does gender affect short term memory experiment

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings
My Bibliography
Collections
Citation manager

Save citation to file

Email citation, add to collections.

Create a new collection
Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

Search in PubMed
Search in NLM Catalog
Add to Search

Gender-linked differences in everyday memory performance

Affiliation.

1 Laboratory of Socio-environmental Studies, National Institute of Mental Health, Washington, DC.
PMID: 1611409
DOI: 10.1111/j.2044-8295.1992.tb02436.x

Recent research has demonstrated that people hold beliefs about how well others perform everyday memory tasks according to another's sex. For example, meta-memory ratings indicate that other men and other women are believed to differ in their success at performing certain memory tasks (Crawford, Herrmann, Holdsworth, Randall & Robbins, 1989). In the present study, two experiments investigated whether gender stereotypes concerning everyday memory have any validity. Experiment 1 presented female and male subjects with two tasks that the aforementioned meta-memory ratings had shown are implicitly gender marked: learning a shopping list (a sterotypically feminine task) and learning directions to go to a particular place (a stereotypically masculine task). The results were consistent with the gender stereotypes, i.e. women recalled more of the shopping list than men whereas men recalled more of the directions than women. The second experiment investigated whether memory performance would be influenced by mere changes in the label of materials in memory tasks to be biased toward male or female gender background: labelling a shopping list as pertaining to 'groceries' or to 'hardware store'; and a set of directions to 'make a shirt' or to 'make a workbench'. The results also indicated that memory performance varied in ways consistent with gender stereotypes.

PubMed Disclaimer

LinkOut - more resources

Full text sources.

Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Gender differences in short term memory and perception

International Journal of Development Research

Memory and perception are valuable possessions of mankind. But these possessions are influenced by various physical, emotional and environmental factors. So, the present study planned to investigate the influence of gender on memory and perceptual ability. Results revealed that short term memory showed statistically significant increase in females compared to males. Perceptual ability showed an insignificant increase in males compared to females. Thus it could be concluded that women performed well in verbal episodic memory tasks and men excelled in visuo-spatial processing.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
HHS Author Manuscripts

Memory Performance and Affect: Are there Gender Differences in Community-Residing Older Adults?

Graham joseph mcdougall.

University of Alabama, Capstone College of Nursing, Tuscaloosa, Alabama, USA

Keenan A. Pituch

University of Texas at Austin, College of Education, Austin, Texas, USA

Marietta P. Stanton

Wanchen chang.

After age 65, the incidence of episodic memory decline in males is greater than in females. We explored the influence of anxiety and depression on objective and subjective memory performance in a diverse sample of community-residing older adults. The study was a secondary analysis of data on three samples of adults from two states, Ohio and Texas: a community sample ( n = 177); a retirement community sample ( n = 97); and the SeniorWISE Study ( n = 265). The sample of 529 adults was 74% female, the average age was 76.58 years (range = 59–100 years), and educational attainment was 13.12 years (±3.68); 68% were Caucasian, and 17% had depressive symptoms. We found no memory performance differences by gender. Males and females were similarly classified into the four memory performance groups, with almost half of each gender in the poor memory category. Even though males had greater years of education, they used fewer compensatory memory strategies. The observed gender differences in memory were subjective evaluations, specifically metamemory. Age was not a significant predictor of cognition or memory performance, nor did males have greater memory impairment than females.

Males had greater age-associated atrophy of the left hemisphere than women did; however, this difference was not manifested in everyday verbal memory ( Larrabee & Crook, 1993 ). However, longitudinal studies found that after the age of 65, the incidence of episodic memory impairment is greater in males than in females ( Federal Interagency Forum on Aging-Related Statistics, 2012 ). We rely on our memory to function in everyday activities, but decline in memory performance frequently occurs during the ages of 65 to 85 years ( Lee et al., 2012 ; Rinn, 1988 ; Schaie, 1989 ).

Nationally, data from the 2011 Behavioral Risk Factor Surveillance System (BRFSS) survey determined that 12.7% of respondents older than 60 years of age reported increased confusion or memory loss in the preceding 12 months. Among those reporting increased confusion or memory loss, 35.2% also reported experiencing functional difficulties ( Centers for Disease Control and Prevention [CDC], 2013 ). Many older adults notice memory lapses and worry about incidents of forgetting ( Haug, Wykle, & Namazi, 1989 ; Herzog & Rodgers, 1989 ; Weaver, Collie, Masters, & Maruff, 2008 ). These memory lapses also are upsetting in the daily lives of adults 40 years of age and older ( Begum et al., 2012 ; Waldorff, Siersma, Vogel, & Waldemar, 2012 ). Changes in memory performance, specifically a decline in episodic memory, are often the initial symptom of declining ability and cognitive impairment ( Albert, 2011 ; Salthouse, 2003 ; R. S. Wilson et al., 2002 ).

Meta-analyses have concluded that depression and older adults’ memory performance are negatively correlated ( Burt, Zembar, & Niederehe, 1995 ; Kindermann & Brown, 1997 ). In addition, other affective and cognitive factors are known to affect memory performance in older adults. The study reported here examined gender differences in metamemory (subjective awareness) and memory (objective performance) and the factors influencing memory in community-residing older adults.

DIFFERENCES IN COGNITIVE AGING

Cohort studies of cognitive aging.

Two studies, Brooks, Friedman, and Yesavage (2003) and Burack and Lachman (1996) , found that healthy older adults who used used memory strategies, specifically list making, recalled more items than did those individuals who did not use a memory strategy. Engagement with friends protected cognitive function, specifically orientation and memory, in women but not in men ( Zunzunegui, Alvarado, Del Ser, & Otero, 2003 ). Using the California Verbal Learning Test, verbal memory declined as age increased for younger men and older women, but not for younger women ( Kramer, Yaffe, Lengenfelder, & Delis, 2003 ). Women generally outperformed men on auditory memory, whereas males performed at a higher level on visual episodic and visual working memory tasks ( Pauls, Petermann, & Lepach, 2013 ). In another study, women excelled in verbal episodic memory tasks—such as remembering words, objects, pictures, or everyday events—but men outperformed women in remembering symbolic, non-linguistic information, known as visuospatial processing ( Herlitz & Rehnman, 2008 ).

Cansino et al. (2013) found that working memory abilities declined with age. Cansino maintains there is no agreement on whether working memory declines equally for both visuospatial and verbal information. However, the comparison across cohort groups showed that discrimination in the visuospatial tasks started to decline earlier in women than in men, but data failed to demonstrate any differences associated with gender for tasks in the verbal domain. Females have been found to have advantages in verbal and autobiographical tasks and general episodic memory ( Andreano & Cahill, 2009 ). Gender differences that are inconsistent have emerged in other autobiographical memory studies ( Grysman & Hudson, 2013 ). Women reported more vivid memory experiences, including more details about emotions, other people, and the meaningfulness of their memories. It was proposed that gender differences in autobiographical memory development may be related to the influence of interactions with others when autobiographical memory skills were developing ( Grysman & Hudson, 2013 ).

Cognitive Aging and Depressive Symptoms

The Baltimore Longitudinal Study on Aging examined the influence of depression on cognitive decline in cognitively normal controls with no Alzheimer’s pathology, individuals with Alzheimer’s pathology, individuals with mild cognitive impairment plus Alzheimer’s pathology but no cognitive decline, and individuals with a clinical diagnosis of dementia plus Alzheimer’s pathology. Depressive symptoms were assessed using the Center for Epidemiologic Studies-Depression Scale. Individuals with Alzheimer’s pathology but without cognitive decline had significantly lower rates of depression than cognitively normal individuals with no Alzheimer’s pathology and individuals with Alzheimer’s pathology plus clinical diagnosis of dementia ( Morgan et al., 2007 ). In a population-based study, 1241 participants, aged 62–85 years, were asked about their physical activity during their early adult years. Researchers found that risk factors associated with depression included female gender, history of cerebrovascular diseases, generalized anxiety disorder, and loneliness ( Polyakova et al., 2013 ). Regular physical activity was positively associated with information processing speed at older ages in men, but not in women ( Dik, Deeg, Visser, & Jonker, 2003 ).

Longitudinal Studies of Cognitive Aging

Zelinski and Burnight (1997) found sixteen year age-related declines in list and text recall among a sample of 106 older adults that were not the result of cohort differences. In the Baltimore Longitudinal Study of Aging, Lamar, Resnick, and Zonderman (2003) tested the memory performance of 385 older adults with the California Verbal Learning Test (CVLT). Regardless of baseline performance, both younger adults and women scored higher on delayed recall. The Health ABC Study followed 2509 black and white elders longitudinally ( Yaffe et al., 2009 ). Over the eight-year study, 30% of the participant’s maintained cognitive function, 53% showed minor declines, and 16% showed major cognitive declines. Even though unique profiles of individuals were found based on age, education, literacy, level of physical activity, and smoking, no gender differences were identified. The Health and Retirement study, published in Older Americans 2012: Key Indicators of Well-Being found that males in every age cohort older than 65 years had a higher percentage of memory impairment than did females. Moderate or severe memory impairment in the study was defined as 4 or fewer words recalled out of 20 ( Federal Interagency Forum on Aging-Related Statistics (2012) . Even though males had more severe memory impairment, females also were impaired. In those 85 years of age or older, 37% of males, and 35% of females had moderate to severe memory impairment.

