Journal of Personality and Social Psychology
1976, Vol. 33, No. 5, 541-546
Personal Space Invasions in the Lavatory:
Suggestive Evidence for Arousal
R. Dennis Middlemist
Oklahoma State University
Eric S. Knowles
Ohio State University
Charles F. Matter
University of Wisconsin—Green Bay
The hypothesis that personal space invasions produce arousal was investigated
in a field experiment. A men's lavatory provided a setting where norms for
privacy were salient, where personal space invasions could occur in the case of
men urinating, where the opportunity for compensatory responses to invasion
were minimal, and where proximity-induced arousal could be measured.
Research on micturation indicates that social stressors inhibit relaxation of
the external urethral sphincter, which would delay the onset of micturation,
and that they increase intravesical pressure, which would shorten the duration
of micturation once begun, Sixty lavatory users were randomly assigned to
one of three levels of interpersonal distance and their micturation times were
recorded. In a three-urinal lavatory, a confederate stood immediately adjacent to a subject, one urinal removed, or was absent. Paralleling the results of
a correlational pilot study, close interpersonal distances increased the delay of
onset and decreased the persistence of micturation. These findings provide
objective evidence that personal space invasions produce physiological changes
associated with arousal.
In the study of person-environment relations, the concept of personal space has been
postulated as a variable that, in part, determines how people respond to their social and
physical environments. Sommer (1969) denned personal space as the "area with invisible boundaries surrounding a person's
body into which intruders may not come"
(p. 26). Investigations of personal space phenomena suggest that individuals seek to maintain psychologically comfortable interpersonal
distances. If an invasion of personal space
takes place, individuals will move away from
others and reestablish the personal space
boundaries (Felipe & Sommer, 1966; Sommer,
1969) or engage in compensatory behaviors that minimize the closeness (Patterson,
The authors thank Daniel Kasten for serving as
the confederate and Anthony G. Greenwald for providing comments on an earlier draft. Portions of
this research were presented at the Midwestern Psychological Association Convention, Chicago, May
1975.
Requests for reprints should be addressed ot Eric
S. Knowles, College of Community Sciences, University of Wisconsin-Green Bay, Green Bay, Wisconsin 54302.
Mullens, & Romano, 1971; Cowan, Note 1).
Other findings suggest that individuals will
avoid invading the personal space of others
(Barefoot, Hoople, & McClay, 1972; Sommer
& Becker, 1969) or will engage in submissive
gestures or verbalized apologies to minimize
the impact of invasion (Efran & Cheyne,
1974; Felipe & Sommer, 1966; Knowles,
1973).
Although these behavioral responses related
to personal space invasions have been documented and described, there has been little
systematic investigation of the reasons why
these responses occur. In a recent review,
Evans and Howard (1973) concluded that
"we do not as yet thoroughly understand all
the variables which are relevant to [personal
space] behavior, and we are even further away
from being able to explain why and how
personal space operates for human beings"
(p. 341). The most common explanatory position is that emotional arousal is an important variable intervening between personal
space and the behavioral responses to personal space invasion. Evans and Howard
(1973) and Sommer (1969) are among those
who have suggested that invasions of personal
541
542
R. D. MIDDLEMIST, E. S. KNOWLES, AND C. F. MATTER
space are interpersonally stressful, increasing
arousal and discomfort, and that it is this
arousal that produces the behavioral responses. These behavioral responses occur
because they reduce the arousal caused by the
personal space invasion.
Although there is a clear relationship between personal space invasions and the behavioral responses to invasions, there is little
unambiguous evidence that arousal plays any
role, much less a mediating role, in this relationship. Findings from animal species other
than man that chronic crowding is related to
adrenal hypertrophy (Christian & Davis, 1966;
Deevey, 1966) suggest prolonged arousal, but
do not imply that similar processes operate
in humans. Various self-report data suggest
that human subjects report discomfort and
negative feelings as a result of personal space
invasions (Efran & Cheyne, 1974; Porter,
Argyle, & Salter, 1970) or crowded conditions
(Dabbs, 1971), but these reports may have
been produced by factors other than arousal.
Several authors have attempted to obtain
more direct indications of arousal. Efran and
Cheyne (1974) attempted to measure changes
in cardiovascular activity as a result of invasions, and Dabbs (1971) attempted to obtain
measures of palmar sweating under conditions of crowding. In both cases the results
were inconclusive. McBride, King, and James
(196S) measured subjects' galvanic skin responses when they were approached at various distances from various angles by male
and female experimenters. They found greater
decreases in skin resistance with closer
approaches, with frontal rather than side
approaches, and with opposite-sex experimenters. Although this study is often cited as
providing the most direct indication that personal space invasions produce arousal, it alsu
is not conclusive, at least by itself. The subjects were instrumented, participating in an
experiment, and aware of the dimension being
manipulated, all of which may have made
their behavior and responses different from
disguised or naturally occurring invasions
(Knowles & Johnsen, 1974).
As an alternative to the laboratory, a men's
lavatory provides a setting where personal
space violations can occur in a natural yet
sufficiently standardized way. Although Kira
(1970) has pointed out that use of the bathroom evokes concerns for privacy among
members of the middle class, public facilities
do not allow complete privacy, particularly
in the case of men urinating. Urinals are
open and placed side by side so that, under
crowded conditions, men stand shoulder to
shoulder, coactively engaging in private elimination. Unlike other settings, including the
laboratory, these personal space intrusions
in the lavatory are minimally confounded
by compensatory responses—moving away,
changing body orientation, using hands and
arms as an interpersonal buffer, reducing eye
contact—that a subject makes to an invasion. If compensatory behaviors occur to reduce the arousal caused by invasions, then it
would be impossible to measure the degree
of arousal accurately if subjects were free to
engage in these compensatory behaviors.
In addition, research on micturation suggests that it is a process sensitive to arousal
(Scott, Quesada, & Cardus, 1964; Straub,
Ripley, & Wolf, 1950; Tanaeho, 1971). At
the onset of micturation, the detrusor muscles
of the bladder contract, increasing intravesical pressure and forcing urine out of the
bladder. At the same time, the two sphincters
of the urethra relax, particularly the external
sphincter, allowing urine to flow. Social
stressors appear to affect both these mechanisms of micturation. Straub et al. (19SO)
showed that a stressful interview produced a
marked and sustained increase in intravesical
pressure. Scott et al. (1964) reported that
fright and embarrassment inhibited relaxation
of the external sphincter of the urethra.
The relationships between social arousal
and micturation suggest that, if an individual
intent on micturating were subjected to a
stressor, the onset of micturation would be
delayed because of a reduction in the degree
of relaxation of the external sphincter, while
the duration of urine flow, once begun, would
be foreshortened because of increased intravesicle pressure. If personal space invasions
produce arousal, then subjects standing closest
to others at lavatory urinals would show increases in the delay of onset of micturation
and decreases in the persistence of mictura-
PERSONAL SPACE AND AROUSAL
tion. Because of the novelty of these hypotheses, a pilot study was first undertaken to
investigate whether any relationship between
interpersonal distance and micturation times
could be observed.
Pilot Study
A field observation conducted at a men's
lavatory at a western U.S. university provided evidence for a correlation between
interpersonal distance and micturation times.
Men entering a restroom to urinate were
allowed to choose a urinal under prevailing
ecological conditions. Data were recorded for
48 subjects, users of the men's lavatory. A
user was included as a subject if the degree
of interpersonal distance between him and the
next nearest user remained constant throughout the duration of his urination. The restroom contained two banks of five urinals,
which were bowl-type receptacles jutting out
of the wall and containing about 3 inches
(8 cm) of standing water, which the user
flushed.
An observer was stationed at the sink
facilities and appeared to be grooming himself. When a potential subject entered the
room and walked to a urinal, the observer
recorded the selected urinal and the placement of the next nearest user. He also
noted (with a chronographic wristwatch) and
recorded the micturation delay (the time between when a subject unzipped his fly and
when urination began) and the micturation
persistence (the time between the onset and
completion of urination). The onset and
cessation of micturation were signaled by the
sound of the stream of urine striking the
water in the urinal.
Of the 48 subjects recorded, none selected
a urinal immediately adjacent to another
user, 23 were separated by one urinal from
the next nearest user, 16 were separated by
two urinals, and 9 were separated by three or
more urinals. The fact that no subjects were
observed choosing an adjacent urinal may reflect active avoidance of the most proximate
interpersonal distance. Even with this restricted range of interpersonal distance, significant correlations were found for both measures. Micturation delay showed a negative
543
correlation with the three levels of interpersonal distance, r(46) = -.315,p < .OS.1 Subjects standing one urinal away had a mean
delay of 7.9 seconds, subjects two spaces
away had a delay of 5.9 seconds, and subjects
three or more spaces away had a delay of 5.7
seconds. Micturation persistence showed a
positive relationship with the three levels
of interpersonal distance, r(46) = +.562, p
< .001. The mean persistence was 19.0 seconds with one space, 24.4 seconds with two
spaces, and 32.0 seconds with three or more
spaces.
