background colors on black words comes from Angela DiGeronimo, Dwayne Guenther, and Amber Adkins of Hanover College
• The use of random letter positions and the selection of different colors is inspired by Matt Moore and Sarah Pollom of Hanover College

Data Skills

2.1 intended learning outcomes.

By the end of this chapter you should be able to:

• Explain what the Stroop effect is and how is it measured
• Load and use packages and functions in R
• Load data using read_csv()
• Check data using summary() , str() , and visual methods

2.2 Walkthrough video

There is a walkthrough video of this chapter available via Zoom . We recommend first trying to work through each section of the book on your own and then watching the video if you get stuck, or if you would like more information. This will feel slower than just starting with the video, but you will learn more in the long-run. Please note that there may have been minor edits to the book since the video was recorded. Where there are differences, the book should always take precedence.

2.3 Activity 1: The Stroop Effect

In this chapter and the next chapter, we're going to develop your data skills by using data from one of the most famous experiments in psychology: The Stroop Effect.

• First, take part in this online version of the Stroop test. It only takes a few minutes to complete. You need to be on a device with a keyboard.
• Second, read the Wikipedia summary of the Stroop Effect and how it is used in psychological research.
• Finally, answer the following questions. Please note that your responses will not save in the browser - if you want to save them, make a note of them somewhere.
• What does the Stroop Effect primarily measure?

Color perception Language comprehension Cognitive interference Reading speed

The Stroop Effect primarily measures cognitive interference, which occurs when the processing of a specific stimulus feature impedes the simultaneous processing of a second stimulus attribute.

• What is one possible explanation for the Stroop Effect?

People are naturally slower at reading than identifying colors Reading is an automatic process that can interfere with color identification Color identification is an automatic process that can interfere with reading None of the above

The Stroop Effect is often explained by the theory of automaticity, which suggests that reading—the task of recognizing words—has become an automatic process for most individuals, and this automatic reading process interferes with the task of naming the color.

• In a typical Stroop test, trials (where the color of the ink matches the word) are completed faster than trials (where the color of the ink does not match the word).

Answer: congruent, incongruent

In a typical Stroop test, there are two types of tasks - congruent and incongruent.

Congruent tasks are those where the color of the ink matches the word. For example, the word "BLUE" is printed in blue ink. In this case, the visual color and the semantic information (the meaning of the word) align or are "congruent."

Incongruent tasks, on the other hand, are those where the color of the ink does not match the word. For example, the word "BLUE" might be printed in red ink. Here, the visual color and the semantic information are in conflict or are "incongruent."

Participants in a Stroop test are typically asked to name the color of the ink, not the word. This is where the interference comes in. Reading words is such an automatic process for most literate adults that the participant's brain automatically reads the word before recognizing the color of the ink. This causes a delay in the response time for incongruent tasks, as the brain has to overcome the initial automatic response to read the word.

Therefore, congruent tasks are typically completed faster than incongruent tasks in a Stroop test. The difference in response times is the measure of the Stroop effect, which demonstrates the nature of cognitive interference and the automatic process of reading.

2.4 Activity 2: New project

• Log in to the to R sever using the link that's on Moodle (and if you haven't already, bookmark the link to make your life easier!).

To help keep things organised, we'll make a new project for the Stroop experiment chapters (this week and next week). To make a new project, the steps are almost the same as what you did in the first chapter with the exception that you don't need to make the Psych 1A folder this time, you just need to find it:

• Click on the "New project" button;
• Then, click on the first option in the list "New Directory";
• Then, click "New Project";
• Then you are given the opportunity to name your project and select which folder it should be stored in. First in the "Directory name" box, type "Stroop Effect";
• The subdirectory should already be set to Psych 1A but if not, click browse and navigate to it then click "Choose";
• Finally, click "Create project".

Figure 1.2: Creating a new project

2.5 Activity 2: Setting check

Now you need to download the data files we're going to use. Before you do, we need to check some settings on your browser because the biggest issue new students face with R is not learning to code, it's knowing where your files are.

• First, if you're not 100% sure, check your browser settings to make sure you know where files you download are going to go. If you're on Windows, there's a good chance the default will be the Downloads folder. However, this folder can sometimes end up a dumpster fire of files so it can be better to change your settings so that your browser asks you where to save each download to so that you have to consciously choose each time. This website explains how to check and change these settings for all different browsers.

We're going to ask you to download zip files which you will then upload to the server. A zip file is a folder that contains files that have been compressed to make the file size smaller (like vacuum packed food) and enables you to download multiple files at once.

• If you're on a Mac and using Safari as your browser, it has a very annoying default habit of unzipping files when you download them. It's trying to be useful but you need the zipped file to upload to the server so it actually causes problems by doing this. We'd strongly recommend just not using Safari at all because it seems to cause a few issues with R and using Chrome or Firefox instead but if you are particularly attached to it, change the settings to stop it unzipping files.

2.6 Activity 3: Data files

Once you've done all this, it's time to download the files we need and then upload them to the server.

• First, download the Stroop data zip file to your computer and make sure you know which folder you saved it in.
• Then, on the server in the Files tab (bottom right), click Upload > Choose file then navigate to the folder on your computer where the zip file is saved, select it, click Open , then OK .

The server will automatically unzip the files for you into your chosen folder and in this case, it's helpful as it means they're now ready to use.

The zip file contains four files:

• stroop_stub1.Rmd and stroop_stub2.Rmd : to help you out in the first semester, we'll provide pre-formatted Markdown files ("stub" files) that contain code chunks for each activity and spaces for you to take notes. Open stroop_sub1.Rmd by clicking on it in the Files tab and then edit the heading to add in your GUID and today's date.
• participant_data.csv is a data file that contains each participant's anonymous ID, age, and gender. This data is in wide-form which means that all of the observations about one subject are in the same row. There are 270 participants, so there are 270 rows of data.

• experiment_data.csv is a data file that contains each participant's anonymous ID, and mean reaction time for all the congruent and incongruent trials they completed. This data is in long-form where each observation is on a separate row so for the Stroop experiment, each participant has two rows because there are two observations (one for congruent trials and one for incongruent trials). So there are 270 participants, but 540 rows of data (270 * 2).

You may be less familiar with this way of organising data, but for many functions in R your data must be stored this way. This semester, we'll provide you with the data in the format it needs to be in and next semester we'll show you how to transform it yourself.

Before we load in and work with the data files we need to explain a few more things about how R works.

2.7 Packages and functions

When you install R you will have access to a range of functions including options for data wrangling and statistical analysis. The functions that are included in the default installation are typically referred to as base R and you can think of them like the default apps that come pre-loaded on your phone.

One of the great things about R, however, is that it is user extensible : anyone can create a new add-on that extends its functionality. There are currently thousands of packages that R users have created to solve many different kinds of problems, or just simply to have fun. For example, there are packages for data visualisation, machine learning, interactive dashboards, web scraping, and playing games such as Sudoku.

Add-on packages are not included with base R, but have to be downloaded and installed from an archive, in the same way that you would, for instance, download and install PokemonGo on your smartphone. The main repository where packages reside is called CRAN , the Comprehensive R Archive Network.

There is an important distinction between installing a package and loading a package.

2.7.1 Installing a package

This is like installing an app on your phone: you only have to do it once and the app will remain installed until you remove it. For instance, if you want to use PokemonGo on your phone, you install it once from the App Store or Play Store; you don't have to re-install it each time you want to use it. Once you launch the app, it will run in the background until you close it or restart your phone. Likewise, when you install a package, the package will be available (but not loaded ) every time you open up R.

The packages you need for this course are already installed on the server so we're not going to show you how to install packages this semester because if you reinstall them on the sever it can cause issues. If you have installed R on your own laptop you should be confident enough to look up how to do this yourself - if not, you can come to office hours (but also, we'd encourage you just to use the server as it will help you follow along with this workbook!).

2.7.2 Loading a package

This is done using the library() function. This is like launching an app on your phone: the functionality is only there when the app is launched and remains there until you close the app or restart. For example, when you run library(cowsay) within a session, the functions in the package cowsay will be made available for your R session. The next time you start R, you will need to run library(cowsay) again if you want to access that package.

2.8 Activity 4: Packages and function

As an example, let's load the cowsay package which is already installed on the server. The cowsay package is just for fun - it will print a message with an animal - but it's useful to show you how packages work. To load a package, you use the function library() and include the name of the package you want to load in parentheses.

• In code chunk 1, type and run the below code to load the cowsay package. If you can't remember how to run code, take a look back at Chapter  1.9.4 .

You'll see library(cowsay) appear in the console. There's no warning messages or errors so it looks like it has loaded successfully.

2.8.1 Using a function

Now you can use the function say() . A function is a name that refers to code that performs some sort of action.

• In code chunk 2, write and run the below code to use the function say() .
• If you get the error could not find function it means you have not loaded the package properly, try running library(cowsay) again and make sure everything is spelled exactly right.

After the function name, there is a pair of parentheses, which contain zero or more arguments . These are options that you can set. If you don't give it any information, it will try and use the default arguments if it has them. say() has two main arguments with a default value : what the text says (default Hello world ), and the animal the message is said by (default is a cat).

To look up all the various options that you could use with say() , run the following code in the console:

The help documentation can be a little hard to read - scrolling to the bottom and looking at the examples can help.

• In code chunk 2, add the below code to produce a different message and animal - below is an example but try your own.

2.8.2 Function Help

If you want more information about what a function does or how to use it, you can look at the help document. If a function is in base R or a package you have loaded, you can type ?function_name in the console to access the help file. At the top of the help it will give you the function and package name.

If the package isn't loaded, use ?package_name::function_name . When you aren't sure what package the function is in, use the shortcut ??function_name which will give you a list of all possible options.

• In the console , type and run the code for the different help options below. The reason you run the help code in the console not a code chunk is that you generally don't want to save this code in your script.

Function help is always organised in the same way. For example, look at the help for ?cowsay::say . At the top, it tells you the name of the function and its package in curly brackets, then a short description of the function, followed by a longer description. The Usage section shows the function with all of its arguments . If any of those arguments have default values, they will be shown like function(arg = default) . The Arguments section lists each argument with an explanation. There may be a Details section after this with even more detail about the functions. The Examples section is last, and shows examples that you can run in your console window to see how the function works.

Use the help documentation to find the answers to these questions:

• What is the first argument to the mean function? trim na.rm mean x
• What package is read_excel in? readr readxl base stats

2.9 Activity 5: Loading data

OK, let's get back to looking at our data. In order to load and work with our Stroop data, we need to load another very important package.

2.9.1 Load the Tidyverse

tidyverse is a meta-package that loads several packages we'll be using in almost every chapter in this book:

• ggplot2 , for data visualisation
• readr , for data import
• tibble , for tables
• tidyr , for data tidying
• dplyr , for data manipulation
• stringr , for strings
• forcats , for factors
• purrr , for repeating things

To use readr to import the data, we need to load the tidyverse .

• In code chunk 3, write and run the code to load the tidyverse . When you run this code, you're going to get something that at first glance might look like an error but it's not, it's just telling you which packages it has loaded.

Figure 2.1: Tidyverse message when successfully loaded

2.9.2 Read in the data

Now we can read in the data. To do this we will use the function read_csv() that allows us to read in .csv files, which are a type of data file. There are also functions that allow you to read in .xlsx (Excel) files and other formats, however in this course we will only use .csv files.

First, we will create an object called dat that contains the data in the experiment_data.csv file. Then, we will create an object called ppt_info that contains the data in the participant_data.csv .

• In code chunk 4, write and run the below code to load the data files.

In order to load the file successfully, the name of the file needs to be in double quotation marks and it must have the file extension .csv . If you miss this out, you'll get the error message ...does not exist in current working directory . To fix it, make sure you've spelled the name of the file right and included the file extension.

Additionally, if you get the message could not find function "read_csv()" it means that you have not loaded the tidyverse - a common error is to write the code but not run it! To fix it, run the code that loads the tidyverse. Another reason you might see this message is if you've made a typo in the name of the function, so check that you've spelled read_csv exactly right.

2.10 Activity 6: Check your data

You should now see that the objects dat and ppt_info have appeared in the environment pane. Whenever you read data into R you should always do an initial check to see that your data looks like how you expected. There are several ways you can do this, try them all out to see how the results differ.

• In the environment pane, click on dat and ppt_info . This will open the data to give you a spreadsheet-like view (although you can't edit it like in Excel).
• In the environment pane, click the small blue play button to the left of dat and ppt_info . This will show you the structure of the object information including the names of all the variables in that object and what type they are.
• In code chunk 5, write and run summary(ppt_info) (and do the same for dat )
• In code chunk 5, write and run str(ppt_info) (and do the same for dat )

What is the mean age pf participants to 1 decimal place?

What is the mean overall reaction time to 1 decimal place?

2.11 Activity 7: Visualise the data

As you're going to learn about more over this course, data visualisation is extremely important. Visualisations can be used to give you more information about your dataset, but they can also be used to mislead.

We're going to look at how to write the code to produce simple visualisations in a few weeks, for now, we want to focus on how to read and interpret different kinds of graphs. Please feel free to play around with the code and change TRUE to FALSE and adjust the values and labels and see what happens but do not worry about understanding this code for now . Just copy and paste it.

• Copy, paste and run the below code in code chunk 6 to produce a bar graph that shows the number of men, women, and non-binary participants in the dataset.

Figure 2.2: Number of participants by gender

Are there more men, women, or non-binary participants in the sample? More men More women More non-binary participants

• Copy, paste, and run the below code in code chunk 7 to create violin-boxplots of reaction times for each condition.

Figure 2.3: Violin-boxplot of reaction times in each condition

• The violin (the wavy line) shows density. Basically, the fatter the wavy shape, the more data points there are at that point. It's called a violin plot because it very often looks (kinda) like a violin.
• The boxplot is the box in the middle. The black line shows the median score in each group. The median is calculated by arranging the scores in order from the smallest to the largest and then selecting the middle score.
• The other lines on the boxplot show the interquartile range. There is a really good explanation of how to read a boxplot here .

Which condition has the longest median reaction time? Congruent Incongruent

2.12 Finished

Finally, try knitting the file to HTML. And that's it, well done! It's absolutely ok if you don't understand 100% of what you're doing at this point, we're going to repeat everything you've done with different datasets so you will get lots of practice and we're going to build it up very slowly. Also remember that you can attend GTA sessions and office hours for help with data skills - you don't have to have specific questions, some students just like to use the GTA sessions as the time to work through the data skills book so that if they need help, they're already there.

Remember to make a note of any mistakes you made and how you fixed them, any other useful information you learned, save your Markdown, and quit your session on the server.

