reaction time experiment in psychology

Simple and choice reaction time tasks

Introduction

In pictures, do it yourself.

In cognitive experimental psychology, we distinguish between simple and choice response time tasks. These two terms are being used in many books papers about cognitive psychology. This lesson explains and demonstrates what we mean with simple and choice response time tasks.

People (and animals) can respond a lot faster when there is just one stimulus and one response type (Simple Response Time task). Also, the more stimuli and responses there are, the slower you get (this is known as Hick’s law ).

It is now well established that a person’s response speed is influenced by age and general intelligence (e.g., Deary, Liewald, and Nissan, 2011 ). It is important to note that many other factors play a role as well, for example the conditions under which you perform the task (are you fit or tired, are you hungry, etc). Also, your speed depends on how accurate you aim to be. If you do not want to make mistakes, you will become slower. This is known as the speed-accuracy trade off (this goes back to the work of Woodworth, 1899 ; for a good review see Heitz, 2014 ).

It is important to understand that response times play a crucial role in experimental cognitive psychology. The basic idea is that response times reflect the time it takes to interpret a stimulus, get information from memory, initiate a muscle response, etc. Thus, response times can be used to find out how long basic thought processes take. This idea goes back to the work of the early experiment psychologists in the second half of the 19th century (when the term "cognitive psychology" did not even exist). One of the leading figures in this area of research was the Dutch ophthalmologist Franciscus Donders .

Below you see an example of the simple and the choice response time paradigm.

In the simple reaction time task, you need to wait until you see a black cross on the white square. When that happens, you press as soon as you can the space bar. Thus, there is one stimulus (black cross) and one response (pressing the space bar).

In the choice reaction time task, you need to wait until you see a black cross on one of the four white squares (e.g., there are four different black cross position, which counts as four different stimuli). When that happens, you press as soon as you can the corresponding key (z, x, . or ,). Thus, there are four stimulus-response associations. In this example trial, you need to press the "x" key.

In the demonstration below, you will do both a simple response time task (20 trials) and a choice response time task (20 trials). At the end, you will see your average response speed for the simple and choice reaction time task. You will be slower in the choice reaction time task.

Here we use the Deary-Liewald paradigm, which uses both a simple and a 4-choice response time task. There are two differences:

In the original Deary-Liewald paradigm, there are training blocks and more trials than in this demo.

In the original Deary-Liewald PC version, the keys of the keyboard chosen are great for UK and US keyboards, but not for German and French keyboards. This implementation uses x, c, b, and n because that works on UK, US, German, French keyboards, and probably cover most keyboards around the world.

If you want to do the full version of the Deary-Liewald paradigm, visit the experiment library .

Click here to try the experiment

Deary, Liewald & Nissan (2011). A free, easy-to-use, computer-based simple and four-choice reaction time programme: The Deary-Liewald reaction time task. Behaviour Research Methods, 43 , 258-268.

Heitz, R. P. (2014). The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Frontiers in Neuroscience, 8 , 1-19.

Woodworth, R. (1899). The accuracy of voluntary movement. Psychological Review, 3 , 1-106

# Precise Timing

# background & context.

Reaction time in psychology research is used to quantify cognitive processes and behaviors. A clear-cut definition of reaction time has to do with the amount of time passed between an appeared stimulus and the response.

There are two components to measuring reaction time, the stimulus’ time of onset and when the participant’s response occurred, illustrated by Fig.1.

Fig. 1: The two main components of measuring reaction time.

For reaction time to be measured accurately, the exact time of the stimulus onset (Point A) must be known, as well as when the participant’s response (Point B) happened as reaction time is the difference between these two points. From the two points, it is easy to determine when a participant’s response occurred, but it is challenging to know exactly when the exact stimulus onset occurred (Point A).

Why is it challenging to determine when Point A occurs? There are three main reasons that influence when a stimulus appears:

Screen refresh rate: The rate of monitor refreshing occurs at 60Hz so if something is scheduled to occur, it can occur only when the monitor is refreshed. While this is on a millisecond scale, it’s an important factor to quantify (which we discuss later how it is measured with the request animation frame) as it directly impacts the experimental sequence.

Nature of programming: All experiments are based on coding and for code to be executed, it must be processed as nothing is instantaneous, this usually takes 1-2 refresh cycles.

Device capacity: Though this is not common, if the participant’s device capacity is really slow, the stimulus presentation can lag as all of the system delays (like a computer freeze). We discuss later on how we check for this issue (the JavaScript Event Loop).

In summary, reaction time is affected by many factors upon which technological processes are built in order to accurately determine the time between stimulus onset and the participant’s response.

# Publication in Behavior Research Methods :

# Our Process: Labvanced’s pipeline for precise timing

Fig. 2: The general pipeline for precision timing and capturing accurate reaction times in Labvanced.

To provide precise timing and reaction times, our software follows these steps (Fig. 2) :

Preloading (caching): Ensuring all experimental stimuli are loaded a priori to the experiment beginning and locally available so loading does not happen in the midst of experimental progress. So, if a participant wants to take part in a study, all the stimuli (images, audio,and video) are already fetched and loaded locally on their computer from our server.

Pre-rendering: When the experiment begins, the content is recursively created so that the next frame and trial is loaded in the background and ready to go as soon as the participant is ready to move on. This is driven by a pre-rendering mechanism.

Participant-Specific Measurements: Since online studies begin in the browser, each participant has finite computer resources (GPU, CPU) which must be kept under consideration as they affect performance. We capture any potential delay and provide it as a correctional variable to the researcher which can also be used as an exclusion criteria.

# Saving Participant’s Responses

However, if the provisions are available, our software is set up so that data recording and responses are saved automatically after each trial. This is important because:

• A local browser cannot hold or cache an infinite amount of memory. By backing up frequently, memory is freed and the system does not risk lagging.
• If a participant stops or drops out, there is at least some data saved for the trials that they did complete and provide responses to prior to terminating their participation.

# About the Timestamp

# About System Architecture and Reaction Time Data Flow

While the pipeline described above captures the basic steps of the reaction time process, below is a more detailed explanation of everything that is going on in Labvanced to make the reaction time measurement accurate and precise.

# Preloading (Caching)

Fig.3: The main steps of the preloading/caching mechanism in Labvanced.

Preloading or caching occurs before the experiment even begins. Labvanced is set up so that all of the study’s experimental stimuli are downloaded before the study starts. This includes all of the elements, such as images and videos. They are all fetched from the Labvanced servers and downloaded locally to the participant’s device so that no downloading has to occur during the experiment itself (Fig. 3).

# Pre-Rendering Mechanism

Fig. 4: The main steps of the pre-rendering mechanism in Labvanced.

We have a pre-rendering mechanism in place to build the structure of the experimental tasks, trials, and frames in advance. For example, if you are in Trial #1 of a task, we pre-render all frames in the current and upcoming trial so that loading does not happen during the experiment, including the instruction, text, audio objects, fixation cross, etc. By building the trials and frames in advance, it prevents the browser from slowing down or being overwhelmed (Fig. 4).

# Participant-Specific Measurements

Because of the innate variability between devices and computers, performance is affected by the definition. Simply by running an experiment on a local system which are inherently limited with resources (ie. speed and memory are not infinite but constrained by their tech specifications), stimuli may not get shown as expected (there may be a delay of a few milliseconds, for example.

To capture these device- and participant-specific fluctuations, we have the following mechanisms in place:

• The request animation frame
• The JavaScript Event Loop

# Request Animation Frame

Fig. 5: Demonstration of the request animation frame mechanism in Labvanced.

Every 60ms the monitor is independently updating and refreshing, this is a constant for all computers and screens. To determine whether there is a delay in presentation of the stimulus (on the millisecond scale), the request animation frame is used for all instances where a timed stimulus is occurring.

Let’s say you execute code to show stimuli at 2000ms, when you execute it nothing happens, the stimuli will be automatically presented at the next refresh rate, 60 milliseconds (Hz) later, at the 240ms mark. You can measure this tiny lag and account for it post-hoc. Because we use the request animation frame, you can know exactly when a command was executed (when it really happened/appeared on the monitor) and adjust accordingly (Fig. 5).

# JavaScript Event Loop

Another example of participant-specific measurements has to do with determining the speed of their device.

If your computer is slow, it may be because there are active system processes running that use available CPU. Thus, the browser is working the limited resources that are available and as a result, everything gets slower.

To determine whether this is happening on the participant level, we use the ** JavaScript Event Loop using CallBack Functions** which runs automatically (by default) in the background to measure the amount of time it takes for the function to call back on itself. If it doesn't return within 5 ms, it means the participant’s browser/computer is slow which could affect the integrity of experimental results measuring reaction time (Fig. 6) . We report the mean value in milliseconds that it takes for the CallBack Function to return for the participant.

For the thousands of studies that have been completed by participants in Labvaned, we have found that over 95% of participants have a reported value that falls below 3ms, sometimes below even 1ms. But in some cases, there are results that average 200-300ms which could indicate to the researcher to consider excluding that particular user’s data from the final data set analysis.

# Key Features of Labvanced’s Reaction Time and Precision Timing Capabilities:

Our top features for measuring participants’ responses include (Fig. 7):

• Temporal accuracy of stimulus presentations
• Spatial accuracy of stimulus presentations

Fig. 7: The key features of Labvanced’s precise timing / reaction time solution.

# Advantages of Labvanced’s Precision Timing

Because of these steps and mechanisms, Labvanced offers an accurate and precise solution to measuring reaction time during online experiments. We highlight the following advantages of our platform:

• Controlled timing of stimuli: Researchers have knowledge of the exact time that stimuli are presented on screen, allowing for adjustment and accurate measurements.
• Strong computational and programming mechanisms: To assure the researcher the most accurate data is being reported, we use strong computational and programming mechanisms in order to accurately quantify the onset of stimuli on the participant’s screen.
• Tried and tested: We have worked with researchers from all over the world to fine-tune our platform and as a result our features have been tried and tested by countless of research and academic institutions using our online reaction timing measuring as a basis for their studies and published works.

# Sample Data & Metrics for Reaction Time

Fig. 8: Data report from a participant’s session performing the Stroop Task using Labvanced; 3rd column from the right demonstrates recorded reaction times.

Things You Can do with Labvanced’s Precision Timing:

• Cognitive decline
• Performance measures
• Feature recognition

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# LV Library Studies:

There are many studies that measure how long it takes for a response to a stimulus to occur, here are a few examples of tasks that have reaction time measurement at their core:

# Popular Areas of Research Utilizing Labvanced’s Precision Timing:

open in new window to your study with this video:

Reaction Time

• Living reference work entry
• First Online: 18 December 2017
• Cite this living reference work entry

• Michael E. Young 3 &
• Angela Crumer 4  

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Historical Background

Reaction Time (RT) data have been used as a measure of human behavior throughout the history of psychology. The Dutch physiologist Franciscus Donders ( 1969 ) believed that RTs were a window into mental chronometry or the duration of various mental operations that included perceiving a stimulus, recognizing it, and making a choice. His methodology heavily relied on a subtraction method in which tasks purportedly with and without various mental components were compared. The difference in reaction time between two tasks that varied in one task component was assumed to measure the duration of that component. For example, the two tasks could require deciding whether to push a button when a stimulus appeared versus pressing any button when it appeared. Trials involving a button press for the former task might produce RTs that average 65 ms longer. Using Donders’ logic, this suggests that the mental process of deciding is 65 ms in duration. The basic assumptions of the...

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Michael E. Young

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Young, M.E., Crumer, A. (2018). Reaction Time. In: Vonk, J., Shackelford, T. (eds) Encyclopedia of Animal Cognition and Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-47829-6_731-1

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• 00:00 Overview
• 02:21 Preparation of ‘No Conflict’ Stimuli for the Stroop Test
• 03:42 Preparation of ‘Conflict’ Stimuli for the Stroop Test
• 04:41 Testing a Participant
• 06:11 Analysis and Results
• 08:12 Applications
• 09:13 Summary

Measuring Reaction Time and Donders’ Method of Subtraction

Source: Laboratory of Jonathan Flombaum—Johns Hopkins University

The ambition of experimental psychology is to characterize the mental events that support the human ability to solve problems, perceive the world, and turn thoughts into words and sentences. But people cannot see or feel those mental events; they cannot be weighed, combined in test tubes, or grown in a dish. Wanting to study mental life, nonetheless, Franciscus Donders, a Dutch ophthalmologist in the early 1800s, came up with a property that he could measure—even back then: he measured the time it took for human subjects to perform simple tasks, reasoning that he could treat those measurements as proxies for the time it takes to complete the unobservable mental operations involved. In fact, Donders went one step further, developing a basic experimental paradigm known as the Method of Subtraction. It simply asks a researcher to design two tasks that are identical in nearly every way, excepting a mental operation hypothesized to be involved in one of the tasks and omitted in the other. The researcher then measures the time it takes to complete each task, and by subtracting the outcomes, he extracts an estimate of the time it takes to execute the one mental operation of interest. In this way, the method allows a researcher to isolate a mental operation. The time it takes to complete a task has become known as reaction time or latency. Even today, reaction time is by a wide margin the most prevalent dependent variable in experimental psychology.  