This pilot study, while lacking controls on
subject self-selection and open to various interpretations, did suggest that the hypotheses
warranted more controlled investigation. The
correlations found were in the direction predicted by the hypotheses. Moreover, the
pilot study suggested that the micturation
measures could be used as the dependent
variables in an experimental study. Thus, the
following experiment was conducted to test
the hypothesis that decreases in interpersonal
distance lead to arousal as evidenced by
increases in micturation delay and decreases
in micturation persistence.
METHOD
Overview
In a field experiment conducted in a men's
lavatory at a midwestern U.S. university, subjects
were randomly assigned to one of three levels of
interpersonal distance. Men who entered a threeurinal lavatory to urinate were forced to use the
leftmost urinal. A confederate was placed immediately adjacent to the subject, one urinal removed, or
was absent from the lavatory. An observer stationed
in a toilet stall timed the delay and persistence of
micturation.
Subjects
Data were gathered on 60 users of the men's
lavatory. A user was included as a subject if no
other user (besides the confederate) was present
during his urination. If someone else was present or
entered during urination, the user was not counted.
Conditions were randomly assigned and prepared
before the subject entered the lavatory. Subjects
were not informed that they had participated in an
experiment.
1
Two-tailed probabilities are used throughout.
R. D. MIDDLEMIST, E. S. KNOWLES, AND C. F. MATTER
544
30"
-•PERSISTENCE
25-
20-
subject's face. The observer started two stop watches
when a subject stepped up to the urinal, stopped
one when urination began, and stopped the other
when urination was terminated. These times allowed
calculation of the two dependent variables: delay of
onset and persistence of micturation.
RESULTS
15-
• DELAY OF ONSET
CLOSE
DISTANCE
MODERATE
DISTANCE
CONTROL
FIGURE 1. Micturation times.
Procedure
The observed lavatory was just off a main hallway, adjacent to a large classroom. The observed
use rate averaged about one person every 6 minutes.
The restroom contained two toilet stalls and three
urinals. The urinals were 18 inches (46 cm) wide
with 18 inches of tile between adjacent urinals and
extended up from the floor about 4 feet (1.2 m).
The urinals were automatically flushed at 10-minute
intervals.
The subjects were forced to use the leftmost urinal
under one of three levels of interpersonal distance.
In the close distance condition, a confederate appearing to urinate was stationed at the middle urinal,
and a "Don't use, washing urinal" sign accompanied
by a bucket of water and a sponge was placed on
the rightmost urinal. This arrangement left a distance of approximately 16 to 18 inches (40 to 46 cm)
between the shoulders of the subject and confederate. In the moderate distance condition, the confederate stood at the rightmost urinal and the
bucket and sign were placed in the middle urinal.
This arrangement left a distance of 52 to 54 inches
(132 to 137 cm) between the subject and the confederate. In a control condition, the confederate was
not present in the lavatory and both the middle and
right urinals had signs on them with the water
bucket in between.
An observer was stationed in the toilet stall immediately adjacent to the subjects' urinal. During
pilot tests of these procedures it became clear that
auditory cues could not be used to signal the
initiation and cessation of micturation. The urinals
were so silent that even the confederate standing
adjacent to the subject could not hear the urine
striking the urinal.2 Instead, visual cues were used.
The observer used a periscopic prism imbedded in
a stack of books lying on the floor of the toilet
stall. An 11-inch (28-cm) space between the floor
and the wall of the toilet stall provided a view,
through the periscope, of the user's lower torso and
made possible direct visual sightings of the stream
of urine. The observer, however, was unable to see a
The hypotheses that decreases in interpersonal distance would lead to increases in the
delay of micturation and decreases in the
persistence of micturation were tested in a
multivariate analysis of variance of the effects
of conditions on the two micturation measures. Each measure was heteroscedastic, but
square root transformations of the data made
the cell variances comparable, and the analysis was performed on these transformed
scores. The multivariate analysis indicated a
significant difference among distance conditions, F(4,112) = 10.38, p < .001. A priori
multivariate comparisons among conditions
showed that the close distance produced responses significantly different from the moderate distance, F(2, 56) = 10.04, p < .001, and
that the confederate-present conditions produced responses significantly different from
the confederate-absent condition, F ( 2 , 5 6 )
— 14.53, p < .001. Figure 1, which presents
the mean seconds for micturation delay and
persistence in each condition, shows that the
effects were in the predicted direction.
A test of the univariate effects of distance
on micturation delay revealed significant differences among conditions, F(2, 57) = 12.44,
p < .001. Micturation delay increased from a
mean of 4.9 seconds in the control condition
to 6.2 seconds in the moderate distance condition to 8.4 seconds in the close distance condition. The a priori tests indicated that the
close condition led to significantly longer
delays than the moderate condition, F(l, 57)
= 9.01, p < .004, and that the confederatepresent conditions led to significantly longer
delays than the confederate-absent condition,
7^(1,57) = 15.86, p< .001.
2
Although the silence of the urinals necessitated
a change from the pilot study in the mode of
observation, it had the advantage of making the
confederate credible. During tests of the experimental procedures, none of the test subjects had any
suspicions about the confederate's activity.
PERSONAL SPACE AND AROUSAL
545
Micturation persistence also showed signifi- iors. Subsequent research is needed to investicant differences among conditions, 7 ? (2,S7) gate the second half of the arousal model.
= 4.41, p < .017. The pattern of means, from
The results of this experiment reproduced
24.8 seconds in the control condition to 23.4 and complemented the results of the pilot
seconds in the moderate distance to 17.4 sec- study. Both micturation delay and persistence
onds in the close distance condition, shows were shown to be related to interpersonal
the predicted decrease in the persistence of distance, and similar patterns of means were
micturation. The close distance produced observed. Although neither set of data is preshorter persistence times than the moder- cise enough to allow assessment of the form
ate distance, F(l, 57) = 4.49, p < .038, and of the relationship between distance and
the confederate-present conditions produced micturation times, it appears that the closest
shorter persistence times than the confederate- distance had much more of an effect than the
absent condition, F(l, 57) = 4.33, p < .042. next closest distance. This pattern is reminisThe analysis of the effects of interpersonal cent of the nonlinear, exponential relationdistance on micturation times supported both ships observed for much greater distances
hypotheses. Closer distances led to increases (Bratfisch, 1969; Ekman & Bratfisch, 1965;
in micturation delay and decrease in mictura- Lundberg, Bratfisch, & Ekman, 1972). The
tion persistence. Both of these effects, which present data are not incompatible with
across conditions produced a negative correla- Ekman's suggestion that emotional involvetion between cell means, appeared in spite of ment decreases as an inverse power function
the fact that the two measures tended to be of distance.
positively correlated. The within-cell correlaFinally, the present study suggests that the
tion between micturation delay and persist- dependent measures may have some utility as
ence was +.349, which reflected compa- unobtrusive measures of social arousal in labrable correlations within each condition (rs oratory as well as field settings. In a labora- +.308, +.304, and +.542 for the control, tory, the effects of intravesical pressure could
moderate, and close distance conditions).
be more sensitively estimated by using the
volume of urine expelled as a covariate to the
persistence measure. Presumably, differences
DISCUSSION
in the amount of urine expelled contributed
Variations in interpersonal distance in a a great deal of variance to the persistence
lavatory appear to be related in systematic measures in the present study. Yet, both
ways to variations in micturation times. The micturation delay and persistence were sensipattern of results supports the hypothesis tive to situational differences. Although the
that arousal increases with decreases in inter- parameters of these measures have not been
personal distance. The arousal model of per- extensively studied, the present study implies
sonal space invasions proposes that close that they have some construct validity as
interpersonal distances are interpersonally indicators of arousal.
stressful, increasing arousal and discomfort,
and that it is this arousal that produces beREFERENCE NOTE
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fornia. 16-mm black and white sound film, 1968.
half of the model, that arousal results from
(Available from University Extension Media Center, University of California, Berkeley, Berkeley,
interpersonal closeness, and the findings supCalifornia 94720.)
port this part of the arousal model. What has
not been shown by this study or earlier
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THIS ARTICLE HAS BEEN CORRECTED. SEE LAST PAGE
Journal of Applied Psychology
2014, Vol. 99, No. 3, 504 –513
© 2014 American Psychological Association
0021-9010/14/$12.00 DOI: 10.1037/a0035559
RESEARCH REPORT
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Rainmakers: Why Bad Weather Means Good Productivity
Jooa Julia Lee and Francesca Gino
Bradley R. Staats
Harvard University
University of North Carolina at Chapel Hill
People believe that weather conditions influence their everyday work life, but to date, little is known
about how weather affects individual productivity. Contrary to conventional wisdom, we predict and find
that bad weather increases individual productivity and that it does so by eliminating potential cognitive
distractions resulting from good weather. When the weather is bad, individuals appear to focus more on
their work than on alternate outdoor activities. We investigate the proposed relationship between worse
weather and higher productivity through 4 studies: (a) field data on employees’ productivity from a bank
in Japan, (b) 2 studies from an online labor market in the United States, and (c) a laboratory experiment.