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Multiple levels of control in the Stroop task

Multiple levels of control may be used in service of reducing Stroop interference. One is list-wide, whereby interference is reduced strategically in lists that include disproportionately more incongruent trials. A second, item-specific control is observed when proportion congruence is manipulated at the level of items. Item-specific control reduces interference for mostly incongruent relative to mostly congruent items. First, we show that item-specific control may drive both list-wide and item-specific proportion congruence effects (Experiment 1). We then show that item-specific control affects Stroop interference similarly when a single feature (a word) as opposed to a feature combination (a word + font type) signals proportion congruence (Experiment 2). Although this suggests that font type offers little advantage for controlling Stroop interference beyond the word, a novel, font-specific proportion congruence effect is observed in Experiment 3, indicating that font type can be used to control interference. These findings support the idea that multiple levels of control are used in reducing Stroop interference.

The Stroop color-naming task ( Stroop, 1935 ) is well suited for evaluating flexibility in the control of cognitive processes and behavior. In the congruent condition of the task, stimulus word matches stimulus color (e.g., BLUE in blue ink) and participants may rely on well-learned reading processes to produce fast and accurate responding. In the incongruent condition, in contrast, accurate responding requires participants to use cognitive control mechanisms to dampen word reading and activate color-naming processes. The additional time that is taken to name the ink color in the incongruent relative to the congruent condition is referred to as Stroop interference . Although the task might seem relatively simple, the literature is replete with reports of robust Stroop interference effects (for a review, see MacLeod, 1991 ). Close to 1,000 articles have been published on the topic, yet the control mechanism(s) used to dampen word reading and activate color-naming processes remain to be fully explicated.

A complicating (or revealing, as we will argue) factor is the different instantiations (e.g., blocked conditions vs. intermixed trials) of the Stroop color-naming task appearing in the literature. Different task contexts appear to elicit different forms of cognitive control, precluding a unitary account of control mechanisms. Proportion congruence is one prominent factor that influences the control mechanisms that are adopted within a given task. Traditionally, proportion congruence is manipulated at a list-wide level by disproportionately presenting congruent and incongruent trials within a list. Participants can use frequencies to predict what type of trial is most likely to occur next, and control processes can be biased toward (as in a mostly congruent list) or away from (as in a mostly incongruent list) word reading prior to stimulus onset on the basis of these expectancies. Such contexts seem to induce a preparatory, goal-driven control mechanism that is implemented in a sustained fashion across trials (i.e., the bias toward or away from word reading remains constant throughout a list), analogous to the proactive control mechanism recently posited in the dual-mechanisms-of-control account ( Braver, Gray, & Burgess, 2007 ). In contrast, in other task contexts, congruent and incongruent trials occur equally often within a list, and one is unable to anticipate the upcoming trial type and prepare control processes accordingly. These contexts demand a more flexible control mechanism that is capable of modulating word-reading and color-naming processes in a transient fashion on a trial-by-trial basis. Because such modulation occurs after stimulus onset, such a control mechanism must operate rapidly.

By this analysis, different cognitive control mechanisms underlie Stroop performance. One control mechanism appears to operate slowly and strategically at a list level, acting prior to stimulus onset. A second appears to operate rapidly at a trial or item-specific level, and acts after the stimulus has been presented. This conception of there being two distinct levels of control may be misleading, however, because these mechanisms largely have been uncovered in independent lines of investigation. In Experiment 1, we simultaneously investigate these two putative control mechanisms. There are at least two possibilities for their interplay. One is that both mechanisms exert observable influences on the magnitude of Stroop interference across different levels of proportion congruence (e.g., mostly congruent, mostly incongruent, or 50/50 contexts). Alternatively, a single level of control may underlie completely the differences in the magnitude of Stroop interference across different levels of proportion congruence. Next, we develop these ideas.

List-Level Control

The list-wide proportion congruence effect refers to the attenuation of Stroop interference that is observed in a mostly incongruent relative to a mostly congruent context (e.g., list, block, or condition) (see, e.g., Lindsay & Jacoby, 1994 ; Logan & Zbrodoff, 1979 ; Logan, Zbrodoff, & Williamson, 1984 ; Lowe & Mitterer, 1982 ; Shor, 1975 ; Tzelgov, Henik, & Berger, 1992 ). The list-wide proportion congruence effect is attributed most commonly to task strategies or to cognitive control settings that uniformly modulate the degree to which word reading and color naming influence performance in a particular context. In the mostly incongruent condition, control is believed to operate in a goal-driven fashion, strategically reducing the influence of the word prior to stimulus onset. In contrast, in the mostly congruent condition, word reading is permitted largely to govern behavior, because word reading is facilitative on the dominant trial type. In this condition, incongruent stimuli are unanticipated and produce greater conflict upon onset than do the same stimuli in the mostly incongruent condition (e.g., Carter et al., 2000 ). These dynamics produce prolonged reaction times (RTs) for incongruent items and greater Stroop interference in the mostly congruent context.

Item-Level Control

An alternative explanation of the list-wide proportion congruence effect refers to item-specific control. 1 This explanation is supported by a recent study in which it was shown that proportion congruence effects occur at the level of particular items ( Jacoby, Lindsay, & Hessels, 2003 ). In their study, Jacoby et al. (2003) assigned color-word sets composed of two to three items to either a mostly incongruent condition (e.g., BLACK, BLUE, and GREEN) or a mostly congruent condition (e.g., RED, YELLOW, and WHITE). Seventy percent of the time, each item (e.g., BLUE) in the mostly incongruent condition occurred in an incongruent color from that set (e.g., black or green), and 30% of the time it appeared in the congruent color (blue). The proportions were reversed for the mostly congruent items, such that RED, for example, appeared 70% of the time in red ink and 30% of the time in yellow or white ink. Jacoby et al. (2003) observed an item-specific proportion congruence effect, whereby Stroop interference was attenuated for the items (e.g., BLACK, BLUE, and GREEN) that were mostly incongruent relative to the items (e.g., RED, YELLOW, and WHITE) that were mostly congruent. Critically, the item-specific proportion congruence effect occurred in a list-wide context wherein trial type was unpredictable because congruent and incongruent trials occurred equally often and were intermixed randomly. This suggests that the locus of the item-specific proportion congruence effect resides not in control strategies based on learned frequencies or expectancies regarding the upcoming trial type. A single word-reading policy per list, whereby participants decide to use or avoid using word information, is ineffectual when participants do not know what type of trial will occur next. Rather, the item-specific proportion congruence effect appears to reflect control at the time of stimulus onset. How might an item-specific control mechanism account for list-wide proportion congruence effects?

For purpose of exposition, consider a typical list-wide proportion congruence experiment in which 70% of trials are congruent and 30% of trials are incongruent. The standard procedure is to design the lists in a manner that holds constant the list-wide proportion congruence level for each item in the stimulus set (e.g., BLUE, GREEN, and RED). Seventy percent of BLUE, GREEN, and RED trials appear in their congruent ink color, and 30% appear equally often in one of the two incongruent ink colors. As such, variations in list-wide proportion congruence are confounded perfectly with variations in item-specific proportion congruence. The implication is that specific items (i.e., words), rather than lists in general, could be associated with particular congruence proportions. This raises the question as to whether list-wide proportion congruence effects are really item-specific effects in disguise.

Additional theorizing also has addressed the interplay between list- and item-specific levels of control in the Stroop task and has questioned whether list-wide proportion congruence has any influence on performance. For example, Blais, Robidoux, Risko, and Besner (2007) recently modified the classic conflict-monitoring model of Botvinick, Braver, Barch, Carter, and Cohen (2001) . Rather than implementing control at the pathway (color-naming) level on the basis of the detection of conflict, Blais et al. implemented control at an item level. In their model, the detection of conflict served to strengthen the association between the color-naming pathway and a specific color rather than color naming in general, as in the Botvinick et al. model. Simulations showed that this new model accounted for both the list-wide and item-specific proportion congruence effects, whereas the prior model fell short in accounting for the latter. Together with the findings of Jacoby et al. (2003) , such modeling raises the possibility that a control mechanism that operates at the item-specific level can alone account for list-wide proportion congruence effects. Alternatively, as noted above, both list-level and item-specific control may be operative in a single task context (e.g., a mostly incongruent list), but existing designs may mask their separate contributions.

EXPERIMENT 1

Experiment 1 tested these possibilities by isolating the list-wide effects from the item-specific influences. This was achieved by using two pairs of color words. One pair (e.g., RED and BLUE) always had an equal number of congruent and incongruent trials—that is, an item-specific proportion congruence level of 50%. A second pair (e.g., GREEN and WHITE) had either a high or a low proportion of congruent trials. More specifically, the item-specific proportion congruence (PC) of the second pair was either 75% or 25%. Presenting the first pair (i.e., 50% item-specific PC) with either the item-specific PC-75 pair or the item-specific PC-25 pair together in a mixed list produced list-wide proportion congruence equal to 67% and 33%, respectively. The primary question of interest was whether Stroop effects for the item-specific PC-50 items would change as a function of the list-wide proportion congruence set by the item-specific PC-75 and item-specific PC-25 items. Such a result would support the existence of a list-level control mechanism because no item-specific influence is expected to be operating when item-specific PC is 50%. Alternatively, no change in Stroop effects for the item-specific PC-50 items as a function of list-wide proportion congruence would indicate an absence of list-wide control. If Stroop effects differ for the item-specific PC-75 and item-specific PC-25 items, then this suggests that an item-specific control mechanism is operative.

A second question of interest concerned the effects of age on list-wide and item-specific control. Past studies indicate that the magnitude of slowing on incongruent relative to congruent trials tends to be larger for older adults and that this pattern does not simply reflect generalized slowing (e.g., Brink & McDowd, 1999 ; but see Verhaeghen & De Meersman, 1998 , for an alternative view). The reason for this increase in Stroop interference is debated. Some researchers suggest that it reflects an age-related deficit in cognitive control related to inhibitory processes ( Spieler, Balota, & Faust, 1996 ) or goal maintenance ( De Jong, Berendsen, & Cools, 1999 ). Little is known, however, about the effect of proportion congruence manipulations on older adults’ Stroop performance. Mutter, Naylor, and Patterson (2005) found that interference was greater in a mostly congruent as compared with a mostly incongruent list for both younger and older adults. West and Baylis (1998) found that age differences in Stroop interference were limited to a mostly incongruent block, with older and younger adults showing similar Stroop effects in a mostly congruent block of the task. The authors attributed this pattern to older adults’ difficulty in actively maintaining the color-naming goal to strategically guide task performance in the mostly incongruent block. In other words, older adults were purported to have a deficit in proactively implementing a list-wide form of control. If strategic control processes such as active goal maintenance underlie list-wide proportion congruence effects and older adults are impaired relative to younger adults in implementing these control processes, then one should expect age-related differences in how Stroop effects are modulated by list-wide proportion congruence, as evaluated by performance on the item-specific PC-50 trials in the present experiment. To our knowledge, item-specific proportion congruence effects have not been investigated in an older adult population, and therefore it is not clear whether older adults will be disadvantaged at implementing item-specific control on the item-specific PC-25 and PC-75 trials. Examining possible age effects on list-wide and item-specific proportion congruence may be revealing as to the mechanisms underlying these manipulations.

Participants

Thirty-six young (18–23 years; M = 19.9) and 36 older (67–87 years; M = 74.8) adults participated in the experiment. The young adults were undergraduate students at Washington University and participated for course credit. The older adults were from the Washington University Older Adult Participant Pool and were each paid $5. All participants were native English speakers with normal color vision and normal or corrected-to-normal visual acuity. A random half of the participants from each age group were assigned to each level of the between-subjects factor, list-wide proportion congruence.

Design and Materials

Four color-words and their corresponding colors were divided into two pairs (RED and BLUE, GREEN and WHITE). Words from one pair (e.g., RED and BLUE) served as item-specific proportion congruency PC-50 items for which an equal number of congruent and incongruent trials were presented; thus, the item-specific PC was 50%. Words from the other pair (e.g., GREEN and WHITE) served as item-specific PC-25 or item-specific PC-75 items for which either a low or high proportion of congruent trials, respectively, were presented. When mixed with the item-specific PC-50 items, the item-specific PC-25 items produced a list-wide PC of 33%. Similarly, the item-specific PC-75 items produced a list-wide proportion congruence of 67% when mixed with the item-specific PC-50 items. This list-wide proportion congruence manipulation was between participants, with 18 young and 18 older adults assigned randomly to the 33% and 67% list-wide PC conditions. The frequencies of the different stimulus types are given in Table 1 . In addition to incongruent and congruent trials, 32 neutral trials were also presented. Neutral trials consisted of eight instances of each of the four colors, presented as strings of percent signs. Word pairs were counterbalanced across participants such that each word served equally often as item-specific PC-50 or item-specific PC-25/PC-75 items. The test list of 320 trials was separated into four blocks of 80 trials, with each block presenting one quarter of all possible word/color combinations. Presentation order within a block was randomized for each participant. The experiment was programmed in E-Prime 1.1, with words presented in E-Prime’s standard color palette (“red,” “blue,” “green,” and “white”) in 24-point Arial font positioned in the center of the screen against a light gray (“silver”) background.

Frequencies of the Word/Color Combinations in the List-Wide PC-33 and List-Wide PC-67 Conditions of Experiment 1

Note—List-wide PC-33 and PC-67 refer to a list-wide proportion congruency of 33% or 67%. In the example above, the words RED and BLUE are serving the role of item-specific PC-50 items, and the words GREEN and WHITE are serving the role of item-specific PC-25 and PC-75 items.

The experiment was conducted in a small room with the experimenter present. Participants were told that words or percent signs would be presented in the center of the screen and that their task was to name the color in which each stimulus was presented as quickly and accurately as possible. After completing 12 practice trials (one of each of the eight possible word/color combinations along with 4 neutral trials), participants performed four blocks of 80 trials, taking short breaks between each block. For each trial, a single stimulus was presented in the center of the screen and remained visible until a vocal response was detected, at which point the stimulus was erased. The experimenter entered the participant’s response via keyboard. Trials on which the voice key was tripped by extraneous noise or imperceptible speech were coded as scratch trials. One second later, the next stimulus was presented. The entire procedure took about 25 min.

For each participant, RTs less than 200 msec and greater than 3,000 msec were removed, which eliminated fewer than 1% of the trials for both the young and older adults. Results reported as statistically significant reached at least the .01 alpha level, except where noted.