This video will demonstrate the measurement of reaction time using Donders’ Method of Subtraction.

1. Pick a task and material to implement it.

• To use Donders’ Method of Subtraction, one first needs a mental operation of interest, and a pair of tasks thought to differ in terms of the operation. For current purposes, this video explores the ability to resolve conflicts between different sources of information—an important aspect of the ability to exert self-control on behavior. The Stroop task is a good basis for measuring the time it takes to resolve a conflict between information sources.
• The Stroop task can easily be programmed on a computer, but one nice feature is that it can also be implemented with just a few index cards and magic markers.
• So, the first things needed are: four magic markers (one each in red, yellow, blue, and green), two large index cards, and a stopwatch.

2. Make the ‘No Conflict’ stimuli.

• Take one of the index cards, placing it in front of you so that the lines are horizontal. Fold it in half creating a vertical meridian for two columns of stimuli.
• On each line in the left column, write in clear, capital letters one of the four color-terms, ‘RED, YELLOW, BLUE, GREEN.’ Ink each word using its corresponding magic marker. Pick colors more-or-less randomly. It might be easier to do this by rolling a die with one of the four colors assigned to each number.
• Repeat 2.2 on each line of the right column, aligned with the crease in your card.
• You now have the stimuli for the ‘No Conflict’ condition of this classic experiment ( Figure 1, left ).

3. Make the ‘Conflict’ stimuli.

• Take your second index card, and repeat step 2.1.
• Now you are again going to write out a color term on each line and in each column. But crucially, ink each term with any marker, except for the corresponding color . In other words, create a conflict between the ink color and the word you write on each line. Again, you want to pick words and colors more or less randomly. If you are using a die, you can roll it once to pick your word, and again to pick your ink (rolling again if they happen to match). Or you can use two dice, of course.
• You now have the stimuli for your ‘Conflict’ condition ( Figure 1, right ). Note, your ‘Conflict’ and ‘No Conflict’ cards should have equal numbers of words.

4. Test a participant.

• You are now ready to test your first participant. You can also test yourself—but you’ll need someone to run the stopwatch.

Place either one of your index cards face down on a table in front of your participant.

• Set your stopwatch to 0.
• Explain to the participant that when you say go, she can turn over the card, and as quickly as possible she should look at each line of the index card, working her way down the left column and then the right column, saying out loud the color of the ink . In other words, she should not read the word, only report its ink color. Emphasize that she must report each line correctly before moving on to the next, but that she should try to go as quickly as possible. She should say ‘DONE’ after reporting the final line.
• You say go, activate the timer, and get ready to stop the timer when your subject says, ‘DONE.’
• Write down the time it took.
• Now repeat 4.5-4.8, but with the other index card. You want the participant to do the task once with the ‘No Conflict’ stimuli, and once with the ‘Conflict’ stimuli. Order does not matter. But if you were to run multiple participants, you would want to counterbalance, with half the participants doing one order and the remaining half doing the other.

5. Analysis

• You should now have two reaction times: the time it took for your participant to get through the ‘Conflict’ card, and the time she took with the ‘No Conflict’ card. Subtract the ‘No Conflict’ time from the ‘Conflict’ time. If the number is positive, it is a sign that resolving the conflict between ink color and written words is a step that is involved in the ‘Conflict’ condition and not the ‘No Conflict’ condition. And the difference is an estimate of how long resolving the conflict takes.
• Note that each card included several words. But because the two cards included the same number of words, the difference between your conditions can be used to derive an estimate of the time to resolve a single instance of conflict. Since the difference between the cards is the difference between the sums of several instances that included a conflict and as many that did not, just divide the time difference between the two cards by the number of words on each card. The result is an estimate of the time to resolve a single conflict.

The ambition of experimental psychology is to characterize the mental events that support the human ability to solve problems, perceive the world, and turn thoughts into words and sentences.

But people can’t see or feel those mental events; they can’t be weighed, combined in test tubes, or grown in a dish.

Wanting to study mental life, nonetheless, Franciscus Donders, a Dutch ophthalmologist in the early 1800s, came up with a property that he could measure-even back then; he measured the time it took for human subjects to perform simple tasks, reasoning that he could treat those measurements as proxies for the time it takes to complete the unobservable mental operations involved.

In fact, Donders went one step further, developing a basic experimental paradigm known as the Method of Subtraction.

This method simply asks the experimental psychologist to design two tasks that are identical in nearly every way, except that one task involves a hypothesized mental operation and the other does not.

The researcher then measures the time it takes to complete each task, and by subtracting the outcomes, she extracts an estimate of the time it takes to execute the one mental operation of interest.

In this way, the method allows the researcher to isolate a mental operation. The time it takes to complete a task has become known as ‘reaction time’ or ‘latency.’ Today, reaction time is the most prevalent dependent variable in experimental psychology.

To use Donders’ Method of Subtraction, one first needs a mental operation of interest, and a pair of tasks thought to differ in terms of the operation. This video will use the Stroop task to explore the ability to resolve conflicts between different sources of information-an important aspect of the ability to exert self-control on behavior.

The Stroop task is a good basis for measuring the time it takes to resolve a conflict between information sources. Here the conflict arises between reading the name of the color and viewing the color in which the word is written.

The Stroop task can easily be programmed on a computer, but one nice feature is that it can also be implemented with just a few index cards and colored pencils. So, the first things needed are four colored pencils, one each in red, orange, blue, and green, and also two large index cards, a stopwatch, and six-sided dice.

Take one of the index cards, placing it in front of you so that the lines are horizontal. Fold it in half, creating a vertical meridian for two columns of stimuli.

On each line in the left column, write in clear, capital letters one of the four color-terms. For each word use its corresponding colored pencil. You want to pick colors more-or-less randomly. It might be easier to do this by rolling a die with one of the four colors assigned to each number.

Repeat the same procedure on each line of the right column, aligned with the crease in your card.

Take your second index card, and again place it in front of you so that the lines are horizontal. Again, fold it in half creating a vertical meridian for two columns of stimuli.

Now, you are again going to write out a color term on each line and in each column. But crucially, print each term with any pencil except for the corresponding color. In other words, create a conflict between the lead color and the word you write on each line.

Again, you want to pick words and colors more or less randomly. If you are using a die, you can roll it once to pick your word, and again to pick your pencil color, rolling again if they happen to match. Or you can use two dice, of course.

You now have the stimuli for your ‘Conflict’ condition. Note, your ‘Conflict’ and ‘No Conflict’ cards should have equal numbers of words.

You are now ready to test your first participant.

Set your stopwatch to 0, and explain the task to the participant.

After the participant confirms that they understand the task, start the timer and say…

The participant should look at each line on the left side of the index card and say out loud the color of the word as quickly as possible . That is, they should not read the word by name, only the color it’s printed in.

The participant must report each line correctly before moving on to the next.

After the colors on the left side of the card have been reported, the participant should repeat the procedure with the colors on the right side of the card.

The participant says “DONE” after reporting the final line, and you stop the timer.

Now repeat the timed test, but with the other index card.

You want the participant to do the task once with the ‘No Conflict’ stimuli, and once with the ‘Conflict’ stimuli. Order does not matter. But if you were to run multiple participants, you would want to ‘counterbalance,’ with half the participants doing one order, and the remaining half doing the other.

You should now have two reaction times: the time it took for your participant to get through the ‘Conflict’ card, and the time she took with the ‘No Conflict’ card.

Subtract the ‘No Conflict’ time from the ‘Conflict’ time. If the number is positive, it is a sign that resolving the conflict between printed color and word names is a step that is involved in the ‘Conflict’ condition, and not the ‘No Conflict’ condition. And the difference is an estimate of how long resolving the conflict takes.

Note that each card included several words, so the reaction time difference is the difference between the total time it takes to get through each card. By dividing that difference by the number of words on the card, you can get an estimate of how long it takes to resolve each individual instance of word-color conflict.

It is hard to draw conclusions from a single subject, and so an experiment typically tests many subjects, around 20 for the Stroop task, aggregating their results to draw reliable conclusions. For each participant, you end up with two reaction times: one from the ‘Conflict’ and one from the ‘No Conflict’ condition. On a spreadsheet, you would organize the results something like this.

These results can be summarized with a simple graph of the average reaction time across all participants in each condition. As seen here, participants read through the card with the No Conflict stimuli about 11.6 sec faster than they read through the card with the Conflict Stimuli. In terms of Donders’ method, this suggests that resolving the conflict between print color and reading takes about 11.6 sec per card. Since each card in this experiment had 12 words written on it, this means that, on average, resolving the conflict between print color and reading takes about 1 sec per word.

Donders’ Method of Subtraction can be used with reaction time measures in a variety of areas in experimental psychology, not just with Stroop or conflict paradigms.

In addition, the Method of Subtraction underpins the basic logic for a wide array of approaches to experimental psychology with dependent variables beyond reaction time. These include measures as diverse as how long an infant glares at a stimulus, and the blood-oxygen-level-dependent (BOLD) response measured in the human brain by sophisticated fMRI machines.

In many fMRI experiments, researchers obtain patterns of brain activity from two experimental conditions that are identical, excepting the involvement of a mental process of interest, such as the conflict and no conflict trials of the Stroop task. By subtracting one pattern from the other they can isolate brain areas involved in that process of interest.

You’ve just watched JoVE’s introduction to Donders’ Method of Subtraction. Now, you should have a good understanding of how to conduct a simple Stroop task in order to determine the time it takes to resolve conflicts and presented stimuli. There are numerous ways to apply to technique. So go out there and make Donders proud.

It is hard to draw conclusions from a single subject, and so an experiment typically tests many subjects, aggregating their results to draw reliable conclusions. For this Stroop experiment, you would test 20 or so participants just the way you tested one. For each participant, you end up with two reaction times, one from the ‘Conflict’ and one from the ‘No Conflict’ condition ( Table 1 ). These results can be summarized with a simple graph of the average reaction time across all participants in each condition ( Figure 2 ).

Table 1. Reaction times by subject. Reaction time data are reported across condition for each subject.

Applications and Summary

Donders’ Method of Subtraction can be used with reaction time measures in a variety of areas in experimental psychology, not just with Stroop or conflict paradigms. In addition, the Method of Subtraction underpins the basic logic for a wide array of approaches to experimental psychology with dependent variables beyond reaction time. These include measures as diverse as how long an infant glares at a stimulus, and the blood-oxygen-level-dependent (BOLD) response measured in the human brain by sophisticated fMRI machines. In many fMRI experiments, researchers obtain patterns of brain activity from two experimental conditions that are identical, excepting the involvement of a mental process of interest. By subtracting one pattern from the other, they can isolate brain areas involved in that process. Indeed, the Stroop is a classic example. Participants have their brains scanned during conflict and no conflict trials. Many brain areas are involved in each kind of trial, including visual cortex and regions involved in reading. But when the no conflict scans are subtracted from the conflict ones, fairly isolated frontal regions of the brain—especially one called the anterior cingulate cortex—appear to be critically active in only the no conflict trials. This makes sense! Those frontal regions are often associated with the ability to control one’s own behavior under difficult conditions.   

The participant should look at each line on the left side of the index card and say out loud the color of the word as quickly as possible. That is, they should not read the word by name, only the color it’s printed in.

Donders Response Types

• Configuration

In 1868, the Dutch physiologist and ophthalmologist F. C. Donders suggested that such mental processes as sensory discrimination, perceptual identification, and motor selection might occur serially, each consuming a certain amount of time. If so, wrote Donders, “interposing into the process some new components of mental action would reveal the time required for the interposed item” (Donders, 1868/1969, p. 418). In other words, if one could devise a pair of tasks differing only in that one required an extra mental process, then the difference in reaction time (RT) to the two tasks would be an estimate of the duration of the extra mental process. To illustrate his subtraction method for studying cognition he compared three tasks: Simple Reaction Time, Go/No-Go Response, and Choice Reaction Time.

Donders assumed that the Simple RT task is identical to the Go/No-Go task, except for an extra process of stimulus discrimination in the latter: Go/No-Go requires the discrimination of squares from diamonds, whereas Simple RT does not. Similarly, the Go/No-Go and Choice RT tasks are identical, except for the extra process of response selection in the latter. Therefore, subtracting mean Simple RT from mean Go/No-Go RT should provide an estimate the duration of the stimulus discrimination stage. Subtracting mean Go/No-Go RT from mean Choice RT should provide an estimate the duration of response selection. Your data in these three tasks likely confirm that Choice RT is more difficult than Go/No-Go, which in turn is more difficult than Simple RT. However, are the RT differences accurate estimates of stimulus discrimination and response selection timing? Or do the three tasks differ in ways other than the “interposed items” proposed by Donders? The validity of any application of the subtraction method depends on the assumption of pure insertion: that a mental process can be added or omitted without in any way altering the speed of the other processes.