Neuro Imaging Studies

Studies of magnetic resonance imaging have found greater atrophy and shrinkage in the brain structures of men than in the brains of women ( Coffey, Lucke, Saxton, Ratcliff, Unitas, Billig, & Bryan, 1998 ; Larkin, 1998 ; Xu et al., 2000 ). Langley and Madden (2000) found that during retrieval in older adults the prefrontal cortex was bilaterally activated. Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) demonstrated evidence for possible over-activation in brain regions, demonstrating that older adults have decreased activity in multiple regions important for memory tasks ( Persson & Nyberg, 2006 ).

Setting, Participants, and Procedure

We analyzed data from three studies of adults aged 59–100 years: the Community Sample study ( n = 177), the Retirement Village ( n = 97), and the Senior WISE Study ( n = 265). Potential participants signed a consent letter before joining the study. The recruitment sites consisted of senior centers, adult learning programs, continuing care retirement communities, and low income housing facilities in Ohio and Texas. There were 396 (73%) females and 143 (27%) males in the sample. The mean age of the sample was 76.6 years ( SD = 6.4 years). Education, in years, was 13.1 years ( SD = 3.68 years). Ethnicity was 68% Caucasian, 23.4% African-American, and 8.5% Hispanic; 31% of the sample was married, 49.7% were widowed, 12.9% were divorced, and 6.0% were single.

Study instruments were administered in a face-face interview. First, individuals were given the Mini-Mental State Exam (MMSE). Next, demographic information was collected, and the metamemory and depression instruments were administered. Then, the memory performance timed test using the Rivermead Behavioural Memory Test and a health questionnaire were administered. Finally, questions from the last part (“delayed” recall) of the memory performance test were administered.

Cognitive Function

The Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975 ) is a 30-item performance measure that assesses cognitive functioning in older adults and is commonly used to diagnosis dementia. Scores can range from 0–30 and higher scores suggest better cognitive functioning. The measure is comprised of 11 areas: orientation to time, orientation to place, registration, attention and calculation, recall, naming, repetition, comprehension, reading, writing, and drawing. Tombaugh and McIntyre (1992) found that test-retest reliability ranged from about .80 to .95, and studies report moderate-to-high levels of predictive validity.

Memory Performance

The Rivermead Behavioural Memory Test (RBMT) tested episodic memory performance and was designed to capture everyday memory ( Cockburn & Smith, 1989 ; B. A. Wilson, Cockburn, & Baddeley, 1991 ; B. A. Wilson, Cockburn, Baddeley, & Hiorns, 1989 ). The assessment asks participants to remember a name (first and surname), a hidden belonging, an appointment, a brief news article, a new route (immediate), a new route (delayed), a message, orientation in place and time, and a date, as well as to recognize a picture and a face. Questions are designed so that normal participants will pass but individuals with everyday memory problems will fail. For each subtest, two scores are produced, a pass/fail screening score and a standardized profile score from 0–2 (0 points = abnormal; 1 point borderline; 2 points = normal). Thus, each participant’s evaluation results in two scores, a Screening Score (SS) ranging from 0–12 and a Standardized Profile Score (SPS) ranging from 0–24. To control for practice effect, the test is available in four alternate (parallel) forms. Test-retest reliabilities have been reported as .78 for the screening score and .85 for the profile score.

The subjective assessment of memory was operationalized as seven scores on the Metamemory in Adulthood (MIA) questionnaire developed by Dixon, Hultsch, and Hertzog (1988) . The MIA is a measure of the memory components of knowledge, beliefs, and affect. The MIA consists of 108 statements, which are rated on a 5-point Likert scale. The seven subscales measure achievement, anxiety, capacity, change, locus, strategy, and task. Achievement is the perception of one’s motivation to perform well in memory tasks (+ = high achievement). Anxiety is rated as the influence of anxiety and stress on performance (+ = high anxiety). Capacity is one’s beliefs about capacity (+ = high capacity). Change is the perception of memory abilities as generally stable or subject to long-term decline (+ = stability). Locus is the individual’s perceived sense of personal control over remembering abilities ( + = internal locus). Task is knowledge of one’s own memory processes (+ = high task).

Strategy is having knowledge of one’s remembering abilities such that performance in given instances is potentially improved (+ = high use). Internal strategies are captured by nine Likert-type questions: rehearsal (4 questions), elaboration (4 questions), and effort (1 question). External memory strategies include nine Likert-type questions related to the use of calendars (1 question), lists (2 questions), notes (3 questions), place (2 questions), and someone (1 question).

The psychometric characteristics of the MIA have been examined with multiple samples of university students and community-dwelling middle-aged and older adults. Cronbach’s alphas from five studies of the instrument’s internal consistency for the two subscales reported here were as follows: Strategy, .82–.86 and Anxiety, .83–.87 ( Dixon, Hultsch, & Hertzog, 1988 ).

Depressive Symptoms

In the various samples, depression was operationalized with two valid and reliable scales. The short version of the Geriatric Depression Scale (GDS) ( Brink et al., 1982 ; Sheikh & Yesavage, 1986 ), a 15-item instrument, has a Yes/No response format. Scores range from zero to 15, with a score ≥5 indicating depression. Depressive symptoms also were measured with the Center for Epidemiologic Studies-Depression (CES-D) scale. Individuals respond on a 4-point scale, ranging from “rarely or none of the time” to “most or all of the time.” There are four subscales: depressed affect, well-being, somatic symptoms, and interpersonal relations; however, a composite score is acceptable ( Hertzog, VanAlstine, Usala, Hultsch, & Dixon, 1990 ; Radloff & Teri, 1986 ). Scores range from zero to 60, with higher scores indicating more depressive symptomatology. The CES-D has been tested with older adults and has been found to be stable when subscale and total scores are reported. High reliability coefficients from .85 to .91 have been obtained and factor structures have remained constant with older adults ( Himmelfarb & Murrell, 1983 ).

Statistical Analysis

Sequential binary logistic regression was used to determine whether gender differences were present for three sets of variables. The covariates (depression, age, ethnic group, and education) were entered first into the logistic regression model, followed by the performance variables, and then the subjective self-report measures. We also conducted an analysis in which the block of self-report measures was entered before the performance measures to assess if results were sensitive to order of entry (they were not). When a given block of variables was found to discriminate between male and females, we examined the impact of individual variables within the block to determine which particular variables contributed to gender differences. For this assessment of individual variables, we used analysis results from the full model with all variables included. We also computed McFadden’s (1974) pseudo r -square, denoted R L 2 , which is an effect size measure that describes the proportional improvement in model fit that is due to each block.

For the logistic regression analysis, gender was the outcome variable. Further, Harell (2001) notes that logistic regression is generally preferred over discriminant analysis because the latter requires more stringent assumptions about the discriminating variables (i.e., multivariate normality and equal variance/covariance matrices across groups). As such, logistic regression is a more robust procedure.

Of the 539 participants, 529 provided responses on all study variables. Given the limited amount of missing data, we removed the ten participants (1.8%) who had missing data. Among the 529 remaining cases, 390 (74%) were female and 139 (26%) were male. Relatively few participants indicated Hispanic ethnicity ( n = 44); therefore, to avoid potential problems associated with small cell sizes, the ethnic variable used in the analysis had two categories: Caucasian ( n = 361, 68%) and other ( n = 168, 32%).

Memory Performance Groups

As shown in Table 1 , males and females were classified into the four RBMT memory performance groups of normal, poor, moderately, and severely impaired categories. There were no differences in the groupings by gender. Table 2 provides descriptive statistics on all study variables by gender. This table shows very similar means on the performance measures for males and females. Males reported, on average, more years of formal education. Females reported more anxiety about their memory function, yet also reported greater memory capacity and more use of external strategies than did males.

Rivermead Memory Performance Categories by Gender

Category	Females = 390	Males = 139
Normal	81 (21%)	27 (19%)
Poor	181 (46%)	67 (48%)
Moderate Impairment	112 (29%)	39 (28%)
Severe Impairment	16 (4%)	6 (4%)

Note. Values in the table represent frequencies and percents.

Descriptive Statistics for All Study Variables

	Females	Males
Variable	= 390	= 139	value
Performance Measures Mean ( )
MMSE	27.56 (2.40)	27.63 (2.11)	.78
RBMT	17.64 (4.38)	17.78 (4.13)	.74
Self-Report Measures Mean ( )
Achievement	3.85 (0.36)	3.81 (0.37)	.33
Anxiety	3.23 (0.62)	3.11 (0.61)	.05
Capacity	3.06 (0.51)	2.95 (0.59)	.03
Change	2.52 (0.55)	2.51 (0.64)	.85
Locus	3.52 (0.49)	3.50 (0.55)	.72
Internal Strategy	3.43 (0.60)	3.41 (0.55)	.71
External Strategy	3.84 (0.67)	3.54 (0.71)	<.001
Task	3.85 (0.36)	3.90 (0.36)	.21
Covariates Mean ( )
Age	76.87 (6.42)	75.76 (6.26)	.08
Education	12.81 (3.57)	14.00 (3.86)	.001
Caucasian (%)	263 (67%)	98 (71%)	.51
Depression (%)	69 (18%)	21 (15%)	.49

Note. MMSE = Mini-Mental State Exam; RBMT = Rivermead Behavioural Memory Test

Prior to conducting the logistic regression analysis, we assessed whether the data supported the use of this procedure. The two performance measures were positively correlated, and the self-report measures were generally moderately correlated, as shown in Table 3 . These correlations supported the use of an analysis model considering associations. While study variables were correlated, excessive multicollinearity was not observed; the largest variance inflation factor for all predictors in the model was 2.4, well under the commonly used value of ten, denoting serious multicollinearity ( Stevens, 2009 ). The statistical assumption of linearity in the logit was assessed by using the Box-Tidwell procedure ( Menard, 2010 ), which found no evidence of nonlinearity. In addition, the p value (.43) associated with the Hosmer-Lemeshow goodness-of-fit test provided support for the assumed functional form of the logistic regression model.

