ArticlePDF Available

Mental Exercising Through Simple Socializing: Social Interaction Promotes General Cognitive Functioning


Abstract and Figures

Social interaction is a central feature of people's life and engages a variety of cognitive resources. Thus, social interaction should facilitate general cognitive functioning. Previous studies suggest such a link, but they used special populations (e.g., elderly with cognitive impairment), measured social interaction indirectly (e.g., via marital status), and only assessed effects of extended interaction in correlational designs. Here the relation between mental functioning and direct indicators of social interaction was examined in a younger and healthier population. Study 1 using survey methodology found a positive relationship between social interaction, assessed via amount of actual social contact, and cognitive functioning in people from three age groups including younger adults. Study 2 using an experimental design found that a small amount of social interaction (10 min) can facilitate cognitive performance. The findings are discussed in the context of the benefits social relationships have for so many aspects of people's lives.
Content may be subject to copyright.
Personality and Social Psychology Bulletin
DOI: 10.1177/0146167207310454
2008; 34; 248 Pers Soc Psychol Bull
Oscar Ybarra, Eugene Burnstein, Piotr Winkielman, Matthew C. Keller, Melvin Manis, Emily Chan and Joel Rodriguez
Mental Exercising Through Simple Socializing: Social Interaction Promotes General Cognitive
The online version of this article can be found at:
Published by:
On behalf of:
Society for Personality and Social Psychology, Inc.
can be found at:Personality and Social Psychology Bulletin Additional services and information for Email Alerts: Subscriptions:
SAGE Journals Online and HighWire Press platforms):
(this article cites 62 articles hosted on the Citations
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
Mental Exercising Through Simple
Socializing: Social Interaction Promotes
General Cognitive Functioning
Oscar Ybarra
University of Michigan
Eugene Burnstein
University of Michigan, University of Warsaw
Piotr Winkielman
University of California, San Diego
Matthew C. Keller
University of Colorado, Boulder
Melvin Manis
University of Michigan
Emily Chan
Colorado College
Joel Rodriguez
U.S. Federal Bureau of Investigation
escartes’ famous philosophical statement, “Cogito
ergo sum” or “I think therefore I am” captures a
core aspect of people’s identity, which also seems to be
reflected in the moniker we have placed on our own
species, Homo sapiens, the wise or knowing man. It
appears that the capacity to cogitate holds a special
place in people’s lives. From the first weeks of human
life parents rush to buy whatever new gadgets will allow
their children to blossom mentally. And at the sunset
stages of life, older adults scramble doing various brain
Authors’ Note: This research was supported in part by a seed grant
from the Center for Aging and Cognition: Health, Education, &
Training (CACHET) at the Institute for Social Research and National
Science Foundation Grant BCS-0217294 to Piotr Winkielman. We
thank Amy Kiefer for her help in conducting literature searches, Lynne
Schaberg for her assistance with some of the analyses, and Hal Pashler
and Tim Rickard for helpful discussions. Correspondence should be
addressed to Oscar Ybarra, University of Michigan, Department of
Psychology and Research Center for Group Dynamics, 530 Church
Street, Ann Arbor, MI 48109-1109; e-mail:
PSPB, Vol. 34 No. 2, February 2008 248-259
DOI: 10.1177/0146167207310454
© 2008 by the Society for Personality and Social Psychology, Inc.
Social interaction is a central feature of people’s life and
engages a variety of cognitive resources. Thus, social
interaction should facilitate general cognitive function-
ing. Previous studies suggest such a link, but they used
special populations (e.g., elderly with cognitive impair-
ment), measured social interaction indirectly (e.g., via
marital status), and only assessed effects of extended
interaction in correlational designs. Here the relation
between mental functioning and direct indicators of
social interaction was examined in a younger and
healthier population. Study 1 using survey methodology
found a positive relationship between social interaction,
assessed via amount of actual social contact, and cogni-
tive functioning in people from three age groups includ-
ing younger adults. Study 2 using an experimental
design found that a small amount of social interaction
(10 min) can facilitate cognitive performance. The find-
ings are discussed in the context of the benefits social
relationships have for so many aspects of people’s lives.
Keywords: cognitive performance; socializing; mental exer-
cise; social intelligence; executive function; group
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
teasers to stave off mental decline. In this research we
propose that more social factors, like simply engaging in
social interaction, can also play a role in helping people
stay mentally sharp.
Up until recent history, people’s lives have been dom-
inated by being socially connected and having relation-
ships with others (Aries, 1962). In their classic book,
Cartwright and Zander (1953) allude to this by refer-
ring to an alien who is visiting earth for the first time.
They note that the alien would be impressed by the
amount of time people spend doing things with others.
For example, people tend to gather and live in the same
dwellings, satisfy various biological needs within the
group, depend on the same economic support, rear
children together, and generally care for one another.
People’s education also tends to occur in groups, and
much of the work that occurs in the world is carried out
by people performing duties and activities interdepen-
dently (Cartwright & Zander, 1953). Human life has
traditionally been a socially connected one, possibly one
with deep phylogenetic roots (cf. Jolly, 1966).
In more recent times, though, social connections
among people seem to be on the decline. As Putnam
(2000) has noted in his book, Bowling Alone, there has
been much loss in social capital in the United States over
the last couple of decades. This is evident, for example,
in the decline of the number of organizations people are
part of and the number of meetings they attend that
involve being around others and having face-to-face
interaction. There has also been a decline in family din-
ners and the occasion with which people have friends
over for a visit. Other research indicates that people
have fewer close others they can talk to about their
innermost thoughts and feelings (McPherson, Smith-
Lovin, & Brashears, 2006). Our society appears to be in
a state of social decline, not one in which the environ-
ment is chaotic and people fear for their lives, but one
in which people have fewer interactions and relations
with others.
Having few social connections has been shown to
have important implications for people’s health. For
example, a preference for being with others is strongly
correlated with well-being (Sinha & Verma, 1990;
Triandis et al., 1986), whereas people with low social
support are more prone to mental illness (McGuire &
Raleigh, 1986), depression (Gladstone, Parker, Malhi,
& Wilhelm, 2007), and lower immunocompetence
(Kiecolt-Glaser, Garner, Speicher, Penn, & Glaser
1984). In addition, socially rejected individuals have
been shown to suffer from self-regulation deficits (e.g.,
Baumeister, DeWall, Ciarocco, & Twenge, 2005).
Studies have also shown that fewer social connections
are related to the risk of death even after controlling for
level of health (House, Landis, & Umberson, 1988).
Social interaction and relationships add to people’s
quality of life in numerous ways.
But social interaction might be good not only for
physical health. In the present research we propose that
social interaction can also facilitate cognitive function-
including long-term and short-term effects on
performance. This prediction is grounded in several
lines of research.
Research in social cognition has long emphasized
that the inferential processes underlying social interac-
tion involve a complex set of computations (Heider,
1958; Mead, 1934). For example, a simple exchange of
views between two people requires that they pay atten-
tion to each other, maintain in memory the topic of the
conversation and respective contributions, adapt to
each other’s perspective, infer each other’s beliefs and
desires, assess the situational constraints acting on them
at the time, and inhibit irrelevant or inappropriate
behavior. Some of these processes are automatic, but
others depend on limited-capacity cognitive resources
often subsumed by the term executive functions, which
include capacities such as attention, working memory,
and cognitive control (Shallice, 1988; E. E. Smith &
Jonides, 1999). For example, a host of social cognition
studies has shown that loading working memory can
influence many strategic aspects of social inference
(Gilbert, Pelham, & Krull, 1988; Trope, 1986).
The involvement of general cognitive capacities in
social inference is also documented by social cognitive
neuroscience (Lieberman, Gaunt, Gilbert, & Trope,
2002). For example, the understanding of others’ beliefs
and desires relies on the prefrontal cortex, especially
medial and orbitofrontal cortex—regions that are tradi-
tionally associated with executive function, especially
working memory and attention (e.g., Baron-Cohen &
Ring, 1994; Brunet, Sarfati, Hardy-Bayle, & Decety,
2000; Fletcher et al., 1995; Frith & Frith, 1999; Royall
et al., 2002; E. E. Smith & Jonides, 1999). Similarly,
control over the expression of beliefs and attitudes is
subserved by prefrontal and parietal regions, the same
regions involved in general cognitive control (Amodio
& Frith, 2006; Nielson, Langenecker, & Garavan,
2002). The critical role of these regions in social inter-
action is illustrated by the profound effects of their dam-
age to social reasoning and behavior (Blumberg
et al., 1999; Damasio, 1994; Kling, 1986; Kling & Steklis,
1976; Moll, de Oliveira-Souza, Bramati, & Grafman,
2002; Myers, Swett, & Miller, 1973; Stone, Baron-
Cohen, & Knight, 1998). Of course, there is some dif-
ferentiation of neural mechanisms supporting social and
nonsocial cognition, but for the purpose of our argu-
ment here, we primarily want to highlight the enormous
importance of executive functions for social cognition
(Adolphs, 2001).