This assumption has long been criticized on many grounds (Boring, 1929; Ilan & Miller, 1994). For instance, changing a simple task to a more complicated one changes the subject’s strategy, and the entire conscious information processing pattern is therefore altered. Introspective accounts have been used to argue that the subtraction method is invalid for studying cognition, because increasing a task’s complexity always affects other stages, both qualitatively and quantitatively (Kuelpe, 1909). Despite such criticisms, Donders’ subtraction method continues to influence modern cognitive psychology. Sternberg’s seminal Additive Factors Method (Sternberg, 1969) is based on Donders’ study, and modern brain-imaging procedures such as PET and fMRI critically rely on subtraction logic to infer what parts of the brain are used to perform basic mental processes.

Publications

Boring, E. G. (1929). A history of experimental psychology. New York: Appleton-Century.

Donders, F. C. (1969). Over de snelheid van psychische processen [On the speed of psychological processes]. In W. Koster (Ed.), Attention and Performance: II (Original work published 1868). Amsterdam: North-Holland.

Gottsdanker, R., & Shragg, G. P. (1985). Verification of Donders’ subtraction method. Journal of Experimental Psychology: Human Perception and Performance, 11, 765-776.

Ilan, A. B., & Miller, J. (1994). A violation of pure insertion: Mental rotation and choice reaction time. Journal of Experimental Psychology: Human Perception & Performance, 20, 520-536.

Kuelpe, O. (1909). Outlines of psychology. London: Swan Sonnenschein Co.

Massaro, D. W. (1989). Experimental psychology: An information processing approach. San Diego: Harcourt, Brace, Javanovich, Inc.

Sternberg, S. (1969). The discovery of processing stages: Extensions of Donders’ method. Acta Psychologica, 30, 276-315.

http://en.wikipedia.org/wiki/Reaction_time

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ORIGINAL RESEARCH article

A reaction time experiment on adult attachment: the development of a measure for neurophysiological settings.

• 1 Institute for Psychology, University of Innsbruck, Innsbruck, Austria
• 2 Forel-Clinic, Ellikon, Zürich, Switzerland
• 3 Rehaklinik Bellikon, Zürich, Switzerland
• 4 Department of Psychology, Mills College, Oakland, CA, USA
• 5 Department of Psychosomatic Medicine and Psychotherapy, Ulm University, Ulm, Germany

In the last few decades, there has been an increase of experimental research on automatic unconscious processes concerning the evaluation of the self and others. Previous research investigated implicit aspects of romantic attachment using self-report measures as explicit instruments for assessing attachment style. There is a lack of experimental procedures feasible for neurobiological settings. We developed a reaction time (RT) experiment using a narrative attachment measure with an implicit nature and were interested to capture automatic processes, when the individuals’ attachment system is activated. We aimed to combine attachment methodology with knowledge from implicit measures by using a decision RT paradigm. This should serve as a means to capture implicit aspects of attachment. This experiment evaluated participants’ response to prototypic attachment sentences in association with their own attachment classification, measured with the Adult Attachment Projective Picture System (AAP). First the AAP was administered as the standardized interview procedure to 30 healthy participants, which were classified into a secure or insecure group. In the following experimental session, both experimenter and participants were blind with respect to classifications. One hundred twenty eight prototypically secure or insecure sentences related to the eight pictures of the AAP were presented to the participants. Their response and RTs were recorded. Based on the response (accept, reject) a continuous security scale was defined. Both the AAP classification and security scale were related to the RTs. Differentiated study hypotheses were confirmed for insecure sentences, which were accepted faster by participants from the insecure attachment group (or with lower security scale), and rejected faster by participants from secure attachment group (or with higher security scale). The elaborating unconscious processes were more activated by insecure sentences with potential attachment conflicts. The introduced paradigm is able to contribute to an experimental approach in attachment research. The RT analysis with the narrative procedure might be of interest for a broader variety of questions in experimental and neurophysiological settings to capture unconscious processes in association with internal working models of attachment. An electrophysiological model based on preliminary research is proposed for assessing the preconscious neuronal network related to secure or insecure attachment representations.

Introduction

Attachment theory is an evolutionary-based theory of a specific type of intimate human social relationship conceived to have a major developmental influence from “the cradle to the grave” ( Bowlby, 1969 , 1973 ). According to attachment theory, the foundation of the attachment relationship is a biologically based behavioral system that evolved in ways that influence and organize motivational, cognitive, emotional and memory processes. These processes are organized in early infancy with respect to significant caregiving figures that extend into adulthood. Bowlby (1980) conceived attachment as a key mechanism related to maintaining biological homeostasis, including the modulation of physiological stress and mental health. Researchers have found physiological correlates of attachment and the affective components of relationships in nonhuman species and humans. Mental representations of early attachment relationships shape emotional and cognitive information, which affects our attention and memory. In order to maintain organization within the attachment system, emotional reactivity is then regulated within the central nervous system ( Bretherton, 1993 ; Main, 1995 ). Over the decades, psychobiological attachment research with infants and adults has increased dramatically ( Coan, 2008 ; Gander and Buchheim, 2015 ). Attachment patterns have been linked to different ways to emotion regulation processes and some researchers even argued that the attachment system is in itself an emotion regulation device ( Vrtička and Vuilleumier, 2012 ).

Most recent findings on attachment and neurobiology in functional magnetic neuroimaging (fMRI) showed that researchers investigated very different systems, often by very different means and a variety of paradigms, ranging from the presentation of individual photos of loved and unknown faces to more complex approaches (e.g., reflecting on attachment-relevant events, priming experiments, talking about attachment-relevant situations; see overview Buchheim and George, 2012 ). At present, the delineation of a neuronal network of attachment is not possible yet. However, there is evidence across fMRI studies that brain regions like the amygdala and orbito/prefrontal cortices are involved in processing attachment-related stimuli. In addition, convergent research results suggest that when caregiving is addressed, dopamine-associated regions of the reward system are active that differ from the neural correlates of the postulated “attachment circuitry” ( Buchheim et al., 2010 ).

Several neurophysiological studies of adult attachment assessing the autonomic nervous system, the hypothalamic-pituitary-adrenocortical axis or frontal electroencephalography (EEG) asymmetry used self-report measures ( Carpenter and Kirkpatrick, 1996 ; Kim, 2006 ; Laurent and Powers, 2007 ; Rochman et al., 2008 ; Zhang et al., 2008 ; Kiss et al., 2011 ; Dan and Raz, 2012 ), while other studies used narrative interview measures of attachment such as the Adult Attachment Interview (AAI) and the Adult Attachment Projective Picture System (AAP; Beijersbergen et al., 2008 ; Buchheim et al., 2009 ; Fraedrich et al., 2010 ; Holland and Roisman, 2010 ; Behrens et al., 2011 ; Leyh et al., 2016 ).

The self-report questionnaire instruments are conceived as personality constructs and assess the subjective evaluation of attachment styles with reported patterns monitored by conscious processing of feelings and experiences related to desires and worries regarding a romantic partner; these measures typically differentiate secure from insecure avoidant or anxious attachment styles ( Ravitz et al., 2010 ). By contrast, developmental attachment measures such as the AAI ( George et al., 1985–1996 ; unpublished manuscript) or AAP ( George et al., 1999 ) are designed to activate the individuals attachment system by introducing attachment-related topics (e.g., separation, illness, abuse and death), and assess attachment representations (secure, insecure-dismissing, insecure-preoccupied and unresolved trauma) based on the analysis of discourse patterns of verbatim transcripts. Interview discourse analysis is less concerned with a specified response (as compared with attachment style measures) as how experiences and feelings are described.

In a very recent fMRI study, Yaseen et al. (2016) investigated the comparison of brain activity correlating with self-report (Relationship Scales Questionnaire, RSQ) vs. a narrative attachment measure (AAI) during conscious appraisal of an attachment figure. Interestingly the two measures elicited different brain responses. While the AAI appeared to disproportionately correlate with conscious appraisal associated activity in Default Mode Network (DMN) and subcortical structures, the RSQ seemed to tap Executive Frontal Network (EFN) structures more extensively. The authors suggested, that the AAI captured more interoceptive, “core-self”-related processes, while the RSQ assessed higher-order cognitions involved in attachment. The authors recommended in their conclusions, that the AAP might be an appropriate alternative in this kind of research, since this measure consists of a set of pictures feasible to present during an experimental setting.

The feasibility of the AAP measure as an attachment-activating stimulus in a neurobiological context (fMRI, neuroendocrinology) has been established in diverse experimental settings in clinical and nonclinical groups (e.g., Buchheim et al., 2006 , 2008 , 2009 , 2012 ). Participants in these different settings were instructed to tell stories to the AAP picture stimuli in the fMRI environment ( Buchheim et al., 2006 , 2008 ) or were presented individualized sentences constructed from their own AAP responses in the fMRI setting ( Buchheim et al., 2012 ).

In the context of a double-blind study with a neuroendocrine research question, we modified the AAP task for a double blind controlled study comparing the effects of oxytocin to a placebo condition. The AAP picture presentations were accompanied by prototypical phrases constructed to represent one of the four established attachment categories (i.e., a generalized attachment-sentence schema for each attachment group). The participants were instructed to rank these phrases from the most to the least appropriate for each presentation. The most interesting finding from this study was that insecurely attached individuals at baseline decided that secure attachment sentences were most appropriate for them under the oxytocin condition ( Buchheim et al., 2009 ). This attachment experiment was a first attempt to assess a combination of conscious and unconscious processes in a self-report perception task. In this present study, we sought to improve on this approach by using this methodology in a reaction time (RT) paradigm. The research question was to examine if the RTs differed with respect to an individual’s attachment representation in order to provide a paradigm to use in a neurobiological setting, like an EEG experiment.

One interesting development in the past few years has been experimental research using the Implicit Association Task (IAT), the goal of which was to explore the domain of automatic cognitive processes concerning the evaluation of the self and others ( Lane et al., 2007 ). The IAT task is based on the measurement of RTs and answers in combination with a target category, for example gender stereotyping and the self. Implicit measures have been successful in predicting verbal behavior, group membership, sexual behavior, and evaluative judgments ( Gawronski, 2002 ) or personality ( Grumm and von Collani, 2007 ).

RT research has a long experimental tradition in psychology, beginning with the experiments by Helmholtz (1850) . Helmholtz was interested in the time relations structured by the nervous systems of living beings not just from a physiological but also from a psychological point of view. In fact, at the time at which he performed his time experiments in frogs, Helmholtz carried out similar studies in human beings ( Schmidgen, 2002 ).

RT experiments are relatively inexpensive to execute and results are easy to obtain, even though the conclusive interpretations are still under discussion. According to Harris et al. (2014) RT experiments have become a standard paradigm for measuring behavioral reactions without taking into account underlying mental processes. Harris et al. (2014) suggested a sophisticated way to improve the analysis and interpretations of RT paradigms.

The idea behind the measurement of RT is that it can be used as a measure in social cognition research, as an index of the complexity of the underlying mental processes. Results showed for example, that more complex processes are associated with longer elaboration/RTs ( Rösler, 1993 ). Moreover, RT experiments have a predictive value for social decisions and have been used successfully in IAT clinical and social experiments ( Lane et al., 2007 ). It was possible to differentiate groups with and without disorders using the IAT in reliable experiments about self-judgments ( Gemar et al., 2001 ). RT measures were also used to understand semantic priming. Reactions were more quickly facilitated when categories were closely related and shared the same reaction as compared to categories that did not share the same reaction. Attribution measures can be interpreted as a measure of relative identity with the objects ( Lane et al., 2007 ). The association of the self as a target category and an attitude dimension provides a measure of implicit self-esteem; it describes the strength between associations of the self and another category. Studies show that emotional relevant primes have an effect on memory performance. One study showed that memory performance was impaired in borderline patients when negatively valued interference was presented ( Mensebach et al., 2009 ). A recent study on autobiographical memories reported that past intentions could be reliably identified with high accuracy using a RT measure ( Zangrossi et al., 2015 ).

A central concept of attachment theory is that individuals develop internal working models, that include expectations about the self, and significant others outside of conscious awareness ( Bowlby, 1969 , 1973 , 1980 ; Bretherton, 1985 ). Furthermore, internal working model content is believed to include knowledge about concrete details of interpersonal experiences as well as the associated affect ( Bretherton, 1985 ). In general, psychoanalytic theory suggests to divide the mind into three different levels: the conscious mind includes everything we are aware of and represents our mental processing that we think of and talk about rationally. A part of this includes memory structures, which are considered not always to be part of consciousness, but can be retrieved and brought into awareness, called preconscious. The unconscious mind constitutes a reservoir of feelings, thoughts, urges and memories that exist outside of conscious awareness. From a psychoanalytic point of view, most of these contents are unacceptable or unpleasant and represent feelings of pain, anxiety or individual conflicts. Unconscious processes are considered to influence our behavior and experience, even though we are unaware of these underlying influences ( Freud, 1915/2001 ). As mentioned above internal working models are also thought to work primarily outside of conscious awareness (unconsciously) and guide attention, interpretation, and memory of attachment experiences and emotions. This allows individuals to generate expectations about the future concerning interpersonal interactions and to develop plans relating to them ( Bowlby, 1969 , 1973 , 1980 ; Bretherton, 1985 , 1990 ). Bowlby (1980) examined possible memory constructs and unconscious processes to explain misrepresentations of mental functioning and behavior, informed by mid-20th century advances in cognitive psychology ( Bowlby, 1980 ). He suggested conscious representations of what parents made the child believe are stored in the semantic memory, while the defensively excluded and traumatic attachment experiences are stored in the episodic memory. Emotional schemata are part of episodic memories and, over time, these schemata can grow into explicit models of the self and the attachment figure ( Liotti, 1999 ). According to information processing theory, the term “unconscious” describes the product of the perceptual systems that work unattended or unrehearsed. Thus from this perspective, nonconscious mental life is identified with early preattentive perceptual processes such as e.g., pattern or face recognition. One of the most common forms of preconscious processing is priming. When investigating the label “automatic”, some processes are intended, others require recent conscious and intentional processing of related information ( Bargh et al., 2012 ). In the following, we use the term “unconscious” in association with the internal working models of attachment and “preconscious” when relating to information processing theory or neurobiological models.