Mean RTs for item-specific PC-50, PC-25, and PC-75 trials are presented in Table 2 . There were two sets of critical analyses. In the first, we analyzed performance on the item-specific PC-50 trials to determine whether Stroop effects were influenced by list-wide proportion congruence. A 2 × 2 × 2 mixed ANOVA was conducted with trial type (congruent vs. incongruent) as a within-subjects factor and list-wide proportion congruence (list-wide PC-33 vs. list-wide PC-67) and age (young vs. old) as between-subjects factors. As expected, RTs were slower for incongruent than for congruent trials [ F (1,68) = 157.14, MS e = 3,388.02], older adults were slower than younger adults [ F (1,68) = 38.65, MS e = 30,809.81], and the magnitude of slowing on the incongruent trials relative to the congruent trials was larger for older adults [ F (1,68) = 17.17, MS e = 3,388.02]. Most importantly, the list-wide proportion congruence manipulation and all interactions involving this factor were not significant ( F s < 1), suggesting that list-wide proportion congruence did not affect Stroop performance. In particular, the nonsignificant interaction between list-wide proportion congruence (33% vs. 67%) and trial type (congruent vs. incongruent) indicated that Stroop effects were almost identical for item-specific PC-50 items in the list-wide PC-33 and list-wide PC-67 conditions. This was the case for both the young adults ( M s = 75 vs. 88 msec, respectively) and older adults ( M s = 160 vs. 163 msec, respectively) ( F s < 1). Given the possibility that participants may have become more sensitive to the list-wide proportion congruence manipulation in the latter blocks of the task, we reconducted the analyses above separately for each half of the task. The results from the first half and second half were identical to the patterns reported above.

Mean Reaction Times (RTs, in Milliseconds) for Item-Specific PC-50, PC-25, and PC-75 Trials in Experiment 1

Note—IS, item-specific; LW, list-wide; PC, proportion congruence, with the number referring to the proportion of congruent trials. Stroop effect = RT(incongruent) - RT(congruent).

Given that list-wide proportion congruence had no significant effect on the magnitude of Stroop interference, we then analyzed the item-specific PC-25 and item-specific PC-75 trials to determine whether performance was affected by item-specific proportion congruence. A 2 × 2 × 2 mixed ANOVA was conducted with trial type (congruent vs. incongruent) as a within-subjects factor and item-specific proportion congruence (item-specific PC-25 vs. item-specific PC-75) and age (young vs. old) as between-subjects factors. RTs were slower for incongruent than for congruent trials [ F (1,68) = 262.70, MS e = 2,689.53], older adults were slower than younger adults [ F (1,68) = 39.12, MS e = 26,844.40], and the magnitude of slowing on the incongruent trials relative to the congruent trials was larger for older adults [ F (1,68) = 20.29, MS e = 2,689.53]. Most critically, there was strong evidence of an item-specific proportion congruence effect. The Stroop effect increased reliably from the item-specific PC-25 condition ( M = 89 msec) to the item-specific PC-75 condition ( M = 190 msec) [ F (1,68) = 33.66, MS e = 2,689.53]. Although the increase was larger for older adults (120 to 238 msec) than for younger adults (60 to 142 msec), the three-way interaction was not significant [ F (1,68) = 1.12, p = .29]. As with the list-wide proportion congruence manipulation, the effects of the item-specific manipulation and all interactions involving this factor did not change as a function of experience (first half vs. second half) with the task.

To verify that this pattern of findings did not change when log-transformed RTs were used to account for differences in baseline response latency, we repeated the analyses above. The pattern of results was identical. 2

Mean error rates for item-specific PC-50, PC-25, and PC-75 trials are presented in Table 3 . The analyses of error rate mirror those reported above for RT, focusing first on the effects of list-wide proportion congruence. The 2 × 2 × 2 mixed ANOVA indicated that error rates were higher for incongruent ( M = .03) than for congruent ( M = .004) trials [ F (1,68) = 32.65, MS e = .001] and higher for younger adults ( M = .03) than for older adults ( M = .01) [ F (1,68) = 5.05, MS e = .001]. Most importantly, the list-wide proportion congruence manipulation and all interactions involving this factor were not significant ( F s < 1), suggesting that list-wide proportion congruence did not affect error rate. Consistent with the RT analysis, the nonsignificant interaction between list-wide proportion congruence (33% vs. 67%) and trial type (congruent vs. incongruent) indicated that the Stroop effect in error rate was almost identical for item-specific PC-50 items in the list-wide PC-33 and list-wide PC-67 conditions. This was the case for younger and older adults.

Mean Error Rates for Item-Specific PC-50, PC-25, and PC-75 Trials in Experiment 1

Note. IS, item-specific; LW, list-wide; PC, proportion congruence, with the number referring to the proportion of congruent trials. Stroop effect = error rate(incongruent) - error rate(congruent).

The effects of the list-wide proportion congruence manipulation were then examined separately for the first and second halves of the task. The entire pattern of findings was consistent with the combined block analysis reported above, except that the main effect of age was not significant [ F (1,68) = 1.61, p = .21] during the first half of the task.

To examine whether item-specific proportion congruence had an effect on error rate, a 2 × 2 × 2 mixed ANOVA was again conducted, this time focusing on the item-specific PC-25 and item-specific PC-75 trials. Error rates were higher for incongruent trials ( M = .03) than for congruent trials ( M = .003) [ F (1,68) = 38.11, MS e = .001], younger adults ( M = .03) made more errors than did older adults ( M = .01) [ F (1,68) = 11.87, MS e = .001], and the relatively larger error rate on incongruent relative to congruent trials was larger for younger adults [ F (1,68) = 5.22, MS e = .001, p = .03]. These main effects were qualified by several interactions. Critically, there was strong evidence of an item-specific proportion congruence effect in error rate. The significant two-way interaction between item-specific proportion congruence and trial type indicated that the Stroop effect in error rate was smaller for the item-specific PC-25 condition ( M = .01) than for the item-specific PC-75 condition ( M = .04) [ F (1,68) = 10.83, MS e = .001]. Furthermore, the three-way interaction was also significant [ F (1,68) = 5.00, MS e = .001, p = .03], indicating that the difference in the magnitude of the Stroop effect between the item-specific PC-25 and PC-75 conditions was greater for younger ( M s = .01 vs. .06) than for older ( M s = .01 vs. .02) adults. Examining the age groups separately, 2 × 2 mixed ANOVAs indicated that the item-specific proportion congruence × trial type interaction was significant for younger [ F (1,34) = 8.59, MS e = .00] but not for older [ F (1,34) = 2.52, p = .12] adults.

When the effects of the item-specific manipulation were examined separately for the first and second halves of the task, the pattern of findings was identical to that reported above for the combined block analysis, with a few exceptions. The main effect of group ( p = .06), age × trial type interaction [ F (1,68) = 1.23, p = .27], and age × trial type × item-specific proportion congruence interaction [ F (1,68) = 2.04, p = .16] were not significant during the first half.

The results of Experiment 1 suggest that an item-level control mechanism influenced the magnitude of Stroop interference such that interference was smaller for the mostly incongruent items. A critical question is whether a list-level control mechanism was also operative. The results strongly suggest that it was not, because list-wide proportion congruence had no effect on the magnitude of Stroop interference. This novel finding is contrary to several past reports (e.g., Logan et al., 1984 ; Lowe & Mitterer, 1982 ). The primary difference between the present study and studies such as these that demonstrated a list-wide proportion congruence effect is the design. Past studies perfectly confounded the list-wide manipulation with an item-specific manipulation. Here, we manipulated list-wide proportion congruence while holding item-specific proportion congruence constant. This approach allowed us to evaluate the distinct contributions of list-level and item-level control. The finding that item-level control exerted a significant influence on Stroop performance, but that list-level control did not, calls into question the locus of list-wide proportion congruence effects in previous studies. What formerly has been described as a list-wide control mechanism may actually be control that is operating on an item-by-item basis.

The analyses of age effects were also informative. Like the younger adults, older adults’ Stroop performance (RT and error rate) was not affected by the list-wide proportion congruence manipulation. In contrast, for RT, older adults’ Stroop performance was significantly modulated by item-specific proportion congruence in a manner that was comparable to the modulation observed for younger adults. That is, older adults’ Stroop interference, like that of younger adults, was smaller for the mostly incongruent as compared with the mostly congruent items. This novel observation suggests that older adults are sensitive to proportion congruence manipulations when they are implemented at the level of particular items.

The item-specific proportion congruence effect was age invariant for RT, but not for error rate. For younger adults, the item-specific proportion congruence effect was large and reliable. However, for older adults, the smaller Stroop effect in error rate in the mostly incongruent (item-specific PC-25) relative to the mostly congruent (item-specific PC-75) condition was not statistically reliable. A survey of the means from the critical cells indicates that younger and older adults had similar error rates in the mostly incongruent condition (1%), but younger adults’ error rates in the mostly congruent condition (6%) were inflated relative to older adults’ error rates (2%). Thus, although both groups appear to exploit item-specific proportion congruence similarly in reducing Stroop interference in RT, there does appear to be an age difference in the degree to which item-specific proportion congruence affects error rate. Focusing solely on younger adults’ performance, what differs between the mostly congruent and mostly incongruent conditions are error rates on the incongruent trials, not error rates on the congruent trials. Inflated error rates on the incongruent trials in the mostly congruent condition most likely reflect prediction errors, whereby participants emit the most frequent (but incorrect) response for a particular word. It is reasonable to assume that such prediction errors are correlated positively with the strength of the association between stimuli (words) and responses, and that younger adults would have stronger representations of this association relative to older adults. By this account, the observed age difference is precisely as expected. Additionally, the observed trend ( p = .10) for younger adults’ Stroop effect in error rate to be magnified in the mostly congruent condition in the second relative to the first block, as associations are presumably strengthened, is consistent with this account. We further consider the contribution of stimulus—response learning and other mechanisms to the item-specific proportion congruence effect in the experiments that follow.

The findings of Experiment 1 have important theoretical implications. Typically, the list-wide proportion congruence effect has been attributed to a single color-naming or word-reading policy that is applied uniformly and strategically to all stimuli within a particular condition (list). For instance, computational modeling traditionally has focused on stronger weighting of the color-naming (goal) pathway in the mostly incongruent condition as the primary locus of the list-wide proportion congruence effect (e.g., Botvinick et al., 2001 ; Cohen, Dunbar, & McClelland, 1990 ). The findings of Experiment 1 provide potential difficulties for this view. Our findings instead suggest that a model with a control mechanism that operates at a single level is sufficient as long as that level is item specific and not list wide. Accordingly, Blais et al. (2007) demonstrated that a model that varies control at an item-specific level, by strengthening the connection between the color-naming pathway and a trial-specific response, can accommodate both item-specific and list-wide proportion congruence effects.

The findings of Experiment 1 also converge with prior research using the process-dissociation procedure ( Jacoby, 1991 ). This procedure yielded stronger estimates of the word-reading process for mostly congruent than for mostly incongruent lists ( Lindsay & Jacoby, 1994 ) and for mostly congruent than for mostly incongruent items ( Jacoby et al., 2003 ). In both cases, variations in the word-reading process occurred independently of color naming, and color naming did not vary as a function of list-wide or item-specific proportion congruence. These studies, like the present experiment, imply that a similar control mechanism acts on the word-reading process to produce list-wide and item-specific proportion congruence effects.

Although a theoretical account of Stroop performance that entails a single item-level control mechanism is intriguing, the possibility remains that there are additional, yet undiscovered levels at which control is implemented. In Experiment 2, we pursued the general question of whether participants exploit several features that are available to control word reading in the Stroop task (i.e., use multiple levels of control) or tend to rely on a single level of control that leads to efficient performance.

EXPERIMENT 2

In Experiment 1, and in prior studies examining item-level control (e.g., Jacoby et al., 2003 ), item-specific proportion congruence was manipulated at the level of word. That is, particular words were grouped together in sets (pairs or triplets), and particular word sets were composed of mostly congruent or incongruent items. As such, particular words were predictive of the likelihood that an item was congruent or incongruent. This item-specific manipulation may encourage use of the word as a “feature” that directs control of the word-reading process on an item-by-item basis.

Similarly, word reading might be controlled on the basis of other features if they too were predictive of proportion congruence. For instance, participants may be capable of extracting low-level, perceptual features of Stroop stimuli (e.g., shapes of particular letters, certain letter combinations, or distinctive font types) that are predictive of (correlated with) proportion congruence. These features then might be used to exert rapid control over the word reading process, such that word reading is permitted to influence performance to varying degrees depending on the specific features of the present item.

In the following experiment, we investigated whether control can operate via multiple levels in the Stroop task. We did this by combining the item-specific manipulation, whereby proportion congruence was manipulated at the level of particular color-word pairs (e.g., BLUE and YELLOW are mostly congruent items and GREEN and WHITE are mostly incongruent items) with a proportion congruence manipulation based on a specific perceptual feature, font type. In one condition, the mostly congruent words appeared in one font type (e.g., Arial), and the mostly incongruent words appeared in a second font type (e.g., Bookman Old Style). We refer to this condition as the multiple-features condition, because particular words and particular features (e.g., BLUE and YELLOW in Arial font) were correlated simultaneously with a proportion congruence level (e.g., mostly congruent). In contrast, in a single-feature condition, proportion congruence was manipulated only at the level of particular color-word pairs. In this condition, the mostly congruent and mostly incongruent words occurred equally often in both font types, and therefore only a single feature, color word, was predictive of proportion congruence.

As demonstrated previously (Experiment 1; Jacoby et al., 2003 ), the manipulation of item-specific proportion congruence at the level of word pairs is expected to produce an item-specific proportion congruence effect, such that interference is greater for mostly congruent than for mostly incongruent word pairs. This effect should occur in the multiple- and single-feature conditions, because both include the item-specific proportion congruence manipulation. The critical question is whether multiple features that are related to proportion congruence can be used simultaneously to control word reading. If participants in the multiple-features condition exploit both the word itself and the font type in service of control over Stroop interference, then the item-specific proportion congruence effect should be magnified in the multiple- relative to the single-feature condition. More precisely, the magnitude of interference should be smaller for mostly incongruent items and larger for mostly congruent items in the multiple-features condition.

Forty Washington University students participated in partial fulfillment of course credit or in exchange for monetary compensation. All participants were native English speakers between the ages of 18 and 25. Older adults did not participate in this or the subsequent experiment.

Four color words and their corresponding colors were divided into two pairs (BLUE and YELLOW, GREEN and WHITE). Words from one pair (e.g., BLUE and YELLOW) were designated mostly congruent (80%), and words from the other pair (e.g., GREEN and WHITE) were mostly incongruent (80%). The two word pairs combined to produce 50% congruent and 50% incongruent trials at the list level.

In the multiple-features condition, the mostly congruent items appeared in one font type (e.g., Arial) and the mostly incongruent items appeared in the second font type (e.g., Bookman Old Style). As a consequence, two features (color word and font type) were correlated with proportion congruence. These features were counterbalanced across participants ( n = 20), such that all possible combinations of font type and color-word pair occurred equally often at each level of proportion congruence. The single-feature condition was identical to the multiple-features condition, with the exception that font type was not correlated with proportion congruence. Rather, items from a particular color-word pair (e.g., BLUE and YELLOW) occurred equally often in both font types. The assignment of word pair to proportion congruence was counterbalanced across participants ( n = 20). The single- versus multiple-features manipulation was carried out between subjects. Participants were assigned randomly to each condition.