Our findings suggest that worker productivity is higher on bad-, rather than good-, weather days and that
cognitive distractions associated with good weather may explain the relationship. We discuss the
theoretical and practical implications of our research.
Keywords: weather, productivity, opportunity cost, distractions
Supplemental materials: http://dx.doi.org/10.1037/a0035559.supp
We theorize that thoughts related to salient outdoor options
come to mind more easily on good weather days than on bad
weather days. Consistent with our theorizing, Simonsohn (2010)
found that cloud cover during visits to a college known for its
academic rigor by prospective students predicted whether they
enrolled in the visited school. Prospective students who visited on
a cloudier day were more likely to enroll than were those who
visited on a sunnier day. Cloudy weather reduced the opportunity
costs of outdoor activities such as sports or hiking and thus
increased the attractiveness of academic activities.
To gain insight into how people intuitively think about this
relationship, we asked 198 adults (Mage ⫽ 38 years, SD ⫽ 14.19;
42% male) to predict the impact of weather on individuals’ work
productivity. Among our respondents, about 82% stated that good
weather conditions would increase productivity, and about 83%
responded that bad weather conditions would decrease productivity. These survey results indicate that people indeed believe that
weather will impact their productivity and that bad weather conditions in particular will be detrimental to it.
This conventional wisdom may be based on the view that bad
weather induces a negative mood and therefore impairs executive
functions (Keller et al., 2005). In contrast to this view, we propose
that bad weather actually increases productivity through an alternative psychological route. We theorize that the positive effects of
bad weather on worker productivity stem from the likelihood that
people may be cognitively distracted by the attractive outdoor
options available to them on good weather days. Consequently,
workers will be less distracted and more focused on bad weather
days, when such outdoor options do not exist, and therefore will
perform their tasks more effectively.
In this article, we seek to understand the impact of weather on
worker productivity. Although researchers have investigated the
effect of weather on everyday phenomena, such as stock market
returns (Hirshleifer & Shumway, 2003; Saunders, 1993), tipping
(Rind, 1996), consumer spending (Murray, Di Muro, Finn, &
Popkowski Leszczyc, 2010), aggression in sports (Larrick, Timmerman, Carton, & Abrevaya, 2011), and willingness to help
(Cunningham, 1979), few studies have directly investigated the
effect of weather on work productivity. Moreover, to date, no
studies have examined psychological mechanisms through which
weather affects individual worker productivity, the focus of our
current investigation.
This article was published Online First January 13, 2014.
Jooa Julia Lee, Harvard Kennedy School, Harvard University; Francesca
Gino, Negotiation, Organizations & Markets Unit, Harvard Business
School, Harvard University; Bradley R. Staats, Operations, Kenan-Flagler
Business School, University of North Carolina at Chapel Hill.
This research was supported by Harvard Business School, the University
of North Carolina at Chapel Hill’s Center for International Business
Education and Research, and the University Research Council at the
University of North Carolina at Chapel Hill. We thank Max Bazerman and
Karim Kassam for their insightful comments on earlier drafts of this article.
We are also grateful to Kanyinsola Aibana, Will Boning, Soohyun Lee,
Nicole Ludmir, and Yian Xu for their assistance in collecting and scoring
the data. We gratefully acknowledge the support of management at our
field site, and the support and facilities of the Harvard Decision Science
Laboratory and the Harvard Business School Computer Laboratory for
Experimental Research (CLER).
Correspondence concerning this article should be addressed to Jooa Julia
Lee, Harvard University, Harvard Kennedy School, 124 Mt. Auburn Street,
Suite 122, Cambridge, MA 02138. E-mail: jooajulialee@fas.harvard.edu
504
BAD WEATHER INCREASES PRODUCTIVITY
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Psychological Mechanisms of the “Weather Effect” on
Productivity
When working on a given task, people generally tend to think,
at least to some extent, about personal priorities unrelated to that
task (Giambra, 1995; Killingsworth & Gilbert, 2010). Taskunrelated thoughts are similar to other goal-related processes in
that they can be engaged in without explicit awareness, though
they are not directed toward the given task (Smallwood &
Schooler, 2006). Thus, when the mind wanders, attention shifts
away from the given task and may lead to failures in task performance (Manly, Robertson, Galloway, & Hawkins, 1999; Robertson, Manly, Andrade, Baddeley, & Yiend, 1997). Prior work notes
that general cognitive interference can have costly effects on
worker productivity (for a review, see Jett & George, 2003).
Workers who experience cognitive interference are distracted,
showing an inability to focus on a task (Fisher, 1998) and a greater
likelihood of committing errors while completing the task (Flynn
et al., 1999).
Thinking about salient and attractive outdoor options is a form
of task-unrelated thinking that serves as a cognitive distraction that
shifts workers’ attention away from the task at hand. Accordingly,
we expect it will be harder for workers to maintain their taskrelated thoughts on good weather days than on bad weather days.
As a result, we also predict that workers will be less productive on
good weather days than on bad weather days. More specifically,
we argue that on a bad weather day, individuals will have a higher
ability to focus on a given work task not because of the negative
mood induced by the weather but because fewer distracting
thoughts related to outdoor options will be readily available in
their minds. Consequently, they will be able to better concentrate
on their tasks and work more productively on bad weather days. In
our research, we consider tasks where productivity requires high
levels of attention and focus, which allow workers to complete
their work faster. Thus, we expect fewer cognitive distractions to
be associated with higher levels of work productivity. Taken
together, these arguments lead to the following hypotheses:
Hypothesis 1. Good weather conditions, such as lack of rain,
will decrease worker productivity on tasks that require sustained attention and focus, compared to bad weather
conditions.
Hypothesis 2. Good weather conditions will increase the salience and attractiveness of outdoor options, compared to bad
weather conditions.
Hypothesis 3. The relationship between good weather conditions and worker productivity will be mediated by greater
cognitive distractions (i.e., salience of one’s outdoor options).
To test our predictions, we used empirical data on worker
productivity, measured by individual performance on tasks conducted in a Japanese bank (Study 1), an online marketplace (i.e.,
Amazon Mechanical Turk, Studies 2 and 3), and the laboratory
(Study 4). We focused on precipitation as the key measure of bad
weather given the previous finding that precipitation is the most
critical barrier to outdoor physical activities (Chan, Ryan, &
Tudor-Locke, 2006; Togo, Watanabe, Shephard, & Aoyagi, 2005).
505
Study 1: Field Evidence From a Japanese Bank
Method
In Study 1, we examined the proposed link between weather
conditions and productivity by matching data on employee productivity from a mid-size bank in Japan with daily weather data.1
In particular, we assessed worker productivity using archival data
from a Japanese bank’s home-loan mortgage-processing line. For
the sake of brevity, we discuss the overall structure of the operations here; more detailed information can be found in Staats and
Gino (2012). Our data includes information on the line from the
rollout date, June 1, 2007 until December 30, 2009, a 2.5-year time
period. We examined all transactions completed by the permanent
workforce, 111 workers who completed 598,393 transactions.
Workers at the bank conducted the 17 data-entry tasks required to
move from a paper loan application to a loan decision. Included
were tasks such as entering a customer’s personal data (e.g., name,
address, phone number) and entering information from a real estate
appraisal. Workers completed one task at a time (i.e., one of 17
steps for one loan); when a task was completed, the system
assigned the worker a new task. The building in which the work
took place had windows through which workers could observe the
weather. Workers were paid a flat fee for their work; there was no
piece-rate incentive to encourage faster completion of work.
In addition to the information on worker productivity, we also
assembled data on weather conditions in Tokyo, the city where the
individuals worked. The National Climactic Data Center of the
U.S. Department of Commerce collects meteorological data from
stations around the world. Information for a location, such as
Tokyo, was calculated as a daily average and includes summaries
for temperature, precipitation amount, and visibility.
Measures
Completion time. To calculate completion time, we took the
natural log of the number of minutes a worker spent to complete
the task ( ⫽ 0.39, ⫽ 1.15). As we detail below, we conducted
our analyses using a log-linear learning curve model.
Weather conditions. Since our main variable of interest is
precipitation, we included a variable equal to the amount of precipitation each day in inches, down to the hundredth of an inch
( ⫽ 0.18, ⫽ 0.53). To control for effects from other weatherrelated factors, we also included temperature ( ⫽ 62.1, ⫽ 14.6)
and visibility ( ⫽ 10.3, ⫽ 5.1). With respect to the former, it
may be that productivity is higher with either low or high temperatures. Therefore, we entered both a linear and quadratic term for
temperature (in degrees Fahrenheit). Finally, because worse visibility could be related to lower productivity, we included the
average daily visibility in miles (to the tenth of a mile).
Control variables. We controlled for variables that have been
shown to affect worker productivity. These included: same-day,
cumulative volume (count of the prior number of transactions
handled by a worker on that day); all prior days’ cumulative
volume (count of transactions from prior days); load (percentage
1
The data reported in Study 1 have been collected as part of a larger data
collection. Findings from the data have been reported in separate articles:
Staats and Gino (2012) and Derler, Moore, and Staats (2013).