There are several recent studies investigating implicit aspects of romantic attachment using self-report measures as explicit instruments for assessing attachment style ( Marks and Vicary, 2015 ; De Carli et al., 2016 ). In the present study, we were interested to capture automatic processes in the moment the attachment system is activated by using a narrative attachment measure with an implicit nature. The AAP is designed to activate the individual’s attachment system and emphasizes the evaluation of unconscious defensive processes in the narratives. In this study, we intended to combine attachment methodology with knowledge from implicit measures by using a RT paradigm.

The general question for this study addressed how a person accepts or rejects prototypic sentences belonging to the two major attachment categories (secure and insecure) using a modified version of the AAP ( Buchheim et al., 2009 ) in a RT paradigm. All participants were administered the standard AAP interview before the experiments started in order to assess their individual attachment representation. The participants did not get any information about their attachment representation during the whole assessments. The experimental design is described in the “Materials and Methods” Section in detail. In short, participants were presented pictures from the AAP accompanied with sentences representing different attachment patterns while assessing how long it took for them to make a decision (i.e., accept or not accept).

(1) We expected that participants would accept the prototypic sentences from the experiment more frequently when these sentences match with their own adult attachment classification.

(2) We expected group differences in reaction speed between participants with secure or insecure adult attachment classification assessed in the previous AAP interview. These expectations were differentiated for four possible configurations of the stimulus (secure, insecure) and the reaction (acceptance, rejection). Comparing both groups, we expected that

(2a) secure sentences will be accepted faster by securely attached participants,

(2b) secure sentences will be rejected faster by insecurely attached participants,

(2c) insecure sentences will be accepted faster by insecurely attached participants,

(2d) insecure sentences will be rejected faster by securely attached participants.

(3) The preference of secure or insecure prototypes in the experimental procedure was expressed by the continuous Adult Attachment Projective Relationship Choices Version 2 (AAP-RC) security scale (see below). We expected following correlations of the security scale with the reaction speed: the higher the security scale, …

(3a) … the faster the acceptance of secure sentences,

(3b) … the slower the rejection of secure sentences,

(3c) … the slower the acceptance of insecure sentences,

(3d) … the faster the rejection of insecure sentences.

Materials and Methods

Adult attachment projective picture system (aap).

The AAP ( George and West, 2012 ) assesses the attachment status in adults using a standardized set of eight picture stimuli. The stimuli are line drawings that include a warm-up scene and seven attachment scenes of individuals in conceptually-defined attachment situations. Four so called “alone pictures” depict scenes of a single person with no other persons visible in the picture. Three so called “dyadic pictures” depict scenes of two or more persons in a potential attachment dyad. The scenes portray characters in different age groups across the life span (e.g., child to old age). The drawings contain only as much details necessary to connote the situation. Features indicating details such as emotion, ethnicity and gender are obscure. Stimulus presentation is standardized so as to introduce increasingly distressing attachment scenes. Participants are asked to tell a story to each picture using a standardized set of instructions: “What is going on in the picture, what led up to this scene, what are the characters thinking or feeling, and what might happen next.” AAP administration is done in person on an individual basis in a quiet location with no distraction and typically takes 30 min. The stories are audio-recorded and analyses are done from verbatim transcripts.

Each stimulus response is coded for attachment related content and defensive processes. Content coding evaluates representation of the presence and degree of integration (as defined by attachment research) of attachment relationships in the response, the actual coding dimensions of which are evaluated on different dimensions for the alone and dyadic pictures. Alone response content is evaluated on two dimensions. The agency of self is defined as the degree to which the character can seek and effectively use attachment figures. The connectedness is defined as the degree to which the character is portrayed as seeking proximity to others. Dyadic response content is evaluated for synchrony, a single dimension that captures the quality of agency of self and connectedness used for the alone pictures. Synchrony is defined as the degree to which responses depict attachment figure sensitivity in the context of distress themes (e.g., a child is sick) or mutual enjoyment in the context of togetherness themes (e.g., couple goes on a trip). Defensive processes are coded for the three standard attachment-defined defenses ( Solomon et al., 1995 ): deactivation (distanced attention from attachment), cognitive disconnection (close attention to and confusion by attachment) and segregated systems (attachment fear and dysregulation).

The AAP designates four attachment classifications based on the evaluation of response content and defensive processes coding patterns across the entire set of seven attachment stories. Individuals are judged secure (F) when the coding patterns demonstrate that attachment figures are present and self and attachment figures manifest integrated interaction (sensitivity, relationship repair, thoughtful action and mutual enjoyment). Defensive processes, which can be depicted in any of the three defense, help integrate and maintain relationships, and manage attachment fears. Individuals with insecure-dismissing (Ds) or insecure-preoccupied (E) attachment are characterized by relative absence of integration, sensitivity and mutual enjoyment in their responses. Descriptions of the alone characters range from themes that portray taking simple action (reactive problem solving behavior without thoughtful consideration) to evidence, that characters cannot move forward. Attachment figures, if included, are described in ways ranging from functional roles without comfort (e.g., just give the sick boy soup), unable to respond (e.g., the mother refuses to hug the child), punishing and sometimes harsh (e.g., an enraged parent who is drunk and abusive). Connections with others, if described, are typically shift to interactions with non-attachment figures (e.g., police, nurse, soccer coach). The dismissing group is characterized by defensive processes, that deactivate attachment needs and shift attention away from attachment distress and themes (e.g., by rejection, power, achievement). The preoccupied group is characterized by defensive processes, that disconnect attachment needs and relationships (i.e., deconstruct the details) shifting attention to elements of frustration and anger, or distorting or blurring story characters and events (e.g., the child is waking up or going to bed; someone died—cannot specify who). Individuals are judged as insecure-unresolved with regard to trauma (U) when they remain dysregulated and overwhelmed by dangerous or threatening story elements (e.g., being frightened, assault, isolation, helplessness). One or more of their stories are void of the content and defensive processing features associated with integration, functional care, or attachment figure or other people providing care. For more complete details of the coding system and classification, see the monograph ( George and West, 2012 ).

Multiple studies have demonstrated excellent convergent validity of the AAP with the AAI ( George et al., 1985–1996 ), test-retest reliability, inter-judge reliability, and discriminant validity in community samples and clinical patients. Results from a large-scale psychometric investigation, including 144 adult participants demonstrated excellent inter-judge reliability; the concordance rate for two judges on the four-group classifications were 90%, κ = 0.85, test-retest reliability (after 3 months 84% remained in the same attachment category; κ = 0.78) and discriminant validity. To evaluate the convergent validity, AAP classifications were compared to independent AAI classifications. The concordance rates for the four-group classifications were 90%, κ = 0.84, and for the two groups (“secure” vs. “insecure”) even 97%, κ = 0.89 ( George and West, 2001 , 2012 ; Buchheim and George, 2011 ).

Development of the AAP Reaction Time Paradigm

Buchheim et al. (2009) developed and used the first experimental adaption of the AAP in a double-blind, placebo-controlled within-subject experimental design. These researchers developed the AAP-RC stimulus set, which is comprised of a set of statements that represent attachment-related sentences that describe the AAP picture stimuli. The statements were schematic descriptions of secure, dismissing, preoccupied, and unresolved attachment stories, as determined by two expert AAP judges (AB, CG) who collectively had experience with over 300 AAP transcripts. Attachment statements described common story response situations. The study compared participant responses to the statement in an oxytocin and a placebo condition. The eight AAP picture stimuli were presented over four sequences, always presented in each sequence in the standardized order. Each of the 32 picture presentations was accompanied by four prototype phrases each of them representing one of the four established attachment categories. The participants were instructed to rank these phrases from the most to the least appropriate for each presentation. The phrases were presented in a randomized balanced sequence in order to minimize simple memory effects across test sessions.

The present study used a modified version of the Buchheim et al. (2009) prototype sentences. Sentences were revised to improve the content and to control for the sentence length. In each group of length-adjusted sentences, all four sentences consisted of the same number of German words in order to minimize the effect of the sentence length on RTs. The modified system of 128 sentences is called Adult Attachment Projective Relationship Choices Version 2 (AAP-RC 2.0). The AAP-RC evaluation procedure uses all eight AAP drawings, including the first dyadic “warm-up” picture with two playing children. Hence, 64 sentences relate to the alone pictures and 64 sentences to the dyadic ones. The revised sentences were rated for content by three certified AAP judges. Table 1 shows example sets of four sentences that represent four attachment categories for two selected AAP picture stimuli. Figure 1 demonstrates an example how a stimulus sentence was presented on the PC screen to the participant. The experimental procedure consisted of 128 such screen sequences.

Table 1. Examples of prototypical sentences from the Adult Attachment Projective Relationship Choices Version 2 (AAP-RC 2.0) instrument .

Figure 1. One of the 128 screen sequences presented in the course of the experimental procedure .

The RT paradigm used in this study followed Bem’s (1981) procedure for RT analysis for gender role schemas. The procedure was to record answers and to assess the time the participants needed to react. The Bem’s (1981) study showed that schema consistent judgments were more quick when the stimuli presented during a selection task matched participants’ gender role schema. We chose this approach because gender role schemas, like attachment representations, are conceived as stable views of self that develop in early childhood that automatically monitor, modulate attentional shifts and appraise new experiences ( Bretherton, 1990 ). Classical experimental designs of self-concept tests using randomized stimulus sequences like the IAT ( Gawronski, 2002 ) could not be used because attachment assessments such as the AAP must adhere to the procedural order in which stimuli are presented ( George and West, 2012 ).

In the current study in the context of a diploma thesis ( Wichmann, 2011 , unpublished diploma thesis), we first administered the AAP and next presented the AAP-RC 2.0. We conducted an Attachment Reaction Time analysis (ART) for the experimental condition. The AAP was administered by a trained interviewer (TW). AAP verbatim protocols were coded by a certified AAP judge (AB). The structure of the administration procedure was as follows: the entire series of AAP picture stimuli were presented 16 times and each series was composed of eight pictures in the standardized AAP administration order. Each picture presentation was accompanied by one stimulus sentence, which was related to one of the four attachment representations. The experimental procedure contained a measure for the individual responses (yes/no) to the prototypical sentences and the recorded RTs.

The interview and the experimental task took place in the same office. The experimental condition was conducted using a computer. The computer was a table mounted Dell computer with no internet-connection and no additionally installed software. The program used for the presentation and RT measurement was E-Prime ( Schneider et al., 2002 ). Participants were alone in the room. Room lighting was artificial and participants sat 0.5 m from the monitor. Answer responses were given via the computer keyboard. At the beginning of the experiment the participants were told to put the index fingers of their hands on the keys: Y for “yes” and the key N for “no”. The keys were marked with a red label. The participants had to press a key to move on with the task. A short practice task was given before to ensure that the participants had understood the task. The practice task included three attachment neutral stimuli with drawings produced in a style similar to the AAP pictures. All instructions were given on the computer screen and, if necessary, explained a second time after the test run.

Participants were told that the task was a speed task so as to avoid participant reflection and distraction. It was emphasized that there were no right or wrong answers, and that responses were simply their preferences. The timing of the presented sentence order (Figure 1 ) was as follows: 1st a fixation cross (1 s); 2nd the sentence (2 s); 3rd a countdown (1.5 s) and 4th the picture along with the decision task. AAP RC sentences were shown one at a time, next showing an AAP picture, with a “3-2-1” countdown shown between the sentence and the picture. The labels “Yes” and “No” were presented on the side of the monitor, analogs to the keys, during the decision tasks. The picture was displayed on the screen until a decision was made. The experiment continued only after a decision was made. We presented the participants first the sentence and then the picture so as to eliminate bias produced by different reading speed. The four attachment categories were presented each with four prototype sentences per picture. The order of attachment representations within the sentence was randomized. In sum there were 128 choices to be made. The choices were forced choice subjective selections, representing their acceptance (yes) or rejection (no) of an AAP RC sentence (see Table 2 ). Task scores are based on counts of agreements and rejections by four attachment representations.

Table 2. Schema of evaluation of the AAP-RC 2.0 by a test person .