The procedure was identical to that of Experiment 1, except as noted below. There were 16 practice trials and two blocks of 108 test trials. The practice trials preserved the proportion congruence manipulation that was implemented in the test trials.

For each participant, RTs less than 200 msec and greater than 3,000 msec were removed. This resulted in the elimination of fewer than 1% of the trials from both the multiple- and single-feature conditions. Overall, errors were low (<1.3%). There were no significant effects in the analysis of error rates other than the finding that more errors were made on incongruent ( M = 2.1%) than on congruent ( M = 0.4%) trials [ F (1,38) = 15.14, MS e = .00, p < .01]. Therefore, to conserve space, we report only the analysis of correct RTs.

A 2 × 2 × 2 mixed-subjects ANOVA was conducted, with condition (multiple features vs. single features) as a between-subjects factor and proportion congruence (mostly congruent vs. mostly incongruent) and trial type (congruent vs. incongruent) as within-subjects factors. Incongruent trials ( M = 700) were responded to more slowly than congruent trials ( M = 612) [ F (1,38) = 214.26, MS e = 1,439]. The proportion congruence × trial type interaction indicated a significant item-specific proportion congruence effect [ F (1,38) = 74.29, MS e = 1,195]. As expected, the magnitude of Stroop interference was smaller for the mostly incongruent condition ( M = 41) than for the mostly congruent condition ( M = 135). As can be seen in Figure 1 , the item-specific proportion congruence effect was observed in both the multiple- and single-feature conditions. Importantly, the absence of a significant three-way interaction indicated that the magnitude of the item-specific proportion congruence effect did not vary as a function of whether multiple features or a single feature was correlated with proportion congruence ( F < 1).

Mean reaction time (RT, in milliseconds) as a function of trial type (C = congruent, I = incongruent) and proportion congruence (MC = mostly congruent, MI = mostly incongruent) for the multiple- and single-feature conditions in Experiment 2.

The findings of Experiment 2 provide some hints regarding the particular features that guide control in the Stroop task. The item-specific proportion congruence effect was observed in the multiple- and single-feature conditions. Both included an item-specific proportion congruence manipulation that varied proportion congruence for particular color-word pairs. This suggests that participants used word information to guide responses. Given the equivalence in the magnitude of the item-specific proportion congruence effect between conditions, it appears that participants in the multiple-features condition did not take advantage of the additional feature, font type, which was also correlated with proportion congruence. This may be because words themselves are highly salient and receive greater attention than do particular perceptual features, such as font type. In this sense, the relationship between particular words and proportion congruence levels may have overshadowed the relationship between particular font types and proportion congruence levels.

Alternatively, this pattern of findings may be related to the underlying mechanisms that reduce susceptibility to Stroop interference when participants respond on the basis of particular words (item-specific manipulation) versus particular word features (font-type manipulation). That is, the words themselves are predictive of the likelihood of congruency (as previously described), and this may lead participants to adopt different word-reading policies for mostly congruent as compared with mostly incongruent words. The words also are associated with particular color responses. For example, when BLUE and YELLOW are mostly congruent, 80% of the time BLUE appears in blue ink and YELLOW appears in yellow. In contrast, BLUE appears rarely in yellow and YELLOW appears rarely in blue. The opposite is true for mostly incongruent items. As suggested by Jacoby et al. (2003) , attending to and responding on the basis of the word can lead to fast and accurate production of the correct response on 80% of the trials (for similar accounts, see Melara & Algom, 2003 ; Musen & Squire, 1993 ; Schmidt & Besner, 2008 ; Schmidt, Crump, Cheesman, & Besner, 2007 ).

Font type, too, predicts the congruency of particular word pairs; therefore, participants may respond differentially to words printed in a mostly congruent relative to a mostly incongruent font type, on the basis of unique word-reading policies that have been established for each font type. In this sense, responding on the basis of item- or font-specific control may represent the action of a similar control mechanism. Unlike the word information that signals item-specific proportion congruence, however, font type does not predict specific responses and therefore does not permit responding on the basis of simple associations. In the present experiment, then, reliance on the word and on the information it carried regarding both the likelihood of congruency and the specific response to produce may have been a sufficiently efficient means of buffering Stroop interference such that other available features (e.g., font type) were not exploited in an effort to implement control over word reading. That is, font type, and the information it carried regarding the likelihood of congruence, may not have offered any additional advantages to performance that the word information alone did not offer already.

This result raises the question of whether font type, if it were the only feature correlated with proportion congruence, would produce a proportion congruence effect. That is, is there a control mechanism that can modulate word reading differentially for mostly congruent and incongruent items when proportion congruence is manipulated at the level of font type rather than at the level of the word itself ? Experiment 3 addressed this question.

EXPERIMENT 3

Incongruent and congruent stimuli were presented equally often in two distinguishable font types. For one font type, items were mostly congruent; for the other, items were mostly incongruent. Stimuli appearing in each font type were presented equally often in the four colors used in the present experiment. As such, font type could not be used to predict specific responses. If font type, due to its ability to predict congruency, is used to control word reading, then Stroop interference should be smaller for items appearing in the mostly incongruent relative to the mostly congruent font type.

Twenty-two Washington University students participated in partial fulfillment of course credit or in exchange for monetary compensation. All participants were native English speakers, with normal color vision and with normal or corrected-to-normal visual acuity.

At the list level, an equal number of congruent and incongruent stimuli were randomly presented along with eight neutral (%%%%%) items. Each of the four stimulus words (BLUE, GREEN, WHITE, and YELLOW) was presented 50% of the time in the congruent color and 50% of the time in an incongruent color (∼33% of the time in each of the three incongruent colors). Bookman Old Style font was used to compose half of the trials; Arial was used in the other. Items presented in one of the two font types were mostly congruent (∼80%), and items presented in the other font type were mostly incongruent (∼80%). The assignment of font type to proportion congruence was counterbalanced across participants. All four stimulus words and colors appeared equally often in the mostly congruent and mostly incongruent font types.

The procedure was identical to that in Experiment 1, with a few exceptions. Participants completed 20 practice trials (8 in Arial font type and 8 in Bookman Old Style that each included all possible word/color combinations and preserved the font-specific proportion congruence of the test blocks, and 4 neutral trials) prior to completing two blocks of 120 test trials.

For each participant, RTs less than 200 msec and greater than 3,000 msec were removed, which eliminated fewer than 1% of the trials. Overall, errors were low (1.4%). There were no significant effects in the analysis of error rates other than the finding that more errors were made on incongruent ( M = 2.4%) versus congruent ( M = 0.4%) trials [ F (1,21) = 18.69, MS e = .00, p < .01]. Therefore, to conserve space, we report only the analysis of correct RTs.

A 2 × 2 within-subjects ANOVA was conducted with proportion congruence (mostly congruent vs. mostly incongruent) and trial type (congruent vs. incongruent) as factors. Incongruent trials ( M = 738) were responded to more slowly than congruent trials ( M = 632) [ F (1,21) = 103.34, MS e = 2,398]. As indicated by the proportion congruence × trial type interaction, the magnitude of Stroop interference was smaller for the mostly incongruent ( M = 96) than for the mostly congruent ( M = 116) font type [ F (1,21) = 4.78, MS e = 4.78, p < .05]. Given that this is the first report in the literature of a font-specific proportion congruence effect of which we are aware, we subsequently analyzed the proportion congruence × trial type interaction for each block of the task to characterize the time course of the effect. Our reasoning was that the association between font type and proportion congruence might develop slowly, such that the reliability of this interaction may be limited to performance in the second block. The analyses confirmed that this was the case. In Block 2, Stroop interference was smaller for items that appeared in the mostly incongruent font type ( M = 94) than for those that appeared in the mostly congruent font type ( M = 121) [ F (1,21) = 4.60, MS e = 886, p < .05]. Although a similar pattern was observed in Block 1, with interference being smaller for items appearing in the mostly incongruent ( M = 99) relative to the mostly congruent ( M = 110) font type, the interaction was less marked and not statistically reliable ( F < 1).

The observation of a font-specific proportion congruence effect implies that word reading can be modulated differentially for mostly congruent and incongruent items when congruency is signaled by a particular font type. Broadly speaking, this finding suggests that control over word reading in the Stroop task can be exerted at the font level in addition to the item level and list level. 3

Font-level control over Stroop interference may be accomplished by a mechanism that, upon onset of the Stroop stimulus, rapidly extracts predictive perceptual features, such as the font type itself or the shape of the word as written in a particular font type, and uses such features to differentially modulate word reading. A more refined hypothesis is that font type is used as an early signal for controlling a word-reading filter ( Jacoby et al., 2003 ; Jacoby, McElree, & Trainham, 1999 ). The idea here is that the word itself would be filtered more quickly for words appearing in the mostly incongruent relative to the mostly congruent font, such that further processing of the word beyond its low-level perceptual features may be inhibited. Importantly, a similar control mechanism might also underlie the item-specific proportion congruence effect observed previously ( Jacoby et al., 2003 ), although the action of this mechanism may be driven by features of the stimulus different from those that produce the font-specific proportion congruence effect. For instance, one might learn that particular words that are longer or shorter tend to come from a mostly incongruent word pair and use this information to filter word reading. This remains to be tested.

The font-specific proportion congruence effect is perhaps part of a larger class of context-specific proportion congruence effects. The term context-specific proportion congruence effect was coined by Crump, Gong, and Milliken (2006) , who showed that the magnitude of Stroop interference was significantly smaller when stimuli appeared in a mostly incongruent relative to a mostly congruent location. As with the font-specific proportion congruence effect, the context-specific proportion congruence effect cannot be accounted for by a simple contingency (e.g., stimulus—response learning) account, because the contextual cue (i.e., location) was associated equally often with all possible responses, just as font type was in the present experiment. Admittedly, a more complex contingency account based on compound font type—word—response associations might at least partially account for the font-specific proportion congruence effect and, similarly, the context-specific proportion congruence effect.

An important difference between the font-specific proportion congruence effect and the context-specific proportion congruence effect relates to the different Stroop paradigms that were used to evaluate these effects. Crump et al. (2006 , Experiment 2A) used a priming procedure whereby a color-word prime was presented briefly in black ink and was followed by a display featuring a colored rectangle that appeared above or below fixation in either the mostly congruent or the mostly incongruent location. The participants’ task was to name the color of the rectangle. Crump et al. speculated that the relevant contextual cue (i.e., location) might be modulating the degree to which the prime word is integrated with the color of the rectangle in the probe display, thus impacting Stroop interference. This control mechanism is very different from the notion of a word-reading filter that may be modulating word reading and producing the font-specific proportion congruence effect in our paradigm. A strong appeal of the latter mechanism is that a word-reading filter might explain Stroop interference effects more broadly, as in other common paradigms involving integrated color/word stimuli.

GENERAL DISCUSSION

Human behavior is incredibly flexible. In some contexts, stimulus—response associations that are acquired via repeated experience are used to guide responding. Novel or unpredictable contexts, however, often necessitate a shift toward responding on the basis of higher level goals or expectations that may change on a moment-to-moment basis. The present analysis suggests that there are multiple approaches to controlling behavior and that such approaches are bound by contextual features. Similarly, the experiments presented here suggest that there are multiple levels at which one can exert control over Stroop interference and that engagement of these levels is triggered differentially for differing contexts.

In the present study, both younger and older adults demonstrated use of item-specific control, which involves the use of word information rapidly upon stimulus onset to modulate the influence of the word-reading process on Stroop performance ( Jacoby et al., 2003 ). When pairs of words (i.e., items) are mostly incongruent, Stroop effects are significantly smaller than when pairs of words are mostly congruent. As first outlined by Jacoby et al. (2003) , at least two mechanisms may underlie item-level control: a cognitive-control mechanism and an associative-learning mechanism. The cognitive control mechanism purportedly involves stronger dampening of the word-reading process upon stimulus onset particularly in the case of items from a mostly incongruent word pair (see also Jacoby, McElree, & Trainham, 1999 ). In contrast, the associative-learning mechanism involves the production of the color response that is associated most frequently with a particular word. For example, for a mostly incongruent word pair (e.g., BLUE and YELLOW), a participant would quickly produce the response “yellow” when presented with the word BLUE because most of the time this is the correct response. However, when the most frequent response is the incorrect response, as in the case of incongruent trials in a mostly congruent condition, reliance on this associative mechanism can create inflated error rates due to response prediction error, which was observed for younger adults in the mostly congruent condition in Experiment 1. Although the present results do not allow us to adjudicate fully between a control account and an associative learning account, it is important to acknowledge that item-level control can be achieved through either or both of these mechanisms.

In the present study, we also observed font-level control, which to our knowledge is a level of control not previously explored. The initial observation of a font-specific proportion congruence effect occurred in Experiment 3. In this experiment, items that were printed in a particular font type were mostly congruent, whereas items printed in a second font type were mostly incongruent. Stroop effects were significantly smaller for items printed in the mostly incongruent font type. The font-specific proportion congruence effect, like the item-specific proportion congruence effect, must reflect a mechanism that acts after stimulus onset, because 50% of the stimuli within a block of trials appear in the mostly congruent font and 50% appear in the mostly incongruent font. These proportions prohibit participants from anticipating the type of font that will occur on the upcoming trial and adjusting control settings prior to stimulus onset.

In the case of item-specific or font-level control, the particular operations that are engaged immediately after stimulus onset remain to be fully explicated. One possibility is that the two levels of control, at least in part, reflect the action of a single control mechanism that is “turned on” by different features of the stimulus. Participants may detect features (e.g., entire words, in the case of item-specific control, or distinctive font types or shapes, in the case of font-specific control) that are predictive of proportion congruence levels and use control mechanisms to gate word-reading processes accordingly. For example, item-specific and/or font-level control may involve processes that modulate word reading on the basis of the degree of interference (i.e., response conflict) each stimulus produces post-stimulus onset. Particular stimuli (or features of stimuli) that are associated with a high probability of incongruence may lead to greater conflict at onset and relatively stronger gating of word reading. Indeed, if item-specific or font-level control act after the detection of conflict, one might anticipate sequential adjustments in the form of conflict adaptation (see, e.g., Botvinick et al., 2001 ) in paradigms where these control processes are operating. Although the present findings and those using process dissociation (e.g., Jacoby et al., 2003 ; Lindsay & Jacoby, 1994 ) anticipate such adjustments to follow the form of weight changes in the reading pathway, changes might take place in the color pathway instead, as evidenced in the item-specific conflict-monitoring model ( Blais et al., 2007 ).

Alternatively, item-specific and/or font-level control may be acting post-stimulus onset, but prior to the occurrence of interference or conflict. For instance, detection of critical features such as font type may be accompanied by rapid gating of word-reading processes before response conflict arises, particularly in the case of incongruent trials from a mostly incongruent condition. Similarly, in the case of item-specific responding on the basis of learned associations, presentation of the stimulus (word) may lead to rapid retrieval of the associated response prior to the occurrence of conflict. According to this view, item-specific and/or font-level control may involve “early selection” processes ( Jacoby, Kelley, & McElree, 1999 ). These processes should not, however, be equated with the proactive control processes described in the context of the dual mechanisms of control account ( Braver et al., 2007 ). This account conceptualizes proactive control processes as acting prior to stimulus onset, whereas the proposed mechanisms just described may be acting post-stimulus onset.