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
Given the evidence for the functional and neural
overlap between social and general cognitive function-
ing, there is remarkably little research testing whether
social interaction can promote cognitive capacities. One
reason for this could be that until recently general cog-
nitive capacities were seen as relatively fixed and not
subject to change, at least in nonclinical populations.
However, this picture is changing in light of several
pieces of evidence. For one, research in cognitive neuro-
science has shown that the actual capacity of working
memory and attention can increase with training and
facilitate performance in novel, unrelated tasks (Olesen,
Westerberg, & Klingberg, 2004; Posner & Rothbart,
2005). Importantly, these effects can be observed in typ-
ical, as well as clinical, populations and can occur after a
few weeks of training, reflecting more permanent effects.
These findings are especially relevant to the present Study
1, which explores long-term effects. However, beneficial
effects of social interaction on cognitive performance
should also be observed with short training episodes, as
we examine in Study 2. In this case, the underlying
mechanisms are better characterized as “resource prim-
ing” or preactivation of general mental operations
involved in both social interaction and cognitive tasks.
This is analogous to how the activation of more specific
procedures and concepts in memory readies semantically
related knowledge and behavioral patterns (Anderson,
1993; E. R. Smith, 1994). Such a mechanism presum-
ably underlies transfer benefits across tasks that share
little common content but engage the same general cog-
nitive skills, such as working memory and attention
(Baddeley, 2002; Singley & Anderson, 1989). In short,
the emerging evidence makes it plausible that engaging
in social interaction should have beneficial effects on
general cognitive skills, even with nonclinical popula-
tions, and that these effects should be observed in both
the long term and short term.
A Possible Relationship Between Social
Interaction and Cognitive Functioning
Some research has considered the relationship
between social interaction and cognitive functioning.
However, in those studies social interaction is usually
not the main focus of the research. Furthermore, many
times it is assessed with very indirect social indicators
(third-party reports) or it is conflated with a variety of
indicators, some of which are poor proxies of actual
social contact (e.g., marital status). In addition, this
research is restricted to elderly populations and tends to
focus on extreme forms of cognitive impairment or even
disease rather than the typical range of mental func-
tioning. Nevertheless, available studies are at least sug-
gestive of a relationship between social interaction and
cognitive functioning.
For example, investigators found that the risk of
developing Alzheimer’s disease was lower for people
who were described by an informant as socially active
than those described as socially inactive (Kondo &
Yamashita, 1990). A recent study has also shown that
the risk of developing Alzheimer’s disease was higher for
people reporting feeling lonely (Wilson et al., 2007).
These findings are additionally buttressed by lab experi-
ments showing that simply imagining that one has been
socially rejected negatively affects cognitive performance
(Baumeister, Twenge, & Nuss, 2002). Additional studies
have also shown that very general indicators of people’s
social environment, such as recreational activities, were
related to a lower incidence of dementia (Fabrigoule
et al., 1995).
Other research has attempted to study more specifi-
cally the features of people’s social networks in relation
to the development of dementia (Fratiglioni, Wand,
Ericsson, Maytan, & Winblad, 2000). In this research
the measure of people’s social networks included mari-
tal status and whether a person lived with a partner or
other persons, number of close social ties (relatives and
close friends), frequency of contact with these various
parties, and the person’s satisfaction with these con-
tacts. The results indicated that low levels of social
engagement were related to an increased risk of devel-
oping dementia.
Finally, some research suggests that at least among
the elderly, social factors are related to general cognitive
functioning and not just extreme impairments. For
example, in studies of noninstitutionalized elderly per-
sons and a high-functioning group of elderly partici-
pants, researchers found that greater social engagement
(e.g., presence of spouse, contact with friends, group
memberships) was associated with better cognitive func-
tioning (Bassuk, Glass, & Berkman, 1999; Seeman,
Lusignolo, Albert, & Berkman, 2001; see also Arbuckle,
Gold, Andres, Schwartzman, & Chaikelson’s, 1992,
study on elderly men).
The Present Research
The preceding studies are encouraging and point to a
potentially important relationship between social fac-
tors and cognitive functioning. However, these studies
bring up various issues. As noted earlier, in some of
these studies vague activities are taken as indicators of
degree of social interaction. In addition, many times a
person’s marital status was not controlled for but included
as an indicator of degree of social interaction and com-
bined with indicators that varied in specificity (e.g., mari-
tal status vs. frequency of contact). Furthermore, the
samples were limited to older adults. Finally, no study
that we know of has shown that social interaction can
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
have a direct, causal influence on cognitive performance
and that such effects can result from even small
amounts of social interaction, a finding with potentially
important implications.
In addition to the preceding points, the available
research also leaves unclear two critical issues. The first
issue is the generality of the social interaction effect.
Does social interaction create cognitive benefits only
after substantial periods of time, say months, years, or
decades, and is this what is being captured with the
study of older adults? Or could the positive effects of
social interaction be apparent even for people who have
undergone little cognitive decline? Furthermore, can
such cognitive benefits be visible only after a short
social interaction episode? The second critical issue is
the causal direction. All the social interaction studies so
far have looked at this via correlational measures. This
obviously raises the issue of whether social interaction
per se promotes cognitive functioning or cognitive func-
tioning promotes interaction.
The present research sought to clarify these issues. As
a first step, Study 1 used a survey methodology to
examine whether there is a relationship between cogni-
tive functioning and specific measures of social interac-
tion, rather than nonspecific indicators. Furthermore,
this study focused on cognitive functioning, not extreme
impairment, and whether it relates to social interaction
across different age groups, not just older adults. If a
relationship exists between social interaction and cogni-
tive functioning across age groups, this would suggest
that anyone can stay sharper mentally by engaging in
more social interaction, assuming the relationship is
causal in nature. Study 2 focused on this latter issue by
examining the cognitive effects of social interaction in a
group of younger adults. If cognitive benefits occur, this
would provide a conservative causal test of the direction
of influence from social interaction to better cognitive
functioning given that on average younger adults have
undergone little cognitive decline (Park, 2000), in addi-
tion to showing that small amounts of interaction can
have cognitive benefits.
Study population. The data for Study 1 came from
personal interviews from the Survey of Americans’
Changing Lives (House, 1986) and consisted of a
national, stratified area probability sample (3,610 of
3,617 interviews available for analysis because of miss-
ing responses). The age range of the participants was
24–96 years of age. The topics covered in the interviews
included interpersonal relationships, sources of satisfac-
tion, social interactions and leisure activities, illness and
traumatic life events, employment and financial status,
physical and psychological well-being, cognitive function-
ing, and other lifestyle and demographic characteristics.
Dependent variables: Cognitive functioning. As part
of the interview, the interviewers assessed participants’
cognitive functioning using the mini-mental exam
(Folstein, Folstein, & McHugh, 1975). The measure
deals with participants’ knowledge of personal informa-
tion (e.g., mother’s maiden name) and current events
(e.g., “Who is the president of the United States?”; range
of scores = 0–7). The mini-mental exam also includes a
simple test of working memory, in which participants
are given a number (i.e., 20) and asked to subtract 3, and
to keep subtracting 3 from each new number they get.
Performance consists of the number of times participants
are able to count backward by three (range of scores =
0–6). Total cognitive performance was computed by
adding the score on the general knowledge questions to
the score on the memory test, with higher scores indicat-
ing better cognitive performance.
Independent variables: Social interaction. The social
interaction variable was simple and clearly got at the
amount of social interaction in which the participants
engaged. The measure consisted of the mean of standard-
ized responses to two questions (r = .29). These questions
assessed the number of times participants talked on the
phone with friends, neighbors, and relatives and how
often they got together with these same parties. These
questions were answered on 6-point scales (1 = never, 3 =
once a week [month], 6 = more than once a day [week]).
Covariates. We controlled for relevant demographic
variables, including participants’ age (measured in
years), level of education (the higher the number, the
higher the grade completed), race and ethnicity, gender,
household income (coded as a continuous variable), and
whether participants were married or living with a part-
ner (0 = no, 1 = yes).
In the analysis we also controlled for measures of
physical health and daily activity levels. The physical
health measure consisted of the number of chronic
health conditions reported by the participant (e.g., inci-
dence of arthritis or rheumatism, high blood pressure,
diabetes, fractures) (range = 0–7). The measure of phys-
ical health is particularly important as a control variable
because it is a strong candidate for explaining variation
not only in sociability (if you are ill you are unlikely to
be very social) but also cognitive functioning (mental
decline reflects physical decline).
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
Participants’ assessments of their activities of daily
living consisted of their responses to two questions that
were scored on 4-point scales (r = .30). These questions
measured the degree to which participants engaged in
active sports or exercise, and how often they took walks
(1 = often, 4 = never). Higher scores on this index indi-
cate lower levels of daily activity.
The experience of depression has been associated
with impaired cognitive functioning in many studies
(Whalley, 2001). Therefore, we also controlled for par-
ticipants’ depressive symptoms. These consisted of four
items that assessed the degree to which participants
endorsed negative self-views and felt low self-efficacy,
for example, “At times I think I am no good at all” and
“There is really no way I can solve the problems I
have.” These questions were answered on 4-point scales
(1 = strongly agree, 2 = agree somewhat, 3 = disagree
somewhat, 4 = strongly disagree; Cronbach’s alpha =
.68). The items were recoded so that higher scores indi-
cate more depressed tendencies.