Participants

Participants were asked for voluntary participation. The sample was comprised of 30 students from the University of Innsbruck (17 women, 13 men; sample mean age: 26.8, SD = 3.4). The participants reported no neurological conditions and were not in psychological or psychiatric treatment. All had normal or corrected eye vision. The study was conducted according to the Helsinki Declaration with informed consent received from all participants. All participants completed the study.

The reported results concern three methodical approaches: the AAP attachment classification assessment; the computerized experimental method AAP-RC; and the ART experiment. The results first describe the findings associated with each of the measures used in the study and second report the relations among them. There were no missing data.

Adult Attachment Projective Picture System (AAP): Distribution of Attachment Classifications

The attachment classification distribution was as follows: 10 (33%) F, 12 (40%) Ds, 6 (20%) E, and 2 (7%) U. Because of the small frequencies in especially the preoccupied and unresolved groups, insecure classifications were collapsed together and data analyses compared only secure ( n = 10, 33%) vs. insecure attachment ( n = 20, 67%).

Relationship Choices, Version 2.0 (AAP-RC): Psychometrical Analysis and Security Index

Reactions to AAP-RC stimuli in the ART test were coded dichotomously as yes (endorsement, acceptance) or no (rejection). The frequencies a, b, c, d shown in Table 2 represent numbers of accepted sequences belonging to the four attachment prototypes. The sets of 32 dichotomous items related to the attachment prototypes F, Ds, E, U, as well as the joint set of 96 insecure type items can be understood as a scale in the psychometric sense. These values of Cronbach α were satisfactory for the secure scale (0.77), for the U scale (0.82) for the joint insecure scale (0.88). However, they were not satisfactory for the Ds scale (0.64) and for the E scale (0.66). The correlation structure was investigated by means of the item-scale correlations and corrected item-scale correlations. The correlation structure was satisfactory for the system of two scales, secure and insecure. However, it was not satisfactory for the more detailed system of four scales F, Ds, E, U.

Guided by the referred psychometric results, we have decided to base the analyses of AAP-RC on the secure-insecure dichotomy. In respect of this aspect we have defined a security index expressing the degree of security vs. insecurity by the formula (see Table 2 ): a/r = a/(a + b + c + d) . The index is a proportion of accepted secure sentences related to all accepted sentences, ranging from 0.00 (completely insecure) to 1.00 (completely secure). By the random answering independent of sentence prototype, it would oscillate around 0.25. The analogously constructed complementary insecurity index (b + c + d)/(a + b + c + d) is mathematically redundant; summing up to one, both indices contain the same information. Hence, the following analyses utilize the security index as a central measure.

Adult Attachment Reaction Time (ART): Reaction Time Analysis

The program E-Prime stored the dichotomous reaction and the needed RT in milliseconds. The hierarchically structured data sample consisted of 30 persons × 128 sentences = 3840 pairs of reactions and RTs.

Figure 2A : the average RT was about 1 s, ranging from 0 up to 15 s; exact values of measures and statistics see in Table 3 . As commonly experienced by the duration time data, the distribution was skewed and its normality was rejected by the Kolmogorov-Smirnov test.

Figure 2. Distribution of reaction time (RT), N = 3840 measurements. (A) Original observation (range 0–15 s), (B) logarithmic transformation, (C) pooled normalization, (D) intra-individual normalization.

Table 3. Kolmogorov-Smirnov test of normality of the reaction time (RT) distribution .

Figure 2B : this was the case after applying the frequently recommended logarithmic transformation. Additional problems were caused by some extreme outlier values. Similarly, other transformations considered by Harris et al. (2014) did not lead to satisfactory results in this case.

Figure 2C : transformation based on quantiles in the total pooled sample of 3840 measurements resulted in a close approximation to the normal distribution; the variable was transformed by the linear function s (z) = 50–10 z . The resulting variable is interpreted as the speed of the reaction. However, there were striking differences in the RTs between the 30 study participants, on an average ranging from 0.32 s up to 2.41 s. The reaction speed differed significantly by ANOVA ( F (29,3810) = 74.17, p < 0.001; η 2 = 0.36); a considerable portion of measurement variance was explained by the individual basic reaction speed.

Figure 2D : in regard to excluding the bias by individual basic reaction speed, we have normalized speed values intra-individually. The RTs for a test person were replaced by ranks 1 for the slowest reaction to 128 for the quickest one, and transformed to the s (speed) values according to the quantiles of the normal distribution N (50,10) 1 . Because of the subsample sizes n = 128, the density curve of the obtained empirical distribution is less smooth than the previous one. Nevertheless, it is very close to the normal distribution N (50,10), and its normality in the sample of 3840 observations was not rejected by the exact Kolmogorov-Smirnov test.

The tests of study hypotheses (correlations, t -tests) were based on the last described intra-individually normalized speed values. The N = 3840 single values were aggregated to the intra-individual means for each of N = 30 study participants. Particularly, the following four aggregated values were relevant: speed of “yes” reactions to secure sentences; speed of “no” reactions to secure sentences; speed of “yes” reactions to insecure sentences; and, speed of “no” reactions to insecure sentences.

Convergent Validity Between the AAP Interview and the AAP-RC Security Index

The convergent validity of the AAP-RC security index was examined by its comparison with the secure and insecure attachment classifications (Figure 3 ). Mean of the AAP-RC security index in the secure group ( n = 10, M = 0.432, SD = 0.105) was greater than the mean in the insecure group ( n = 20, M = 0.293, SD = 0.087); this difference was significant according to the two-sided two-group t -test: t (28) = 3.866, p = 0.001, Cohen’s d = 1.50 indicated a strong effect.

Figure 3. Adult attachment projective relationship choices version 1 (AAP-RC) security index in attachment groups by AAP .

The predictability of attachment classifications on the basis of the security index in the attachment RT experiment was estimated by the discriminant analysis. The cross-validated classification was used, which is by small sample sizes particularly important (see “discriminant—cross-validation” in the IBM SPSS software system). The procedure recommended predicting AAP classification as secure when the security index exceeded the threshold 0.362 shown by horizontal line in Figure 3 . Appling this threshold, 8 of 10 secure participants and 16 of 20 insecure participants were recognized correctly; the prediction was successful in 80% of cases in both groups.

Reaction Time to Accept or Reject Secure or Insecure Prototype Sentences

For each participant, the set of 128 measurements was divided by sentence prototype stimulus (secure, insecure) and his/her answer reaction (yes, no) into 2 × 2 = 4 subsets, as described above (see “Adult Attachment Reaction Time (ART): Reaction Time Analysis” Section). Within each subset, the intra-individual mean values of speed were computed, resulting in the speed values of the following four stimulus-reaction combinations: (1) accept secure sentences; (2) reject secure sentences; (3) accept insecure sentences; and (4) reject insecure sentences. These computations were based (a) on all 128 sentences and alternatively; (b) on 64 sentences relating to the alone pictures; and (c) on 64 sentences relating to the dyadic pictures.

These speed variables were compared by ANOVAs for 2 × 2 repeated measures in the whole sample of N = 30 participants. Results of analyses based on all, alone and dyadic stimuli are shown in Table 4 : (a) The analysis based on the complete material has shown that the interaction of sentence prototype and answer was significant ( p = 0.011), whereas both main effects were not. As can be seen, participants answered more quickly to “yes to secure” and “no to insecure” and more slowly to “no to secure” and “yes to insecure”. It means that the “secure-conform” answers were given more quickly than “insecure-conform” ones. (b) For the alone pictures, none of the three ANOVA effects was significant. (c) For the dyadic pictures, the interaction effect ( p = 0.005) and the main effect sentence prototype ( p = 0.020) were significant. The highest speed was observed for the combination “yes to secure”; the lowest speed and hence the highest time needed to answer was observed for the combination “yes to insecure”.

Table 4. Speed of yes/no answers to secure/insecure prototype sentences .

Reaction Times in Secure and Insecure Attachment Groups According to AAP

The four speed variables (1–4) described in “Reaction Time to Accept or Reject Secure or Insecure Prototype Sentences” Section were compared between secure and insecure AAP attachment classification groups. As shown in Table 5 , the significant group differences were found for “RC-insecure” prototype sentences:

1. The insecure participants accepted the RC-insecure sentences more quickly than the secure participants.

2. The secure participants rejected the RC-insecure sentences more quickly than insecure participants.

Table 5. Speed of answers in participants with secure and insecure attachment according to the AAP classification .

The first result was also confirmed for both subsets of alone and dyadic sentences. The second result was confirmed for sentences connected to the dyadic pictures. In sum, the differences between the secure and the insecure attachment group according to the AAP were significantly manifest for the RC-insecure sentences.

Reaction Times in Correlation to the Security Index AAP-RC in ART

The AAP-RC security index (see “Relationship Choices, version 2.0 (AAP-RC): Psychometrical Analysis and Security Index” Section) ranges from completely insecure (0.0) to completely secure (1.0) reactions to the 128 stimuli. The correlations of the AAP-RC security index with variables concerning the reaction speed by four stimulus-reaction pairings are shown in Figures 4A–D .

Figure 4. Correlations between AAP-RC security-insecurity index and reaction speed. (A) Accepted secure sentences, (B) rejected secure sentences, (C) accepted insecure sentences, (D) rejected insecure sentences.

(A) The RT speed of acceptance of secure sentences (Figure 4A ) was not significantly correlated with the security index; according to ART both rather securely or insecurely attached persons reacted comparable quick in this case.

(B) The rejection speed of secure sentences (Figure 4B ) was negatively correlated with the security index; the rather insecurely attached persons rejected the secure sentences more quickly.

(C) The acceptance speed of insecure sentences (Figure 4C ) was negatively correlated with the security index; the rather insecurely attached persons accepted the insecure sentences more quickly.

(D) The rejection speed of insecure sentences (Figure 4D ) was positively correlated with the security index; the rather securely attached persons rejected the insecure sentences more quickly.

Summarizing, the results of the experiment—especially for insecure prototype sentences—indicate a consistency between the attachment preferences and the higher speed of the corresponding reaction. The complete results of the experiment are summarized visually in Figures 4A–D , which underlines the consistency of the different results.

Discussion of the Methodology and Results

Bowlby (1980) proposed that inner working models of attachment function automatically and outside of conscious awareness. RT analyses are a way to observe the implicit automatic reactions. Pietromonaco and Barrett (2000) recommend the use of implicit measures like RTs to capture the unconscious parts of attachment, which are obscured when relying on self-report measures. Therefore we chose a narrative attachment measure, the AAP, designed to elicit unconscious elements by focusing on defensive processes, in combination with a RT measure. Our results reflect that implicit methodology can bring new and interesting insights in attachment related processes in the domain of neuroscience of human attachment.

Participants in our study were tested in an attachment RT experiment using the AAP picture stimuli accompanied by prototypic sentences representing different attachment representations (AAP-RC). Participants were not informed about their attachment classification prior to beginning the experimental session; therefore their reaction to the sentences was considered to be automatic and outside of conscious appraisal (i.e., unconscious). We hypothesized that the participants would accept the prototype sentences in the attachment RT paradigm: (1) more frequently; (2) more quickly when sentences matched with their own representations of attachment classification; and (3) that this would correspond with their attachment prototype preferences in the RT experiment (ART). Overall most of our expected results were confirmed for secure vs. insecure attachment groups.

The distribution of attachment classifications in our sample showed an overrepresentation of dismissing participants as compared to the distributions in samples with healthy controls ( Bakermans-Kranenburg and van Ijzendoorn, 2009 ). Therefore, one caveat of our study is that we did not have a representative distribution of attachment classifications. Another caveat is the small sample size. The consequence was that data analyses for separate attachment groups was not possible and we were confined to comparisons of participants with secure and insecure attachment representations. This remains a challenge for our next studies.

Item-scale analyses confirmed the internal consistency for the secure and insecure scales. Discriminant analysis showed that AAP-AAP RC 2.0 convergence prediction was successful in 80% of the cases in both groups; 8 of 10 secure and 16 of 20 insecure participants. Although a 100% correspondence was not reached, there was a sufficient agreement in this study to demonstrate the validity of the paradigm. This association was stronger, for example, than the results of studies that correlated narrative and self-report attachment measures (e.g., Roisman, 2007 ).

Given that participants did not know their own attachment classifications by the standard AAP procedure, we can assume that they were not guided by informed conscious appraisals of attachment while evaluating the prototype sentences, rather by unconscious processes. The fact that the different measures showed a considerable convergence supports the conclusion that we were able to capture both conscious and unconscious automatic reactions to attachment related stimuli. The average RTs differed significantly between the study participants. The considerable portion of variance of the originally measured RTs is explained by the individual basic speed of reactions to the presented stimuli. This empirically found fact can be caused by different plausible reasons, like overall speed or slowness of mental processes of the subject, extended rational reasoning on the presented sentences, or intensive imagination triggered by them.