A third level of control, which operates at the list level, is the most pervasive control mechanism identified in the extant literature regarding proportion congruence effects in Stroop paradigms (e.g., Logan & Zbrodoff, 1979 ; Logan et al., 1984 ). Unlike the item-specific and font levels of control, list-level control is purported to involve proactive global strategies, operating prior to stimulus onset. List-level strategies largely reflect expectations. For example, in mostly incongruent lists, incongruent items are expected. To reduce Stroop interference in such a context, participants are believed to use a consistent trial-to-trial strategy that involves adjusting cognitive control settings away from word reading and toward color naming. In the present study, we did not, however, find evidence of list-level control. In fact, the results of Experiment 1 raise the question of whether prior observations of list-wide proportion congruence effects may reflect item-level control, at least partially and perhaps fully.

Across three experiments, then, we have uncovered two levels of control (item level and font level) that were implemented in service of reducing Stroop interference. The second key question we addressed is whether participants would simultaneously exploit more than one level of control. Experiments 1 and 2 were informative on this issue. Neither showed clear evidence of the simultaneous implementation of two control levels. There are several potential reasons for this finding.

Above, we described both the item-level and font-level mechanisms as being more flexible in nature, rapidly adjusting control settings after stimulus onset on a word-by-word or trial-by-trial basis. If this is the case, one reason that item- and font-level control may not operate simultaneously in contexts that allow for control to be implemented at both levels, as in Experiment 2, is that the two levels of control may be redundant. That is, using both levels simultaneously may not produce reductions in interference proportional to the additional effort that may be required to do so. Alternatively, item- and font-level control may interfere with one another. For instance, if participants divide attention between the two dimensions (word and font type), performance may suffer as compared with when they choose a single dimension and respond accordingly. Of course, both of these explanations assume that the font type manipulation in Experiment 2 was sufficiently salient for participants to have the option of using item- and font-level control simultaneously.

Although a redundancy, interference, or saliency explanation may suffice in explaining why multiple levels of control were not operative when the available levels were item level and font level, these explanations do not fare well in explaining the results of Experiment 1, wherein only an item-level effect was observed even though a list-wide proportion-congruence manipulation was also implemented. An account based on the mechanisms that underlie each level of control is elaborated next in an attempt to explain the patterns observed across Experiments 1 and 2. In these experiments, participants may have elected to attend to the item-specific proportion congruence manipulation that occurred at the level of words, rather than the list-wide or font-type proportion congruence manipulation, because item-level control afforded participants the opportunity to respond to each word on the basis of stimulus—response associations. This type of responding may eliminate the need for engagement in effortful modification of control settings, because stimulus—response associations may be retrieved prior to the occurrence of interference or conflict on incongruent trials. Other levels of control may have little value in such a context. Two predictions follow from this account. The first is that increased use of list-wide or font-specific levels of control should occur to the extent that one reduces the efficiency of item-level control. Second, if two levels of control (e.g., list level and font level) were available in a single task context and neither level permitted responding on the basis of simple stimulus—response learning, one might observe the simultaneous implementation of both levels of control. These predictions remain to be tested.

The present set of experiments indicates that multiple levels of control are used in service of control over Stroop interference. An item level of control surfaced in task contexts in which other levels of control were available but were not used. A second level of control, the font level, was revealed for the first time in the present study. Although a third level of control, list-wide, has been observed in prior reports, we did not observe it here when any item-specific influence that could contribute to its observation was controlled. This finding suggests caution in interpreting list-wide proportion congruence effects in Stroop tasks as reflecting solely strategic or global forms of control. Rather, our findings indicate that an account of proportion congruence effects in Stroop experiments must consider multiple levels of control that are used to modulate Stroop interference and that these levels can operate differentially from trial to trial.

Acknowledgments

This research was supported partially by National Institute on Aging Grants 5T32AG00030 and AG13845. We are grateful to Swati Chanani, Rachel Edelman, Carlee Beth Hawkins, and Danielle Hirschfield for assistance with data collection.

1 This explanation was elaborated and tested initially in a poster presented by Toth and Jacoby (2003) at the 44th Annual Meeting of the Psychonomic Society in Vancouver, BC.

2 Analysis of the log-transformed RTs indicated that older adults showed larger Stroop interference effects than did younger adults on both the item-specific PC-50 trials [ F (1,68) = 8.11, MS e = .001] and the item-specific PC-25 and PC-75 trials [ F (1,68) = 10.92, MS e = .001]. List-wide proportion congruence had no influence on the magnitude of the Stroop effect ( F < 1), whereas item-specific proportion congruence had large effects on this measure [ F (1,68) = 38.84, MS e = .001]. Most critically, the age × proportion congruence × trial type interactions were nonsignificant for the list-wide and item-specific analyses ( F s < 1), indicating that the list-wide and item-specific manipulations had a similar effect on the magnitude of Stroop interference for younger and older adults. In fact, the increase in Stroop interference from the item-specific PC-25 to the item-specific PC-75 condition was almost identical for older ( M = .06) and younger ( M = .05) adults.

3 List-level control was not observed in Experiment 1 in the present study; however, this is not to say that list-wide control will never emerge in Stroop paradigms. List-wide control may be used, for instance, in task contexts that do not afford item-specific control.

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• Published: 23 August 2024

Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau

• Abdulhakim Al-Ezzi 1 ,
• Rebecca J. Arechavala   ORCID: orcid.org/0000-0002-9799-2610 2 ,
• Ryan Butler 1 ,
• Anne Nolty 3 ,
• Jimmy J. Kang 4 ,
• Shinsuke Shimojo   ORCID: orcid.org/0000-0002-1290-5232 5 ,
• Daw-An Wu   ORCID: orcid.org/0000-0003-4296-3369 5 ,
• Alfred N. Fonteh 1 ,
• Michael T. Kleinman 2 ,
• Robert A. Kloner 1 , 6 &
• Xianghong Arakaki 1  

Communications Biology volume  7 , Article number:  1037 ( 2024 ) Cite this article

Metrics details

• Cerebrospinal fluid proteins
• Cognitive control
• Diagnostic markers
• Neurophysiology

Alterations in functional connectivity (FC) have been observed in individuals with Alzheimer’s disease (AD) with elevated amyloid ( A β ) and tau. However, it is not yet known whether directed FC is already influenced by A β and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) A β tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF A β /tau.

Introduction

Alzheimer’s disease (AD) is a neurological disorder in which progressive neurodegeneration and synaptic dysfunction result in impairments in a range of cognitive domains. With the continual rise of the global population and life expectancy, it is anticipated that the prevalence of neurocognitive disorders or dementia will experience a substantial surge, reaching an estimated 74.7 million individuals by 2030 and more than 131.5 million by 2050 worldwide 1 , 2 . Recent research reported early impairments in executive functions and memory among individuals afflicted with A β and/or tau pathologies 3 , 4 , 5 . These findings provide validation for the notion that executive functions and episodic memory 6 , 7 , 8 are indeed affected during the initial stages of AD, primarily due to the alteration or pathology of the frontal and temporal cortices 9 , 10 . More specifically, inhibitory abilities 11 , attentional processes 12 , 13 , and visuospatial functions 14 appear to be particularly compromised. A defining feature of the progression of AD is the reduction in A β protein (resulting in low levels of CSF amyloid- β ( A β ) and the rise in neuronal degeneration biomarkers (such as increased levels of CSF total tau and phosphorylated tau) in individuals with AD. The reduction in CSF A β levels seems to occur in the early progression of AD, becoming apparent more than twenty years before the onset of any clinical symptoms 15 . In individuals with AD or those who are at risk of developing AD, the amyloid- β -to-tau ratio is often low, indicating an accumulation of A β plaques and/or tau tangles in the brain 6 , 16 , 17 . Lei Wang et al, found that lower CSF A β 42 levels and higher tau/ A β 42 ratios were strongly correlated with a reduction in hippocampal volume and indicators of progressive atrophy of the cornu ammonis subfield in pre-clinical AD individuals, but not cognitively healthy (CH) individuals 18 . Compared to the A β 42 and/or tau, the A β 42/tau ratio demonstrated greater sensitivity in detecting pre-symptomatic AD and distinguishing it from frontotemporal dementia 19 . Consequently, it is plausible that A β 42/tau ratio may serve as a sensitive biomarker in detecting the earliest stages of preclinical AD compared to individual biomarkers. Preclinical investigations offer robust evidence supporting functional connectivity as a probable intermediary mechanism linking A β to tau secretion and accumulation 20 . Despite the significant research dedicated to unraveling AD pathogenesis, there is currently a lack of sensitive, specific, reliable, objective, and easily scalable biomarkers or endpoints to guide clinical trials and facilitate early risk detection in clinical settings.

Several large prospective studies attempt to characterize the early diagnostic criteria in people at risk of developing AD. These assessments include pathological markers (both Beta Amyloid ( A β ) and tau pathologies) 21 , neuropsychological scores (Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE)) 22 , neuroimaging (Magnetic resonance imaging (MRI), Magnetoencephalography (MEG), and electroencephalogram (EEG) 23 , and heart rate variability (HRV) 24 . EEG Brain connectivity, MRI brain structures, neuropsychological assessments, and HRV are intricately correlated measures that can predict early AD pathology. For example, a recent examination of HRV from the Multi-Ethnic study of atherosclerosis revealed a correlation between higher HRV and superior cognitive function across various cognitive domains 25 . We previously reported a significant association between high resting HR and less negative alpha event related resynchronization (ERD) during Stroop testing in individuals with pathological A β /tau, compared with those with normal A β /tau 26 . These findings prompt further investigation into brain connectivity involved with pathological A β /tau presence during task-switching tasks. Therefore, we aim to integrate brain activity, neuropsychology, and HRV assessments in this study to facilitate early detection of AD risks, understand disease mechanisms, and ultimately help improving outcomes for individuals affected by AD by addressing the multifaceted nature of the disease.

The abnormality of brain connectivity measured by MRI in regions with early A β -burden (e.g., default mode network (DMN) has been shown when A β fibrils just start to accumulate 27 . However, this abnormality has not been reported or tested in EEG investigations to our knowledge. Both A β and tau pathologies have been shown to impact brain network’s structural and FC 28 . Abnormal FC has been consistently identified in the early stages of AD before the appearance of clinical symptoms or brain structural changes 29 . For instance, a recent study has achieved 90% accuracy in classifying brain A β and tau pathology in subjective cognitive decline from mild cognitive impairment (MCI) individuals using EEG coherence 30 . FC studies have found that abnormal cerebrospinal fluid (CSF) levels of phosphorylated-tau and A β in early AD are linked with disrupted cortical networks involving the anterior and posterior cingulate cortex, and temporal and frontal cortices 31 . We previously reported that ERD increased in CH with pathological A β tau ratio (CH-PATs) 32 , compared to CH with normal A β tau ratio (CH-NATs) in alpha band. Using regional interconnectivity methods, a previous study found that the temporal and frontal regions’ connection is a characteristic pattern for the pathological transition of normal to MCI and the density of edges in these networks is a differential pattern between HC and MCI 33 . The decreased patterns of regional hemispheric interconnectivity in the metabolic network rely on the pathology severity 33 . Therefore, EEG can be a promising, diagnostic, noninvasive, high temporal resolution method, which is a cost-effective biomarker and easily accessible to track and predict the severity of cognitive dysfunction in degenerative diseases. As memory (predominantly localized in the temporal region) and executive functions (mainly associated with the frontal region) are the two sensitive cognitive activities that were abnormal in early AD 9 , 10 , it will be compelling to study frontal and temporal FC in the early stage of AD spectrum.

In the present study, we aimed to: (1) compare effective connectivity (EC) between CH-NATs and CH-PATs during task switching, and explore the potential contribution of task difficulty levels; (2) study the links between EC and Neuropsychological measures, structural MRI brain volumes, and HRV.

Participant characteristics

The demographic and clinical characteristics of our subjects have been reported in our previous work 32 ). The participants’ age in CH-PATs and CH-NATs were comparable and both groups also had similar educational levels, with mean years of education. There were no differences in cognitive reserve (CR) and intelligence quotient (IQ) scores between CH-NATs and CH-PATs.

Behavioral analysis

The difference between the accuracy (ACC) and reaction time (RT) scored under the effect of trial types (repeat or switch) was notable with a significantly improved RT ( p  < 0.0001) and ACC ( p  = 0.048) during repeat trials than during switch trials. In addition, the results of this study showed no significant differences in group  × trial type interaction in RT, F(1, 108) = 0.0001, p  = 0.991 and ACC, F(1, 108) = 0.003, p  = 0.960 between the two groups of all CH-PAT and CH-NAT participants. A comparison of the main effect of trial types and group  × trial between CH-NATs and CH-PATs was reported previously in our work 32 .

EEG power spectral density

The comparison of the normalized alpha power in resting-state and task-switching at the five regions in the CH-PATs and CH-NATs is shown in Fig.  1 . The CH-NATs showed significantly stronger spectral power of task-switching alpha at temporal, parietal, and occipital electrodes when compared to CH-PATs ( p  < 0.0001), ( p  = 0.0005 and p  < 0.0001), respectively as shown in (Fig.  1 a, b). On the contrary, there were no significant differences between CH-PATS and CH-NATs in frontal or central power. In the resting state, there were no significant differences in alpha power changes between all brain regions (Fig.  1 b, d).

a , b A group comparison between CH-PATs and CH-NATs using t -test in different brain regions (Frontal, temporal, parietal, central, occipital) within the range (200–550 ms), where 0 ms is the onset of the stimulus during task switching and resting-state, respectively. c , d Shows the avereged topographical distribution of alpha power in CH-PATs and CH-NATs during task switching and resting-state, respectively. e , f Shows the mean normalized absolute PSD for all electrodes in the frequency domain (0–50Hz) for CH-NATs and CH-PATs during the switching task and resting state. Frequency bands are decomposed into the following: delta (0.4–4 Hz), theta (4.1–8 Hz), alpha (8.1–12 Hz), and beta (12.1–30 Hz). * P  < 0.05, ** P  < 0.01, *** P  < 0.001.