506
LEE, GINO, AND STAATS
of individuals completing work during the hour that the focal task
occurred; see Kc & Terwiesch, 2009); overwork (a comparison of
current load to the average, see Kc & Terwiesch, 2009); defect;
day-of-week, month, year, stage (an indicator for each of the 17
different steps); and individual indicators.
errors and correcting them). To account for the potential influence
of weather-driven moods, in addition to new productivity measures, we collected data on whether workers felt positive or negative affect while completing the task.
Method
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Results and Discussion
We used a log-linear learning curve model because individuals’
performance improves over time with experience. Using this approach, we conducted our analyses at the transaction level. Therefore, in our models, we controlled for the effects of the worker,
task, and time, and then examined the effect of weather on worker
productivity. For our primary model, we used a fixed effects linear
regression model with standard errors clustered by individual.
Column 1 in Table 1 shows our main model, which we used to
test Hypothesis 1. Examining rain, we found that the coefficient is
negative and significant (coefficient ⫽ ⫺0.01363). In terms of the
effect size, we found that a one-inch increase in rain is related to
a 1.3% decrease in worker completion time for each transaction.
Given that there are approximately 100 workers in the operation,
a 1.3% productivity loss is approximately equivalent to losing one
worker for the organization on a given day. Based on the average
yearly salary of the associate-level employees at this bank and the
average frequency of precipitation, this loss could cost approximately $18,750 for this particular operation a year. When accumulated over time for the entire bank of nearly 5,000 employees,
a 1.3% productivity loss could be interpreted as a significant loss
in revenue for the bank: at least $937,500 a year. Further, in a city
the size of Tokyo (approximately 9 million people) our identified
effect could translate into hundreds of millions of dollars in annual
lost productivity.
Next, it is important to properly account for the standard errors
in our model as we have many observations nested within a small
number of individual workers. Therefore, in Column 2, we clustered the standard errors by day, not by worker. In Column 3, we
used Prais-Winsten regression with panel-corrected standard errors
adjusted for heteroskedasticity and panel-wide, first-order autocorrelation. Then, in Column 4, we used the fixed effects regression
model from Columns 1–3 but used block-bootstrapped standard errors.
In each model, the coefficient on rain is negative and statistically
significant. Finally, in Columns 5 and 6 we added additional
controls with first individual fixed effects interacted with monthly
fixed effects and then individual fixed effects interacted with stage
fixed effects. In conclusion, using a within-subject design, this
study showed that greater rain is related to better worker productivity.
Study 2: Online Study of Weather and Productivity
Although Study 1 offers valuable information on employees’
actual work productivity, only the time taken to complete a task
was used as an outcome variable, as error rates were low (less than
3%) and showed little variation across employees. In Study 2, we
sought a conceptual replication of the effect of weather on completion time while also using a task that would permit us to
measure error rates. We could thus investigate productivity not
only in terms of quantity (speed at which workers completed their
given task) but also in terms of quality (accuracy of detecting
Participants and procedure. We recruited U.S. residents to
participate in an online survey in early March, when weather
conditions vary significantly depending on where workers are
located. Three hundred twenty-nine online workers (Mage ⫽ 36.52
years, SD ⫽ 12.79; 48% male) participated in a 30-min study and
received a flat fee of $1. We first gave all workers a threeparagraph essay that included 26 spelling errors; we asked them to
find as many errors as they could and correct the errors they
found.2
Once all the workers had completed the task, they completed a
questionnaire that included measures of state emotions to control
for potential effects of affect. Finally, we asked workers to complete a demographics questionnaire that also included questions
about the day’s weather and their zip code.
Measures.
Productivity. We computed the time (in seconds) workers
spent on the task of correcting spelling errors (i.e., speed). Given
that each worker spent a different amount of time on the task, we
calculated speed by dividing the number of typos detected by the
total time taken in seconds. We then log-transformed the variable
to reduce skewness. In addition, we computed how many spelling
errors were correctly identified and fixed as a measure of accuracy.
State emotions. We used the 20-item form of the Positive and
Negative Affect Scale (PANAS; Watson, Clark, & Tellegen,
1988). Participants indicated how much they felt each emotion
“right now” using a 7-point scale. We calculated two summary
variables for each participant: positive (␣ ⫽ .90) and negative
affect (␣ ⫽ .91).
Weather questionnaire. Workers were asked to report their
zip code, which enabled us to find the daily weather data of the
specific area on a specific day (http://www.wunderground.com).
To ensure that workers’ perceived weather matched actual weather
conditions, we also asked them to think about the weather conditions of the day, relative to their city’s average weather conditions,
using a 5-point scale (1 ⫽ one of the best to 5 ⫽ one of the worst).
Results and Discussion
We first tested whether actual weather matched workers’ perceptions of the day’s weather. Indeed, subjective perceptions of
bad weather were associated with lower temperature (r ⫽ ⫺.24,
p ⬍ .001), higher humidity (r ⫽ .21, p ⬍ 0.001), more precipitation (r ⫽ .23, p ⬍ 0.001), more wind (r ⫽ .31, p ⬍ 0.001), and
lower visibility (r ⫽ ⫺.26, p ⬍ 0.001).
Table 2 reports summary statistics. Table 3 summarizes a series
of regression analyses. Consistent with Hypothesis 1, more rain
was associated with higher productivity, measured in terms of both
speed and accuracy (Model 1). This relationship holds even after
controlling for key demographic variables and state emotions
2
More detailed instructions and materials are available online as supplemental materials (Appendix A).
B
0.006068 ⫺0.01363ⴱ
0.004340
0.006964ⴱ
3.710e⫺05 ⫺6.425e⫺05ⴱ
7.311e⫺04 9.799e⫺04
SE
B
0.006869 ⫺0.01284ⴱⴱⴱ
0.003341
0.006789ⴱⴱⴱ
2.680e⫺05 ⫺6.323e⫺05ⴱⴱⴱ
6.991e⫺04 8.483e⫺04ⴱ
SE
SE
ⴱ
B
B
B
0.006055
0.004438
3.756e⫺05
7.102e⫺04
SE
6
Individual ⫻ Stage fixed
effects
0.004827 ⫺0.01336ⴱ
0.003473
0.006863
2.946e⫺05 ⫺6.449e⫺05
5.755e⫺04 7.808e⫺04
SE
5
Individual ⫻ Month fixed
effects
0.005686 ⫺0.01167ⴱ
0.004364
0.004519
3.819e⫺05 ⫺4.588e⫺05
7.040e⫺04 8.176e⫺04
SE
4
Block bootstrap
0.002788 ⫺0.01363ⴱ
0.001773
0.006964
1.382e⫺05 ⫺6.425e⫺05
3.443e⫺04 9.799e⫺04
3
Prais-Winsten
—
—
598,393
0.4591
—
—
598,393
0.3563
—
598,393
0.3374
—
—
598,393
0.3563
—
6.198e⫺11 1.524e⫺09ⴱ
0.01030
⫺0.4181ⴱⴱⴱ
0.009857
0.2603ⴱⴱⴱ
0.006690
0.2206ⴱⴱⴱ
0.08566
⫺0.3350
—
598,393
0.08806
Yes
7.360e⫺10 1.380e⫺09
0.05965
⫺0.3283ⴱⴱⴱ
0.05339
0.1898ⴱⴱⴱ
0.03900
0.2398ⴱⴱⴱ
0.2394
0.1733
Yes
598,393
0.04908
—
5.905e⫺10
0.04788
0.04108
0.03500
0.2154
1.205e⫺09 1.323e⫺09ⴱ
0.05141
⫺0.3651ⴱⴱⴱ
0.04601
0.2166ⴱⴱⴱ
0.03609
0.2487ⴱⴱⴱ
0.2160
1.0083ⴱⴱⴱ
1.461e⫺10 1.508e⫺09ⴱⴱⴱ
0.02195
⫺0.4014ⴱⴱⴱ
0.02583
0.2468ⴱⴱⴱ
0.01661
0.3108ⴱⴱⴱ
0.1693
⫺2.4212ⴱⴱⴱ
1.524e⫺09ⴱ
⫺0.4181ⴱⴱⴱ
0.2603ⴱⴱⴱ
0.2206ⴱⴱⴱ
⫺0.3350
6.132e⫺10 1.524e⫺09ⴱⴱⴱ
0.05738
⫺0.4181ⴱⴱⴱ
0.04925
0.2603ⴱⴱⴱ
0.03507
0.2206ⴱⴱⴱ
0.2192
⫺2.1010ⴱⴱⴱ
2.380e⫺05 ⫺3.477e⫺05ⴱ 1.661e⫺05
⫺4.507e⫺05ⴱ 1.801e⫺05 ⫺4.507e⫺05ⴱⴱⴱ 3.674e⫺06 ⫺4.581e⫺05ⴱⴱⴱ 1.672e⫺06 ⫺4.507e⫺05ⴱ 1.823e⫺05 ⫺1.809e⫺05
⫺1.696e⫺04ⴱ 6.511e⫺05 ⫺1.696e⫺04ⴱⴱⴱ 2.259e⫺05 ⫺1.040e⫺04ⴱⴱⴱ 9.373e⫺06 ⫺1.696e⫺04ⴱⴱ 6.031e⫺05 ⫺1.274e⫺04ⴱⴱⴱ 2.909e⫺05 ⫺1.661e⫺04ⴱⴱ 5.830e⫺05
⫺0.01363ⴱ
0.006964
⫺6.425e⫺05
9.799e⫺04
B
2
Cluster by day
Model
Note. n ⫽ 598,393. All models include indicators for the individual, stage, month, year, and day of week.