With regard to the RT results, we found that all participants had a tendency to answer “yes” to secure and “no” to insecure sentences quickly and more slowly when the cases were inverted (i.e., “no” to secure and “yes” to insecure). ANOVA did not show significant results for both main factors sentence prototype stimulus and answer reaction; the interaction effect prototype and answer was significant however. The participants accepted secure prototype sentences and rejected the insecure prototype sentences more quickly. One possible explanation of this finding is social desirability, because the perception of secure sentences could be expected to be ideal. This is in line with findings by De Carli et al. (2016) . In their IAT study about caregiving and attachment, which they proposed as two different systems, the authors found that adult attachment style had a role in shaping the implicit attitude, but not the explicit attitude, concerning the category “mother.” The explicit attitude did not appear to be influenced in that study by experimental manipulation or the participants’ attachment style. The authors discussed that this can be explained by social desirability, because the perception of mother is expected to be mostly positive. In sum the IAT findings of De Carli et al. (2016) in the context of the transmission of attachment are in line with our results by showing that the participants preferred a particular style of caregiving coherent with their own attachment style. However the authors pointed out that their attachment measure was a self-report instrument that captured explicit thoughts only. However a notable strength in our study is that we used a free-response narrative attachment assessment measure, which seems to be more appropriate for this kind of experimental approach because of its implicit nature. Yet the role of social desirability should be clarified in future studies.

Despite the results, that all participants in our study accepted the prototype secure sentences faster than insecure prototypes, there were significant differences between the two adult attachment groups. Secure participants accepted more prototype secure sentences and showed faster RTs than insecure participants. Insecure participants accepted more insecure sentences, and did so faster than secure participants. This result underscores the presence of automatic unconscious detection and appraisal processes when responding to attachment relevant information.

Parallel patterns were found in the AAP-RC with the RTs in the experiment. Participants with higher preference for secure prototype secure sentences rejected insecure sentences more quickly. Participants with higher preference for insecure prototype sentences accepted insecure sentences more quickly and rejected secure ones more quickly.

Our differential hypotheses addressed secure and insecure prototype sentences. Findings supported our hypotheses, and we confirmed all hypotheses concerning the insecure prototypes. In other words: “ accept secure and reject insecure” goes fast , and “ reject secure and accept insecure” goes slow . It seems that the “insecure-type” reactions demand more time.

In a study by Rösler (1993) , more complex processes took longer elaboration time than more simple ones. What makes the insecure reaction more demanding than the secure one in our study? We can nearly exclude that the linguistic or cognitive complexity would play a role: the grouped sentences had the same length and they were clear and understandable. However, the complexity of the relationship related decision processes might differ. It is reasonable then to conclude that the differences were due to story content. George and West (2012) described, how different insecure attachment representations are connected to different defensive mechanisms. The insecure attachment prototypes have the potential to address inner conflicts (e.g., ambivalence or deactivation of attachment relevant information), which must be recognized first, and then accepted or rejected. This unconscious process might request the additional “working time” 2 .

Our findings are in line with those of Vrtička et al.’s (2012) study of attachment style. These researchers used an explicit choice paradigm and found distinct effects of attachment avoidance and anxiety on subjective emotional judgments. Their results supported the assumption that anxious attachment is associated with a hyperactivating tendency for the appraisal of social threat, but may also involve an ambivalence influencing the judgment of information. Although, the authors did not use a RT experiment, their results support thinking that proposes that insecure attachment seems to need more mental elaboration time.

Therefore we could have assumed in our study that individuals with preoccupied attachment representations associated with heightened emotional reactivity would show different RT patterns compared to dismissing individuals, characterized by deactivating attachment related emotions. This important differentiation should be the next step in future studies with a larger sample size.

According to previous research with the AAP we might have also expected particularly differentiated results for the analyses based on “alone pictures” compared to “dyadic” ones. Alone pictures represent scenarios of emptiness and loneliness and seem to elicit high affective arousal in participants ( Buchheim and George, 2011 ). However the results of the present study showed that insecure individuals needed longer elaboration times confronted with the dyadic pictures. This type of sentences (like in the AAP picture of the couple in the scenario “departure”) represents explicit attachment related scenarios between two or more persons (potential separation, need for care). We might conclude that insecure individuals needed more elaboration time for processing these attachment related conflicts. The observed differences should be verified in further investigations using a larger sample.

In sum, high security index scores were associated with prompt rejection of insecure prototype sentences. Lower security index scores were associated with prompt acceptance of insecure sentences, as well as rejection of secure sentences. Some other hypotheses could not be confirmed significantly; there were no contrary findings nevertheless. We might have demonstrated that the secure vs. insecure attachment classification groupings could be observed with the implicit measure, by observing the activation of inner working model in “real time.”

Our results support the conceptualization of inner working models of attachment as guiding attention and interpretation outside of conscious awareness and the coherency of the association between mental representation and interpretation of attachment situations ( Bowlby, 1980 ).

From a methodological perspective, we suggest that the observation of RTs is valuable to complement the spectrum of mainstream measures in human neuroscience, like brain mapping or EEG analyses. These highly advanced measures focus on brain localizations and processes associated with different psychological tasks and events. The RT approach investigates the overall time of participants to specific stimuli analogous to the time complexity theory in computer science ( Sedgewick and Wayne, 2011 ). The more operations are needed for the problem solution, the more time is needed. The time needed for the problem solution might then constitute an operationalization of the problem complexity and depends on numerous biasing factors. Human processing time consists of the individual’s basal or momentary reaction speed including external disturbing influences, which could cause long outlier RTs. The data-analytic procedure proposed in this article was designed with the aim to be robust against the mentioned biasing factors and could be a fruitful additional approach in an EEG analysis when using a similar paradigm.

Limitations

The size of our sample of 30 participants was sufficient, albeit small, for the experimental investigation of the RT phenomena. The number of 128 attachment prototype sentences was considerably larger than the sample size; this circumstance limited the use of more advanced psychometrical analyses (e.g., factor analysis). Similarly, the sample sizes and the distribution of four particular attachment groups led us to the decision to confine the analyses to the two basic attachment classifications secure and insecure. In fact, secure vs. insecure analyses are often chosen as a comparison in the field of attachment. However as we discussed it would have been valuable to differentiate the insecure attachment groups and the individuals’ RTs. This aspect should be tested in further research.

In sum the present study served as a pilot study to test its feasibility in healthy participants. The next steps are the application of the RT experiment in clinical studies with a larger sample. Moreover, the AAP measure is constructed and validated for adults and adolescents only, so the application is limited to that age group and not feasible for children, where other measures should be used, like the Separation Anxiety Test ( Klagsburn and Bowlby, 1976 ).

Despite these limitations, the study has shown that the concept of immediate reactions to stimulus sentence could be beneficial for experimental attachment research contributing to measure the intensity of unconscious processes empirically. As a following research step, we intend using psychometric procedures to continue and improve the development of the AAP-RC instrument in order to implement it in a neurobiological setting.

Outlook: Neurobiological Model Using the Reaction Time Experiment on Adult Attachment

In the presented study, we have seen that stimuli with more distressing attachment content might need a longer RT for its elaboration than stimuli with more harmonious content. Future studies need to replicate these findings using larger samples. A further next step is to adapt the experiment for an EEG setting, which could give further insight into the neural mechanisms of potential response delays during an implicit task.

One of the most interesting areas in the research of preconscious perception is the investigation of early brain potentials. Until now, there are only a small number of studies examining the perception of emotional stimuli in individuals with different attachment patterns. In an EEG setting the N1 potential, which is also called N170 component, is considered to be a very sensitive representation of early perceptual processing. Spatio-temporal analyses of brain activity patterns during the first 200 ms after stimulus presentation have characterized the timing of attentional selection processes and different stages of feature encoding and pattern analyses ( Hillyard et al., 1998 ). In an attachment study on face recognition Zhang et al. (2008) reported distinct differences in N1 activation using self-reports. The perception of angry faces was followed by high N1 amplitudes in anxious and secure individuals in contrast to the smaller amplitudes in avoidant individuals. Given that N1 is considered to be an index of the level of attention, the authors suggested that individuals with anxious attachment “use most, and avoidant individuals use least attentional resources to face stimuli than secure individuals”. The authors considered these differences as the results of automatic processes in association with conscious and preconscious emotional information processing. In contrast to the latter study Fraedrich et al. (2010) focused on event-related potentials (ERPs) in mothers during the perception of infant emotions by presenting positive, negative and neutral facial expressions as well as non-facial stimuli within an oddball paradigm. Dismissing mothers exhibited elevated N170 amplitudes for facial target stimuli within conditions that contained frequent non-facial stimuli. In summary, the findings suggested that insecure mothers require more cognitive resources to process infant faces, while secure mothers allocate more attention to infant faces and clearly show a perceptual bias toward social information. The differences between the study results of Zhang et al. (2008) and Fraedrich et al. (2010) might be due to the different stimulus material.

In a very recent study by Leyh et al. (2016) , the association between maternal attachment representation and brain activity (ERPs) underlying the perception of infant emotions was examined. Securely attached mothers recognized emotions of infants more accurately than insecurely attached mothers. ERPs yielded amplified N170 amplitudes for insecure mothers when focusing on negative infant emotions. Secure mothers showed enlarged P3 amplitudes to target emotion expressions of infants compared to insecure mothers, especially within conditions with frequent negative infant emotions. In these conditions, P3 latencies were prolonged in insecure mothers.

One potential limitation of attachment research of preconscious perception with the help of the early brain potentials so far might be the predominant focus on face processing as the stimulus material. Neural processing in secure and insecure subjects were not examined by attachment related material directly linked to the individuals’ own attachment representations using a paradigm where spontaneous preferences had to be given in a defined time frame.

In a recently published article by Matheus-Roth et al. (2016) early occipital ERP’s (e.g., P100 and N170) have been shown to be sensitive for a “preference” for stimuli with alcohol association in patients with alcohol dependance. The authors used a Go-NoGo paradigm with three visual stimuli: tea, juice and beer. The N170 amplitudes were elevated in response to the alcohol-related (beer) stimuli in the NoGo condition in these patients compared to controls. The patients had to react to the frequent tea stimuli and ignore the beer and the juice stimuli. While the higher N170 component correlated with a relapse within the following 3-month, the shorter P100 latencies were related to higher depression scores. The latencies of these early ERPs represent the “RTs” of the brain, presumably independent of deliberate influence. In another study, the so called “mismatch negativity” (MMN) has been demonstrated to react pre-attentively to syntactic or semantic errors ( Menning et al., 2005 ). The authors used an auditory oddball design with frequent standard sentences to elicit a memory trace, which was interrupted by rare deviant (erroneous) sentences. Moreover, Hietanen and Nummenmaa (2011) revealed that N170 is sensitive to stimuli of naked bodies. In their studies it is even greater for nudes than to faces. Overall N170 seems to be an indicator for the preconscious individual importance of visual stimuli.

Finally the analysis of P300 component—an indicator for emotional operations—might reveal interesting results ( Nieuwenhuis et al., 2005 ; Schupp et al., 2007 ; Flaisch et al., 2008 ). However, assuming that P300 is a correlate of conscious perception ( Dehaene et al., 2006 ), more early EEG components like cited above should be considered first to capturing modes of more unconscious processes.

In sum these neurophysiological and the other cited attachment studies investigating implicit aspects of romantic attachment using self-report measures as explicit instruments for assessing attachment style ( Marks and Vicary, 2015 ; De Carli et al., 2016 ) suggest that early visual and auditory stimuli could be used as a change detector of emotionally preferred stimuli. Thus, transposed to our tested and validated AAP RT paradigm, we would expect that the specific (secure or insecure) attachment system paves the way for a specific ERP, e.g., higher amplitudes or shorter latencies of the N170 or P300 to individual preferred stimuli which represent the own attachment representation. One advantage of our paradigm would be to use attachment related material linked to the individuals’ inner working models of attachment in a RT setting. This might extend previous studies in healthy samples and may provide some feasibility for clinical studies.

The measures based on RT reflect the overall activity of the brain needed for the elaboration of different stimuli. The results of the referred study suggested that the overall time needed for the processing of “unpleasant”, discomforting stimuli was higher than for “pleasant”, comforting ones. The fact that RT showed convergence with the individual’ inner working model of attachment in our study, has the potential to contribute to the validity of neurobiological experiments, like EEG. Therefore RT analysis with the proposed evaluation procedures might be of interest for a broader variety of questions concerning attachment in experimental and neurophysiological settings to capture automatic, unconscious processes in association with internal working models of attachment.

Author Contributions

The study was conceptualized by AB, CG, TW and DP. The attachment experiment was developed by AB. The study setup and data collection were organized and conducted by TW. Coding of attachment interviews were conducted by AB. DP performed the statistical data analysis and contributed substantially to the result interpretation. DP developed the statistical procedure for RT analyses. CG, DP, TW, HM, IS and AB provided important intellectual contribution in commenting and revising the manuscript. AB, DP and TW wrote major parts of the manuscript and edited its final version.

The publication is funded by the Faculty of Psychology and Sports Science, University of Innsbruck, Austria; Research Funding for Young Scientists.

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

This would not have been possible without the support of many colleagues. Many thanks also to Florian Juen and Ann-Christine Jahnke-Majorkovits for rating the diverse attachment sentences and to Stefan Fischer for methodological advice at the beginning of the study.