Correlations among the Discriminating Variables

Variable	1	2	3	4	5	6	7	8	9	10
1. MMSE	—
2. RBMT	.52	—
3. Achievement	.04	−.06	—
4. Anxiety	–.09	–.18	.32	—
5. Capacity	.01	.01	.01	–.49	—
6. Change	.04	.16	–.19	–.58	.64	—
7. Locus	.13	.13	.32	–.25	.36	.38	—
8. Internal Strategy	.14	.10	.32	.19	–.09	–.13	.20	—
9. External strategy	.19	.13	.27	.23	–.21	–.23	.11	.49	—
10. Task	.21	.14	.31	.14	–.11	–.27	.11	.32	.35	—

While there were outlying values in the solution (3 cases with standardized residuals greater than 4), these outliers did not influence study results; the largest value of Cook’s distance for these observations was 0.26, well below the value of 1 that is often used to characterize excessive influence ( Stevens, 2009 ). These outlying cases were males who had below average years of formal education but had higher than average memory performance, capacity, and use of external strategies. There also was one participant whose Cook’s distance value (.50) was larger than for other cases. However, this case did not influence results, which we determined by temporarily removing the case and rerunning the logistic regression analysis. Thus, all 529 cases were used in the logistic regression analysis.

Covariates Influence on Memory Performance

Table 4 presents the results of the analysis. When the covariates were entered as a block, the likelihood ratio test ( χ 2 = 14.00, df = 4, p = .007, R L 2 = 2) indicated that the set of covariates distinguished between males and females. As Table 4 indicates, females reported fewer years of education than males. Adding the performance variables to the model that contained the covariates did not improve the fit of the model ( χ 2 = 2.07, df = 2, p = .499, R L 2 = .002), indicating that there were no performance differences between males and females. However, adding the self-report measures to the model improved the fit ( χ 2 = 56.41, df = 8, p < .001, R L 2 = .09). As the odds ratios in Table 4 indicate, females were more likely to have greater anxiety as well as greater capacity and greater beliefs in better memory and were more likely to use external strategies than males.

Logistic Regression Estimates for Gender as Function of Covariates and the Discriminating Variables

	95% CI
Variable	Odds Ratio	LL	UL
Depression	.98	.53	1.78
Age	1.04	1.00	1.08
Education	.89	.83	.95
Caucasian	1.09	.62	1.93
MMSE	1.01	.90	1.13
RBMT	1.04	.98	1.10
Achievement	.90	.44	1.84
Anxiety	1.76	1.11	2.80
Capacity	3.15	1.79	5.54
Change	.86	.49	1.53
Locus	.95	.56	1.61
Internal Strategy	.71	.46	1.10
External Strategy	3.27	2.20	4.86
Task	.53	.26	1.06
Constant	.01	—

Note. CI = confidence interval; LL = lower limt; UL = upper limit, MMSE = Mini-Mental State Exam; RBMT = Rivermead Behavioural Memory Test

This study provided evidence of subjective and objective evaluations of memory function in a diverse group of community-residing older adults from Ohio and Texas. The data were collected using a cohort design that measured affective and cognitive function one time. Since the sample was not random, we cannot extrapolate these findings to all older adults. However, the results contribute to the cognitive aging literature, providing new data on gender differences in memory evaluation. In a random sample of older adults between the ages of 60 and 94, these adults ranked problems with memory among the five most frequently occurring daily symptoms; however, memory concerns were ranked among the lowest 10% of symptoms that these individuals felt required attention ( Haug, Wykle, & Namazi, 1989 ). Based on the Rivermead memory performance scores, almost half the sample in this study had everyday memory problems. Forty-six percent of the females and 48% of the males were in the poor memory performance category. An additional 30% of the sample was in the memory impaired category.

Age was not a significant predictor of cognition or memory performance in this large sample. Additionally, males and females were equally represented in the four memory performance groups. There were no significant differences in global cognitive function or memory performance scores.

In our sample, females reported that they were using significantly more external memory strategies than were the males. Females in this sample also were more anxious about their memory, which may have increased their use of memory strategies. Memory strategies may be considered a compensatory mechanism to deal with the difficulties experienced by adults in their everyday memory performance as they age. Hutchens et al. (2013) found no association between control beliefs and memory strategy use, or control beliefs and memory performance for older adults with amnestic mild cognitive impairment (MCI); these researchers did find a strong association between strategy use and memory performance in healthy older adults.

Researchers have found that a number of risk factors, such as being a female, having a history of cerebrovascular diseases, experiencing a generalized anxiety disorder, having loneliness, and living in a long-term care institution, are strongly associated with mild cognitive impairment ( Polyakova et al., 2013 ). After a comprehensive review of studies from adults 55 years of age and older, the investigators determined that although minor depression is rarely investigated in elderly persons with MCI, nearly 20% of patients with MCI seem to suffer from depression.

Neuroscience Issues

Neuroscience is providing more in-depth knowledge of the physiological basis of memory function and aging while assisting clinicians in understanding the behavioral and functional manifestations encountered in the aging adult population. For instance, the integrity of specific white matter tracts of the prefrontal cortex have been investigated and are evident in studies of healthy older adults. Three cognitive processes that utilize the tracts of the prefrontal cortex involve episodic memory, working memory, and reasoning. Strenziok (2013) concluded that episodic memory, working memory, and reasoning were related to the integrity of specific fibers that can be tracked using current medical technology in the prefrontal cortex. This current investigation explored not only behavioral changes encountered in subjective and objective measures but also the emerging information from the field of neuroscience.

Substantial progress has been made in understanding the behavioral aspects of learning and memory. However, examining the neurochemical aspects of memory, covariates of functional plasticity, may provide a deeper understanding of age as well as gender related differences. Neurochemical analyses have been found to be more sensitive than behavioral analyses in delineating gender based differences ( Sebastian et al., 2013 ). Current Alzheimer’s disease research is focused on the combination of neurochemical indicators, such as decreased cerebrospinal fluid (CSF) levels of beta-amyloid (1–42), and increased levels of phosphorylated tau (ptau-181) or total tau protein are known to be biomarkers of AD ( Galvin, Fagan, Holtzman, Mintun, & Morris, 2010 ). Other studies have found that basal and feedback indices of cortisol regulation varied by gender; women without memory complaints had lower cortisol levels ( Wolf et al., 2005 ). The researchers hypothesized that memory complaints may be related to enhanced hypothalamus-pituitary-adrenal (HPA) axis activity leading to stress-related increases in plasma cortisol levels.

The brain achieves approximately 75% of its adult weight by the age of 2 but the regional brain structure continues to change throughout life. These changes in regional brain structure were modified by activity in a particular portion of the neostriatum. One study measured the structural covariance of various portions of the neostriatem and determined that aging and gender were correlated with changes in regional brain volumes and produced gender related differences in memories and how they are processed ( Soriano-Mas et al., 2013 ). These neuroscience findings are providing a more dynamic view of memory changes in aging and are enhancing our understanding not only of gender differences in memory but also those associated with other characteristics.

Clinical Relevance of Men’s Mental Health

The Centers for Disease Control and Prevention (2009) reported that for all races, the top ten causes of death in males were led by heart disease (25.2%) and cancer (24.4%). Even though suicide accounted for 2.4% and Alzheimer’s disease only 2% of deaths in males, these top chronic illnesses profoundly affect mental health. Affective and cognitive impairments are often inversely related to chronic illness ( Burt, Zembar, & Niederehe, 1995 ). For example, cognitive impairment was significantly greater in individuals with heart failure, independent of all other variables except comorbid conditions ( Zuccala`, Pedone, Cesari, Onder, Pahor, et al., 2003 ).

Our limited measures of memory performance were not able to determine if mild cognitive impairment (MCI) was present in the sample. However, in a study of community elders ( McDougall, Becker, & Arheart, 2006 ), 46 individuals, or 17% of the sample met the criteria of poor everyday memory functioning and had memory complaints, whereas 81 (31%) were considered to be at-risk based on other MCI criteria ( Winblad et al., 2004 ). Memory function is the conundrum for determining what healthy aging is, and what dementia is, for not only adults and their families, but also for the health care professionals who advise and counsel elders. The correct diagnosis of these nuances in cognitive function and memory performance in seemingly normal older adults is the emphasis of cognitive aging research and allows for the differentiation of MCI from Alzheimer’s disease ( Knopman, 2013 ).

Men’s health is often ignored and men do not seek health care as frequently as women do. The federal government offered a policy level solution in 2003 through the Office of the Secretary General in Departmental Management. The introduction of HR Report 107–229 provided a public statement declaring that there was no entity responsible for the coordination and oversight of activities across the agency concerning men’s health. This recommendation was based on reports that men were 25% less likely than women to receive regular health screenings, and that men were less likely to visit a doctor when they noticed a problem. The Committee recommended that the Secretary expand Departmental disease prevention and health promotion activities among men and to give consideration to establishing an office for men, similar to the Office of Women’s Health. This initiative has not been codified.

In summary, women had higher anxiety scores and a greater memory capacity, and they used more strategies to maintain memory performance than did men. Females believed that they remembered more information than did males, but there were no memory performance differences by gender on either standard profile scores or the screening scores for the subscales. Older women’s greater memory capacity should have resulted in improved memory performance. These findings indicate that, in this sample, the gender differences in memory were subjective, not objective, measures of performance.

Declaration of Interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Contributor Information

Graham Joseph McDougall, University of Alabama, Capstone College of Nursing, Tuscaloosa, Alabama, USA.