p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
Rain (inches)
Temperature (degrees)
Temperature2
Visibility (miles)
Same-day, cumulative
volume
All prior days’
cumulative volume
All prior days’
cumulative volume2
Load
Overwork
Defect
Constant
Individual ⫻ Month
fixed effect
Individual ⫻ Stage
fixed effect
Observations
2
R
Variable
1
Main model
Table 1
Summary Regression Results on Completion Time for Study 1
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BAD WEATHER INCREASES PRODUCTIVITY
507
LEE, GINO, AND STAATS
508
Table 2
Summary Statistics for Study 2
Variable
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Speed
Accuracy
Precipitation
Perceived bad weather
Positive affect
Negative affect
Female
Age
Education
Income
M
1
2
3
4
5
6
2.84
17.87
0.28
3.01
4.00
1.53
1.52
37.23
4.19
3.84
0.64
4.82
0.93
0.82
1.14
0.83
0.50
12.88
1.49
2.68
.82
.12
.01
⫺.04
⫺.03
.02
⫺.08
.13
.13
.11
.01
⫺.04
⫺.05
.05
.05
.18
.13
.24
.02
⫺.02
⫺.10
.00
.05
⫺.01
⫺.09
.01
.06
.06
.02
⫺.06
⫺.06
.08
.10
.06
.01
⫺.09
ⴚ.15
⫺.05
.03
7
8
.13
.05 .11
.04 ⫺.02
9
.25
Note. Bold denotes significance of less than 5%.
(Model 2). These findings suggest that bad weather is associated
with both indicators of productivity, increased speed, and accuracy.
Study 3: Online Study of Weather and Salience of
Outdoor Options
We conducted a third study to test Hypothesis 2, which suggests
that good weather conditions raise the attractiveness of outdoor
options compared to bad weather conditions.
Method
Participants and procedure. We recruited 77 online workers
(Mage ⫽ 33.02 years, SD ⫽ 11.99; 53% male) on MTurk to
participate in a 5-min study for a flat fee of $0.20. We randomly
assigned participants to one of two weather conditions (good vs.
bad). Participants were primed on the weather; half of them read,
“Please imagine that it is a beautiful, sunny day outside for the next
10 seconds,” and the rest read, “Please imagine that it is raining
outside for the next 10 seconds.” We then asked all workers to
write down as many non-work-related activities as possible that
they would like to engage in (up to 10). Workers were also asked
to rate the attractiveness of these activities using a 5-point scale
(from 1 ⫽ the least attractive to 5 ⫽ the most attractive). Among
all activities listed, we counted the number of outdoor and indoor
activities separately.
Results and Discussion
Workers who were told to imagine good weather conditions
listed significantly more outdoor activities they would like to
engage in (M ⫽ 4.47, SD ⫽ 2.91) than did workers who imagined
bad weather conditions (M ⫽ 1.31, SD ⫽ 2.10), t(75) ⫽ ⫺5.48,
p ⬍ .001, although the total number of non-work-related activities
(which include both indoor and outdoor activities) did not differ
across weather conditions, t(75) ⫽ 1.48, p ⫽ .14. Similarly,
attractiveness ratings for these outdoor activities were higher for
those who imagined good weather (M ⫽ 3.77, SD ⫽ 0.14),
compared to bad weather (M ⫽ 1.38, SD ⫽ 0.29), t(75) ⫽ –7.32,
p ⬍ .001. This finding suggests that outdoor activities were indeed
more salient and attractive when workers perceived weather to be
good than bad.
Study 4: Laboratory Study of Outdoor Options and
Productivity
In Study 4, we carefully chose the days on which we conducted
our study sessions to take advantage of natural variation, then we
experimentally manipulated subjects’ exposure to outdoor options.
Through moderation, we seek to provide evidence in support of
our mediation hypothesis that the salience of attractive outdoor
options is directly linked to cognitive distractions. To test for the
mediating role of outdoor options and cognitive distractions
through a moderation approach (Spencer, Zanna, & Fong, 2008),
we chose weather conditions and manipulated the mediating factor
(in our case, exposure to outdoor options).3 Using a 2 ⫻ 2 design,
we expect to find an interaction between weather conditions and
exposure to outdoor options in predicting work productivity (consistent with Hypothesis 3). Further, we predict that productivity
will be lower on good weather days compared to bad weather days,
regardless of the outdoor-options manipulation, as these options
are already salient and attractive without our prompt. Thus, we
expect to see our predicted effect (better performance on bad
weather days) in the condition in which we do not introduce
outdoor options as distractions.
Method
For our first manipulation, we varied whether the task was
undertaken on days with poor weather (rainy) or good weather
(sunny). For our second manipulation, the participants either were
primed by exposure to a variety of outdoor options prior to the task
or were not primed by exposure to outdoor options. We used this
second manipulation to vary the level of cognitive distraction
created by thinking about outdoor activities one may engage in, a
manipulation based on prior research (e.g., Simonsohn, 2010).
During the entire experiment, the laboratory’s lighting and temperature levels were fixed at the same level, and participants were
3
We selected this method of manipulating the availability of outdoor
options instead of relying on self-reports, which are less reliable and more
likely to be biased (i.e., asking participants how distracted they felt or how
frequently they thought about outdoor options). This approach is considered a stronger test of the mediation hypothesis than measuring the mediating factor through the use of self-reported measures (Rucker, Preacher,
Tormala, & Petty, 2011; Spencer et al., 2005).
BAD WEATHER INCREASES PRODUCTIVITY
509
Table 3
Summary Regression Results in Study 2
Speed
Model 1
B
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Variable
Precipitation
Female
Age
Education
Income
Positive affect
Negative affect
Constant
Observations
R2
Root MSE
ⴱ
p ⬍ .05.
ⴱⴱ
SE
ⴱⴱ
0.07
0.02
2.81ⴱⴱⴱ
321
0.01
0.62
0.04
p ⬍ .01.
ⴱⴱⴱ
Accuracy
Model 2
B
Model 1
SE
ⴱⴱ
0.07
0.04
⫺0.00
0.05ⴱ
0.02
⫺0.02
⫺0.03
2.72ⴱⴱⴱ
321
0.05
0.62
0.02
0.07
0.00
0.02
0.01
0.04
0.04
0.24
B
Model 2
SE
ⴱⴱ
0.54
0.15
17.62ⴱⴱⴱ
321
0.01
4.79
0.28
B
SE
ⴱⴱ
0.52
0.30
0.01
0.47ⴱ
0.18
⫺0.23
⫺0.39
15.45ⴱⴱⴱ
321
0.06
4.71
0.15
0.54
0.02
0.01
0.09
0.28
0.36
1.72
p ⬍ .001.
able to see the outside weather through the lab’s window. There
was no significant difference in show-up rates between bad versus
good weather days.
Participants and procedure. We recruited 136 students
(Mage ⫽ 21.82 years, SD ⫽ 3.51; 48.89% male) through the study
pool at the Harvard Decision Science Laboratory. Students signed
up online in advance to participate in an hour-long study and were
paid a $10 participation fee. They were also told that, depending
on the completion time of their data entry, they could receive an
additional $10 bonus.
Participants in the exposure-to-outdoor-options condition
viewed photos of outdoor activities taking place in good weather
conditions and were asked to evaluate the attractiveness of each
activity. Participants were then asked to pick their favorite depicted activity or the activity in which they engaged most frequently and to discuss as vividly as possible what they would do
in the depicted scene. By contrast, participants in the control group
were asked to describe their typical daily routine.
Next, all participants completed the data-entry task, which involved entering five sets of questionnaire responses written in
Italian from printed copies into a spreadsheet.4 All participants
finished entering five surveys and received the additional $10.
After all participants completed their data-entry task, they answered a questionnaire that included state emotions, subjective
weather perceptions, and demographic questions.
Measures
Productivity. We assessed speed and accuracy as measures of
productivity. For speed, we first calculated the number of words
entered, then divided this number by the amount of time spent
completing the task, given that each survey data consisted of a
different number of words. We assessed accuracy by counting the
number of correct words entered for each person.
State emotions. Similar to Study 2, we controlled for the
potential influence of affect by measuring both positive (␣ ⫽ .93)
and negative affect (␣ ⫽ .89) using PANAS.