Results and Discussion
Table 1 presents the means for the social interaction
and cognitive performance measure as a function of age
group. The regression analyses were conducted as a
two-stage elimination procedure. In the first stage only
the covariates were entered into the model predicting
cognitive performance. Any covariate that failed to
meet the p < .10 criterion was excluded from the subse-
quent regression models. The excluded covariates were
income level, daily activity level, and physical health.
The second stage of the analysis involved classifying
participants into one of three groups as a function of
their age, young (24–41), middle age (42–64), and older
age (65–96; see Table 2 for regression model results).
Then we performed separate regression analyses within
each age group that included the covariates that met the
stay criterion described previously and, more impor-
tantly, the social interaction variable. A backward elim-
ination method, with a stay criterion of p < .05, was
used to determine whether social interaction predicted
cognitive functioning at the final stage of the regression
analysis. The results indicated that social interaction was
a reliable predictor of cognitive performance. The more
participants interacted socially by talking to and visiting
friends and relatives, the better their performance on
the measure of cognitive functioning. Importantly, this
predictive relationship was reliable for each of the age
groups, including the youngest.
The findings from Study 1 indicate that the more
socially engaged participants were, the higher their level
of cognitive performance. This goes beyond available
research in several ways. We had a direct measure of the
amount of social interaction, and we studied a typical
range of cognitive performance. More importantly, we
examined people of various ages and found that this
relation holds across the whole age spectrum, in the
elderly, middle-age, and youngest groups.
The finding of a reliable association between specific
measures of social interaction and cognitive performance
among the youngest group is important not only empir-
ically but theoretically as well. Specifically, it suggests
that the effect of social interaction does not require an
expanded time line before the benefits become apparent.
And the implications of this possibility are not trivial, as
even a few social interactions may potentially have rec-
ognizable benefits on cognitive performance.
It is important to note that the evidence for Study 1,
and that of the previously reviewed research showing a
relationship between social factors and cognitive func-
tioning, is correlational in nature. Thus, one goal of
TABLE 1: Means and Standard Deviations for the Measures of
Social Interaction and Cognitive Performance as a
Function of Age Group
Age Group MSD
Age range 24–41 (n = 1,183)
Social interaction 4.54 1.10
Cognitive performance 11.74 1.76
Age range 42–64 (n = 1,220)
Social interaction 4.33 1.19
Cognitive performance 11.28 2.25
Age range 65–96 (n = 1,207)
Social interaction 4.43 1.25
Cognitive performance 10.54 3.02
NOTE: The means for the measure of cognitive performance are
based on 7 extra participants (3 for middle age and 4 for older age)
who had missing social interaction data.
TABLE 2: Regression Models Testing the Relationship Between the
Measure of Social Interaction and the Measure of Cognitive
Performance as a Function of Age Group, Controlling for
Reliable Covariates From Stage 1 of Analysis
Cognitive Performance
Multivariate Model bp
Age range 24–41
Social interaction .08 .007
Age range 42–64
Social interaction .05 .05
Age range 65–96
Social interaction .094 .0001
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
Study 2 was to conduct an experiment to determine
whether social interaction in a group of younger adults
can have a causal influence on cognitive performance.
Related to this goal, Study 2 also tested the idea of
whether a small amount of social interaction can have
positive cognitive effects.
The participants in Study 2 were randomly assigned
to one of three conditions. These included a social
interaction condition, an intellectual activities condi-
tion, and a control condition. The control and intellec-
tual activities conditions were included as comparisons.
Given the proposed, stimulating nature of “intellectual”
and more academic activities (e.g., Schooler, Mulatu, &
Oates, 1999), we hypothesized that participants in the
intellectual activities condition would outperform those
in the control condition. Of greater interest, given our
argument that social interaction also helps exercise
people’s brains and minds, and the correlational findings
from Study 1, we hypothesized that participants in the
social interaction condition would also outperform the
controls. We had no strong theoretical basis for expect-
ing differences in performance between the two experi-
mental conditions, but a lack of a difference between the
two would help support the idea that social interaction
is similarly stimulating as activities traditionally charac-
terized as “intellectual” in nature.
Seventy-six participants (age range 18–21) took part
in the experiment. The participants were randomly
assigned to one of three conditions in which they were
run as dyads by an experimenter blind to the hypotheses.
Participants in the social interaction condition engaged
in a discussion of a social issue for 10 min. The issue,
which was the same for all participants, involved privacy
protection, especially in light of recent technological
advances and political events. Through the toss of a coin
the experimenter assigned participants to either the pro
or con position. Then they were given 4 min to read
through the description of the topic and formulate their
positions. They were then given the remaining 6 min to
carry out their discussion, with the participant assigned
the pro position asked to start the discussion.
Participants in the intellectual activities condition did
not interact with each other. Their activity consisted of
three tasks, a reading comprehension task (3 min), a
crossword puzzle (4 min), and a mental rotation task (3
min; total time for the tasks combined was always 10
min). The participants were told that it was okay if
they did not finish the tasks in the allotted time. Control
participants also did not interact with each other, but
their task, which involved watching a 10-min clip of the
sitcom Seinfeld, did have a social component. This
aspect of the control condition helps to additionally dis-
tinguish the processes operating in the social interaction
condition, as research has shown that watching televi-
sion can help people satisfy social needs for interaction
(Gardner, Pickett, & Knowles, 2005). If the social inter-
action participants cognitively outperform the controls,
such a finding would suggest that face-to-face interac-
tion or interacting with a live person is important in
realizing cognitive benefits.
Immediately following the 10 min, regardless of con-
dition, we asked participants to evaluate the activity
they were asked to perform (i.e., social interaction,
intellectual activities, or film clip), indicating how
engaging, stimulating, and enjoyable they found the
task to be (answers ranged from 1 = not at all to 6 =
very much so; Cronbach’s alpha = .80).
Following the evaluation of the respective activities,
for the next phase of the experimental session we
assessed participants’ cognitive functioning. Although
we could have used the cognitive functioning measure
from Study 1 for ease of administration, in this study we
used two different cognitive performance measures that
were more stringent and intensive. They included a mea-
sure of processing speed and working memory. Both of
these types of measures have been associated with vari-
ous forms of cognitive performance and measures of IQ
(e.g., Schatz, Kramer, Ablin, & Matthay, 2000).
The speed of processing task involved making same/
different judgments as quickly as possible about two
patterns of dots that were presented side by side on a
sheet of paper (Park et al., 1996). On each sheet of
paper there were multiple pairs for the participants to
compare. The participants were timed and given 45 s
per sheet (three sheets total) to do as many pattern com-
parisons as possible. The speed of processing score was
determined by taking the number of comparisons par-
ticipants answered correctly (across the three sheets)
and dividing this number by the number of comparisons
attempted to take accuracy into account.
The working memory task (reading span task)
required participants to answer questions about sen-
tences read aloud to them by the experimenter (process-
ing component) while maintaining an element from each
sentence in memory (storage component; Salthouse &
Babcock, 1991). Initially participants were presented
with three sets of two objects. Then participants imme-
diately had to recall the objects. If they recalled at least
two of the three sets of two objects, they moved to the
next trial. For this next trial participants were presented
with three sets of three objects. If they could recall
two of the three sets (of three objects) correctly they
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
proceeded to the next trial, and this could have continued
until participants were presented with three sets of eight
objects. The total number of correctly recalled sets
(across trials) was taken as the measure of working
memory performance.
Finally, after the working memory task participants
filled out some demographic questions and were then fully
debriefed and given course credit for their participation.
Results and Discussion
Table 3 shows the results of the experiment. For each
dependent measure (speed of processing, working
memory), we had two questions. First, does each of the
two experimental groups (intellectual, social) differ
from the control group? Second, do the two experimen-
tal groups differ from each other? We first conducted
overall ANOVAs on each measure of cognitive perfor-
mance and then followed up with planned contrasts to
answer these questions.
In terms of performance on the speed of processing
task (number correct/number attempted), there was an
overall difference in cognitive performance among the
conditions, F(2, 73) = 3.91, p < .02. Planned contrast
analysis on the means shown in Table 3 revealed that
the participants in the intellectual activities condition
outperformed controls, F(1, 48) = 4.23, p < .04. Of
greater interest, social interaction participants also out-
performed controls, F(1, 49) = 5.72, p < .02. In addi-
tion, the two experimental groups did not differ from
each other, F(1, 49) < 1.00. Importantly, there was no
difference in the number of attempts by participants
across the three conditions (M
= 17.12, M
17.77, M
= 18.40), F(2, 73) = 1.17, p < .31. Thus,
all participants were equally engaged with the second
phase of the experimental session.
Working memory performance yielded equivalent
results, with overall performance differences across con-
ditions, F(2, 73) = 3.46, p < .04. Contrasts revealed that
intellectual activities condition participants outper-
formed controls in terms of working memory perfor-
mance, F(1, 48) = 4.49, p < .04. Similarly, social
interaction participants also outperformed controls,
F(1, 49) = 7.23, p < .01. Again, both experimental
groups did not differ from each other, F(1, 49) < 1.00.