Keenan A. Pituch, University of Texas at Austin, College of Education, Austin, Texas, USA.

Marietta P. Stanton, University of Alabama, Capstone College of Nursing, Tuscaloosa, Alabama, USA.

Wanchen Chang, University of Texas at Austin, College of Education, Austin, Texas, USA.

Albert MS (2011). Changes in cognition. Neurobiology of Aging , 32 ( Suppl 1 ), S58–S63. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Andreano JM, & Cahill L (2009). Sex influences on the neurobiology of learning and memory. Learning and Memory , 16 , 248–266. [ PubMed ] [ Google Scholar ]
Begum A, Whitley R, Banerjee S, Matthews D, Stewart R, & Morgan C (2012). Help-seeking response to subjective memory complaints in older adults: Toward a conceptual model. The Gerontologist , 53 ( 3 ), 462–473. [ PubMed ] [ Google Scholar ]
Brink TL, Yesavage JA, Lum O, Heersema PH, Adey M, & Rose TL (1982). Screening tests for geriatric depression. Clinical Gerontologist , 1 ( 1 ), 37–43. [ Google Scholar ]
Brooks JO, Friedman L, & Yesavage JA (2003). Use of an external mnemonic to augment the efficacy of an internal mnemonic in older adults. International Psychogeriatrics , 15 ( 1 ), 59–67. [ PubMed ] [ Google Scholar ]
Burack RO, & Lachman EM (1996). The effects of list-making on recall in young and elderly adults. Journal of Gerontology , 51B , 226–233. [ PubMed ] [ Google Scholar ]
Burt DB, Zembar MJ, & Niederehe G (1995). Depression and memory impairment: A meta-analysis of the association, its pattern, and specificity. Psychological Bulletin , 117 ( 2 ), 285–305. [ PubMed ] [ Google Scholar ]
Cansino S, Herna´ndez-Ramos E, Estrada-Manilla C, Torres-Trejo F, Mart´ınez-Galindo JG, Ayala-Herna´ndez M, & Rodr´ıguez-Ortiz MD (2013). The decline of verbal and visuospatial working memory across the adult life span. Age , 35 ( 6 ), 2283–2302. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Centers for Disease Control and Prevention. (2009). Leading causes of death in males United States Washington, DC: Author; Retrieved from http://www.cdc.gov/men/lcod/ [ Google Scholar ]
Centers for Disease Control and Prevention. (2013). Self-reported increased confusion or memory loss and associated functional difficulties among adults aged 60 ≥ years—21 States, 2011. Morbidity and Mortality Weekly Report , 62 ( 18 ), 347–350. [ PubMed ] [ Google Scholar ]
Cockburn J, & Smith PT (1989). The Rivermead Behavioural Memory Test. Supplement 3: Elderly people Suffolk, UK: Thames Valley Test Company. [ Google Scholar ]
Coffey CE, Lucke JF, Saxton JA, Ratcliff G, Unitas LJ, Billig B, & Bryan RN (1998). Sex differences in brain aging: A quantitative magnetic resonance imaging study. Archives of Neurology , 55 ( 2 ), 169–179. [ PubMed ] [ Google Scholar ]
Dik M, Deeg DJ, Visser M, & Jonker C (2003). Early life physical activity and cognition at old age. Journal Clinical and Experimental Neuropsychology , 25 ( 5 ), 643–653. [ PubMed ] [ Google Scholar ]
Dixon RA, Hultsch DF, & Hertzog C (1988). The metamemory in adulthood (MIA) questionnaire. Psychopharmacological Bulletin , 24 , 671–688. [ PubMed ] [ Google Scholar ]
Federal Interagency Forum on Aging-Related Statistics. (2012). Older Americans 2012: Key indicators of well-being Washington, DC: US Government Printing Office. [ Google Scholar ]
Folstein MF, Folstein SE, & McHugh PR (1975) Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research , 12 , 189–198. [ PubMed ] [ Google Scholar ]
Galvin JE, Fagan AM, Holtzman DM, Mintun MA, & Morris JC (2010). Relationship of dementia screening tests with biomarkers of Alzheimer’s disease. Brain , 133 ( 11 ), 3290–3300. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Grysman A, & Hudson JA (2013). Gender differences in autobiographical memory: Developmental and methodological considerations. Developmental Review , 33 , 239–272. [ Google Scholar ]
Harrell FE (2001). Regression modeling strategies, with applications to linear models, survival analysis and logistic regression New York, NY: Springer. [ Google Scholar ]
Haug MR, Wykle MY, & Namazi KH (1989). Self-care among older adults. Social Science in Medicine , 29 , 171–183. [ PubMed ] [ Google Scholar ]
Herlitz A, & Rehnman J (2008). Sex differences in episodic memory. Current Directions in Psychological Science , 17 ( 1 ), 52–56. [ Google Scholar ]
Hertzog C, Van Alstine J, Usala PD, & Hultsch DF (1990). Measurement properties of the Center for Epidemiological Studies Depression Scale (CES-D) in older populations. Psychological Assessment , 2 , 64–72. [ Google Scholar ]
Herzog AR, & Rodgers WL (1989). Age differences in memory performance and memory ratings as measured in a sample survey. Psychology and Aging , 4 , 173–182. [ PubMed ] [ Google Scholar ]
Himmelfarb S, & Murrell SA (1983). Reliability and validity of five mental health scales in older persons. Journal of Gerontology , 38 , 333–339. [ PubMed ] [ Google Scholar ]
Hutchens RL, Kinsella GJ, Ong B, Pike KE, Clare L, Ames D, Saling MM, Storey E, Mullaly E, Rand E, & Parsons S (2013). Relationship between control beliefs, strategy use, and memory performance in amnestic mild cognitive impairment and healthy aging. Journals Gerontology B: Psychological Sciences Social Sciences , 68 ( 6 ), 862–871. [ PubMed ] [ Google Scholar ]
Kindermann SS, & Brown GG (1997). Depression and memory in the elderly: A meta-analysis. Journal of Clinical and Experimental Neuropsychology , 19 ( 5 ), 625–642. [ PubMed ] [ Google Scholar ]
Knopman DS (2013). Alzheimer disease biomarkers and insights into mild cognitive impairment. Neurology , 80 ( 11 ), 978–980. [ PubMed ] [ Google Scholar ]
Kramer JH, Yaffe K, Lengenfelder J, & Delis DC (2003). Age and gender interactions on verbal memory performance. Journal of the International Neuropsychological Society , 9 ( 1 ), 97–102. [ PubMed ] [ Google Scholar ]
Lamar M, Resnick SM, & Zonderman AB (2003). Longitudinal changes in verbal memory in older adults: Distinguishing the effects of age from repeat testing. Neurology , 60 ( 1 ), 82–86. [ PubMed ] [ Google Scholar ]
Langley LK, & Madden DJ (2000). Functional neuroimaging of memory: Implications for cognitive aging. Microscopy Research and Technique 51 ( 1 ), 75–84. [ PubMed ] [ Google Scholar ]
Larkin M (1998). Brain shrinkage more rapid in men than in women. Lancet , 351 ( 9102 ), 575. [ PubMed ] [ Google Scholar ]
Larrabee GJ, & Crook TH (1993). Do men show more rapid age-associated decline in simulated everyday verbal memory than do women? Psychology and Aging , 8 ( 1 ), 68–71. [ PubMed ] [ Google Scholar ]
Lee T, Crawford JD, Henry JD, Trollor JN, Kochan NA, Wright MJ, Ames D, Brodaty H, & Sachdev PS (2012). Mediating effects of processing speed and executive functions in age-related differences in episodic memory performance: A cross-validation study. Neuropsychology , 26 ( 6 ), 776–784. [ PubMed ] [ Google Scholar ]
McDougall G, Becker H, & Arheart KL (2006). Older adults in the SeniorWISE study at-risk for mild cognitive impairment. Archives of Psychiatric Nursing , 20 ( 3 ), 126–134. [ PMC free article ] [ PubMed ] [ Google Scholar ]
McFadden D (1974). Conditional logit analysis of qualitative choice behavior. In Zarembka P (Ed.), Frontiers in econometrics (pp. 105–142). New York, NY: Academic Press. [ Google Scholar ]
Menard S (2010). Logistic regression: From introductory to advanced concepts and applications Los Angeles, CA: Sage. [ Google Scholar ]
Morgan MD, Mielke MM, O’Brien R, Troncoso JC, Zonderman AB, & Lyketsos CG (2007). Rates of depression in individuals with pathologic but not clinical Alzheimer disease are lower than those in individuals without the disease: Findings from the Baltimore Longitudinal Study on Aging (BLSA). Alzheimer Disease & Associated Disorders , 21 ( 3 ), 199–204. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Pauls F, Petermann F, & Lepach AC (2013). Gender differences in episodic memory and visual working memory including the effects of age. Memory , 21 ( 7 ), 857–874. [ PubMed ] [ Google Scholar ]
Persson J, & Nyberg L (2006). Altered brain activity in healthy seniors: What does it mean? Progressive Brain Research , 157 , 45–56. [ PubMed ] [ Google Scholar ]
Polyakova M, Sonnabend N, Sander C, Mergl R, Schroeter ML, Schroeder J, & Scho¨nknecht P (2013). Prevalence of minor depression in elderly persons with and without mild cognitive impairment: A systematic review. Journal Affective Disorders , January;152–154:28–38. doi: 10.1016/j.jad.2013.09.016 Epub 2013 Sep 25. [ PubMed ] [ CrossRef ]
Radloff LS, & Teri L (1986). Use of the Center for Epidemiological Studies-Depression Scale with older adults. Clinical Gerontologist , 5 ( 1/2 ), 119–136. [ Google Scholar ]
Rinn WE (1988). Mental decline in normal aging: A review. Journal of Geriatric Psychiatry and Neurology , 1 , 144–158. [ PubMed ] [ Google Scholar ]
Salthouse TA (2003). Memory aging from 18 to 80. Alzheimer Disease Associated Disorders , 17 ( 3 ), 162–167. [ PubMed ] [ Google Scholar ]
Schaie KW (1989). The hazards of cognitive aging. The Gerontologist , 29 ( 4 ), 484–493. [ PubMed ] [ Google Scholar ]
Sebastian V, Vergel T, Baig R, & Schrott LM (2013). PKMζ differentially utilized between sexes for remote long-term spatial memory. PLoS One , 8 ( 11 ): e81121. doi: 10.1371/journal.pone.0081121 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sheikh JI, Hill RD, & Yesavage JA (1986). Long-term efficacy of cognitive training for age-associated memory impairment: A 6-month follow-up study. Developmental Neuropsychology , 2 , 413–421. [ Google Scholar ]
Soriano-Mas C, Harrison BJ, Pujol J, Sola M, Hernandez-Ribas R, Alonso P, Contreras-Rodrieguez O, Giminez M, Blanco-Hinjo L, Ortiz H, Deus J, Menchon JM, & Cardover N (2013). Structural covariance of the neostratium with regional grey matter volumes. Brain Structure Function , 218 , 697–709. [ PubMed ] [ Google Scholar ]
Stevens J (2009). Applied multivariate statistics for the social sciences (5th ed.). New York, NY: Routledge. [ Google Scholar ]
Strenziok M, Greenwood PM, Cruz SAS, Thompson JC, & Parasuraman R (2013). Differential contributions of dorso-ventral and rostro-caudal prefrontal white matter tracts to cognitive control in healthy older adults. PLoS One , 8 ( 12 ). doi: 10.1371/journal.pone.0081410 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tombaugh TN, & McIntyre NJ (1992). The Mini-Mental State Examination: A comprehensive review. Journal of the American Geriatrics Society , 40 ( 9 ), 922–935. [ PubMed ] [ Google Scholar ]
Waldorff FB, Siersma V, Vogel A, & Waldemar G (2012). Subjective memory complaints in general practice predicts future dementia: A 4-year follow-up study. International Journal Geriatric Psychiatry , 27 ( 11 ), 1180–1188. [ PubMed ] [ Google Scholar ]
Weaver CJ, Collie A, Masters C, & Maruff P (2008). The nature of cognitive complaints in healthy older adults with and without objective memory decline. Journal of Clinical Experimental Neuropsychology , 30 ( 2 ), 245–257. [ PubMed ] [ Google Scholar ]
Wilson BA, Cockburn J, & Baddeley AD (1991). The Rivermead Behavioral Memory Test Suffolk, UK: Thames Valley Test Company. [ Google Scholar ]
Wilson BA, Cockburn J, Baddeley A, & Hiorns R (1989). The development and validation of a test battery for detecting and monitoring everyday memory problems. Journal of Clinical and Experimental Neuropsychology , 11 , 855–870. [ PubMed ] [ Google Scholar ]
Wilson RS, Beckett LA, Barnes LL, Schneider JA, Bach J, Evans DA et al. (2002). Individual differences in rates of change in cognitive abilities of older persons. Psychology and Aging , 17 ( 2 ), 179–193. [ PubMed ] [ Google Scholar ]
Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund LO … et al. (2004). Mild cognitive impairment—Beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment. Journal Internal Medicine , 256 ( 3 ), 240–246. [ PubMed ] [ Google Scholar ]
Wolf OT, Dziobek I, McHugh P, Sweat V, de Leon MJ, Javier E, & Convit A (2005). Subjective memory complaints in aging are associated with elevated cortisol levels. Neurobiology of Aging , 26 ( 10 ), 1357–1363. [ PubMed ] [ Google Scholar ]
Xu J, Kobayashi S, Yamaguchi S, Iijima K, Okada K, & Yamashita K (2000). American Journal Neuroradiology , 21 ( 1 ), 112–118 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Yaffe K, Fiocco AJ, Lindquist K, Vittinghoff E, Simonsick EM, Newman AB, Satterfield S, Rosano C, Rubin SM, Ayonayon HN, & Harris TB (2009). Predictors of maintaining cognitive function in older adults: The Health ABC Study. Neurology , 72 ( 23 ), 2029–2035. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Zelinski EM, & Burnight KP (1997). Sixteen-year longitudinal and time lag changes in memory and cognition in older adults. Psychology and Aging , 12 ( 3 ), 503–513. [ PubMed ] [ Google Scholar ]
Zuccala` G, Pedone C, Cesari M, Onder G, Pahor M, Marzetti E, Monaco MR, Cocchi A, Carbonin P, & Bernabei R (2003). The effects of cognitive impairment on mortality among hospitalized patients with heart failure. American Journal Medicine , 115 ( 2 ), 97–103. [ PubMed ] [ Google Scholar ]
Zunzunegui MV, Alvarado BE, Del Ser T, & Otero A (2003). Social networks, social integration, and social engagement determine cognitive decline in community-dwelling Spanish older adults. Journals Gerontology B: Psychological Sciences Social Sciences , 58 ( 2 ), S93–S100. [ PMC free article ] [ PubMed ] [ Google Scholar ]