Subjective weather perceptions. As a manipulation check for
our weather manipulation, we asked participants whether they
thought the weather on the day of their participation was “good” or
“bad.”
Results and Discussion
We excluded 10 participants who failed to follow our instructions, as their completion time was not recorded correctly. Table 4
reports the descriptive statistics and correlations among the key
variables used in our analyses.
Manipulation check. Almost 90% of the participants who
participated on a good weather day (60 out of 67) felt that the
weather was good; almost 93% of participants who participated on
a rainy day (64 out of 69) felt that the weather was bad, 2(1, N ⫽
136) ⫽ 92.29, p ⬍ .001. These weather variables were not significantly correlated with our manipulation of exposure to outdoor
options, which we randomized.
Main analyses. Hypothesis 3 predicted that bad weather conditions increase productivity by decreasing thoughts about outdoor
options, which should reduce cognitive distractions. Given the
design of Study 4, this hypothesis would be supported by a
significant interaction between weather conditions and exposure to
outdoor options in predicting productivity. To test this hypothesis,
we conducted a series of regression analyses (Table 5). As shown
in Model 1, exposure to outdoor options decreased data-entry
speed and accuracy. We did not find a statistically significant
effect of bad weather on productivity (for speed,  ⫽ 1.60, p ⫽
.10; for accuracy,  ⫽ 13.33, p ⫽ .10). As predicted, the effect of
weather on speed was qualified by a significant interaction between exposure to outdoor options and weather conditions, while
the interaction effect on accuracy did not reach significance criteria. We conducted similar analyses while controlling for demographics and state emotions (Model 2). After holding these variables constant, the interaction effect on speed remained robust, and
the interaction effect on accuracy became statistically significant.
A simple slope analysis supports Hypothesis 3 (see Figure 1).
When no outdoor options were made salient to participants, bad
weather significantly increased data-entry speed ( ⫽ 3.04, p ⫽
.04). When participants were exposed to outdoor options, however,
4
Further details of the instructions and materials used in this study are
available online as supplemental materials (Appendix B).
LEE, GINO, AND STAATS
510
Table 4
Summary Statistics for Study 4
Variable
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Speed
Accuracy
Good weather indicator
Outside option indicator
Age
Female
Income
Education
Positive affect
Negative affect
Note.
M
1
2
3
4
5
6
7
8
9
30.03
190.49
0.48
0.52
21.94
1.52
4.90
3.42
35.93
19.59
3.55
31.81
0.50
0.50
3.57
0.50
3.40
1.03
12.16
9.67
.90
⫺.02
⫺.14
ⴚ.20
.18
.13
⫺.11
.03
.00
⫺.01
ⴚ.20
⫺.16
.10
.09
⫺.04
.07
⫺.01
⫺.04
.05
.01
.00
.09
.09
.04
⫺.06
⫺.07
.06
.00
⫺.07
.13
⫺.07
ⴚ.19
.71
.11
⫺.15
.06
⫺.07
⫺.09
.10
ⴚ.26
.06
⫺.05
⫺.00
.01
⫺.13
Bold denotes significance of less than 5%.
weather conditions no longer predicted speed significantly ( ⫽
0.19, p ⫽ .76). Similarly, when there were no outdoor options, bad
weather significantly increased data-entry accuracy ( ⫽ 24.90,
p ⫽ .05), a relationship that no longer held for those distracted by
outdoor options ( ⫽ 1.87, p ⫽ .74).
To summarize, we found that having attractive outdoor options
decreased productivity through increased cognitive distractions. In
line with previous work (Bailey & Konstan, 2006; Speier, Valacich, & Vessey, 1999), we demonstrate that making outdoor options
salient in people’s minds alone could impair their ability to concentrate. Good weather conditions were harmful for productivity,
an effect that seemed to disappear when outdoor options were
made salient. This interaction effect between weather conditions
and exposure to outdoor options suggests that people can be
relatively more productive at work on rainy days, unless they are
actively distracted. On sunny days, participants are likely to already be distracted, as outdoor options are salient in their minds.
Together, consistent with Hypothesis 3, these findings show that
cognitive distractions created by the salience of outdoor options
may serve as a mechanism through which bad weather conditions
increase productivity.
General Discussion and Conclusion
Our main goal in this article was to provide an alternative
psychological route of limited attention through which bad
weather conditions influence productivity, even when we hold
affective influences constant. Our evidence from both the field and
the lab was consistent with the predictions of our theoretical
model.
Although numerous previous studies used weather to induce
either positive or negative moods (Cunningham, 1979; Goldstein,
1972; Keller et al., 2005; Parrott & Sabini, 1990; Schwarz &
Clore, 1983) to study the effect of moods, our result does not
support this weather-mood hypothesis. Using a meta-analysis,
Shockley, Ispas, Rossi, and Levine (2012) found that positive
affect is associated with enhanced overall job performance. In
Studies 2 and 4, however, weather conditions did not induce
positive nor negative affect, and affect did not predict productivity.
Yet it is not our goal to suggest that the weather-mood hypothesis
is unwarranted or that affect plays no role in cognition. Although
these influences were not realized in our study, they may still be in
place, even if to a lesser extent than previous research posited.
Table 5
Summary Regression Results in Study 4
Speed
Accuracy
Model 1
B
Variable
Exposure to outdoor options
Good weather indicator
Interaction (Outdoor Options ⫻ Weather)
Age
Female
Income
Education
Positive affect
Negative affect
Constant
Observations
R2
Adjusted R2
Root MSE
ⴱ
p ⬍ .05.
ⴱⴱ
p ⬍ .01.
ⴱⴱⴱ
p ⬍ .001.
Model 2
SE
ⴱ
⫺2.20
⫺1.49
2.51ⴱ
0.87
0.91
1.27
31.28ⴱⴱⴱ
123
0.05
0.03
3.50
0.64
B
Model 1
SE
ⴱ
⫺2.26
⫺1.58
2.60ⴱ
⫺0.27
1.14
0.14
0.46
0.02
⫺0.01
32.70ⴱⴱⴱ
122
0.14
0.07
3.43
0.87
0.92
1.27
0.13
0.63
0.63
0.44
0.03
0.03
2.69
B
Model 2
SE
ⴱⴱ
⫺22.82
⫺12.02
20.61
203.27ⴱⴱⴱ
125
0.07
0.04
31.08
7.68
8.09
11.16
5.67
B
SE
ⴱⴱ
⫺23.90
⫺13.61
21.88ⴱ
⫺2.62ⴱ
5.42
1.10
6.50
0.26
⫺0.14
219.07
124
0.13
0.07
30.82
7.75
8.26
11.34
1.14
5.65
0.87
3.97
0.24
0.03
24.22
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BAD WEATHER INCREASES PRODUCTIVITY
511
could measure other aspects of job performance. For example,
weather-induced positive moods may improve workers’ productivity on tasks that require creativity, as well as affective interpersonal skills such as empathy and emotional intelligence.
Research also shows that bad weather conditions may lead
people to prefer spending time at work because attractive outdoor
options are not available to them (e.g., Connolly, 2008; Zivin &
Neidell, 2010). Although our studies did not allow for testing this
possibility, future studies should investigate the potential role of
differing incentives. If workers have incentives to finish their work
early on sunny days, rather than having fixed work hours per day,
their motivation to leave early might offset productivity loss due to
cognitive distractions.
In addition, there might be individual differences in people’s
responses to weather conditions (see Klimstra et al., 2011, for
“weather reactivity”) and their preference for outdoor activities.
Such dispositions may contribute to the variance in how outside
weather conditions are perceived and may also explain the lack of
significant correlations between weather and moods. Future studies should further examine the role of such individual differences
in modulating the role of outside weather in influencing worker
productivity.
Theoretical and Practical Implications
Figure 1. Exposure to outdoor options moderates the relationship between weather conditions and productivity.
One potential moderator that could address these seemingly
contradictory results is workers’ exposure to outside weather,
either by spending time and working outside or by looking outside
through windows. In fact, Keller et al. (2005) found that the
amount of time spent outdoors moderated the effects of weather on
mood and cognition. Both of our studies were conducted in a
climate-controlled environment where individuals were asked to
complete a series of tasks requiring attention and focus, such as a
workplace (Study 1), an online labor market (Studies 2–3), and the
laboratory (Study 4). Thus, this may explain why outside weather
conditions played a lesser role in influencing workers’ affective
state but created a more significant variation in the level of
cognitive distraction. In such contexts, weather may primarily act
on people’s cognition rather than on their affective states, as
weather influences their level of distraction when they think about
attractive outdoor options, as we have shown. Future research
examining the role of weather across these different contexts (i.e.,
workers who typically work outside the office, or workers who
work in an office without windows) would further our understanding of the relationship between weather, affect, and cognition.