Before the assessment of cognitive performance, we
asked participants to evaluate the activities they per-
formed (i.e., how enjoyable, engaging, stimulating they
found the activities to be; 1 = not at all, 6 = very much
so). Responses to these questions are of interest because
it could be argued that mood or related motivational
differences due to the experimental manipulation could
have influenced cognitive performance. For example,
participants in the video clip condition might have
really enjoyed their activity and might have wanted to
prolong it, which could have subsequently lowered their
motivation when they were put through the cognitive
performance measures. Although the lack of a differ-
ence on the number of attempts on the speed of pro-
cessing task helps argue against this possibility, we
wanted to examine the effects, if any, of how the activ-
ities were evaluated. We first analyzed whether there
were any differences in evaluation. A between-subjects
ANOVA across the three conditions showed no reliable
differences, F(2, 73) = 2.74, p < .07. And even though
there was a trend toward an overall difference, both the
control (video) and social interaction conditions were
rated more favorably than the intellectual activities con-
dition and not differently from each other (M
3.39, M
Social Interaction
= 4.05, M
= 3.91). This in itself
also argues against such an alternative account.
To further buttress this aspect of the results, we
included the activity evaluation score as a covariate and
repeated the analyses for speed of processing and work-
ing memory. The addition of this covariate did not alter
the results for either measure of cognitive performance:
speed of processing, F(2, 72) = 3.91, p < .02; working
memory, F(2, 72) = 3.38, p < .04.
The results from Study 2 showed that short-term
social interaction lasting 10 min boosted participants’
cognitive performance to a comparable extent as having
participants engage in so-called intellectual activities for
the same amount of time. To our knowledge, this experi-
ment represents the only causal evidence of the facilitative
TABLE 3: Cognitive Performance as a Function of Experimental Condition
Social Interaction Intellectual Control
26) (n
25) (n
Cognitive Performance
Speed of processing 0.95 0.042 0.94 0.045 0.91 0.071
Working memory 10.54 1.42 10.52 2.02 9.48 1.39
NOTE: The greater the score, the better the performance.
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
effects of direct social interaction on cognitive perfor-
mance (and maybe the only causal test of the influence
of intellectual activities as well).
The focus on cognitively thriving younger adults in
the present study, in addition to extending the purview
of the social interaction–cognition link, served as a con-
servative test of the proposed causal influence of social
interaction on cognitive functioning given that older
adults perform more poorly than younger adults on a
variety of cognitive functioning tasks (refer to Study 1;
see also Craik, & Byrd, 1982; for a review see Park,
2000). Along more theoretical lines, the findings indi-
cate that the effects of social interaction can be rela-
tively immediate and can result from just a small
amount of social interaction.
Experimenter observations of the social interactions
in the present study indicated that participants planned
their positions, took turns sharing them, anticipated the
partner’s points, and readied their counterpoints. Save
for the constraints in which the interaction took place,
the interactions appeared for the most part natural and
coordinated. Although we do not have direct evidence
for this, the interactions likely triggered the use of per-
spective taking, planning, and inference generation,
processes that have been associated with executive func-
tioning (Amodio & Frith, 2006; Baddeley, 2002). We
posit that it is this that allows social interaction,
through the preactivation of cognitive resources, to
boost subsequent mental performance in a similar way
to how the activation of specific concepts in memory
readies semantically related knowledge and processes
(Anderson, 1993; E. R. Smith, 1994). We argue that
this process of resource priming (brief exercises of exec-
utive functions), which helps explain the effects of Study
2, if practiced regularly can create a more chronic ele-
vation of cognitive resources, which may be the process
captured in Study 1 (Olesen et al., 2004; Posner &
Rothbart, 2005).
At this point, our findings and interpretation agree
with cognitive and social neuroscience research, but
only through future research will we be in a position to
accept these interpretations more confidently. Some of
this work might involve, for example, studying different
types of interactions (getting to know someone or chit-
chatting vs. discussion of an issue), manipulating the
issue under discussion, introducing greater structure
into the social interaction to target specific social cogni-
tive processes (e.g., separately manipulating perspective
taking, mentalizing, or inhibition components), and
using neuroscience techniques to understand neural
changes underlying the observed effects. We will con-
tinue our considerations of how social interactions can
differ and their likely effects on cognition in the general
Both studies suggest a facilitative effect of social inter-
action on intellectual performance. Study 1 showed that
specific indicators of social interaction predicted cogni-
tive performance among cognitively healthy participants
and that this effect extends across a wide age spectrum,
including the youngest participants. This study extended
previous research with elderly and cognitively impaired
populations. Study 2 followed up on these results by
focusing on younger adults and the possibility that small
amounts of social interaction can have causal effects on
boosting cognitive performance. Compared to control
participants, participants who interacted socially for 10
min showed better cognitive performance, performance
equivalent to that displayed by participants engaged in
so-called intellectual activities.
The findings showing that younger adults can reap
cognitive benefits from socializing expand our concep-
tions of the social interaction–cognition link. Not only
do the results show that the effect is causal but that the
process is very sensitive to small amounts of social inter-
action. Reliance on the survey results with elderly par-
ticipants, barring any consideration of measurement
issues, provides little clue that such an effect could
occur so immediately.
The process thus seems aligned with the possibility
that social interaction can “exercise” general cognitive
processes (working memory, speed of processing, inhi-
bition) in the service of social cognition (e.g., empathy,
mentalizing, symbolic interaction). It is possible that as
people engage socially and mentally with others, they
receive relatively immediate cognitive boosts, which
then facilitate subsequent social interactions, receiving
additional cognitive boosts, and so forth. This perspec-
tive suggests that anyone, older and younger alike, can
do things that come naturally to most of us to stay cog-
nitively engaged.
More generally, the current findings fit with the
emphasis in the social cognition literature on the role of
general processing resources in person perception and
judgment (Lieberman et al., 2002). They also fit with
recent research showing real practice-related changes in
brain substrates underlying such fundamental functions
as attention, working memory, and processing speed
(Olesen et al., 2004; Posner & Rothbart, 2005).
Importantly, these changes can occur in typical popula-
tions and can have short-term influences, presumably
based on general mechanisms of activation, as well as
long-term influences, presumably based on structural
changes (Draganski et al., 2004). These findings are also
consistent with research on other species showing that
social enrichment improves cognitive performance, neu-
ronal growth, and overall brain mass (Bennet, Rosenzweig,
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
& Diamond, 1969; Cummins, 1973; Lipkind, Nottebohm,
Rado, & Barnea, 2002; Lomassese et al., 2000; Menzel,
Davenport, & Rogers, 1970; Sandeman & Sandeman,
The Nature and Tenor of the Social Interaction
One issue that remains unclear from the present
research is whether all types of social interaction can
have a positive effect on cognitive performance. Some
available research, although varying in method from the
present research, suggests that some social interactions
can be cognitively depleting (Finkel et al., 2006;
Richeson & Trawalter, 2005; Richeson, Trawalter, &
Shelton, 2005). For example, using a measure of inhibi-
tion, Richeson et al. (2005) showed that individuals
high in prejudice or who had concerns about being
viewed as prejudiced were worse at inhibiting interfer-
ing responses on a Stroop task. The research by Finkel
et al. (2006) using as outcome measures anagrams and
analytical tests from the Graduate Record Exam found
depletion effects following interactions that were asyn-
chronous or high-maintenance in nature. Both sets of
findings were explained in terms of self-regulation
deficits, although the emphases differed.
At a broader level, though, it may be possible to inte-
grate these findings with the present research. Whether
a social interaction is cognitively draining or cognitively
energizing may depend on several factors. Most gener-
ally, it is worth noting that the emergence of facilitation
or depletion effects with executive function tasks
depends on the extent of resource pre-use. As an anal-
ogy, before a competition, athletes engage in warm-up
exercises, without overdoing them and tiring themselves
out. Similarly, performance on executive function tasks
should benefit from earlier tasks with low and moder-
ate difficulty and self-timing that allows for rest (e.g.,
our Study 2), but performance may be temporarily
impaired by earlier tasks with high levels of difficulty,
as typically used in depletion experiments (Muraven &
Baumeister, 2000).
More specific to the nature of the social interactions,
their effects should generally depend on whether the
context involves approach or avoidance tendencies, two
core aspects of how people tend to relate to each other
(Chance, 1988). Social interactions that trigger avoid-
ance are likely to be surrounded by a lack of structure
or norms or an asynchronous dynamic, or they may
involve people who under normal circumstances might
not interact with each other or do so warily (e.g.,
strangers, outgroup members). Such social interactions
may induce uncertainty and anxiety about what the
other persons are like, how they will react, or what they
are likely to do (cf. Stephan & Stephan, 1985), out-
comes that may be particularly lethal to the ability to
self-regulate and to concomitant cognitive performance.
In the research by Richeson et al. (2005), participants
were made to feel concerned that they might be viewed
as prejudiced, which might have induced anxiety and
uncertainty, especially when they were told they would
be videotaped and asked to disclose their opinions on
racially sensitive topics. It is also possible that the high-
maintenance conditions in the research by Finkel et al.