Social Sciences

Individuals are exposed to gender stereotypes not only in daily interactions, but from mediums that pervade every society. Children’s books, for example, have an overwhelming predisposition to include gender-stereotypic material. This content can be influential as the children reading these books develop a sense of self (Taylor, 2009). While reading books and receiving external messages from a social point of view (e.g., women are nurses and men are doctors), children learn what it means to be a certain gender and in a stereotypical manner (Taylor, 2009). These implications of gender can be pervasive throughout an individual’s lifetime.

Gender stereotyping impacts many aspects of life. Good, Woodzicka, and Wingfield (2010) examined how stereotypes influenced performance in school. Freshman and sophomore high school students, age 13 to 17, were exposed to gender-stereotypical and counter-stereotypical images added to excerpts from their science textbooks (Good et al., 2010). Good et al. (2010) found that when females saw images of female scientists (counter-stereotypical) in their science books, they performed at a higher level than when using the same science textbook with male-stereotypical images. There was a corresponding finding for males viewing images of male scientists (Good et al., 2010). Simply viewing stereotypical and atypical photos in a science text changed how students of each gender performed with regard to the material contained therein.

Few differences are found in basic memory abilities between genders (Herrmann, Crawford, & Holdsworth, 1992), however, gender stereotypes do affect memory as Herrmann et al. (1992) showed in a study utilizing shopping lists. Hermann et al. (1992) had 48 undergraduate participants, age 18 to 23, intentionally remember shopping lists with specific titles (e.g., Groceries or Hardware Store) as the only difference. Females recalled more items from the grocery list and males recalled more items from the hardware list (Herrmann et al., 1992). Although female and male memory appears similar, researchers have identified and continue to investigate differences that exist between the two.

Signorella, Bigler, and Liben (1997) researched children’s memory in relation to their gender. A meta-analysis of memory research indicated children’s gender schemas showed a gender-congruent bias in delayed memory (Signorella et al., 1997). Liben and Signorella (1980) also found first and second grade children’s attitudes informed their memories. Liben and Signorella (1980) tested recognition memory with pictures of female and male individuals in stereotypical and atypical occupations. Liben and Signorella (1980) measured each participant for gender-stereotyped attitudes utilizing a previously published measure adapted for the purposes of their study. The measure indicated what each child believed only males could do, only females could do, and what both genders could do (Liben & Signorella, 1980). Children were shown pictures of individuals in gender-traditional, gender-nontraditional, and gender-neutral occupations and were later asked to identify which images had been seen previously (Liben & Signorella, 1980). Children who scored higher on gender stereotyping recognized more gender-traditional pictures than non-traditional (Liben & Signorella, 1980). Gender related memory appeared higher in the children who hold more gender stereotypical beliefs.

Humans have multiple ways of processing information and categorizing perceptions. One way is through the use of heuristics (i.e., rules of thumb) to categorize information for storage (Cherney, 2005). Individuals often use a type of heuristic called a schema (Bem, 1981). Bem (1981) explains schemas as a memory tool individuals use to better assign meaning to particular information. A person with a particular gender schema processes and codes information congruent with sex and gender related information (Bem, 1981). As Valian (2005) discussed, the human mind categorizes information for ease of recall. Schematic categories are the first steps to organizing memories. To avoid overload and in attempt to minimize categorization difficulties, as few categories as possible are used to accomplish the task at hand (Valian, 2005). Here, Valian (2005) points out individuals are not necessarily sexist when using gender categories to organize and process information, but rather vulnerable to the accessibility of such schemas in memory. Gender schemas are used to help organize information for later recall, as are other gender related processes.

Gender stereotypes can help facilitate memory (Wood, Groves, Bruce, Willoughby & Desmarais, 2003). Wood et al. (2003) examined undergraduate memory for the ability to draw upon gender stereotypical information when using an elaborative strategy to remember facts about females and males. Participants were given sentences about an individual, with a specific gender, doing an activity. Participants were, then, asked to elaborate on “why” the actor was doing the activity as way help recall the information later. Wood et al. (2003) found that, for the most part, memory regarding both genders was equal, however, both females and males would elaborate more with gender stereotypical information for the female actors. Research also showed when the stereotypical information helped define facts pertaining to females and males, information was recalled with more accuracy (Wood et al., 2003). Gender stereotypes help categorize information which produces improved recall performance for this information.