The mean partial directed coherence (PDC) FC pattern of CH-PATs and CH-NATs were shown in Fig.  2 a, b, respectively, and we qualitatively observed that the main difference between the two groups was that CH-PATs patients exhibited much more and much weaker long-range connections from left and right temporal cortices than CH-NATs. Specifically, compared with the CH-NATs group (0.135 ± 0.017), the averaged information flow in the alpha frequency of CH-PATs was enhanced in the frontal regions during task switching processing (0.223 ± 0.014); t (44) = 18.06, p  < 0.0001. On the contrary, CH-NATs showed increased information flow from temporal region (0.187 ± 0.016) compared to CH-PATs (0.09 ± 0.015), t (44) = 21.06, p  < 0.001. On the contrary, central, parietal, and occipital regions did not show any significant differences between CH-NATs and CH-PATs. It is also interesting to note that resting-state EC showed a significant difference between CH-NATs and CH-PATs only in the occipital cortex (0.082 ± 0.03), (0.105 ± 0.036), t (44) = 2.36, p  = 0.023 as shown in Fig.  2 c, d. Differences in brain connectivity (Switching task connectivity values - Resting state connectivity values) were calculated across five distinct brain regions (Fig.  2 e, f). Results revealed differences between CH-NATs and CH-PATs in frontal ((0.036 ± 0.041), (0.084 ± 0.037), t (44) = 6.985, p  < 0.0001) and temporal ((0.072 ± 0.039),(−0.028 ± 0.027), t (44) = 10.21, p  < 0.0001)), while no differences were observed in the parietal, occipital, or central regions.

Representation of the functional networks as graphs in the Alpha frequency band at stimuli time (200–550 ms) after the onset of the stimulus. PDC from an area i to j is represented by an arrow. a , c , e Group connectivity comparison between CH-PATs and CH-NATs during task switching, resting state, and the differences between task switching and resting (Task-rest), respectively in the alpha band. b , d , f The directed connectivity of the CH-PATs (left) and CH-NATs (right) during task switching, resting state, and the differences between task switching and resting (Task-rest), respectively in the alpha band. The brain regions are graphically represented with connections depicting causal influence at (200–550 ms). The brain surface templates we used to visualize these connections in Fig.  2 are primarily generated from a commonly used template known as MNI/Talaraich (ICBM152).

To validate the outcomes concerning directed connectivity as measured by PDC, we employed multiple functional phase connectivity methodologies, including Weighted Phase Lag Index (wPLI) and Phase locking value (PLV). Although FC and EC can be associated, they estimate distinct characteristics of brain interactions, and the presence of one does not inherently imply the presence of the other. During task-switching, wPLI results showed that CH-PATs demonstrated significantly higher phase connectivity in frontal and central regions (Supplementary Fig.  1 ). In addition, PLV analysis showed decreased phase coherence in frontal and occipital regions (Supplementary Fig.  2 ). Detailed results, including additional analyses and comparisons utilizing wPLI and PLV algorithms, are presented in this manuscript’s supplementary file.

Task difficulty

To examine the potential contribution of task difficulty level to the EC differences, we selectively compared connectivity between “good performers” in CH-PATs and “bad performers” from CH-NATs to bring down the potential differences and check if the frontal and temporal EC differences remained. Task difficulty was determined through the calculation of performance indicators, namely ACC or RT, and brain connectivity. In this context, the ACC results demonstrated a significant difference between CH-NATs (0.79 ± 0.13) and CH-PATs (0.96  ± 0.03) with p  < 0.0001. Similarly, the results indicated that CH-NATs showed a significant increase in RT (1743.63 ± 265.6) compared to CH-PATs (1218.47 ± 167.66) with p  < 0.0001. For this condition, we sorted the EC based on the ACC and RT data (i.e., low-vs-high-connectivity) and constructed a statistical analysis between CH-PATs and CH-NATs. For instance, using EC based on the ACC classification, the CH-PATs showed increased frontal connectivity (0.22 ± 0.02) compared to CH-NATs (0.131 ± 0.02) with p  = 0.0009. Additionally, the CH-PATs showed decreased temporal connectivity (0.09 ± 0.02) compared to CH-NATs (0.187 ± 0.006) with p  < 0.0001. Furthermore, using EC based on the RT classification, the CH-PATs showed increased frontal connectivity (0.22 ± 0.01) compared to CH-NATs (0.13 ± 0.02) with p  = 0.0008. The CH-PATs showed decreased temporal connectivity (0.09  ± 0.02) compared to CH-NATs (0.188  ± 0.012) with p  < 0.0001. This rigorous comparative analysis supported that the EC differences are independent of task difficulty levels. Figure  3 illustrates mean scores for task difficulty based on ACC and RT classifications for the color-Word (cW) test.

a The Reaction Time (RT) was different between CH-NATs and CH-PATs. The values represent the best 50% performance of RT (lowest RT values) in CH-PAT participants and the worst 50% performance (Highest RT values) in CH-NATs during high-load color-word switch trials. The comparison was performed between the two groups for frontal ( b ) and temporal ( c ) connectivity for the same participants. d The ACC was significantly different between CH-NATs and CH-PATs. The values represent the good 50% performance of ACC scores (highest ACC values) in CH-PATs and the worst 50% performance (lowest ACC values) in CH-NATs during high-load color-word switch trials. The comparison was performed between the two groups for frontal ( e ) and temporal ( f ) connectivity for the same participants using a parametric t -test. * p  < 0.05, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001.

Functional connectivity and neuropsychological and cognitive reserve analysis

Supplementary Table  1 presents several correlations between task-switching brain connectivity in temporal and frontal brain regions and different neuropsychological tests (i.e., processing speed, working memory, and executive functions) between CH-NATs and CH-PATs. Processing speed tests were used to assess the ability to process information rapidly. The higher the score, the more time it has taken, and the worse the performance. Executive function and working memory tests can provide an estimation of a wide range of skills (i.e., working memory and organization). The higher the score, the more time it has taken, and the better the performance. While CH-PATs showed greater scores in performance in speed processing tests, CH-NATS showed a better performance in executive function and working memory tests. Also, the present study aimed to investigate the relationship between cognitive reserve (CR) and parietal connectivity in two groups, CH-NATs and CH-PATs. Our findings revealed a significant negative correlation between CR and parietal connectivity in the CH-NATs group (r = −0.61, p  = 0.030), as shown in Fig.  4 . This suggests that individuals with higher CR tend to exhibit lower parietal connectivity in this group. However, in contrast, the CH-PATs group did not show any differences in CR and parietal EC.

A linear regression model was used to estimate the coefficients of linear correlations (Confidence Intervals = 0.95) that relate a set of predictor variables to a response variable.

Functional connectivity and MRI brain volumes

In the CH-PATs group, significant negative correlations were observed during task switching between temporal EC and several brain volumetrics: Fusiform Right Side Volume ( r  = −0.42, p  = 0.043), Hippocampal Occupancy Score (HOC) Norm Percentile ( r  = −0.46, p  = 0.023), and Fusiform Asymmetry Norm Percentile ( r  = −0.41, p  = 0.049) as shown in Fig.  5 . However, CH-NATs did not show significant correlations between temporal EC and the same regions. Moreover, CH-NATs showed significant correlations between temporal EC and Entorhinal Cortex Asymmetry Norm Percentile ( r  = −0.67, p  = 0.013), Fusiform Left Percent Of intracranial volume (ICV) ( r  = −0.58, p  = 0.041), and Fusiform Asymmetry Norm Percentile ( r  = −0.67, p  = 0.015). Additional correlations between frontal and temporal connectivity with brain volumetrics in CH-NATs and CH-PATs are reported in Supplementary Table  2 .

a A Correlation analysis between Hippocampal Occupancy Score (HOC) Norm Percentile and temporal EC in two groups;in two groups CH-NATs (blue scatter plots) and CH-PATs (red scatter plots). b Correlation between Fusiform Right Side Volume and temporal EC between two groups; CH-NATs and CH-PATs. A correlation analysis reveals the strength and direction of the association between brain volumetrics and brain connectivity. Spearman correlation was applied and the p  < 0.05 and r (association directionality values) are shown.

Functional connectivity and HRV analysis

Spearman’s correlation analysis was also conducted in both groups to explore the relationship between HRV metrics and EC during task-switching paradigms. In the task-switching condition, CH-PATs exhibited noteworthy findings, revealing significant negative correlations between frontal EC with Root mean square of the successive differences (RMSSD) ( r  = −0.52, p  = 0.020). Conversely, CH-NATs demonstrated significant negative correlations between frontal connectivity and mean RR ( r  = −0.87, p  = 0.002), as shown in Fig.  6 . On the contrary, CH-NATs did not reveal significant correlations in RMSSD measures and brain connectivity as shown in Fig.  6 .

a A correlation between mean RR and frontal EC during task switching for two groups CH-NATs and CH-PATs. b A correlation between resting RMSSD and frontal connectivity for the two groups. Spearman correlation was applied and p values were set to  < 0.05 and r (association directionality values) are shown.

The main objective of the present biomarker study was to characterize the effects of the accumulation of A β pathologies and tau concentrations on the directed brain networks in CH individuals. Participants categorized into our CH-NAT and CH-PAT groups were asymptomatic, with normal neurocognitive tests, and were diagnosed based on CSF A β 42 and Tau measures that were within the published ranges 6 . The CSF A β 42/Tau ratio outperforms the CSF A β 42 or tau levels individually to identify dementia and preclinical phases of AD. Abnormal amyloid levels and tau accumulation can disrupt synaptic function by interfering with neurotransmitter release and synaptic plasticity. This disruption can lead to neurotoxicity, microtubule destabilization, neuroinflammation, and alterations in the strength and efficiency of synaptic connections between neurons, ultimately affecting overall brain connectivity. In this exploratory study, we report on several important findings: (1) CH-PATs compared to CH-NATs, presented higher frontal EC, and lower temporal EC, independent from task difficulties. (2) CH-PATs presented significant correlations between temporal or frontal EC and other measures, including neuropsychological measurements (i.e., processing speed, executive functions, and working memory tests), MRI regional volumetrics, and HRV, supporting compensatory mechanisms. These changes are potentially linked to a less strategic approach while performing the task in CH-PATs, or no improvement in efficiency. These results may indicate that CH-PATs may present compensating mechanisms and may lack learning and self-improvement with functions that as seen in advanced intelligence for self-improving mode. Another similar example is during coding, using functions (temporal lobe in CH-NATs) can improve efficiency, while always using whole codes (frontal lobe) but limited functions (temporal lobe) can be exhaustive for CH-PATs.

The identification of effective EEG biomarkers associated with AD pathology holds substantial promise in unraveling the neural mechanisms underlying this neurodegenerative disorder and facilitating its early diagnosis. Growing evidence suggests that EEG measurements reflect the capacity of AD neuropathology on brain neural signal transmission underlying cognitive processes 34 , 35 , 36 . However, the accuracy and reliability of different types of EEG biomarkers, i.e., power and entropy in facilitating the early detection and prediction of AD progression remain largely unknown. To our knowledge, this is the first study to evaluate the impact of promising EEG connectivity on detecting early AD pathology in CH individuals. We provide strong evidence supporting that the inclusion of multidimensional information (i.e., EEG biomarkers, CSF measures, brain volumetrics, and HRV) is highly effective in assessing patients’ pre-symptomatic clinical status. Taken together, our findings suggest that brain connectivity has the potential for the early detection of risk for cognitive decline in CH individuals independently or in association with other measures. Notably, our study corroborates these findings and highlights the significance of EEG metrics and connectivity as pivotal biomarkers for revealing CH-PATs.

The Behavioral results of this study showed no significant differences between all CH-PATs and CH-NATs groups (results were reported previously in ref. 32 ). Additionally, the after-test survey suggested no subjective difficulty levels between the two groups. This indicates that, at the behavioral level, there is no evidence of cognitive decline in CH-PATs at this early stage of the disease. One possible explanation is that the cognitive deficits associated with CH-PATs are not yet severe enough to manifest at the behavioral level 37 . It is also possible that compensatory mechanisms and/or CR may contribute to similar behavioral performance in both groups 38 . When the brain switches attention between tasks, it successfully alternates, but consistent mental replacement of one task with another requires additional effort in terms of time and cognitive resources. This leads to switching costs, which we also observed in our study. Both CH-PATs and CH-NATs exhibited longer RTs during switch trials compared to repeat trials, indicating the presence of a successful switching cost effect 32 , 39 , 40 . Moreover, to investigate the possible contribution of task difficulty level to the EC differences, we selectively compared connectivity values between the best 50% performance of CH-PATs (Highest accuracy scores and lowest RT scores) and the lowest 50% performance from CH-NATs (lowest accuracy scores and highest RT scores) to bring down the potential differences and check if the frontal and temporal EC differences remained. Presumably, these subsets will bring CH-PATs and CH-NATs data closer in subjective difficulty/concentration. If connectivity analysis continues to exhibit consistent differences, it suggests that the connectivity patterns are fundamental, rather than solely results of task difficulty. Conversely, if the connectivity differences diminish, it indicates that they may indeed be influenced by subjective task difficulty. This approach was motivated by the desire to control for the influence of task difficulty on EC alterations and isolate the effects of intrinsic brain connectivity differences. By focusing on individuals with comparable task performance levels, we were able to minimize the confounding effects of task difficulty, ensuring that any observed EC differences were more likely attributable to inherent neurobiological factors rather than variations in task performance 41 . The good performance (increased ACC values and decreased RT values) in Fig.  3 may suggest that this group of CH-PATs did benefit from cognitive reserve, at least on neural activity during task switching. This also could be due to a compensatory increase in the number of neurons and/or synapses in CH-PATs.

During task switching processing compared to CH-NATs, CH-PATs exhibited higher alpha power values in the frontal region, while lower values were observed in temporal and parietal areas as shown in Fig.  1 . These aberrations may signify two distinct pathophysiological alterations: the reduction in alpha power in AD pathology could be attributed to alterations in cortico-cortical connections 42 . We previously reported increased event-related resynchronization (ERD) in CH-PATs, compared to CH-NATs in the alpha band 32 . In contrast to ERD (Negative values calculated by wavelet transform and corrected with baseline), absolute alpha power (Positive values calculated by welch power) is a measure of the overall power that may detect changes in excitability alterations in the brain and does not provide specific information about task-related processing. ERD and absolute alpha power are both measures used in EEG analysis, but they capture different aspects of brain activity. Furthermore, evidentiary results have found higher resting-state alpha power manifestations in the frontal regions among MCI individuals compared to CH individuals 43 . This increase may suggest the recruitment of compensatory mechanisms. Individuals with a cognitive decline may show less vigilance to external stimuli in the resting state and may exhibit diminished capacity to recruit relevant brain regions when performing a task. Previous investigations have consistently reported slowing EEG activity among individuals with MCI and AD. For instance, a recent work substantiates the presence of distinct power resting state EEG rhythms in older individuals with subjective memory complaints (awareness of memory loss), notably showing greater theta power and a subtle reduction in EEG reactivity 44 . In addition, a decreased alpha/beta power and increased theta/delta power across various brain regions, including the frontal, temporal, parietal, and occipital areas 45 were reported. The degeneration of cholinergic neurons in the basal forebrain projecting to the hippocampus and neocortex is believed to play a pivotal role in this process 46 . The present study also examined the resting-state EEG power analysis in CH-PATs and CH-NATs. Our findings revealed no significant differences in EEG power between the two groups during the resting state. This lack of significant differences suggests that the EEG resting-state brain activity may not be significantly affected by the pathological amyloid/tau. Such results may indicate compensatory mechanisms or variability within the groups, which might contribute to the absence of significant differences. The absence of significance also may indicate that cognitive challenge can help in revealing subtle changes in brain activities 47 , 48 .