(2006) might have induced frustration and facilitated
inferences that the partner was not to be trusted, an out-
come with the potential to short-circuit processes such
as perspective taking.
Other interactions, in contrast, are more structured
or allow for coordination, or may involve people who
are more likely to interact with each other (e.g., friends,
ingroup members). Such interactions, instead of elicit-
ing a self-protective, reactive stance, may induce greater
security, less anxiety, and at times greater communality.
This captures to some degree the nature of the social
interaction used in Study 2, in which participants inter-
acted in a structured setting in which they were rela-
tively interdependent and had to try to predict and
understand each other’s position. It may be this aspect
of the social interaction that promotes a cognitive style
that supports consequent cognitive boosts.
Finally, the possibility exists that the outcome mea-
sures used across the different studies may also play a
role in determining whether cognitive depletion or cog-
nitive boosts occur. Richeson et al. (2005) used a test of
cognitive inhibition, whereas Finkel et al. (2006) used
anagram and analytical reasoning tasks, in addition to
tasks that involved handgrip effort and fine motor con-
trol. In the present research we used two general mea-
sures of cognitive resources, working memory and
speed of processing. Further research is thus needed to
more clearly determine what aspects of social interac-
tion lead to cognitive boosts or cognitive depletion and
the possible role of how cognitive performance is mea-
sured. The former emphasis strikes us as a particularly
exciting area of inquiry. It has potential implications for
research dealing with, for example, the effects of inter-
group contact and diversity on cognitive performance,
and how supportive and coordinated relationships,
both romantic and nonromantic, may allow people to
blossom cognitively.
Limitations of the Present Research
In addition to the issues discussed previously, a limi-
tation of the present research concerns the correlational
nature of Study 1. Although the findings from Study 2
provide causal evidence for the effect of social interac-
tion on cognitive functioning, we cannot draw this con-
clusion with the same level of confidence for Study 1.
The findings from Study 1 are equally suggestive of the
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
idea that people who are functioning better cognitively
are better able to enter into social interactions with oth-
ers, or there could be a third variable that accounts for
both of these possibilities. At a broader level, though,
the idea that more cognitively able people may enter
into more social interactions is not inconsistent with the
thrust of the present research. Such a relationship sug-
gests that social interaction demands a requisite level of
cognitive functioning, in line with the suggestions put
forth by evolutionary biologists (Dunbar, 1992, 1995;
Humphrey, 1976). It may indeed be this possibility that
allows for social interaction to produce boosts in cogni-
tive functioning, as demonstrated in Study 2.
The Social Is What We Do, and
Thinking Is for Doing
In the Introduction we discussed how an alien visit-
ing from another world would be impressed by the
amount of time people spend doing things with other
people. What is important to note is that such an alien
could pay a visit now, could have visited 50,000 years
ago, or 2 million years ago (in the time of Homo habilis)
and in all cases be impressed by the role of social rela-
tions in people’s lives. Social connections are at the core
of primate life (Jolly, 1966) and are central to the
human survival strategy (Barash, 1986; Baumeister,
2005; Dunbar, 1992, 1998).
Given the continual and tonic role of social interac-
tion in people’s lives, more specifically, the necessity to
navigate a complex web of social relations in a mixed-
motive world (Humphrey, 1976), it makes sense that our
brains and minds would be very sensitive and responsive
to that dimension of experience. Research in evolution-
ary biology has shown, for example, that the size of the
primate neocortex is better predicted by how cognitively
taxing a primate’s species social environment is (as
reflected in group size) rather than the demands of the
physical ecology (Dunbar, 1992, 1995; Humphrey,
1976). In humans, research in social cognition has
shown that compared to information of a nonsocial
nature, people are faster at recognizing information that
has social implications (Ybarra, Chan, & Park, 2001).
Other research has shown that when people are first get-
ting to know someone, they seek more information
about the person’s social rather than non-social-related
tendencies (Wojciszke, Bazinska, & Jaworski, 1998).
People’s conversations with others also tend to revolve
around the social dimensions of life (Dunbar, Marriott,
& Duncan, 1997), and even while sleeping and dream-
ing people’s minds are preoccupied with social relations
(McNamara, McLaren, Smith, Brown, & Stickgold,
2005). Social neuroscience research has also shown that
the default neural activity present in the human brain is
more aligned with the way people process social versus
nonsocial information (Mitchell, Heatherton, &
Macrae, 2002). People’s psychology appears to be con-
sistently attuned to the social world.
The social readiness of people’s minds makes sense
given that people’s cognitive abilities many times oper-
ate in the service of social coordination and staying
socially connected. In turn, social interaction and rela-
tionships not only sharpen our knowledge and social
skills but also strengthen the cognitive processes that
underlie those skills, which may then ready people for
greater connection and effectiveness in dealing with oth-
ers. Thus, an important outcome of social interaction
appears to be mental sharpness, which in itself may play
a central role in helping us enjoy the many other bene-
fits that come from being socially connected. Thinking
many times is for being social, and being social supports
our thinking.
The current analysis complements other recommen-
dations given for maintaining a healthy brain and mind
(e.g., Verghese et al., 2003; Wilson et al., 2002). Usually
it is assumed that activities that work the brain and
mind are of a more “intellectual” and “technical”
nature, such as reading, developing new hobbies, and
learning to appreciate new aspects of one’s culture (e.g.,
art, music). Such activities should undoubtedly play a
role in keeping the brain and mind healthy.
But in light of current research and discussion, it may
not be inappropriate to rephrase Descartes’ philosophi-
cal statement as “I think about and with others, there-
fore I am.” Other needs that are basic to us, such as
being socially connected (Baumeister & Leary, 1995),
by their very nature when fulfilled will engage people
mentally. The mental gymnastics that come with social
interaction need not take the place of other activities
that exercise our minds, but they may produce the
byproduct, whether intentional or not, of making our
social lives more gratifying while providing boosts to
our cognitive functioning.
1. Some critics challenge Putnam’s (2000) “Bowling alone” con-
clusion and suggest that people have as many social contacts now as
in the past but that much of this social interaction has shifted to the
Internet. However, what might be critical for proper cognitive and
physical functioning is actual, face-to-face interaction with its
dynamic and fast character. No amount of email, even “instant mes-
saging,” can supplement face-to-face interaction, as it does not have
the same computational demands.
2. The analyses were also performed with all of the covariates
included as part of the regression models (no exclusion). These analy-
ses produced the same results as those reported.
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
Adolphs, R. (2001). The neurobiology of social cognition. Current
Opinion in Neurobiology, 11, 231-239.
Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: the medial
frontal cortex and social cognition. Nature Reviews Neuroscience,
7, 268-277.
Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence
Arbuckle, T. Y., Gold, D. P., Andres, D., Schwartzman, A., &
Chaikelson, J. (1992). The role of psychosocial context, age, and
intelligence in memory performance of older men. Psychology and
Aging, 7, 25-36.
Aries, P. (1962). Centuries of childhood: A social history of family
(Robert Baldick, Trans.). New York: Knopf.
Baddeley, A. D. (2002). Fractionating the central executive. In
D. Stuss & R. T. Knight (Eds.), Principles of frontal lobe function
(pp. 246-260). New York: Oxford University Press.
Barash, D. P. (1986). Sociobiology and behavior. New York: Elsevier.
Baron-Cohen, S., & Ring, H. (1994). The relationship between EDD
and ToM: Neuropsychological and neurobiological perspectives.
In P. Mitchell & C. Lewis (Eds.), Origins of an understanding of
mind (pp. 183-207). Hillsdale, NJ: Lawrence Erlbaum.
Bassuk, S. S., Glass, T. A., & Berkman, L. F. (1999). Social disen-
gagement and incident cognitive decline in community-dwelling
elderly persons. Annals of Internal Medicine, 131, 165-173.
Baumeister, R. F. (2005). The cultural animal: Human nature, mean-
ing, and social life. New York: Oxford University Press.
Baumeister, R. F., DeWall, C. N., Ciarocco, N. J., & Twenge, J. M.
(2005). Social exclusion impairs self-regulation. Journal of
Personality and Social Psychology, 88, 589-604.
Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire
for interpersonal attachments as a fundamental human motiva-
tion. Psychological Bulletin, 11, 497-529.
Baumeister, R. F., Twenge, J. M., & Nuss, C. (2002). Effects of social
exclusion on cognitive processes: Anticipated aloneness reduces
intelligent thought. Journal of Personality and Social Psychology,
83, 817-827.
Bennet, E. L., Rosenzweig, M. R., & Diamond, M. C. (1969). Rat
brain: Effects of environmental enrichment on wet and dry
weights. Science, 163, 825-826.
Blumberg, H. P., Stern, E., Ricketts, S., Martinez, D., de Asis, J.,
White, T., et al. (1999). Rostral and orbital prefrontal cortex dys-
function in the manic state of bipolar disorder. American Journal
of Psychiatry, 156, 1986-1988.