The brain forms patterns that aid in coding information and memory. These patterns form and link meaning to events and circumstances. In this way, the mind is selective in what is retained (Martinez, 2010). Martinez (2010) found human memory codes information primarily relevant to the individual and new information connects to what is already known. This theory of memory could lead to how stereotypes might aid in connections made during learning and memorization. For example, McKlevie (1981) found memory was linked to participant gender. Undergraduate participants were asked to view a series of different faces and later indicate previously seen faces from a large selection (McKlevie, 1981). Facial recognition studies showed an ease for both females and males to recognize faces congruent with their own gender (McKlevie, 1981). Memory, in a multitude of facets, shows improvement when gender congruent or drawing upon gender schemas and stereotypes.

Cherney (2005) found gender schemas influenced instructed memory (i.e., participants informed information would need to be recalled later) quite heavily when studying for recall of gender-stereotyped toys. Incidental memory (i.e., participants not informed information would need to be recalled later) was also affected by gender schemas, as stereotypes are often prompted when using heuristics and processing ambiguous information (Cherney, 2005). Cherney (2005) informed half the participants they would be asked to recall the toys they would soon be shown at a later time (instructed memory) and the other half were not informed they would be asked to recall the toys (incidental memory). Participants used gender schemas to process information and, thus, recalled more gender-congruent than gender-atypical toys (Cherney, 2005). Gender schemas appear to facilitate both female and male memory.

Chipman, Kimura, and Fraser (1998) investigated why females outperform males in recall tasks of information they were not specifically instructed to remember. Chipman et al. (1998) found evidence this pattern could be attributed to the verbal aspect of previous studies, as females generally perform better than males on verbal tasks. Cherney and Ryalls (1999) conducted a study to examine the theory females remember more information from their environment than males. Children, age 3 to 6, and adults were shown female- and male-stereotypical toys and objects in a toy room for the children, and an office for the adults, for 2 min. Females and males in each age group recalled more objects congruent with their own gender, but females did not show the hypothesized advantage over males (Cherney & Ryalls, 1999). Females and males show some differences in memory patterns, but both show a tendency for gender-congruent memory in different situations.

Wagner (1974) theorized incidental memory comes from development and experiences. For information to be coded into memory at all, the information must be focused and fixated on long enough to code (Castelhano & Henderson, 2005). If objects, events, facts, and information are not properly fixated on, they will not be stored in short-term memory or processed for promotion into long-term memory. Wagner (1974) also discussed incidental memory is selective for what is most relevant to an individual, potentially, including gender.

The current study examines the impact of gender on long-term incidental memory. Based on gender schema theories (Bem, 1981; Signorella et al., 1997) and gender-facilitated memory (Herrmann et al., 1992), we predict gender will improve female and male recall of gender-congruent words. , (4), 354-364.

Blank, H. (2005). Another look at retroactive and proactive interference: A quantitative analysis of conversion processes. , (2), 200-224.

Castelhano, M., & Henderson, J. (2005). Incidental visual memory for objects in scenes. , (6), 1017-1040.

Cherney, I. (2005). Children's and adults' recall of sex-stereotyped toy pictures: Effects of presentation and memory task. , (1), 11-27.

Cherney, I. D., & Ryalls, B. O. (1999). Gender-linked differences in the incidental memory of children and adults. , , 305-328.

Chipman, K., Kimura, D., & Fraser, S. (1998). An investigation of sex differences on incidental memory for verbal and pictorial material. , (4), 259-272.

Crawford, J. T., Leynes, P. A., Mayhorn, C. B., & Bink, M. L. (2004). Champagne, beer, or coffee? A corpus of gender-related and neutral words. , (3), 444-458.

Fernandes, M. A., & Grady, C. (2008). Age differences in susceptibility to memory interference during recall of categorizable but not unrelated word lists. , , 297-322.

Good, J., Woodzicka, J., & Wingfield, L. (2010). The effects of gender stereotypic and counter-stereotypic textbook images on science performance. , (2), 132-147.

Herrmann, D.J., Crawford, M., & Holdsworth, M. (1992). Gender-linked differences in everyday memory performance. , , 221-231.

Keller, J. (2007). Stereotype threat in classroom settings: The interactive effect of domain identification, task difficulty and stereotype threat on female students’ math performance. , , 323-338.

Kimura, D., & Clarke, P. (2002). Women’s advantage on verbal memory is not restricted to concrete words. , , 1137-1142.

Liben, L. S., & Signorella, M. L. (1980). Gender-related schemata and constructive memory in children. , (1), 11-18.

Loftus, E. (1997). Creating false memories. , (3), 70-75.

Martinez, M. (2010). Human memory: The basics. , (8), 62-65.

Maylor, E. A. (2002). Serial position effects in semantic memory: Reconstructing the order of verses of hymns. , (4), 816-820.

McDonald, J. H. (2009, September 6). Wilcoxon signed-rank test [Webpage]. Retrieved from http://udel.edu/~mcdonald/statsignedrank.html

McKelvie, S. (1981). Sex differences in memory for faces. , (1), 109-125.

Rivardo, M. G., Rhodes, M. E., Camaione, T. C., & Legg, J. M. (2011). Stereotype threat leads to reduction in number of math problems women attempt. , (1), 5-16.

Signorella, M. L., Bigler, R. S., & Liben, L. S. (1997). A meta-analysis of children’s memories for own-sex and other-sex information. , , 429-445.

Taylor, F. (2009). Content analysis and gender stereotypes in children’s books. , 5-22.

Valian, V. (2005). Beyond gender schemas: Improving the advancement of women in academia. , (3), 198-213.

Wagner, D. (1974). The development of short-term and incidental memory: A cross-cultural study. , (2), 389-396.

Wood, E., Groves, A., Bruce, S., Willoughby, T., & Desmarais, S. (2003). Can gender stereotypes facilitate memory when elaborative strategies are used? , (2), 169-180.

FEMALE	MALE
blush	beard
bra	cologne
doll	fire
flower	fishing
glitter	football
gorgeous	gamer
gossip	garage
jewelry	gun
lipstick	handsome
maid	lawnmower
makeup	mechanic
mascara	muscular
nurturing	police
pedicure	prince
perfume	truck
pink	tuxedo
pretty	war
purse	weapon
secretary	weights
skirt	wrestling

Note . 40-item word list generated for the purpose of Study 1. Words were rated for stereotype value and balanced for word length.

List of Female- and Male-Stereotypical Words used in Study 2

FEMALE	MALE
barbie	beard
blossom	bicep
blouse	bowtie
bouquet	boxing
bunny	burly
corsage	cigars
corset	devil
dress	fishing
fairy	goatee
flowers	hairy
gossip	hockey
makeup	hunting
mascara	necktie
nanny	pirate
petite	plumber
pretty	poker
purse	sheriff
rainbow	soldier
skirt	umpire
teacher	veteran

Note . 40-item word list (balanced for stereotype value and word length) sourced from a 600-item gendered and neutral word list (Crawford et al., 2004) designed to balance stereotype value.

Save Citation » (Works with EndNote, ProCite, & Reference Manager)

Jarschke, A. K., & Frederick, C. M. (2014). "The Influence of Gender on Long-Term Incidental Memory." Inquiries Journal/Student Pulse , 6 (05). Retrieved from http://www.inquiriesjournal.com/a?id=897

Jarschke, Anna K., and Christina M. Frederick. "The Influence of Gender on Long-Term Incidental Memory." Inquiries Journal/Student Pulse 6.05 (2014). < http://www.inquiriesjournal.com/a?id=897 >

Jarschke, Anna K., and Christina M. Frederick. 2014. The Influence of Gender on Long-Term Incidental Memory. Inquiries Journal/Student Pulse 6 (05), http://www.inquiriesjournal.com/a?id=897

JARSCHKE, A. K., & FREDERICK, C. M. 2014. The Influence of Gender on Long-Term Incidental Memory. Inquiries Journal/Student Pulse [Online], 6. Available: http://www.inquiriesjournal.com/a?id=897

From the Inquiries Journal Blog

Related reading, monthly newsletter signup.

The newsletter highlights recent selections from the journal and useful tips from our blog.

Latest in Psychology

What are you looking for, from our blog.

Inquiries Journal

Disclaimer: content on this website is for informational purposes only. It is not intended to provide medical or other professional advice. Moreover, the views expressed here do not necessarily represent the views of Inquiries Journal or Student Pulse, its owners, staff, contributors, or affiliates.

Need an Account?

Forgot password? Reset your password »

Effects of sampling healthy versus unhealthy foods on subsequent food purchases

Original Empirical Research
Published: 04 September 2024

Cite this article

does gender affect short term memory experiment

Dipayan Biswas ORCID: orcid.org/0000-0002-6015-8585 1 ,
Annika Abell 2 ,
Mikyoung Lim 1 ,
J. Jeffrey Inman 3 &
Johanna Held 4

Food sampling at retail stores and restaurants (e.g., amuse bouche) is a widespread practice. These food samples vary considerably in healthfulness levels. Prior research has primarily focused on the effects of sampling on evaluations and sales of the sampled item. However, can there be unintended consequences of sampling a healthy versus an unhealthy item on subsequent purchases of other food items? Also, would the degree of dissimilarity between the sampled item and subsequent options moderate the effects? The results from a series of experiments, including four studies conducted in field settings, show that sampling a healthy (vs. unhealthy) item paradoxically leads to greater subsequent purchase/choice of unhealthy foods – but only when consumers perceive a relatively high level of dissimilarity between the sampled item and subsequent options. This effect reverses when the sampled food and subsequent options are perceived as being relatively low on dissimilarity (i.e., high on similarity).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

Get 10 units per month
Download Article/Chapter or eBook
1 Unit = 1 Article or 1 Chapter
Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Annas, J. (2004). Being virtuous and doing the right thing. Proceedings and Addresses of the American Philosophical Association, 78 (2), 61–75.