It should also be noted that our measure of job performance was
limited to the data entry task, which requires attention, and thus
more likely to be affected by cognitive distractions, rather than
affective influences. Positive affect tends to encourage less constrained, less effortful, and more creative problem solving
(Schwarz & Clore, 1983). In fact, positive moods induced by good
weather conditions may broaden workers’ cognition, thus increasing the flexibility of their thoughts (Keller et al., 2005). Consequently, future research should include different types of tasks that
Our research extends previous work on the influence of weather
conditions on behavior. Prior work has focused on the effects of
weather on behavior through people’s affective reactions to
weather conditions (e.g., Larrick et al., 2011). Our work demonstrates that weather conditions also influence individuals’ cognition. By reducing the potential for cognitive distractions, bad
weather was actually better than good weather at sustaining individuals’ attention and focus, and, as a result, increasing their
productivity.
Our results also deepen understanding of the factors that
contribute to work productivity. Prior research has focused
primarily on factors that are directly under one’s control or the
control of the organization (e.g., Staats & Gino, 2012). We
document the influence of weather conditions, incidental factors that affect work productivity. Distractions that arise at
work have been studied under the assumption that they can be
avoided. In fact, engaging in distractions, such as Internet
surfing, may have positive effects on productivity (due to
increased stimulation, Jett & George, 2003). Similarly, perceived autonomy over lunch breaks reduced fatigue at the end
of the day (Trougakos, Hideg, Cheng, & Beal, in press). Thus,
a concerted effort to take advantage of good weather for break
purposes could offset potential negative effects on productivity.
Future studies may explore the consequences of different types
of distractions at work, including how to structure break programs to restore the workers’ cognitive resources.
Weather is one of the many factors that may lead workers to
engage in non-work-related thoughts. Bad weather eliminates
only one type of distracting thoughts; other factors may influence worker productivity to a larger degree (i.e., explicit incentives and implicit goal-oriented motives). Despite our small
effect size in Study 1, our findings shed light on how seemingly
irrelevant, uncontrollable factors may influence workers’ productivity and also learning over time. In fact, operational im-
LEE, GINO, AND STAATS
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512
provement efforts often focus on issues that have effect sizes
less than 1%. Companies realize that even small efficiency
improvements can translate to cost advantages. This finding
calls for further investigation of the factors that can increase
task-unrelated thoughts that may adversely affect productivity.
Research could also examine how expectations of certain conditions (e.g., rain when sunshine was expected) might moderate
the effect of task-unrelated thoughts.
Our research also has practical implications. Although weather
conditions are exogenous and uncontrollable, to tap into the effects
of bad weather on productivity, organizations could assign more
clerical work of the type that does not require sustained attention
but does allow for more flexible thinking on rainy days than sunny
days. Since we found that cognitive distractions led to higher error
rates, individuals may wish to avoid working on a task in which
errors would be costly when they have task-unrelated priorities. In
addition, organizations may give productivity feedback to each
employee and allow flexible working hours that could maximize
productivity. We also note that if an organization wishes to maintain a consistent work output, then the weather forecast might be
a valuable input to a staffing model. Finally, as Cachon, Gallino,
and Olivares (2011) noted, weather is an important variable for
facility location. Our results suggest that, holding all other factors
constant, locating operations in places with worse weather may be
preferable.
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Received May 22, 2013
Revision received November 25, 2013
Accepted December 2, 2013 䡲
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Correction to Lee et al. (2014)
In the article “Rainmakers: Why Bad Weather Means Good Productivity” by Jooa Julia Lee,
Francesca Gino, and Bradley R. Staats (Journal of Applied Psychology, Advance online publication.
January 13, 2014. doi: 10.1037/a0035559), there is an error in the last paragraph. The sentence
“Although weather conditions are exogenous and uncontrollable, to tap into the effects of bad
weather on productivity, organizations could assign more clerical work of the type that does not
require sustained attention but does allow for more flexible thinking on rainy days than sunny days”
should have read . . . “to tap into the effects of bad weather on productivity, organizations could
assign more clerical work of the type that requires sustained attention on rainy days, and more
creative work that allows for more flexible thinking on sunny days.”
DOI: 10.1037/a0036192
ORIGINAL RESEARCH ARTICLE
published: 02 December 2013
doi: 10.3389/fnhum.2013.00824
The impact of physical exercise on convergent and
divergent thinking
Lorenza S.Colzato 1 *, Ayca Szapora 1 , Justine N. Pannekoek 2 ,3 and Bernhard Hommel 1
1
Cognitive Psychology Unit, Institute for Psychological Research and Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
3
Leiden University Medical Centre and Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
2
Edited by:
Carsten De Dreu, University of
Amsterdam, Netherlands
Reviewed by:
Marieke Roskes, Ben Gurion
University of the Negev, Israel
Simone Ritter, Radboud University
Nijmegen, Netherlands
*Correspondence:
Lorenza S. Colzato, Cognitive
Psychology Unit, Institute for
Psychological Research and Leiden
Institute for Brain and Cognition,
Leiden University, Wassenaarseweg
52, 2333 AK, Leiden, Netherlands
e-mail: colzato@fsw.leidenuniv.nl
Anecdotal literature suggests that creative people sometimes use bodily movement to
help overcome mental blocks and lack of inspiration. Several studies have shown that
physical exercise may sometimes enhance creative thinking, but the evidence is still
inconclusive. In this study we investigated whether creativity in convergent- and divergentthinking tasks is affected by acute moderate and intense physical exercise in athletes
(n = 48) and non-athletes (n = 48). Exercise interfered with divergent thinking in both
groups. The impact on convergent thinking, the task that presumably required more
cognitive control, depended on the training level: while in non-athletes performance was
significantly impaired by exercise, athletes showed a benefit that approached significance.
The findings suggest that acute exercise may affect both, divergent and convergent
thinking. In particular, it seems to affect control-hungry tasks through exercise-induced
“ego-depletion,” which however is less pronounced in individuals with higher levels
of physical fitness, presumably because of the automatization of movement control,
fitness-related neuroenergetic benefits, or both.
Keywords: physical exercise, creativity, convergent thinking, divergent thinking, fitness
INTRODUCTION
Anecdotal literature suggests that creative people sometimes use
bodily movement to help overcome mental blocks and to get
deeper into a problem. Indeed, the philosopher Henry David
Thoreau stated: “the moment my legs begin to move my thoughts
begin to flow – as if I had given vent to the stream at the lower
end and consequently new fountains flowed into it at the upper”
(Thoreau, 1851). Several studies have indeed shown that physical
exercise in healthy adults may sometimes enhance creative thinking – even though the size of this effect can vary substantially
(Gondola and Tuckman, 1985; Gondola, 1986, 1987; Steinberg
et al., 1997; Blanchette et al., 2005). Gondola and Tuckman (1985)
investigated the effects of long-term physical exercise on creativity performance, showing small but significant improvements
in Alternate Uses (spontaneous flexibility) and Remote Consequences (originality) tasks, but not for an Obvious Consequences
(different ideas) task. Gondola (1986) used the same creativity
tasks to compare the effect of long-term and acute physical exercise
and found improvements for both conditions and all three creativity measures. Gondola (1987) tested another form of acute aerobic
activity (dance) and reported comparable enhancing effects. Steinberg et al. (1997) found only small improvements in a group of fit
participants, and only in one of the three measures of the Torrance
test of creative thinking. Blanchette et al. (2005) used the same test
and found enhancing effects of exercise over a 2 h period. It is
possible that in some or all of these previous studies physical exercise provided the opportunity for mind-wandering or incubation
in trained (and, thus, less challenged) people. Indeed, Baird et al.
(2012) have reported that engaging in simple external tasks that
allow the mind to wander may facilitate creative problem solving.
Frontiers in Human Neuroscience
The methodological diversity across the available studies with
regard to sample characteristics and creativity assessment (mainly
targeting aspects of divergent thinking) is considerable, which renders it questionable whether they were actually assessing the same
constructs and processes. Moreover, there is still no mechanistic
model explaining how creative processes operate and how physical exercise might affect these operations. To address this issue, we
tried to avoid addressing creativity as a whole but focused on particular components of creative performance – components that
are more transparent at the process level and thus easier to investigate. More concretely, we investigated the impact (during and
after) of acute moderate and intense physical exercise on creativity tasks tapping into convergent and divergent thinking. Guilford
(1950, 1967) has considered these two as the main ingredients of
most creative activities, even though other processes are also likely
to contribute (Wallas, 1926).
Divergent thinking is taken to represent a style of thinking that allows many new ideas being generated, in a context
where more than one solution is correct. The probably best
example is a brainstorming session, which has the aim of generating as many ideas on a particular issue as possible. Guilford’s
(1967) alternate uses task (AUT) to assess the productivity of
divergent thinking follows the same scenario: participants are
presented with a particular object, such as a pen, and they
are to generate as many possible uses of this object as possible. Convergent thinking, in turn, is considered a process of
generating one possible solution to a particular problem. It
emphasizes speed and relies on high accuracy and logic. Mednick’s
(1962) remote associates task (RAT) that aims to assess convergent thinking fits with this profile: participants are presented
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Colzato et al.