Brunet, E., Sarfati, Y., Hardy-Bayle, M. C., & Decety, J. (2000). A
PET investigation of the attribution of intentions with a nonverbal
task. Neuroimage, 11, 157-166.
Cartwright, D., & Zander, A. (1953). Group dynamics: Research and
theory. Evanston, IL: Row, Peterson.
Chance, M. R. A. (1988). Social fabrics of the mind. Hillsdale, NJ:
Lawrence Erlbaum.
Craik, F. I. M., & Byrd, M. (1982). Aging and cognitive deficits: The
role of attentional resources. In F. I. M. Craik & S. Trehub (Eds.),
Aging and cognitive processes (pp. 191-211). New York: Plenum.
Cummins, R. A. (1973). Environmentally-induced changes in the
brains of elderly rats. Nature, 243, 516-518.
Damasio, A. R. (1994). Descartes’ error: Emotion, reason, and the
human brain. New York: Putnam.
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., &
May, A. (2004). Neuroplasticity: changes in grey matter induced
by training. Nature, 427, 311–312.
Dunbar, R. I. M. (1992) Neocortex size as a constraint on group size
in primates. Journal of Human Evolution, 20, 469-493.
Dunbar, R. I. M. (1995). Neocortex size and group size in primates: A
test of the hypothesis. Journal of Human Evolution, 28, 287-296.
Dunbar, R. I. M. (1998). The social brain hypothesis. Evolutionary
Anthropology, 6, 178-190.
Dunbar, R. I. M., Marriott, A., & Duncan, N. D. C. (1997). Human
conversational behavior. Human Nature, 8, 231-246.
Fabrigoule, C., Letenneur, L., Dartigues, J. F., Zarrouk, M.,
Commenges, D., & Barberger-Gateau, P. (1995). Social and
leisure activities and risk of dementia: A prospective longitudinal
study. Journal of the American Geriatrics Society, 43, 485-490.
Finkel, E. J., Campbell, W. K., Brunell, A. B., Dalton, A. N., Scarbeck,
S. J., & Chartrand, T. L. (2006). High maintenance interaction:
Inefficient coordination impairs self-regulation. Journal of
Personality and Social Psychology, 91, 456-475.
Fletcher, P. C., Happe, F., Frith, U., Baker, S. C., Dolan, R. J.,
Frackowiak, R. S., et al. (1995). Other minds in the brain: A func-
tional imaging study of “theory of mind” in story comprehension.
Cognition, 57, 109-128.
Folstein, M., Folstein, S., & McHugh, P. (1975). Mini-mental state: A
practical method for grading the mental state of patients for the
clinician. Journal of Psychiatric Research, 12, 189-198.
Fratiglioni, L., Wand, H., Ericsson, K., Maytan, M., & Winblad, B.
(2000). Influence of social network on occurrence of dementia: A
community-based longitudinal study. Lancet, 355, 1315-1319.
Frith C. D., & Frith, U. (1999). Interacting minds—A biological basis.
Science, 286, 1692-1695.
Gardner, W. L., Pickett, C. L., & Knowles, M. (2005). Social snack-
ing and shielding: Using social symbols, selves, and surrogates in
the service of belonging needs. In K. D. Williams, J. P. Forgas, &
W. V. Hippel (Eds.), The social outcast: Ostracism, social exclu-
sion, rejection, and bullying (pp. 227-242). New York: Psychology
Gilbert, D. T., Pelham, B. W., & Krull, D. S. (1988). On cognitive
busyness: When person perceivers meet persons perceived. Journal
of Personality and Social Psychology, 54, 733-740.
Gladstone, G. L., Parker, G. B., Malhi, G. S., & Wilhelm, K. A.
(2007). Feeling unsupported? An investigation of depressed
patients’ depression. Journal of Affective Disorders, 103, 147-154.
Heider, F. (1958). The psychology of interpersonal relations. New
York: John Wiley.
House, J. S. (1986). The survey of Americans’ changing lives [Data
file]. Ann Arbor, MI: Institute for Social Research.
House, J. S., Landis, K. R., & Umberson, D. (1988). Social relation-
ships and health. Science, 241, 540-545.
Humphrey, N. K. (1976). The social function of intellect. In P. P. G.
Bateson & R. A. Hinde (Eds.), Growing points in ethology
(pp. 303-317). Cambridge, UK: Cambridge University Press.
Jolly, A. (1966). Lemur social behavior and primate intelligence.
Science, 153, 501-506.
Kiecolt-Glaser, J., Garner, W., Speicher, C., Penn, G., & Glaser, R.
(1984). Psychosocial modifiers of immunocompetence in medical
students. Psychosomatic Medicine, 46, 7-14.
Kling, A., & Steklis, H. D. (1976). A neural substrate for affiliative
behavior in nonhuman primates. Brain, Behavior and Evolution,
13, 216-238.
Kling, A. S. (1986). Neurological correlates of social behavior.
Ethology and Sociobiology, 7, 175-186.
Kondo, K., & Yamashita, I. (1990). A case-control study of
Alzheimer’s disease in Japan: Association with inactive psychoso-
cial behaviors. In K. Hasegawa & A. Homma (Eds.),
Psychogeriatrics: Biochemical and social advances (pp. 49-53).
Amsterdam: Excerpta Medics.
Lieberman, M. D., Gaunt, R., Gilbert, D. T., & Trope, Y. (2002).
Reflection and reflexion: A social cognitive neuroscience approach
to attributional inference. Advances in Experimental Social
Psychology, 34, 199-249.
Lipkind, D., Nottebohm, F., Rado, R., & Barnea, A. (2002). Social
change affects the survival of new neurons in the forebrain of adult
songbirds. Behavioural Brain Research, 133, 31-43.
Lomassese, S. S., Strambi, C., Strambi, A., Charpin, P., Augier, R.,
Aouane, A., et al. (2000). Influence of environmental stimulation
on neurogenesis in the adult insect brain. Journal of
Neurobiology, 45, 162-171.
McGuire, M. T., & Raleigh, M. J. (1986). Behavioral and physiolog-
ical correlates of ostracism. Ethology and Sociobiology, 7,
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
McNamara, P., McLaren, D., Smith, D., Brown, A., & Stickgold, R.
(2005). A “Jekyll and Hyde” within: Aggressive versus friendly
interactions in REM and non-REM dreams. Psychological Science,
16, 130-136.
McPherson, M., Smith-Lovin, L., & Brashears, M. E. (2006).
Discussion networks over two decades. American Sociological
Review, 71, 353-375.
Mead, G. H. (1934). Mind, self and society from the standpoint of the
social behaviorist. Chicago: University of Chicago Press.
Menzel, E. W., Davenport, R. K., & Rogers, C. M. (1970). The devel-
opment of tool-using in wild-born and restriction-reared chim-
panzees. Folia Primat, 12, 273-283.
Mitchell, J. P., Heatherton, T. F., & Macrae, C. N. (2002). Distinct
neural systems subserve person and object knowledge. Proceedings
of the National Academy of Sciences, 99, 15238-15243.
Moll, J., de Oliveira-Souza, R., Bramati, I. E., & Grafman, J. (2002).
Functional networks in emotional moral and nonmoral social
judgments. Neuroimage, 16, 696-703.
Muraven, M., & Baumeister, R. F. (2000). Self-regulation and deple-
tion of limited resources: Does self-control resemble a muscle?
Psychological Bulletin, 126, 247-259.
Myers, R. E., Swett, C., & Miller, M. (1973). Loss of social group
affinity following prefrontal lesions in free-ranging macaques.
Brain Research, 64, 257-269.
Nielson, K. A., Langenecker, S. A., & Garavan, H. (2002). Differences
in the functional neuroanatomy of inhibitory control across the
adult life span. Psychology and Aging, 17, 56-71.
Olesen, P. J., Westerberg, H., & Klingberg, T. (2004). Increased pre-
frontal and parietal activity after training of working memory
Nature Neuroscience, 7, 75-79.
Park, D. C. (2000). The basic mechanisms accounting for age-related
decline in cognitive function. In D. C. Park & N. Schwarz (Eds.),
Cognitive aging: A primer (pp. 3-22). Philadelphia: Psychology Press.
Park, D. C., Smith, A. D., Lautenschlager, G., Earles, J., Frieske, D.,
Zwahr, M., et al. (1996). Mediators of long-term memory perfor-
mance across the life span. Psychology and Aging, 11, 621-637.
Posner, M. I., & Rothbart, M. K. (2005). Influencing brain networks:
Implications for education. Trends in Cognitive Science, 9, 99-103.
Putnam, R. D. (2000). Bowling alone: The collapse and revival of
American community. New York: Simon & Schuster.
Richeson, J. A., & Trawalter, S. (2005). Why do interracial interac-
tions impair executive function? A resource depletion account.
Journal of Personality and Social Psychology, 88, 934-947.
Richeson, J. A., Trawalter, S., & Shelton, N. J. (2005). African
Americans’ implicit racial attitudes and the depletion of executive
function after interracial interactions. Social Cognition, 23, 336-352.