Article Google Scholar

Auer, J., & Papies, D. (2020). Cross-price elasticities and their determinants: A meta-analysis and new empirical generalizations. Journal of the Academy of Marketing Science, 48 (3), 584–605.

Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A., Barndollar, K., & Trötschel, R. (2001). The automated will: Nonconscious activation and pursuit of behavioral goals. Journal of Personality and Social Psychology, 81 (6), 1014–1027.

Biswas, D., & Szocs, C. (2019). The smell of healthy choices: Cross-modal sensory compensation effects of ambient scent on food purchases. Journal of Marketing Research, 56 (1), 123–141.

Biswas, D., Grewal, D., & Roggeveen, A. (2010). How the order of sampled experiential products affects choice. Journal of Marketing Research, 47 (3), 508–519.

Biswas, D., Labrecque, L. I., Lehmann, D. R., & Markos, E. (2014). Making choices while smelling, tasting, and listening: The role of sensory (Dis) similarity when sequentially sampling products. Journal of Marketing, 78 (1), 112–126.

Biswas, D., Lund, K., & Szocs, C. (2019). Sounds like a healthy retail atmospheric strategy: Effects of ambient music and background noise on food sales. Journal of the Academy of Marketing Science, 47 (1), 37–55.

Blanken, I., van de Ven, N., & Zeelenberg, M. (2015). A meta-analytic review of moral licensing. Personality and Social Psychology Bulletin, 41 (4), 540–558.

Brown, R. E., Sharma, A. M., Ardern, C. I., Mirdamadi, P., Mirdamadi, P., & Kuk, J. L. (2016). Secular differences in the association between caloric intake, macronutrient intake, and physical activity with obesity. Obesity Research & Clinical Practice, 10 (3), 243–255.

Carels, R. A., Harper, J., & Konrad, K. (2006). Qualitative perceptions and caloric estimations of healthy and unhealthy foods by behavioral weight loss participants. Appetite, 46 (2), 199–206.

Chandon, P., & Wansink, B. (2007). The biasing health halos of fast-food restaurant health claims: Lower calorie estimates and higher side-dish consumption intentions. Journal of Consumer Research, 34 (3), 301–314.

Chernev, A. (2011a). Semantic anchoring in sequential evaluations of vices and virtues. Journal of Consumer Research, 37 (5), 761–774.

Chernev, A. (2011b). The dieter’s paradox. Journal of Consumer Psychology, 21 (2), 178–183.

Chernev, A., & Gal, D. (2010). Categorization effects in value judgments: Averaging bias in evaluating combinations of vices and virtues. Journal of Marketing Research, 47 (4), 738–747.

Conrad, P. (1994). Wellness as virtue: Morality and the pursuit of health. Culture, Medicine and Psychiatry, 18 (3), 385–401.

Dhar, R., & Simonson, I. (1999). Making complementary choices in consumption episodes: Highlighting versus balancing. Journal of Marketing Research, 36 (1), 29–44.

Dhar, R., & Wertenbroch, K. (2000). Consumer choice between hedonic and utilitarian goods. Journal of Marketing Research, 37 (1), 60–71.

Dhar, R., Nowlis, S. M., & Sherman, S. J. (1999). Comparison effects on preference construction. Journal of Consumer Research., 26 (3), 293–306.

Effron, D. A., & Conway, P. (2015). When virtue leads to villainy: Advances in research on moral self-licensing. Current Opinion in Psychology, 6 , 32–35.

Finkelstein, S. R., & Fishbach, A. (2010). When healthy food makes you hungry. Journal of Consumer Research, 37 (3), 357–367.

Fitzsimons, G. M., Chartrand, T. L., & Fitzsimons, G. J. (2008). Automatic effects of brand exposure on motivated behavior: How apple makes you “think different.” Journal of Consumer Research, 35 (1), 21–35.

Geyskens, K., Dewitte, S., Pandelaere, M., & Warlop, L. (2008). Tempt me just a little bit more: The effect of prior food temptation actionability on goal activation and consumption. Journal of Consumer Research, 35 (4), 600–610.

Gholipour, B. (2013). Physical activity and obesity: Both rising. Retrieved December 1, 2023, from http://www.livescience.com/38067-activity-obesity-rising.html

Harris, J. L., Bargh, J. A., & Brownell, K. D. (2009). Priming effects of television food advertising on eating behavior. Health Psychology, 28 (4), 404.

Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach . Guilford.

Google Scholar

Herman, C. P., & Mack, D. (1975). Restrained and unrestrained eating. Journal of Personality, 43 (4), 647–660.

Higgins, E. T. (2000). Making a good decision: Value from fit. American Psychologist, 55 (11), 1217–1230.

Higgins, E. T., Bargh, J. A., & Lombardi, W. J. (1985). Nature of priming effects on categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11 (1), 59–69.

Khan, U., & Dhar, R. (2006). Licensing effect in consumer choice. Journal of Marketing Research, 43 (2), 259–266.

Kim, J., Novemsky, N., & Dhar, R. (2013). Adding small differences can increase similarity and choice. Psychological Science, 24 (2), 225–229.

Kim, J., Kim, J. E., & Park, J. (2018). Effects of physical cleansing on subsequent unhealthy eating. Marketing Letters, 29 , 165–176.

Krishna, A., & Hagen, L. (2019). Out of proportion? The role of leftovers in eating-related affect and behavior. Journal of Experimental Social Psychology, 81 , 15–26.

Labroo, A. A., & Patrick, V. M. (2009). Psychological distancing: Why happiness helps you see the big picture. Journal of Consumer Research, 35 (5), 800–809.

Laran, J., & Janiszewski, C. (2009). Behavioral Consistency and Inconsistency in the Resolution of Goal Conflict. Journal of Consumer Research, 35 (6), 967–984.

Liberman, N., Trope, Y., & Stephan, E. (2007). Psychological distance. In A. W. Kruglanski & E. T. Higgins (Eds.), Social psychology: Handbook of basic principles (pp. 353–381). The Guilford Press.

Liu, P. J., Haws, K. L., Lamberton, C., Campbell, T. H., & Fitzsimons, G. J. (2015). Vice-virtue bundles. Management Science, 61 (1), 204–228.

McArdle, M. (2016). Virtuous eating: Feeling-good posturing over food. Retrieved December 1, 2023, from https://www.sandiegouniontribune.com/opinion/commentary/sdut-food-posturing-moral-farm-fresh-2016apr21-story.html

Merritt, A. C., Effron, D. A., & Monin, B. (2010). Moral self-licensing: When being good frees us to be bad. Social and Personality Psychology Compass, 4 (5), 344–357.

Mick, D. G., & DeMoss, M. (1990). Self-gifts: Phenomenological insights from four contexts. Journal of Consumer Research, 17 (3), 322–332.

Monin, B., & Miller, D. T. (2001). Moral credentials and the expression of prejudice. Journal of Personality and Social Psychology, 81 (1), 33–43.

Mukhopadhyay, A., & Johar, G. V. (2009). Indulgence as self-reward for prior shopping restraint: A justification-based mechanism. Journal of Consumer Psychology, 19 (3), 334–345.

Nowlis, S. M., & Shiv, B. (2005). The influence of consumer distractions on the effectiveness of food-sampling programs. Journal of Marketing Research, 42 (2), 157–168.

Okada, E. M. (2005). Justification effects on consumer choice of hedonic and utilitarian goods. Journal of Marketing Research, 42 (1), 43–53.

Perry, L. C., Perry, D. G., & English, D. (1985). Happiness: When does it lead to self-indulgence and when does it lead to self-denial? Journal of Experimental Child Psychology, 39 (2), 203–211.

Pinsker, J. (2014). The psychology behind Costco’s free samples. Retrieved December 1, 2023, from http://www.theatlantic.com/business/archive/2014/10/the-psychology-behind-costcos-free-samples/380969

Polivy, J., & Herman, C. P. (2020). Overeating in restrained and unrestrained eaters. Frontiers in Nutrition, 7 , 30.

Ramanathan, S., & Williams, P. (2007). Immediate and delayed emotional consequences of indulgence: The moderating influence of personality type on mixed emotions. Journal of Consumer Research, 34 (2), 212–223.

Ratner, R. K., & Kahn, B. E. (2002). The impact of private versus public consumption on variety-seeking behavior. Journal of Consumer Research, 29 (2), 246–257.

Redden, J. P., & Haws, K. L. (2013). Healthy satiation: The role of decreasing desire in effective self-control. Journal of Consumer Research, 39 (5), 1100–1114.

Romero, M., & Biswas, D. (2016). Healthy-left, unhealthy-right: Can displaying healthy items to the left (versus right) of unhealthy items nudge healthier choices? Journal of Consumer Research, 43 (1), 103–112.

Sanchez, J., Abril, C., & Haenlein, M. (2020). Competitive spillover elasticities of electronic word of mouth: An application to the soft drink industry. Journal of the Academy of Marketing Science, 48 (2), 270–287.

Scott, M. L., Nowlis, S. M., Mandel, N., & Morales, A. C. (2008). The effects of reduced food size and package size on the consumption behavior of restrained and unrestrained eaters. Journal of Consumer Research, 35 (3), 391–405.