In our investigation, we employed directed EC measures in the alpha frequency band, which are reliable, valid, and less influenced by confounding factors such as volume conduction 49 . Unlike undirected functional connectivity (i.e., coherence, phase lag index (PLI), and Phase Locked Value (PLV)) or Structural connectivity (anatomical links between neuronal populations), Effective connectivity (EC) (i.e., partial directed coherence (PDC)) among different EEG features examines the causal and directional influences between distant brain networks. Our study provides evidence of EEG changes associated with pre-clinical AD neuropathologies ( A β and tau). Specifically, we found a significant association between the A β /tau in CSF and an increase in CH-PATs frontal alpha connectivity. Reduced levels of A β peptides in the CSF indicate heightened A β deposition in the brain, while elevated levels of CSF tau protein, derived from damaged neuronal microtubules, serve as reliable biological indicators of AD and predictors of MCI conversion 50 . It has been found that synaptic dysfunction is a fundamental deficit in AD, preceding the emergence of hallmark pathological changes 51 . Soluble A β oligomers and tau fibrillar lesions disrupt synaptic plasticity and contribute to synaptic loss, resulting in the impairment of neural networks. Consequently, MCI and pre-symptomatic AD are better characterized as disruptions in functional and structural integration of neural systems rather than localized abnormalities. Our study observed increased frontal connectivity in CH-PATs. This finding suggests possible compensatory responses within executive networks and the presence of synaptotoxicity and neuronal dysfunction associated with presymptomatic AD-related pathology 52 . Furthermore, preclinical studies have suggested that increased synchrony in cortical circuits among individuals with pathological A β /tau may be attributed to reduced inhibitory neurotransmission mediated by GABAergic mechanisms rather than increased excitatory transmission 53 . Furthermore, tau was hypothesized to be associated with a breakdown in predictive neural coding 54 .

We speculate that CH-NATs exhibited pronounced temporal lobe connectivity in terms of causal interactions, surpassing those observed in CH-PATs. These results align with existing evidence indicating that the preclinical stages and MCI are characterized by significant atrophy and hypometabolism primarily in the posterior hippocampal, cingulate, temporal, and parietal regions. Particularly, these affected regions collectively resemble the memory network and default mode network as delineated in healthy individuals using task-free fMRI paradigms 55 . In summary, decreased brain connectivity is believed to be associated with memory decline 56 .

Furthermore, our analysis of EEG data using three distinct connectivity measures (that is, PDC, PLV, and wPLI) revealed complementary insights into neural connectivity patterns 57 . PDC analysis showed significant EC between the frontal and temporal cortices, pointing to greater information flow from these regions. In contrast, PLV identified significant phase synchronization between the frontal and occipital cortices, suggesting coupled activity potentially related to top-down visual attention processes 58 . WPLI results found significant phase synchronization in the frontal, parietal, and central cortices, indicating inhibitory effects of visuospatial attention, inhibition of return, and inhibitory control 59 . Different brain regions might be more involved in either directional influence or synchronization depending on the cognitive task. The synchronization (Measured by PLV and wPLI) between two regions could be due to a third region influencing both, or other indirect interactions. Conversely, PDC can be established without strong functional synchronization if the causal influence is strong enough to create a statistically significant correlation in their activities. The differences in how these methods handle noise, artifact, spatial, temporal resolutions, and the assumptions they create about neural dynamics can lead to variations in results. Notably, these three methods consistently identified the frontal cortex as a key hub of connectivity. These findings underscore the importance of the frontal cortex in the neural network and illustrate how different connectivity measures can provide a multifaceted understanding of brain activity 60 .

Our results revealed significant associations between brain connectivity and performance on these neuropsychological measures. Regarding memory tasks (e.g., REY-O 3-MINUTE DELAY), CH-PATs show a strong positive correlation between frontal connectivity and performance on episodic memory tasks. This finding suggests that greater connectivity within the working memory brain networks is associated with greater efficiency 61 . Furthermore, we observed a significant negative correlation between frontal connectivity and executive functions tasks (that is, language animals tasks), indicating that decreased connectivity in the temporal or frontal regions is associated with poorer executive functions tasks and may indicate a neural compensatory mechanism. CH-PATs showed a positive correlation between frontal connectivity and Stroop color-naming task as compared to CH-NATs. This suggests that CH-PATs compensate for their processing speed decline by increasing their frontal cortex connectivity (specifically in regions linked with executive functions) to perform better on the Stroop color naming task. Moreover, we found many negative correlations between brain connectivity and attention tasks, indicating that greater connectivity in these regions is associated with decreased attentional performance 62 . These results underscore the importance of brain connectivity with memory and cognitive functioning 63 . The strong correlations observed between specific brain regions and performance on memory and cognitive tasks provide evidence for the role of neural networks in cognitive processes.

Furthermore, results suggest that the relationship between CR and parietal connectivity may vary across different patient populations, with the CH-NATs group showing a distinct pattern of negative correlation. Adults with higher levels of cognitive reserve (CR) are more likely to use other cognitive resources, such as memory strategies, to compensate for their memory impairments. Previous studies showed that individuals with a higher CR use additional brain regions associated with better memory task performance 64 . CR is assumed to reduce the risk of cognitive decline associated with brain changes related to aging by promoting the use of compensatory cognitive processes 65 . CR indicates the efficiency, capacity, and flexibility of cognitive processes in the presence of a challenge, which helps to explain the individual’s ability to cope better with brain pathology (e.g., brain aging, delay of dementia symptoms, stroke) via more adaptable functional brain processes. Although actual biomarkers of CR are still questioned, a possible mechanism for CR has been hypothesized 66 . Neural reserve theory postulates that there exists an inter-individual variability in brain networks that function as a basis of any task. In CH-NATs, a higher CR was correlated with a lower parietal EC (more efficient), which was not observed in CH-PATs. This result may suggest that CR may be exhausted in CH-PATs during this task switching processing.

The association of MRI structural volumes and EEG brain connectivity in alpha may explain how neural structures and brain functions are coupled. The negative correlations in CH-PATs between temporal EC and these brain volumes suggest that a decrease in the volume of these brain regions may be associated with an increase in EC 67 . This could be due to a compensatory increase in the number of neurons and/or synapses in these brain regions. Previous studies have found that people with AD typically have smaller hippocampus than in HCs 68 . This suggests that the reduction of neurons in the hippocampus may constitute one of the initial alterations observed in AD 69 . Other brain regions that are often affected in AD include the temporal, the parietal, and the frontal lobes 69 , 70 . In late-onset AD, cortical atrophy initiates in the temporal cortex and subsequently extends to the parietal cortex via the cingulum bundle. In contrast, in early-onset AD, cortical atrophy originates in the parietal cortex and then spreads to the temporal cortex 71 , 72 . These regions are involved in a variety of cognitive functions, including language, memory, and executive function. Furthermore, recent research observed that both AD and MCI patients showed altered FC of the fusiform gyrus in a resting state compared to normal controls 73 , 74 , which can help explain our findings in supplementary Table  2 . As the disease progresses, the brain tissue in these regions may shrink, leading to further cognitive decline. Our findings uphold the notion that greater connectivity within the frontal regions is associated with brain compensation in pre-symptomatic AD 75 . This relationship aligns with previous studies highlighting the involvement of the frontal cortex in early AD pathology 10 . The results could provide evidence that enlarged regional volumes in CH-PATs may link with greater frontal EC and play a role in compensating for behavioral performance in the presence of AD pathologies.

Despite the probable clinical relevance of autonomic dysfunction in CH individuals with pathological A β /tau, only a few studies have evaluated HRV in presymptomatic AD. Our study observed a significant negative correlation between frontal connectivity and RMSSD in CH-PATs but not in CH-NATs. The level of brain connectivity may serve as a predictor of cognitive flexibility during a cognitive task, whereas HRV may specifically predict cognitive flexibility when influenced by neuronal oscillations 76 . The association of HRV, which measures autonomic function, and cardiovascular disease as well as cognitive dysfunction has been evidenced. There is a strong relationship between cardiovascular risk and an elevated likelihood of developing neurodegenerative diseases 77 . During the initial phases of AD, perturbations in the autonomic nervous system play a role in sustaining chronic hypoperfusion, thereby impacting the self-regulation of the brain and the functioning of the neurovascular unit. Conversely, neurodegenerative alterations characteristic of AD can exert an influence on autonomic functions and HRV by disrupting the vegetative networks situated in the insular cortex and brainstem 78 . This is in line with the previous findings that the preclinical dementia patients demonstrated parasympathetic regulation of slow waves is strongly associated with disrupted FC in the central nervous system 79 . Our data suggested that higher HRV (mean RR or RMSSD) is related to lower temporal and frontal connectivity in CH-NATs. CH-PATs also suggest that higher RMSSD is associated with decreased brain connectivity. Our study supports that memory and executive function networks are related to autonomic regulation and are affected by AD pathology.

The current study has several limitations. Firstly, the study’s sample size was relatively modest, potentially limiting the generalizability of our findings to broader populations or distinct groups. Second, we used the 21-electrode EEG system to study brain connectivity (scalp potentials) in the brain cortex. Future research should focus on a high-density EEG system (i.e., 64, 128, or 256 electrodes) and compare the results with our findings. Third, a notable limitation of this study is the time difference in data collection, with MRI and EEG data being acquired at different time points. This misalignment could potentially introduce confounding variables related to alterations in the participants’ electrophysiological states or external environmental factors over time. Fourth, our analysis was performed at the sensor-space level rather than at the source-space level. While sensor-level analysis offers valuable insights into neural activity patterns, it lacks the precision and specificity that source-level analysis can provide in localizing the origins of these signals within the brain. Fifth, we considered CSF A β and tau in classifying our cohorts. Future research endeavors may explore brain connectivity using PET or plasma A β and tau to study pre-clinical AD progression. Lastly, a significant limitation in estimating causal information flow among brain regions, such as with PDC, particularly with multichannel non-invasive recordings, is the influence of volume conduction arising from surrounding active neuronal sources. Future investigations should explore alternative connectivity algorithms less sensitive to volume conduction and validate findings using high-temporal-resolution methodologies such as MRI or DTI.

To conclude, AD pathology manifests several years before clinical symptoms are recognized, termed the preclinical stage. We investigated this stage to assess whether brain connectivity could detect this early pathophysiology. The results of this study showed the potential of EC as a noninvasive tool in isolating the asymptomatic participants with normal CSF biomarker (CH-NATs)levels from asymptomatic participants with AD (CH-PATs) during task switching. Reduced temporal EC and increased frontal EC were reported in CH-PATs compared to CH-NATs, independent of task difficulties. The increased frontal EC and/or decreased temporal EC in CH-PATs are linked with disrupted brain volumes, neuropsychological, HRV, and CR, suggesting a compensatory mechanism in the presence of AD pathology to retain the same behavioral performance. Our findings indicate that A β /tau pathology may affect specific EEG networks with systemic structural/functional compensations. Overall, EC is a useful, non-invasive tool for assessing EEG-functional-network activities and provides a better understanding of the neurophysiological mechanisms underlying Alzheimer’s disease.

Participants

Forty-six cognitively healthy elderly participants were recruited locally through local newspapers and newsletters, the Pasadena Huntington Hospital Senior Health Network, and visits to the senior centers. All participants consented via an Institutional Review Board (IRB) approved protocol (HMRI # 33797). Assessments included collecting demographic data, physical exams, fasting blood studies, disease severity and disability scales, and CSF A β /tau measurements 6 . Inclusion criteria: over 60 years, classified as CH after a comprehensive neuropsychological battery, as referenced in detail 6 . Exclusion criteria: other active, untreated disease, use of anticoagulants, or other contraindications to lumbar puncture.

CSF Amyloid/tau analysis

We reported a cutoff ratio of A β 42/total tau (2.7132) provided at least 85% sensitivity in discriminating AD from non-AD participants; we then used this regression to assign CH participants (CH) into 2 groups, one with normal CSF A β /total tau (CH-NATs) and the other with pathological A β /total tau (CH-PATs). As provisional evidence for the capacity of this CSF A β /total tau to predict clinical decline, a longitudinal study found that 40% of CH-PATs declined cognitively over 4 years to MCI, or AD, while none of the CH-NATs declined 39 , 40 . A detailed description of the data collection, methodological aspects of the entire process, and CSF data analysis procedures have been documented in our prior studies 6 , 32 , 39 .

Task switching paradigms

During the resting state baseline, participants were instructed to remain still and relax for 5 min with their eyes open, followed by another 5 min with their eyes closed. For the task-switching testing each trial consisted of two sequential stimuli, both presenting incongruent colored words (e.g., the word ‘Red’ in green color or the word ‘Green’ in red color), with or without an underline (see Fig.  7 ). Participants were instructed to press a button labeled ‘1’ for red and ‘2’ for green, indicating either the color (c) when underlined or the word (w) when not underlined. The trials were categorized into low-load repeat (color-color (cC) or word-word (wW)) or high-load switching (cW or wC) trials, with the second stimulus denoted using superscript to indicate the study target. The task-switching phase was comprised of three mixed blocks, each containing 64 trials. The blocks included all four conditions (cC, wW, cW, wC) in a random sequence with equal weightage. Our analysis focused on the cW task due to the presence of the persisting task-set inhibition 80 .

Each trial includes two sequential stimuli. Each stimulus is incongruent colored word. Participants were requested to respond to the word itself (no-underline), or to the color of the ink (underlined), by pressing a button (“1” for red, “2” for green). Tasks include a random mixture of low-load repeat trials ( a ) or high-load switch trials ( b ). The paradigm is described from our previous work 32 .

EEG data acquisition and processing

All EEG data were collected during the resting state (eyes closed) or during the switching-task challenge 32 . A 21-head-sensor, dry electrode system (Quasar Wearable Sensing, DSI-24, San Diego, CA, USA) was used to collect EEG signals. Sensor configuration followed the international 10–20 system. EEG signals were sampled at 300 Hz, and bandpass filtered between 0.4 and 45 Hz. For artifact rejection, we applied a  − 100 to 100 μV voltage threshold to detect bad epochs. In short, the visual inspection of epochs was performed based on a minimum of artifacts (e.g., excessive muscle activity, eye blinks) and drowsiness. In our study, drowsiness was inspected in EEG signals through careful visual inspection. Specifically, trained individuals examined EEG recordings for characteristic patterns associated with drowsiness, such as slowing of brainwave frequencies, increased theta activity, or intermittent bursts of alpha waves. Inspecting drowsiness is crucial to keep participants awake and alert (verbal notification) to ensure data quality, participant safety, and the validity of our experimental findings in our recordings. When an adequate level of quality was not obtained, we either substituted the epochs with alternative ones or eliminated the EEG data from further analysis if there were no sufficient epochs from the same subject available for analysis. Data quality refers to the standard of quality considered acceptable for the EEG data to be considered reliable for further analysis. This standard encompasses different factors including signal clarity, absence of artifacts, and adherence to predefined criteria for data integrity. For better signal processing, electrooculographic, electrocardiographic, and electromyography were recorded by 3 auxiliary sensors. A trigger channel encoded the time of color-word stimuli onset, the participants’ responses, and the type of test (C or W) for further analysis.