Royall, D. R., Lauterbach, E. C., Cummings, J. L., Reeve, A.,
Rummans, T. A., Kaufer, D. I., et al. (2002). Executive control
function: A review of its promise and challenges for clinical
research. Journal of Neuropsychiatry and Clinical Neuroscience,
14, 377–405.
Salthouse, T. A., & Babcock, R. L. (1991). Decomposing adult age
differences in working memory. Developmental Psychology, 27,
Sandeman, R., & Sandeman, D. (2000). “Impoverished” and
“enriched” living conditions influence the proliferation and sur-
vival of neurons in crayfish brain. Journal of Neurobiology, 45,
Schatz, J., Kramer, J. H., Ablin, A., & Matthay, K. K. (2000).
Processing speed, working memory, and IQ: A developmental
model of cognitive deficits following cranial radiation therapy.
Neuropsychology, 14, 189-200.
Schooler, C., Mulatu, M. S., & Oates, G. (1999). The continuing
effects of substantively complex work on the intellectual function-
ing of older workers. Psychology and Aging, 14, 483-506.
Seeman, T. E., Lusignolo, T. M., Albert, M., & Berkman, L. (2001).
Social relationships, social support, and patterns of cognitive
aging in healthy, high-functioning older adults: MacArthur studies
of successful aging. Health Psychology, 20, 243-255.
Shallice, T. (1988). From neuropsychology to mental structure.
Cambridge, UK: Cambridge University Press.
Singley, M. K., & Anderson, J. R. (1989). Transfer of cognitive skill.
Cambridge, MA: Harvard University Press.
Sinha, J. B. P., & Verma, J. (1990, July). Social support as a modera-
tor of the relationship between allocentrism and psychological
well-being. Paper presented at the Individualism–Collectivism
Conference, Seoul, Korea.
Smith, E. E., & Jonides, J. (1999). Storage and executive processes in
the frontal lobes. Science, 283, 1657-1661.
Smith, E. R. (1994). Procedural knowledge and processing strategies
in social cognition. In R. S. Wyer Jr. & T. K. Srull (Eds.),
Handbook of social cognition (2nd ed., Vol. 1, pp. 99-151).
Hillsdale, NJ: Lawrence Erlbaum.
Stephan, W. G., & Stephan, C. W. (1985). Intergroup anxiety.
Journal of Social Issues, 41, 157-175.
Stone, V. E., Baron-Cohen, S., & Knight, R. T. (1998). Frontal lobe
contributions to theory of mind. Journal of Cognitive
Neuroscience, 10, 640-656.
Triandis, H. C., Bontempo, R., Betancourt, H., Bond, M., Leung, K.,
Brenes, K., et al. (1986). The measurement of etic aspects of indi-
vidualism and collectivism across cultures. Australian Journal of
Psychology, 38, 257-267.
Trope, Y. (1986). Identification and inferential processes in disposi-
tional attribution. Psychological Review, 93, 239-257.
Verghese, J., Lipton, R. B., Katz, M. J., Hall, C. B., Derby, C. A.,
Kuslansky, G., et al. (2003). Leisure activities and the risk of
dementia in the elderly. New England Journal of Medicine, 348,
Whalley, L. (2001). The aging brain. New York: Columbia University
Wilson, R. S., Krueger, K. R., Arnold, S. E., Schneider, J. A., Kelly, J.
F., Barnes, L. L., et al. (2007). Loneliness and risk of Alzheimer
disease. Archives of General Psychiatry, 64, 234-240.
Wilson, R. S., Mendes de Leon, C. F., Barnes, L. L., Schneider, J. A.,
Bienias, J. L., Evans, D. A., et al. (2002). Participation in cogni-
tively stimulating activities and risk of incident of Alzheimer dis-
ease. Journal of the American Medical Association, 287, 742-748.
Wojciszke, B., Bazinska, R., & Jaworski, M. (1998). On the domi-
nance of moral categories in impression formation. Personality
and Social Psychology Bulletin, 24, 1251-1263.
Ybarra, O., Chan, E., & Park, D. (2001). Young and old adults’ con-
cerns about morality and competence. Motivation and Emotion,
25, 85-100.
Received March 11, 2007
Revision accepted August 10, 2007
© 2008 Society for Personality and Social Psychology, Inc.. All rights reserved. Not for commercial use or unauthorized distribution.
by on January 24, 2008 http://psp.sagepub.comDownloaded from
... 'Most clients just chat about their day, but for some it's an important opportunity to discuss something affecting their life' (Respondent 22, avg client age 18-25) 'A lot of guys just like to sit in silence for half an hour and relax' (Respondent 18, avg client age [26][27][28][29][30][31][32][33][34][35] 'There are different types of male customers […] some are happy to talk, some will completely blank you' (Respondent 8, avg client age [18][19][20][21][22][23][24][25]. ...
... 'People after the first lockdown were more desperate to come back to have a conversation and see people' (Respondent 11, avg client age [26][27][28][29][30][31][32][33][34][35] 'We are here to make people feel good about themselves, haircut, chat, people have missed this over lockdown' (Respondent 26, avg client age [26][27][28][29][30][31][32][33][34][35]. ...
... 'People after the first lockdown were more desperate to come back to have a conversation and see people' (Respondent 11, avg client age [26][27][28][29][30][31][32][33][34][35] 'We are here to make people feel good about themselves, haircut, chat, people have missed this over lockdown' (Respondent 26, avg client age [26][27][28][29][30][31][32][33][34][35]. ...
Background: Previous research has highlighted the need to promote help-seeking by men with mental health problems. Aims: To investigate barbers' views about offering mental health support for men in barbershops, with a specific focus on the psychosocial impacts of the COVID-19 pandemic. Method: We used a sequential mixed-methods qualitative design with online data collection. In Phase 1, 30 barbers in Southern England completed surveys exploring perceptions of their clients' mental health during the COVID-19 pandemic, experiences of informal supportive roles and scope for providing formal mental health support in barbershops. Phase 2 involved member validation interviews and explored practice implications with three Phase 1 respondents. Results: Thematic analysis identified three overarching themes: 'more than a haircut' (describing how the physical and relational contexts of barbershops can offer a supportive environment for clients); 'impacts of COVID-19' (describing stressors related to the pandemic and implications for clients' mental health and barber-client relationships); and 'formal mental health strategies' (describing opportunities for, and potential barriers to, formalising mental health support in barbershops). Conclusions: Barbers were aware of their clients' worsening mental health during the COVID-19 pandemic. Barbershops were generally considered to be a suitable setting in which to promote good mental health, monitor for signs of mental ill health and provide information about local mental health services. Future work is needed to co-produce and evaluate formal mental health promotion and prevention strategies in barbershops. Particular attention should be given to service innovations that preserve the credibility and trust that are fundamental to the barbershop experience for many males.
... Moreover, Langan-Fox et al. (2004) summarized the potential benefits of the relationship between TMMs and performance, some of which include more effective communication by less communication actions through using shared models such as common language (Langan-Fox, 2001), more prompt mutual learning, and improving the allocation of responsibilities by considering the strengths and weaknesses of team members (Langan-Fox et al., 2004). Van den Bossche (2006) drew the attention to the close relationship between cognition and interaction and then, Ybarra, et al. (2008) described that these two concepts have direct influence on each other (McNeese et al., 2014). Nevertheless, Houghton et al. (2000) proposed that TMMs may cause "groupthink" biases, which is defined as a possible disadvantage that groups may experience when conformity pressure leads to faulty decision-making (Janis, 1982) and can be seen in a wide range of groups working together in various fields (Rose, 2011). ...
Conference Paper
Full-text available
Collaboration between Lean Construction and BIM teams is a key factor in exploiting the synergies between Lean and BIM. Although various studies have underlined the importance of team cognition and Team Mental Models (TMM) in the success or failure of collaboration amongst teams, those concepts have not been sufficiently explored from a Lean/BIM perspective. Therefore, this study attempts to introduce the concept of TMM to the Lean-BIM domain by conducting a cognitive review of the Lean-BIM joint implementation at an engineering design firm in the UK with the principal aim of developing a set of suggestions to improve the collaboration between BIM and Lean experts. To collect data, this study used a mixed research approach including secondary research, a case study and semi-structured interviews. Data analysis was conducted through Thematic Analysis to find the main barriers hindering an effective Lean-BIM joint implementation. Findings also suggest that improving the components of TMM can result in an improved Lean-BIM joint implementation. A set of recommendations for Lean and BIM teams' collaboration is also given in the paper.
... We recruited older adults from the Bournemouth University Aging and Dementia Research Centre participant pool (specifically older adults without identified cognitive deficit) and from the Bournemouth branch of the University of the Third Age. Participants from these groups are typically very physically, and socially active which helps to maintain high-level of EF abilities as individuals age (Kramer et al., 1999;Derwinger et al., 2005;Carlson et al., 2008;Ybarra et al., 2008;Berryman et al., 2013). ...