Abbreviations

AAP, Adult Attachment Projective Picture System; AAPRC, Adult Attachment Projective Relationship Choices Version 1; AAPRC 2.0, Adult Attachment Projective Relationship Choices Version 2; ART, Attachment Reaction Times; Ds, dismissing attachment; E, preoccupied attachment; EEG, Electroencephalography; ERP, Event related potential; F, secure attachment; fMRI, Functional magnetic resonance imaging; RT, reaction times; U, unresolved trauma.

• ^ Technical note on ties: the ties were resolved by the replacing values by the average quantile value. For instance, by the subject p09, the first three quickest reactions needed the same time 0.185 s. Without ties, the ranks 1, 2 and 3 would correspond to the speed scores 76.6, 72.7 and 70.6. Because of ties, the mean value of three of these scores 73.3 was considered, rather than 72.2 corresponding to the mean rank 2.0. Resolving ties in this way, the intra-individual mean value was exactly 50.000 for each participant, the intra-individual standard deviations were very close to the value 10.000 (9.981–9.989), depending on the number of ties.
• ^ An analogous approach is being used in the computer science: the complexity of a problem is classically operationalized by the number of needed steps of the problem solving algorithm, and consequently by the time demanded for the problem solution; see Sedgewick and Wayne (2011) .

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Keywords: adult attachment projective picture system, reaction times, decision task

Citation: Wichmann T, Buchheim A, Menning H, Schenk I, George C and Pokorny D (2016) A Reaction Time Experiment on Adult Attachment: The Development of a Measure for Neurophysiological Settings. Front. Hum. Neurosci. 10:548. doi: 10.3389/fnhum.2016.00548

Received: 24 June 2016; Accepted: 14 October 2016; Published: 02 November 2016.

Reviewed by:

Copyright © 2016 Wichmann, Buchheim, Menning, Schenk, George and Pokorny. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution and 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: Anna Buchheim, [email protected]

† These authors have contributed equally to this work.

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.

Psychology Discussion

Experiments on reaction time | experimental psychology.

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List of top two psychological experiments on reaction time!

Experiment # 1. Simple Reaction Time:

To determine the simple reaction time of a subject to visual and auditory stimuli.

Materials Required:

A reaction-time apparatus and a chronoscope.

Description of the Reaction-Time Apparatus:

It consists of a wooden board with a screen in the middle. On one side of the screen (the subject’s side) there are two lights, red and white and two press keys. Each one of the keys is connected to one of the lights. If the key is pressed while the light is on, the circuit will automatically break and the light will go off.

On the other side, i.e., experimenter’s side, there are a series of keys which serve to switch on the two lights on the subject’s side. Along with this, a buzzer is also fixed on the experimenter’s side, and there is a key to which the buzzer is connected. With the help of these keys, the experimenter can switch on or switch off the lights or ring the buzzer.

Chronoscope:

A chronoscope is an electrically operated timing instrument which measures very short time interval up to a millisecond.

The batteries or the electrical main, the chronoscope and the reaction time apparatus are connected such that they forma circuit. Whenever the experimenter switches on the lights or the buzzer, the chronoscope also starts working and when the subject switches off the light, the chronoscope also stops. The reading in the chronoscope gives the reaction time, i.e., the time between the experimenter’s switching on the light and the subject’s switching it off.

Give the following instructions to the subject:

‘You have got a red light here. As soon as you see it burning, press the key on your left side, as quickly as you can.’ The experimenter gives a few trials for demonstration. Now the experimenter starts the experiment. He presses the key for the red light and as soon as the subject presses the key, he notes the time recorded on the Chronoscope.

Before switching on the light, each time a ready signal is given. The experiment is repeated about twenty times. The average reaction time for the twenty trials is calculated. The experiment is then repeated using the buzzer as the stimulus instead of the red light.

Tabulate the results for the entire group as follows:

Calculate the average for the group and discuss the variability among the subjects.

Experiment # 2. Discrimination Reaction Time:

To study the reaction time when the subject has to discriminate one stimulus from another, and then respond.

Reaction-time apparatus, chronoscope, batteries.

This experiment involves presentation of two stimuli, i.e., the red light and the white light on different trials. The experimenter, therefore prepares a preliminary list of stimuli, 40 in number (20 with red light, and 20 with white light) presented in a random order.

Instructions:

“This time on some occasions you will see a red light and on others a white light, but you have to respond to only the white light. Whenever you see the white light, you press the key on your right side.” The experimenter as in the previous experiment presents the 40 stimuli preceded by a ‘ready’ signal each time. The reaction time for the 20 white light trials are noted down.

Calculate the average reaction time for the white light stimuli.

Tabulate the results as follows:

Study the Variability of the Group:

Repeat the experiment with two auditory stimuli, a buzzer and a bell, included in the circuit. It may also be interesting to employ one visual stimulus and one auditory stimulus and study the discrimination reaction time for them. Compare these results with those obtained under simple reaction time.

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• Experiments on Perception | Experimental Psychology
• Establishing Controls in Psychological Experiments | Experiments | Psychology
• Top 2 Experiments on Attention | Experimental Psychology
• Top 9 Experiments on Sensation | Experimental Psychology

Experiments , Experimental Psychology , Reaction Time , Experiments on Reaction Time

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A Comparative Study on Visual Choice Reaction Time for Different Colors in Females

Grrishma balakrishnan.

1 Department of Physiology, Yenepoya Medical College, Yenepoya University, Deralakatte, Mangalore, Karnataka 575018, India

Gurunandan Uppinakudru

2 Department of Surgery, Yenepoya Medical College, Yenepoya University, Deralakatte, Mangalore, Karnataka 575018, India

Gaur Girwar Singh

3 Department of Physiology, Jawaharlal Institute of Post Graduate Medical Education and Research, Institute of National Importance, Ministry of Health and Family Welfare, Puducherry 605006, India

Shobith Bangera

Aswini dutt raghavendra, dinesh thangavel.

4 Department of Physiology, Dhanalakshmi Srinivasan Medical College, Perambalur, Tamil Nadu 621113, India

Reaction time is one of the important methods to study a person's central information processing speed and coordinated peripheral movement response. Visual choice reaction time is a type of reaction time and is very important for drivers, pilots, security guards, and so forth. Previous studies were mainly on simple reaction time and there are very few studies on visual choice reaction time. The aim of our study was to compare the visual choice reaction time for red, green, and yellow colors of 60 healthy undergraduate female volunteers. After giving adequate practice, visual choice reaction time was recorded for red, green, and yellow colors using reaction time machine (RTM 608, Medicaid, Chandigarh). Repeated measures of ANOVA and Bonferroni multiple comparison were used for analysis and P < 0.05 was considered statistically significant. The results showed that both red and green had significantly less choice visual choice reaction ( P values <0.0001 and 0.0002) when compared with yellow. This could be because individual color mental processing time for yellow color is more than red and green.

1. Introduction

Reaction is a purposeful voluntary response to an external stimulus. There is certain time period between application of external stimulus and appropriate motor response to the stimulus called the reaction time. Reaction time is defined as interval of time between presentation of stimulus and appearance of appropriate voluntary response in a subject [ 1 , 2 ]. It is usually expressed in milliseconds. It reflects the speed of the flow of neurophysiological, cognitive, and information processes which are created by the action of stimulus on the person's sensory system. The receipt of information (visual or auditory), its processing, decision making, and giving the response or execution of the motor act are the processes which follow one another and make what we call the reaction time [ 3 – 5 ].

Concept of the reaction time of man appeared in science in the forties of the last century. Hermann von Helmholtz worked on nerve conduction velocity, a component of reaction time. He stimulated first one point of the nerve near to the muscle and then another point far from the muscle. The difference between the times from the stimulation of nerve to the muscle contraction in those two situations is the nerve conduction velocity. Later experiments were done to study the time taken for a specific response which was called reaction time [ 6 ]. Reaction time is very important for our everyday lives and needs intact sensory system, cognitive processing, and motor performance. Reaction time is a good indicator of sensorimotor coordination and performance of an individual. Reaction time determines the alertness of a person and must be lesser in certain occupations, for example, drivers, military people, pilots, sportsmen, doctors, nursing staff, and security guards where alertness is a must for them [ 1 ].

Many factors have been shown to affect reaction time including gender, age, physical fitness, level of fatigue, distraction, alcohol, personality type, limb used for test, biological rhythm, and health and whether the stimulus is auditory or visual [ 5 ]. Reaction time is independent of social-cultural influences. Prolonged reaction time denotes decreased performance [ 7 ].

There are 3 different types of reaction time experiments, simple, recognition, and reaction time experiments. In simple reaction time experiments, there is only one stimulus and one response. In recognition reaction time experiments, there are some stimuli (the “memory set”) that should be responded to and others (the “distracter set”) that should not be responded to. In choice reaction time experiments, there are multiple stimuli and multiple responses and subject must give a response that corresponds to the stimulus [ 8 ]. It was reported that the time for motor preparation (e.g., tensing muscles) and motor response was the same in all three types of reaction time tests, implying that the differences in reaction time are due to processing time [ 5 , 8 ].

Many studies have been undertaken to examine the influence of color on the simple reaction time. In few, reaction time has been shown to be independent of wavelength while others have found that reaction time to red stimuli was shorter than that to green or blue stimuli [ 9 – 12 ]. The issue of variation in RT with changing stimulus chromaticity therefore merits reexamination. Also, in everyday life choice reaction time becomes more important than the simple reaction time.

The choice reaction time can be studied by using visual inputs or by using auditory inputs. When studied using visual inputs it is called visual choice reaction time. Contemporary models of color vision assume that chromatic information is extracted through two independent postreceptoral cone-opponency channels, processing red-green (L-M) and blue-yellow (S-[L-M]) information (where S, M, and L represent input from short, middle, and long wavelength sensitive cones, resp.) [ 11 ]. Because of this, red, green, and yellow colors were used for the study. Reaction time is faster when the dominant hand is used when compared with the opposite side. Visual choice reaction time using the dominant limbs was studied. Reaction time is faster in men compared with women [ 13 ]. For uniformity, we had analyzed the visual choice reaction time on 60 female subjects.

Aim of the Study. The aim is to compare the visual choice reaction time for red, green, and yellow colors of 60 healthy undergraduate female subjects.

2. Materials and Methods

The study was conducted in Department of Physiology, Jawaharlal Institute of Post Graduate Medical Education and Research (JIPMER), Pondicherry, India. Prior to commencement of study approval of JIPMER scientific advisory committee and ethics committee was obtained. Sixty healthy female volunteers without visual defects or with corrected (with glasses) visual defects were recruited for the study. Visual choice reaction time for red, green, and yellow colors was compared. All tests were carried out in Autonomic Function Testing Laboratory in the Department of Physiology, JIPMER, between 3.00 pm and 5.00 pm. The laboratory environment was quite and the temperature was maintained between 22 and 25°C. The participants were informed in detail about study protocol and written informed consent was obtained from them. The subjects were advised to have lunch at 1.00 pm and come for tests at least two hours after lunch with empty bowel and bladder. The subjects were instructed to avoid caffeine and nicotine 12 hours before and sympathomimetics and parasympathomimetic agents, psychotropic drugs (sedatives, hypnotics, and tranquilizers), and antihistamines 48 hours prior to the study. The parameters were recorded 6–8 days after menstruation. The anthropometric measurements were taken. Subject's height was measured to the nearest millimeter by a wall mounted stadiometer. Weight was measured with an electronic weighing scale (Microgene, New Delhi) with LCD with accuracy of ±0.1 kg. BMI was calculated by Quetelet's index that is weight/[height] 2 , weight in kg and height in meters. Visual reaction time was done in subjects using reaction time machine (RTM-608, Medicaid Systems, Chandigarh) with resolution of 0.001 sec, accuracy ±1 digit, and 3 different lights, red, green, and yellow and 3 different sounds, high, medium, and low pitch sounds.

The subjects were instructed about the procedure and after adequate practice the tests were performed. Each of the three lights, namely, yellow, red and green lights, had its corresponding button equidistant from centre button. Keeping the same luminance for all three colors, we studied the reaction response to changes only in chromaticity. The subject was asked to keep the index finger of the dominant hand on the center button and press the corresponding light button as soon as yellow, red, or green light appears. The reaction values were directly read from digital display.

2.1. Statistical Analysis

Ten values of visual reaction time were recorded, two lowest and two highest values were deleted, and the average for the middle six values was calculated. The data were summarized using descriptive statistics, mean and standard deviation. Repeated measures of ANOVA and Bonferroni multiple comparison were used for analysis using appropriate statistical software. P < 0.05 was considered statistically significant.

Table 1 shows mean age group and anthropometric measurements of sixty study subjects. When the visual choice reaction time among the colors was compared using repeated measures ANOVA, it showed visual choice reaction time in milliseconds for yellow, red, and green was statistically significant with P value <0.001 and F value 15.01 ( Figure 1 ). Visual choice reaction time among colors was compared in pairs using Bonferroni multiple comparison test and it showed that both red and green color choice visual reaction times were significantly less when compared with yellow with P values <0.0001 and 0.0002, respectively ( Table 2 ).