The continuous baseline EEG data were initially converted from the DSI-24 format to MATLAB format (R2022a) 38 . To ensure data quality and remove artifacts, a preprocessing pipeline designed explicitly for developmental EEG data, known as the Harvard Automated Processing Pipeline for EEG (HAPPE), was employed 38 . This subset consisted of 21 channels; Frontal (Fp1, Fp2, F7, F3, Fz, F4, F8), Temporal (T3, T4, T5, T6), Parietal (P3, PZ, P4), Occipital (O1, O2), Central (C3, CZ, C4), and mastoidal (A1, A2), as shown in Fig.  8 . The EEG signals were then referenced to the two mastoids/earlobes electrodes A1 and A2. Before independent component analysis (ICA), a 0.4 Hz digital high-pass filter, and a 45 Hz low-pass filter were applied to the EEG data to remove non-stationary signal drifts across the recording. HAPPE’s artifact removal steps encompassed the elimination of 60 Hz electrical noise using CleanLine’s multi-taper approach, rejection of bad channels, and removal of participant-related artifacts (e.g., eye blinks, movement, muscle activity) through ICA with automated component rejection via EEGLAB and the Multiple Artifact Rejection Algorithm (MARA) 81 . After artifact rejection, any channels removed during bad channel rejection were reconstructed using spherical interpolation to mitigate spatial bias in re-referencing. The resting-state EEG data were segmented into contiguous 2-s windows, and segments containing retained artifacts were rejected based on HAPPE’s amplitude and joint probability criteria, consistent with prior research on developmental EEG 82 . Importantly, there were no significant differences between outcome groups in terms of the mean lengths of the processed EEG data or any of the HAPPE data quality measures. Significant features were determined ( p  < 0.05), and assessed for the between groups using Student’s t -test) 82 .

From raw EEG signals, cortical activity is achieved by means of high-resolution EEG techniques. It shows the HAPPE pipeline’s pre-processing steps including ICA, the estimation of directed FC from the cortical time series, threshold application, and eventually the statistical analysis. The Schematic figure also shows the proportional threshold on the PDC metrics by maintaining a proportion p (0  < p  < 1) of the high dense connections and setting these connections to the same connectivity value, with all other connections set to 0 86 . The selection of the optimum thresholding value was based on global cost efficiency 96 . The brain network statistics are performed by t -test and Spearman’s rank correlation coefficient or Pearson correlation coefficients where appropriate.

A Fast Fourier Transform (FFT) with multitaper windowing was used to decompose the EEG signal into power for each 2-s segment for each of the channels of interest. For each of the four frequency bands, the summed power across all frequencies within the band was calculated as the measure of total power in that frequency band. All segmentation parameters and analysis windows are consistent with connectivity metrics and FFT was conducted using a Hanning window. Each participant’s data was averaged across the epochs for each electrode and the mean alpha power was computed for each of the following frequency bands: delta (0.4–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz). We selected a time window from 200 ms to 550 ms after stimulus onset as it detects brain responses associated with diverse cognitive functions, such as attention, working memory, decision-making, integration of incoming words, and emotion processing 83 . The data analysis process is illustrated in the block diagram in Fig.  8 .

Brain connectivity networks and information flow

Partial directed coherence (PDC) is one measure of the Granger causality which provides insights about the directionality of information between the brain nodes. PDC is based on the consideration that knowledge of the “driver’s” past increases the prediction of the “receiver’s” present state, compared to only using the receiver’s past. In the presence of volume conduction, however, all EEG channels mutually “drive” each other in this respect. PDC is derived from coefficients of a multivariate autoregressive (MVAR) model, which additionally depends on the scaling of the data. Interestingly, this scaling dependency is sufficient to yield significant spurious information flow from low-variance to high-variance temporally and spatially white noise channels. The MVAR uses Akaike information criterion (AIC) and Schwarz information to select the model order of MVAR 84 , 85 . In this study, the average MVAR model order p for all subjects was 7. Models with lower AIC are principally preferred. PDC is a frequency-domain approach to denote the direct linear relationship between two different signals y i ( t ) and y j ( t ) (equation 1 ) once remarked jointly with a set of other signals. Considering Y ( t ) the set of all observed time series, it can be depicted as an autoregressive model as follows: where p represents the model order, ε (t) is the prediction error matrix and A k are the coefficients matrix with a i j elements in which denotes the relation between signals at lag k . ε (t) has a covariance matrix ξ and their coefficients are usually a white noise with zero mean. This results in PDC factor ( π i j ) and partial coherence function ( ∣ k i j [ f ] ∣ 2 ) that indicates the strength and the direction of communication at frequency f .

Therefore, the PDC value from channel j to channel i can be expressed as follows:

where, \(({\bar{a}}_{i})(\, f)(i = 1,2,\ldots M)\) represents the i t h column of the matrix \(\bar{A(f)}\) and π i j represents the strength of causal interaction of the information flow from channel j to channel i at a frequency of f .

H [ f ] is the Hermitian matrix which is equal to \({\bar{A}}^{-1}[ \, f]\) . \({\bar{A}}_{ij}(f)\) is the complement of A i j ( f ) and represents the transfer function from y j [ t ] to y i [ t ] being also an element of A [ f ] matrix. Finally, a j [ f ] is the j t h column of A [ f ] and π i is the i t h row of π i j .

We applied the proportional threshold on the PDC metrics by maintaining a proportion p (0 <  p  < 1) of the highly dense connections and setting these connections to the same connectivity value, with all other connections set to 0 86 . The selection of the optimum threshold value was based on global cost efficiency. Proportional thresholding is a commonly used analysis step in reconstructing functional brain networks to ensure equal density across patient and control samples. The proportional threshold method is employed to highlight the most robust and significant connections while reducing visual clutter caused by weaker or less relevant connections. The MVAR model is a mathematical model commonly used in time series analysis to describe the relationship between an observation and a linear combination of its past observations. This MVAR model is generated using the two-time series for a specific frequency f ,  A k is the MVAR model in the discrete domain, is the covariance of the cross-spectral density matrix, k is the number of EEG channels, and I is the identity matrix. Calculation of the PDC values leads to a large matrix that describes the connectivity between the EEG channels, as shown in equation ( 1 ), where i j (  f  ) is the individual PDC value calculated from the time series i to j at frequency f . The PDC values range between 0 and 1 depending on how well one time series predicts the other. The strength of a measure such as PDC is apparent in its formulation because it is normalized according to the destination. When analyzing time series, this operation is taken in a short time Fourier transform approach. A 50 percent overlap between windows, with a window length of 400 Ms, is chosen to capture events that may fall on the border between windows. To reduce memory requirements, frequencies are divided into evenly spaced bins, typically a power of 2; here we chose 30. For k channels, there will be a k x k x 30 x t matrix of PDC values. The first dimension is the source channel and the second corresponds to the destination.

Brain phase synchronization

Phase synchronization analysis is crucial in understanding undirected functional connectivity in brain networks derived from EEG data. The Phase Lag Index (PLI) is a widely used measure in neuroscience that quantifies the consistency of phase differences between neural oscillations across different brain regions. Nonetheless, the Weighted Phase Lag Index (wPLI) extends the PLI by considering the magnitudes of the phase differences. This enhancement accounts for the strength of phase coupling between neural oscillations in addition to their consistency. The wPLI is a robust functional connectivity approach used in EEG connectivity analysis, because of its high insensitivity to common sources and volume conduction effects. The formula for wPLI is given by:

Where, ∣ Δ ϕ ( t ) ∣ represents the magnitude of the phase difference. wPLI provides a more refined measure of functional connectivity, capturing both the consistency and strength of phase coupling between brain regions. In contrast to PLI, the wPLI adjusts the weighting of the cross-spectrum based on the magnitude of its imaginary component. It eliminates the influence of cross-spectrum elements (phase lacking) near the real axis (0, π , or 2 π ), which are susceptible to small noise perturbations that might alter their true sign due to the volume conduction effects.

Moreover, the phase locking value (PLV) method is commonly used for calculating the correlation between two electrodes. The PLV is a statistic that can be used to investigate EEG data for task-induced changes in the long-range synchronization of neural activity. To calculate the PLV, two time series are first spectrally decomposed at a given frequency, f 0 , to obtain an instantaneous phase estimate at each time point. Phase synchronization between two narrow-band signals is frequently characterized by the PLV. Consider a pair of real signals s 1 ( t ) and s 2 ( t ), that have been band-pass filtered to a frequency range of interest. Analytic signals \({z}_{i}(t)={A}_{i}(t){e}^{j{\phi }_{i}(t)}\) for i  = {1, 2} and \(j=\sqrt{-1}\) are obtained from s i ( t ) using the Hilbert transform:

where H T ( s i ( t )) is the Hilbert transform of s i ( t ) defined as:

and P .  V . denotes the Cauchy principal value. Once the analytic signals are defined, the relative phase can be computed as:

The instantaneous PLV is then defined as 87 :

where E [. ] denotes the expected value. The PLV takes values on [0, 1] with 0 reflecting the case where there is no phase synchrony and 1 where the relative phase between the two signals is identical in all trials. PLV can therefore be viewed as a measure of trial-to-trial variability in the relative phases of two signals. In this work, we use the Hilbert transform, but the continuous Morlet wavelet transform can also be used to compute complex signals, producing separate band-pass signals for each scaling of the wavelet 88 . The connectivity results associated with wPLI and PLV are presented in the supplemental section (Figs.  S1 and S2 ).

Neuropsychological tests and cognitive reserve

Several tests of working memory, language, executive function, and processing speed were considered in our analysis. A full description of these tests and their references were reported in these studies 6 , 89 .

One crucial factor that has not been taken into account in the previously described studies on strategy use is the potential role of Cognitive reserve (CR) (brain’s ability to withstand aging or pathology by employing compensatory mechanisms) 90 . CR indicates the effectiveness, capability, and adaptability of cognitive processes during cognitive challenges or pathology. This phenomenon elucidates an individual’s capacity to manage brain-related issues such as aging, and delayed onset of dementia symptoms. Various proxies are used to measure CR, including educational level, verbal intelligence quotient (IQ), engagement in work, social interactions, and/or participation in leisure activities 91 . Presently, composite measures offer the most comprehensive assessment of CR, such as education, occupational complexity, and leisure activities. In our study, both IQ estimation and education level were used as proxies for CR. First, scores were transformed into Z-scores. Subsequently, the education and IQ Z-scores were averaged into a single cognitive reserve (CR) score 64 , 66 , 92 .

Structural MRI data acquisition

All MRI images were acquired at the Advanced Imaging and Spectroscopy Center of the Huntington Medical Research Institutes (Pasadena, CA) using a 1.5 Tesla General Electric (GE) clinical scanner with an 8-channel high-resolution head coil. A brief description of MRI and NeuroQuant (Cortechs Labs.ai Inc, San Diego, CA, USA) analyses was reported in our published work 93 . Several brain regions were selected to examine the correlation between brain connectivity and brain atrophy in CH-NATs and CH-PATs. These regions include the fusiform cortex, frontal cortex, hippocampus, entorhinal Cortex, and Amygdala. The normalization factors are often based on automated intracranial volume (ICV) measurements or scaling factors from skull-based or whole-head-based registration to a standard template 94 , 95 .

ECG and HRV analysis

We examined the correlation between EC and HRV measures. Raw electrocardiogram (ECG) data were collected during the task-switching using AcqKnowledge software (BIOPAC Systems, Inc., Goleta, CA). ECG and HRV recording and analysis details were reported in our previous work 32 . A correlation between CH-PATS and CH-NATs was conducted between brain connectivity and HRV time domain measures (i.e., NN intervals (RR), heart rate (HR), standard deviation of NN (SDNN), and root mean squared successive differences (RMSSD)) and frequency domain (i.e., low frequency (LF) and high-frequency (HF)).

Statistics and reproducibility

We employed a parametric two-sample t-test, using Bonferroni–Holm correction method, to assess the connection metrics between CH-NATs and CH-PATs. Before conducting the statistical analysis, we used the Kolmogorov-Smirnov method to test the normal distribution of the data. A p -value ( p  < 0.05) was used to identify the significant differences between CH-NATs and CH-PATs at the group level. All data are presented as (mean ± SD). Finally, Spearman’s or Pearson correlation was applied to study the association between brain connectivity and neuropsychological, CR scores, brain volumetric, and HRV scores. The p  < 0.05 and r (association directionality values) are shown.

All statistical analyses were performed using GraphPad prism statistics software (version 9.5.0) and R programming language (version 2023.06.0), and Matlab (version 2022A, The Mathworks, Inc).

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Raw data were generated at Huntington Medical Research Institutes (HMRI). All data generated or analyzed during this study are included in this published article and its supplementary information files, specifically in Supplementary Data  1 . Derived data and Matlab codes supporting the findings of this study are available from the corresponding author AA on valid request.

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Acknowledgements

The authors thank the study participants for their altruistic participation in this research. They also thank Dr. Astrid Suchy-Dicey for her support in submitting this manuscript, as well as Cathleen Molloy for revising the manuscript and handling some data, Shant Rising, and Rachel Woo for taking part in handling some data. Some data relied on in this study were derived from research performed at HMRI by Dr. Michael G. Harrington. This work was supported by the National Institute on Aging, National Institutes of Health (NIH) (grant numbers R56AG063857 and R01AG063857).

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Abdulhakim Al-Ezzi, Ryan Butler, Alfred N. Fonteh, Robert A. Kloner & Xianghong Arakaki

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Conceived and designed the experiments: X.A.; performed the experiments: X.A.; neuropsychological data: A.N.; MRI data: HMRI Brain Imaging Center; Analyzed data: A.A.; Wrote the paper: A.A.; Behavioral analysis: D.W.; Heart rate variability analysis: M.K. and R.A.; Edited the paper: R.A., R.B., A.N., J.J.K., S.S., D.W., A.F., M.K., R.K., and X.A.; All authors contributed to the final manuscript.

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Al-Ezzi, A., Arechavala, R.J., Butler, R. et al. Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau. Commun Biol 7 , 1037 (2024). https://doi.org/10.1038/s42003-024-06673-w

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DOI : https://doi.org/10.1038/s42003-024-06673-w

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