Full-text available
As we age, many physical, perceptual and cognitive abilities decline, which can critically impact our day-to-day lives. However, the decline of many abilities is concurrent; thus, it is challenging to disentangle the relative contributions of different abilities in the performance deterioration in realistic tasks, such as road crossing, with age. Research into road crossing has shown that aging and a decline in executive functioning (EFs) is associated with altered information sampling and less safe crossing decisions compared to younger adults. However, in these studies declines in age and EFs were confounded. Therefore, it is impossible to disentangle whether age-related declines in EFs impact on visual sampling and road-crossing performance, or whether visual exploration, and road-crossing performance, are impacted by aging independently of a decline in EFs. In this study, we recruited older adults with maintained EFs to isolate the impacts of aging independently of a decline EFs on road crossing abilities. We recorded eye movements of younger adults and older adults while they watched videos of road traffic and were asked to decide when they could cross the road. Overall, our results show that older adults with maintained EFs sample visual information and make similar road crossing decisions to younger adults. Our findings also reveal that both environmental constraints and EF abilities interact with aging to influence how the road-crossing task is performed. Our findings suggest that older pedestrians' safety, and independence in day-to-day life, can be improved through a limitation of scene complexity and a preservation of EF abilities.
... Social activity frequency has also been tied to cognitive functioning, including working memory and executive functioning across adulthood (Lee & Kim, 2016;Ybarra et al., 2008). Social activity involves participating in meaningful social activities and maintaining intimate relationships with others (Adams et al., 2011;Rowe & Kahn, 1997), including activities such as attending social events or providing informal help to others (Lee et al., 2021). ...
Objectives Active lifestyles are related to higher levels of cognitive functioning. Fewer studies have examined the importance of engaging in different activities (activity variety) for cognitive functioning. Moreover, it is unclear whether activity variety in specific domains (i.e., cognitive, physical, or social) is important for cognitive health. The current study examined whether overall activity variety as well as variety in specific domains relates to cognitive functioning. Method In Waves 2 and 3 of the Survey of Midlife Development in the United States (MIDUS), 3,337 adults reported their activity engagement and completed a cognitive battery. For longitudinal analyses, 2,049 participants were classified into four groups based on their rank-ordering of activity variety across nine years (remained high, increased, decreased, or remained low). Results Cross-sectional analyses revealed that overall activity variety was related to higher cognitive functioning over and above activity frequency; physical and social activity variety each contributed significantly and uniquely to this association. Longitudinal analyses revealed that those with consistently low overall activity variety at both waves had lower cognitive functioning at Wave 3 than those with high activity variety at either wave, after adjusting for cognitive functioning at Wave 2. Those with consistently high or increasing social activity variety had higher cognitive functioning at Wave 3 than participants with low activity variety at both waves. Discussion Findings suggest that activity variety, particularly in the social domain, is related to concurrent and future cognitive function across adulthood.
... Lee, Kim, Lee, Chung과 Park (2012) (Kim, 2010;Kwon & Paek, 2014;Won & Kim, 2003), 교육은 건강한 삶을 도모하는 방식으로 생활을 영위하고 조절하는 데 필요한 자 원을 제공해 주며 연령이 증가할수록 그 영향력이 더욱 커지는 것 으로 알려져 있다 (Mirowsky & Ross, 2005). 또한, Jeon (2013) (Fratiglioni, Wang, Ericsson, Maytan, & Winblad, 2000;Hwang & Kwon, 2009;James, Wilson, Barnes, & Bennett, 2011;Kim, 2015;Seeman et al. 2011;Ybarra et al., 2008 (Burstein, 1980;Cooley, Bond, & Mao, 1981;Cronbach & Webb, 1975;Kang, 2016 1수준: 2nd 3rd 4th 5th 6th 7th Time Figure 1A). 반면, 연령 (Baik, 2015;Wilson et al., 2002;Yang, Jeong, & Choi, 2017 (Choi & Sung, 2019;Hultsch, Hertzog, & Dixon, 1990;Klencklen et al., 2017;Lee et al., 2012;Sung & Kwag, 2012 (Christensen et al., 2001;Tucker-Drob, Johnson, & Jones, 2009;Van Dijk et al., 2008;Zahodne et al., 2011). ...
Objectives: The purpose of this study is to examine changes in the cognitive function of the elderly over time and to identify factors affecting the cognitive decline by dividing them into multidimensional factors such as socio-demographic factors, physical and mental health factors, hearing factors, and social contact factors.Methods: This study used the Korean Longitudinal Study of Aging (KLoSA). A multilevel growth model analysis was conducted on 992 elderly people aged 60 or older who had been measured repeatedly seven times from 2006 to 2018.Results: First, the results showed that the cognitive function of the elderly decreased linearly over the years. Second, the initial status of cognitive function decreased as the age increased and as education level and economic condition decreased. The change rate of the cognitive function increased as education level increased. Third, at each time point, depression level had a negative effect on cognitive function, and subjective hearing condition had a negative effect on cognitive function. These influences decreased over time.Conclusion: The lower the education level, the higher the depression level; and the worse the subjective hearing condition, the more likely the elderly are to experience cognitive decline. Because the impact on cognitive function is large in the early stages of depression and hearing loss, it is necessary to detect them early and to make appropriate intervention to prevent cognitive decline.
Social participation is associated with cognitive function; however, their causal relationships have not been reported yet. This study was designed to examine the autoregressive effects and bidirectional causal relationship between social participation and cognitive function. In this secondary longitudinal data analysis, we enrolled 4,834 Korean adults. A cross-lagged panel model with fixed effects was used to examine the causal relationships between social participation and cognitive function. Both participation (unstandardized coefficient = .370, p < .001) and cognitive function (unstandardized coefficient = .151, p < .001) had positive autoregressive effects over time. Participation had a cross-lagged effect on cognitive function (unstandardized coefficient = .061, p < .001). However, the cross-lagged effects of cognitive function on participation were not statistically significant (unstandardized coefficient = .051, p = .312). Various health-care programs that promote social participation and improve cognitive function must be established. Additional studies are required to confirm the causal effects of cognitive function on participation.
Social participation has tremendous implications for the physical and mental health of older adults. A growing body of Canadian literature has examined social participation among older adults, including frequency of participation; gender, age, and regional differences in participation; and associations with self-perceived health, loneliness, and life dissatisfaction. The current study adds to this important body of research, using a large, nationally representative sample of adults 45–85 years of age (Canadian Longitudinal Study on Aging [CLSA] baseline data [ n = 51,338]), to examine nuanced characteristics associated with social participation (socio-demographics, social support, cognitive ability, mental health, physical conditions), frequency of participation, and the relationship between the aforementioned characteristics and frequency of participation. Findings indicated that compared with those who reported infrequent/no participation, more frequent participation was associated with greater social support, higher cognitive abilities, increased satisfaction with life, fewer depressive symptoms, reduced odds of self-reported mood and anxiety disorders, and fewer self-reported physical conditions. Findings highlight the importance of active social participation, and have important implications for the development and implementation of accessible community programs across Canada.
Full-text available
Does the structure of an adult human brain alter in response to environmental demands? Here we use whole-brain magnetic-resonance imaging to visualize learning-induced plasticity in the brains of volunteers who have learned to juggle. We find that these individuals show a transient and selective structural change in brain areas that are associated with the processing and storage of complex visual motion. This discovery of a stimulus-dependent alteration in the brain's macroscopic structure contradicts the traditionally held view that cortical plasticity is associated with functional rather than anatomical changes.
Full-text available
no abstract
Three studies examined the effects of randomly assigned messages of social exclusion. In all 3 studies, significant and large decrements in intelligent thought (including IQ and Graduate Record Examination test performance) were found among people told they were likely to end up alone in life. The decline in cognitive performance was found in complex cognitive tasks such as effortful logic and reasoning: simple information processing remained intact despite the social exclusion. The effects were specific to social exclusion, as participants who received predictions of future nonsocial misfortunes (accidents and injuries) performed well on the cognitive tests. The cognitive impairments appeared to involve reductions in both speed (effort) and accuracy. The effect was not mediated by mood.
Conventional wisdom over the past 160 years in the cognitive and neurosciences has assumed that brains evolved to process factual information about the world. Most attention has therefore been focused on such features as pattern recognition, color vision, and speech perception. By extension, it was assumed that brains evolved to deal with essentially ecological problem-solving tasks. 1.
Introduction. Chance, A Systems Synthesis of Mentality. Emory, Social Geometry & Cohesion in Three Primate Species. Pitcairn, Social Attention and Social Awareness. Power, The Cohesive Foragers: Human and Chimpanzee. de Waal, The Reconciled Hierarchy. Itani, The Origin of Human Equality. Price, Alternative Channels for Negotiating the Definitions of Social Relationships. Gardner, Psychiatric Syndromes as Infrastructure for Intraspecific Communication. Scott-Lewis, The Therapeutic Use of an Ethogram in a Drug Addiction Unit: Social Referent Allegiances. Montagner et al, Social Interactions of Young Children with Peers and their Modification in Relation to Environmental Factors. Barner-Barry, The Structure of Politically Relevant Behaviours in Pre-School Peer Groups. Masters, Nice Guys DON'T Finish Last: Aggressive & Appeasement Gestures in Media Images of Politicians. Kemper, The Two Dimensions of Sociality. Wedgewood-Oppenheim, Organizational Culture and the Agonic, Hedonic Bimodality. Glossary. Indices.