Comparison of visual choice reaction time among colors. Values are expressed as mean ± SD; analysis was done by repeated measures ANOVA. * P < 0.05; ** P < 0.01; *** P < 0.001. F value, 15.01. VRT: visual reaction time.

Age and anthropometric measurements of the subjects.

Values are expressed as mean ± SD. BMI: body mass index.

Pairwise comparison of visual choice reaction time among colors.

VRT yellow: visual reaction time to yellow light, VRT red: visual reaction time to red light, and VRT green: visual reaction time to green light.

4. Discussion

Reaction time is one of the important methods to study a person's central information processing speed and coordinated peripheral movement response [ 1 ]. Cognitive processes are typically inferred from behavioral data such as accuracy and reaction time [ 14 ]. Choice reaction time is very important in driving vehicles. Most of the time people drive their vehicles based on the conditioned reflexes, learned through experience, but sometimes when unexpected situation arises, like when they suddenly spot a traffic signal, their reaction to it is an example of visual choice reaction time. Henry and Rogers proposed “memory drum” theory according to which complex responses, like responses for choice reaction, required more stored information and hence take longer time to react [ 15 ].

The results obtained by different authors show that when a color stimulus changes in both luminance and chromaticity, the visual reaction time of an observer in detecting this chromatic change depends on nothing more than the luminance change and is regulated by Pieron's law [ 16 ]. The purpose of our study was to compare the visual choice reaction time for red, green, and yellow colors keeping the luminance constant in 60 healthy undergraduate female subjects. We had included only females in our study because reaction time is known to be faster in men compared with women [ 13 ]. The findings of our study revealed both red and green color choice visual reaction times were significantly less when compared with yellow. Our findings are consistent with Venkatesh et al. who had reported that green color evoked a faster response due to its stronger stimulation on the visual receptors and refute the findings of study conducted by Hita et al. who reported that there was no correlation between reaction time and chromaticity [ 17 , 18 ]. But both their studies were on simple reaction time.

The components of choice reaction time are (1) mental processing time, (2) nerve conduction time, (3) movement time (including motor preparation and motor response), and (4) device response time [ 19 , 20 ]. Since the nerve conduction time, movement time, and device response time are the same for all the three colors, the difference in visual choice reaction time should be in individual color mental processing time. Choice reaction time is also a function of stimulus information but only up to some amount of practice, after which it is independent of the number of alternatives; to minimize its influence on results we have used the same number of colors with adequate practice [ 21 ].

A three-state conceptualization of the central mechanisms or mental processing time operative during the latent period-preprocessing sensation (the time it takes to detect the sensory input from an object), stimulus categorization (according to Donders it includes stimulus-stimulus translation and stimulus-response translation), and response selection is proposed. The stimulus detection could contribute to increased visual choice reaction time for yellow when compared to the other two colors, as red-green activates (L-M) cone and blue-yellow activates S-[L-M]. It is reported that simple RTs generated in response to S cone-isolating stimuli are longest, whereas the shortest RTs are generated by L-M cone-isolating stimuli [ 12 , 21 ].

The difference in visual choice reaction time among colors with increased time for yellow color when compared with red and green colors could be because of difference in time taken for stimulus categorization and response selection. Stimulus categorization includes process-template matching versus feature testing [ 20 ]. Increased neuronal gamma-band synchronization and shortened neuronal response latencies to stimulus have direct effects on visually triggered behavior and reflect visuomotor integration. Hence we can say that gamma-band synchronization is better for red and green colors when compared with yellow color [ 22 ]. We could not separately measure the components of mental processing time to strengthen our findings. In addition, visual choice reaction time for shorter wave length light could also be recorded which forms the future scope of our study.

5. Conclusion

The study results indicated visual choice reaction time for yellow color was significantly more than red and green colors. This could be because individual color mental processing time for yellow color is more than red and green. The difference could be in either preprocessing sensation and stimulus, stimulus categorization, or response selection or all of them. Hence we suggest that yellow color and its variants should be less used in places where reaction time becomes very important like in traffic signals and so forth.

Acknowledgment

The authors would like to acknowledge the support received from the Jawaharlal Institute of Post Graduate Medical Education and Research (JIPMER), Puducherry, India (affiliated to the Institute of National Importance under the Ministry of Health and Family Welfare).

The authors hereby state that the paper has been read and approved by all the authors, that the requirements for authorship have been met, and that each author believes that the paper represents honest work.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Grrishma Balakrishnan helped in concept, design, data analysis, and interpretation. Gurunandan Uppinakudru helped in data analysis and preparation of the paper. Gaur Girwar Singh revised the paper. Shobith Bangera contributed to data analysis and editing the paper. Aswini Dutt Raghavendra edited the paper. Dinesh Thangavel helped in concept and revising the paper.

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Reaction Time

Cognitive ability- neuropsychology, get access to a complete battery of cognitive tests to assess reaction time, identify and assess the presence of alterations or deficits, stimulate and improve your reaction time and other cognitive functions, what is reaction time or response time.

Reaction time or response time refers to the amount of time that takes places between when we perceive something to when we respond to it . It is the ability to detect, process, and respond to a stimulus.

• Perception : Seeing, hearing, or feeling a stimulus with certainty is essential to having good reaction time. When the starter shoots the gun at the beginning of a race, the sound is received by the athlete's ears (they perceive the stimulus).
• Processing : In order to have good reaction time, it's necessary to be focused and understand the information well. Following the previous example, the runners, after hearing the gun, will be able to distinguish the sound from other background noise and know that it is time to start running (process the stimulus).
• Response : Motor agility is necessary in order to be able to act and have good response time. When the runners perceived and correctly processes the signal, they started moving their legs (respond to the stimulus).

If any part of these processes is altered, reaction time will be affected as a consequence. In other words, if one of the athletes had poor reaction time, they would have a disadvantage against the other runners. Reaction time necessarily includes a motor component , unlike processing speed . This is why having good reaction time is associated with having good reflexes.

• Complexity of the stimulus- The more complex the stimulus, the more information that has to be processed, the longer this process will take.
• Familiarity, preparation, and expectations : If you have to respond to a known stimulus that you've responded to before, the reaction time will be lower. The less information that you have to process, the quicker the reaction time will be. If, as in the example with the runners, you are expecting the stimulus (waiting for the gun), reaction time will be even lower.
• Stimulated sensory modality : Reaction time is shorter when the stimulus that triggers the response is auditory than if it is visual because auditory stimuli require less processing. Each sensory modality has a different reaction time.
• Simple : There is one single response to a single stimulus. For example, pressing the space bar on the on the computer when a word appears.
• Choice : There are different responses to different stimuli. For example, pressing the right arrow key if a word appears in Spanish, and pressing the left arrow key if the word appears in another language.
• Selection : There are different stimuli, but you only have to respond to one. For example, press the space bar only when the word appears in English. If it appears in Spanish, you don't do anything.

Why is reaction time so important and how does it affect daily life? Good reaction time allows us to be agile and efficient when it comes to responding to stimuli and situations like driving, having a conversation, playing sports, etc. Good response time benefits us in a variety of ways, but it's important that we properly process the information that we receive. If someone asks you a question in an interview, they will be expecting you to answer quickly and well. The same is true for other examples, like if your car breaks down, or if you have to act on your toes- you will have to respond quickly and accurately. Luckily, reaction time can be trained and improved.

Examples of response time

• If you are driving and you come across a crosswalk, the time that it takes from when you see the crosswalk to when you break and stop the car would be reaction time. This cognitive ability can prevent us from many dangerous car accidents.
• In a boxing match or football game, it is very important to detect the opponents move and know what they're going to to in order to react as quickly and carefully as possible. Good reaction time is the key to scoring and winning.
• A child is in gym class and has to start running when the teacher gives the signal. The time it takes between when the teacher gives the signal and when the child starts running would be reaction time.
• You're in a building and you smell smoke all of the sudden. Reaction time would be the time it takes you to find and use the closest fire extinguisher after detecting a fire.
• When a security guard sees suspicious behavior, the time that it takes him or her to react may be crucial for a successful intervention. If they see, for example, a robbery, response time would be the time between when they see the robbery and start taking action to prevent it.

Problems and disorders associated to reaction time

Any type of disorder that is characterized by perception, information processing, or motor problems will also affect reaction time. This is why reaction time is so sensitive to alterations . For example, visual or auditory problems like blindness or hearing impairments may lead to problems that affect reaction time due to the problems with perception. People with bradypsychia or dementia like Alzheimer's Disease may cause poor processing, and thus affect response time. People with inhibition control problems or oeople with ADHD may also have processing speed affected, which in turns affects response time. When it comes to carrying out the action, people with akinesia or bradykinesia, as is the case with Parkinson's patients, or motor problems like hemiparesia or other paralisies may also have problems when giving a motor response. In general, any neurodegenerative disorder like Alzheimer's, Parkinsons, MS, or Huntington's disease will also find that their reaction time is affected as well. Finally, brain problems caused by brain injury or stroke may affect any of these processes, which affects response time as a consequence.

One disorder that can most affect how quickly you are able to process information is called diffuse axonal injury . This usually happens after suffering from a concussion and the neural connections become damaged. The blow to the head or accident causing the concussion to break or tear the axons (the part of the neuron that allows it to connect with other neurons, white matter in the brain). This damage to the axons doesn't affect one specific area of the brain, but rather it affects all of the axons in the brain, causing diffuse damage. This translates into slowed processing and, as a result, a slower response time. Unfortunately, this type of injury is quite common and generally has a bad prognosis.

Reaction time isn't only affected by injury or some kind of disease or disorder. There are a number of different circumstances that may lower and weaken reaction time, like sleep, mood, anxiety, or lack of concentration in general. However, unlike the other factors, recovering reaction time affected by these circumstances is quicker and easier.

How to measure and assess response time?

Reaction time plays a role in the majority of our day-to-day activities. Our ability to interact with out surroundings and react to unexpected changes and events depends directly on this cognitive skill. Being able to evaluate reaction time and understand how it functions could be very helpful in a variety of situations and areas. For example, academics , as it allows teachers or parents to understand if the child has perception, processing, or motor problems and the academic repercussions this may have, medical , as it can help detect mild problems in patients with perceptive, processing, or motor areas, or in the professional field, where it makes it possible for workers to know and understand if they are best prepared to carry out certain activities that may require them to act quickly in certain circumstances.

We are able to measure different cognitive functions, including reaction time, with a complete neuropsychological assessment . The tests that CogniFit created to measure reaction time were based on the classic NEPSY test, Test of Variables of Attention (TOVA), Continuous Performance Test (CPT), Test of Memory Malingering (TOMM), and the Visual Organization Task (VOT). Aside from measuring reaction time, these tests also measure working memory, visual scanning, hand-eye coordination, inhibition, cognitive flexibility, naming, visual perception, contextual memory, recognition, sustained attention, and spatial perception.

• Inquiry Test REST-COM : Objects will appear for a short period of time. The user must select the word that correspond the image as quickly as possible.
• Decoding Test VIPER-NAM : Images will appear on the screen for a short period of time an then disappear. Four letters will then appear, only one of which will correspond to the letter of the object. The user must choose the correct letter as quickly as possible.
• Recognition Test WOM-REST : A series of three objects will appear on the screen. The user must memorize the order in which they are displayed and later choose the correct order from a selection.
• Resolution Test REST-SPER : A number of moving stimuli will appear on the screen. The user must click on the objective stimuli while avoiding irrelevant stimuli.
• Speed Test REST-HECOOR : A blue square will appear on the screen. The user must click as quickly and as many times as possible in the middle of the square. The more times the user clicks, the higher the score.
• Processing Test REST-INH : In this task, two different sized blocks with numbers inside will appear. The user will first have to click on the bigger block. The next step is to click on the block with the highest number .

How can you improve or rehabilitate response time?

Like our muscles, response time and our other cognitive skills can be trained and improved, and CogniFit may help with professional tools and training programs. The rehabilitation of reaction time is based on the science of neuroplasticity . CogniFit also has a battery of exercises available to help rehabilitate problems with response time and other cognitive functions. Training and challenging your brain can help strengthen the brain and its neural networks. If you frequently train reaction time, the brain's connections will become stronger and healthier, which means that when it comes time to use response time, it will be quicker and require less mental resources.

CogniFit's professional team is made up of a number of specialists in the area of neuropsychology, neurogenesis, and synaptic plasticity, which is what allowed us to create the personalized cognitive stimulation program to meet each user's needs. This program starts with a precise assessment of the user's response time and other fundamental cognitive functions, and then uses the results to create a training program created to their specific needs.

Consistent and adequate training are necessary for improving reaction time, and CogniFit has assessment and rehabilitation toold to optimize this cognitive function. The program only requires 15 minutes two to three times a week. .

You can use CogniFit online . There are a number of interactive online games and exercises that can be played on the computer or mobile device. After each session, CogniFit will provide a detailed graphic outlining the user's cognitive progress .

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