Shiv, B., & Nowlis, S. M. (2004). The effect of distractions while tasting a food sample: The interplay of informational and affective components in subsequent choice. Journal of Consumer Research, 31 (3), 599–608.

Suher, J., Raghunathan, R., & Hoyer, W. D. (2016). Eating healthy or feeling empty? How the “healthy= less filling” intuition influences satiety. Journal of the Association for Consumer Research, 1 (1), 26–40.

Wadhwa, M., Shiv, B., & Nowlis, S. M. (2008). A bite to whet the reward appetite: The influence of sampling on reward-seeking behaviors. Journal of Marketing Research, 45 (4), 403–413.

Webb, E. C., & Shu, S. B. (2018). The effect of perceived similarity on sequential risk taking. Journal of Marketing Research, 55 (6), 916–933.

Wilcox, K., Vallen, B., Block, L., & Fitzsimons, G. J. (2009). Vicarious goal fulfillment: When the mere presence of a healthy option leads to an ironically indulgent decision. Journal of Consumer Research, 36 (3), 380–393.

Wohl, J. (2021). How the return of in-store sampling is going over with consumers. Ad Age , last retrieved December 1, 2023, from https://adage.com/article/cmo-strategy/how-return-store-sampling-going-over-consumers/2341906

Yum Sin, N. L., & Vartanian, L. R. (2012). Is counter-regulation among restrained eaters a result of motivated overeating? Appetite, 59 , 488–439.

Download references

Author information

Authors and affiliations.

University of South Florida, Tampa, FL, 33620, USA

Dipayan Biswas & Mikyoung Lim

University of Tennessee, Knoxville, TN, 37996, USA

Annika Abell

University of Pittsburgh, Pittsburgh, PA, 15260, USA

J. Jeffrey Inman

Bosch Power Tools and Bayreuth University, Bayreuth, Germany

Johanna Held

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dipayan Biswas .

Ethics declarations

Conflict of interest.

The authors have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Kelly Goldsmith served as Area Editor for this article.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 832 KB)

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Biswas, D., Abell, A., Lim, M. et al. Effects of sampling healthy versus unhealthy foods on subsequent food purchases. J. of the Acad. Mark. Sci. (2024). https://doi.org/10.1007/s11747-024-01047-4

Download citation

Received : 28 July 2020

Accepted : 24 July 2024

Published : 04 September 2024

DOI : https://doi.org/10.1007/s11747-024-01047-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Retail sampling
Healthy and unhealthy foods
Product choice
Dissimilarity
Field studies
Find a journal
Publish with us
Track your research

IMAGES

Does Gender Affect Short Term Memory? by Davenia Boothe
Gender Difference in Short Term Memory by Jisoo Park on Prezi
Does Gender Affect Short Term Memory by Hamad Al Huwaidi
Gender and short term memory by Bethany Houston
Effect of Gender On Short Term Memory Finished
Does Gender Affect Memory? by Valorie Henderson

VIDEO

Does Gender Affect Ministry Debunking Misconceptions
Love vs Submission in Marriage
Are You Suffering from Digital Dementia?
Does gender affect reaction time?
Short term memory Experiment 1
SHORT TERM MEMORY PSYCHOLOGY EXPERIMENT B.A,M.A

COMMENTS

PDF Short Term Memory Based on Gender
The results from our experiments show that gender has an affect on short term memory. We ran the same experiment with all subjects and there was a significant gap in the accuracy of men in comparison to women.
Gender differences in memory test performance among children and
Abstract Gender differences among children and adolescents were examined on 14 separate measures of short-term memory. A nationally stratified sample of 1,279 children and adolescents, 637 males and 642 females, ranging in age between 5 and 19 years, were assessed on the 14 subtests of the Test of Memory and Learning (TOMAL).
The Role of Sex in Memory Function: Considerations and Recommendations
Abstract There is evidence to suggest that biological sex plays a critical role in memory function, with sex differentially influencing memory type. In this review, we detail the current evidence evaluating sex-specific effects on various memory types.
Are Women Outperforming Men in Short-Term Memory?
Abstract. Enormous amount of experimental research on short-term memory have been conducted through the years. This research is focused on highlighting gender differences and making inferences ...
PDF WHO REMEMBERS WHAT?: GENDER DIFFERENCES IN MEMORY
To place these questions in perspective, it will be helpful to know something about how memory researchers think about memory. Consequently, in the process of outlining the influence of gender differences on various aspects of human memory, we will attempt to familiarize the reader with important concepts and procedures that have been used to guide memory research. One such concept, the ...
PDF Gender Differences in Memory Recall Among College Students
Overall, women scored higher in performance on the auditory memory task compared to men. It was determined that adolescents and male adults scored higher in performance on both visual memory tasks. The purpose of the present study was to examine gender differences in memory recall among college students.
Sex-related differences in attention and memory
Conclusions Current study showed no sex differences in the mean values of cognition, whereas higher intra-individual variability of short-term memory and attention switching was identified in women, indicating that their performance was lower on these cognitive abilities.
Memory & Cognition: What difference does gender make?
Small but significant gender differences, typically favoring women, have pre-. viously been observed in experiments measuring human episodic memory. performance. In three studies measuring episodic memory, we compared. performance levels for men and women. Secondary analysis from a paired-.
New perspectives on sex differences in learning and memory
Females have historically been disregarded in memory research, including the thousands of studies examining roles for the hippocampus, medial prefrontal cortex, and amygdala in learning and memory. Even when included, females are often judged based on male-centric behavioral and neurobiological standards, generating and perpetuating scientific stereotypes that females exhibit worse memories ...
Remember Memory: The Effect of Age and Gender on Visual Short Term Memory
This project is designed to test the extent of people's short-term memory, and reveal how age and gender cause people's minds to function differently. The hypothesis was if the age of female test subjects decreases, then their visual short-term memory will improve because as you age, your ability to recall recent information worsens, and statistics show that females tend to recall information ...
Gender differences in episodic memory and visual working memory
Abstract Analysing the relationship between gender and memory, and examining the effects of age on the overall memory-related functioning, are the ongoing goals of psychological research. The present study examined gender and age group differences in episodic memory with respect to the type of task.
Effects of Age and Gender on Recall and Recognition Discriminability
Research also has shown that women outperform men on verbal episodic memory tests. However, gender differences in recall and recognition discriminability and the age-by-gender interaction on these constructs have not been thoroughly examined.
Women versus Men: A Critical Comparison for Understanding the
The authors present results from eight studies examining differences in brain activation between women and men, during performance of long-term memory tasks:, memory for words, faces and shapes, autobiographical memory (visual/verbal cues) and memory for abstract shapes. Using functional magnetic resonance imaging (fMRI), findings indicate ...
University of Tennessee at Chattanooga
Explore research articles on gender differences in memory recall and other topics at the University of Tennessee at Chattanooga.
Sex Differences In Memory: Women Better Than Men At ...
Psychologists determine significant sex differences in episodic memory, a type of long-term memory based on personal experiences, favoring women. Specific results indicated that women excelled in ...
Gender identity better than sex explains individual differences in
Advances in the literature of sex-related differences in autobiographical memory increasingly tend to highlight the importance of psychosocial factors such as gender identity, which may explain these differences better than sex as a biological factor.
Size of Audience, Gender, and Digit-Rate Effects on Short-Term Memory
In summary, the specific aims of the present exploratory study were (1) to measure effects of size of audience on short-term memory for digit-span, (2) to study how gender effects may be influenced by size of audience, and. (3) to replicate systematically the effects of digit rate on short-term memory. METHOD. Subjects.
Gender-linked differences in everyday memory performance
The results were consistent with the gender stereotypes, i.e. women recalled more of the shopping list than men whereas men recalled more of the directions than women. The second experiment investigated whether memory performance would be influenced by mere changes in the label of materials in memory tasks to be biased toward male or female ...
Gender differences in short term memory and perception
But these possessions are influenced by various physical, emotional and environmental factors. So, the present study planned to investigate the influence of gender on memory and perceptual ability. Results revealed that short term memory showed statistically significant increase in females compared to males.
PDF Relationship between Short- Term Memory and Gender of 18- 20 Years
Research Paper. Term Memory and Gender of 18- 20YearsSwagata Adyalkar1*ABSTRACTThe aim of the research was to assess Relati. nship between short- term memory (STM) and gender of 18- 20 years. Short term memory is a storage capacity which is extremely small. Memory is very important because cognitive tasks can be completed o.
Memory Performance and Affect: Are there Gender Differences in
Even though males had greater years of education, they used fewer compensatory memory strategies. The observed gender differences in memory were subjective evaluations, specifically metamemory. Age was not a significant predictor of cognition or memory performance, nor did males have greater memory impairment than females.
Does Gender affect Memory? by Kathleen Steeves on Prezi
Conclusion This experiment was done on 10 students (5 girls, 5 boys) to test their short term memory abilities. The focus is exploring the possibilities of gender differences in short term memory.
The Influence of Gender on Long-Term Incidental Memory
Given the prevalence of gender stereotypical information, this article examines how gender effects the information individuals unintentionally retain in their short term memory. Individuals are exposed to gender stereotypes not only in daily interactions, but from mediums that pervade every society.
Effects of sampling healthy versus unhealthy foods on ...
Food sampling at retail stores and restaurants (e.g., amuse bouche) is a widespread practice. These food samples vary considerably in healthfulness levels. Prior research has primarily focused on the effects of sampling on evaluations and sales of the sampled item. However, can there be unintended consequences of sampling a healthy versus an unhealthy item on subsequent purchases of other food ...