Physical exercise impacts creativity
with three unrelated words, such as “time,” “hair,” and “stretch,”
and are to identify the common associate (“long”). Interestingly for our purposes, performance on the AUT and the RAT
were found to be uncorrelated (Akbari Chermahini and Hommel,
2010) and differently affected by the same experimental manipulations (Hommel et al., submitted), which supports Guilford’s
(1967) suggestion that convergent and divergent thinking represent different, separable components of human creativity. Such a
scenario would fit with considerations of De Dreu et al. (2008),
who proposed the Dual Pathway to Creativity model suggesting
that creative performance arises from the interaction between
cognitive flexibility and cognitive persistence – two dissociable
cognitive control functions (Goschke, 2000; De Dreu et al., 2012).
Consistent with this, divergent thinking was less pronounced
in avoidance-motivated than in approach-motivated individuals,
suggesting that the former need to compensate for their inflexible processing style by effortful and controlled processing (Roskes
et al., 2012).
Along the same lines, Colzato et al. (2012) have argued that convergent thinking requires strong top-down control because it represents the tightly constrained search of very few or just one item.
In contrast, divergent thinking should rely on weak top-down control, given that it implies a broad, loosely defined search space so
to activate many items that satisfy the often relatively soft criteria (Hommel, 2012). Hence, convergent and divergent thinking
are likely to differ in their reliance on executive control for the
processing of information. If so, acute exercise should affect these
two processes differently. According to the ego-depletion hypothesis (Baumeister et al., 1998), the cognitive resources required for
cognitive-control operations are tightly limited and thus deplete
quickly during and after control-demanding tasks. Following a
similar, though more motivational rationale, Inzlicht and Schmeichel (2012) have developed a process model to explain self-control
failure. According to that model, “exerting self-control at Time 1
reduces success at self-control at Time 2 by initiating shifts in
motivation and attention that conspire to reduce self-control and
increase immediate gratification” (p. 460). According to this reasoning, poorer self-control at Time 2 is attributed to reduced
motivation to exert control and to reduced attention to cues
that signal a need for control, as well as more impulsive behavior and more attention to reward cues. Given that exercising
must use up some amount of control resources, more controldemanding tasks (like convergent thinking) should suffer more
from exercise than less control-demanding tasks (like divergent
thinking).
However, how resource-hungry exercise should not only
depend on the kind of exercise (e.g., the complexity of the
coordination required) but also on the skill level of the exercising individual. The same exercise that exhausts the resources
of the less sportive student may have little impact on the highly
practiced athlete. In athletes, many movement routines are overlearned and automatized, which can lead to dramatic reductions
of conscious monitoring and control demands (Beilock and Carr,
2001; Schneider and Chein, 2003). Moreover, long-term fitness training leads to an increase of oxygenation and glucose
in the frontal brain regions, which has been found to produce rather selective benefits for executive-control processes
Frontiers in Human Neuroscience
(Colcombe and Kramer, 2003). This means that athletes may not
exhibit the same effects as non-athletes. While the latter should
show exercise-induced costs in more control-demanding tasks
(like convergent thinking), the former might either not show such
costs or perhaps even show exercise-induced benefits.
To investigate these possibilities, we tested the impact of acute
physical exercise on convergent and divergent thinking in athletes
and non-athletes. We also took into account possible moderating factors, such as the intensity of the exercise (which was
moderate or high, in different sessions) and the temporal overlap between exercise and creativity task (with the latter being
performed during or after the exercise).
METHODS
PARTICIPANTS
Ninety-six healthy, native Dutch speakers (48 females and 48
males), of which 48 were athletes (mean age = 20.6 years;
mean body mass index, BMI = 22.3) and 48 non-athletes (mean
age = 20.7 years; mean BMI = 22.2), participated for an energy
bar and a sports drink or one study credit. Participants were considered athletes if they exercised at least three times a week during
the recent 2 years and non-athletes if they did not exercise on
a regular basis (less than 1 time per week). All participants had
normal systolic and diastolic blood pressure at rest (mean systolic blood pressure, SBP = 122 and diastolic blood pressure,
DPB = 74), and reported no current or history of medication
or drug use. Informed consent was obtained from all participants
after the nature of the study was explained to them. The protocol
was approved by the local ethical committee (Leiden University,
Institute for Psychological Research).
REMOTE ASSOCIATION TASK (CONVERGENT THINKING)
In this task, participants are presented with three unrelated words
(such as “time,” “hair,” and “stretch”) and asked to find a common
associate (“long”). Our Dutch version comprised of 30 previously
validated items (Akbari Chermahini et al., 2012). In each of the
three sessions, participants completed 10 different items.
ALTERNATE USES TASK (DIVERGENT THINKING)
In this task, participants were asked to list as many possible uses
for six common household items (“pen,” “towel,” “bottle”). In
the three sessions, participants completed 1 of these items. The
results can be scored in several ways with flexibility, the number
of different categories used, being the theoretically most transparent and the empirically most consistent and reliable score
(Akbari Chermahini and Hommel, 2010). In the case of the item
“pen,” “writing an essay,” and “writing a letter” would fall into
the same category, but “drumming on the table” would fall into a
different category.
In this study we considered four scores:
Flexibility: The number of different categories used.
Originality: Each response is compared to the total amount of responses
from all of the subjects. Responses that were given by only 5% of the
group count as unusual (1 point) and responses given by only 1% of
them count as unique (2 points).
Fluency: The total of all responses.
Elaboration: The amount of detail (e.g., “a door stop” counts 0, whereas “a
door stop to prevent a door slamming shut in a strong wind” counts
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Physical exercise impacts creativity
2 (1 point for explanation of door slamming and another for further
detail about the wind).
moment in which participants carried out the creativity tasks (during vs. after exercise) as between-group factor. A significance level
of p < 0.05 was adopted for all tests.
EXERCISE CONDITIONS
During the rest condition, participants sat on a cycle ergometer (Kettler Cycle) without cycling. During the moderate cycling
condition, participants cycled at a normal pace (level 8) without
exhausting themselves. During the intense cycling condition, the
resistance level on the bicycle was adjusted to high (level 16), and
the participants cycled at a maximum level of effort.
RESULTS
PARTICIPANTS
No significant group differences were obtained for age,
t(94) = 0.05, p = 0.95, and BMI, t(94) = 0.34, p = 0.73, but there
was a significant difference for sport units per week, t(94) = 21.68,
p = 0.00001: athletes exercised more often per week (3.4) than
non-athletes did (0.5).
PHYSIOLOGICAL AND MOOD MEASUREMENTS
Heart rate (HR) and systolic and diastolic blood pressure (SBP
and DPB) were measured from the non-dominant arm with an
OSZ 3 Automatic Digital Electronic Wrist Blood Pressure Monitor
(Speidel and Keller). BMI was measured by Omron BF511 medical
device. Mood was rated on a 9 × 9 Pleasure × Arousal grid (Russell
et al., 1989) with values ranging from –4 to 4.
PROCEDURE AND DESIGN
A between-group (athletes vs. non-athletes) randomized
cross-over design with counterbalancing of the order of the
exercise conditions (rest vs. moderate vs. intense) was used (Latinsquare design). All participants were tested individually. Half of
the participants in each group (n = 24) executed the creativity tasks
during cycling, the other half (n = 24) thereafter. Upon arrival,
participants were asked to rate their mood and HR, SBP, DPB, and
BMI were collected (baseline measurement). Next, the participant
was introduced to the assigned exercise condition. When the rest
condition was preceded by the moderate or intense exercise condition, the participant started the next exercise condition only after
a couple of minutes (never more than 5) when HR returned to the
baseline measurement level.
After each condition, HR, SBP, DPB, and mood were measured
again. The creativity tasks (AUT and RAT) were performed either
during or after the physical exercise, depending on the condition
subjects had been randomly assigned to, see Figure 1. Participants
had 3 min to execute the RAT (10 items per test condition) and
3 min for the AUT (1 item per test condition). Participants were
confronted with a printed version of the creativity tasks on a clipboard positioned on the cycle ergometer in front of them so that
they could fill in their responses comfortably while cycling. After
the experimental session was ended, participants were rewarded
for their participation in the study.
STATISTICAL ANALYSIS
Independent t-tests were performed to test differences between the
two groups. Mood, HR, BPS, and BPD, and five creativity measures
(from the two tasks) were extracted for each participant: flexibility, originality, fluency, and elaboration scores from the AUT, the
number of correct items from the RAT. All four AUT measures
were scored by two independent raters [Cronbach’s alpha = 1.00
(fluency); 0.85 (flexibility); 0.71 (originality); 0.74 (elaboration)].
All measures were analyzed separately by means of repeatedmeasures ANOVAs with Session (rest vs. normal vs. intense) as
within-subjects factor and group (athletes vs. non-athletes) and
Frontiers in Human Neuroscience
PHYSIOLOGICAL AND MOOD MEASUREMENTS
We found a main effect of session on HR, F(2,184) = 768.01,
p < 0.00001, MSE = 109.063, η2p = 0.89, SBP, F(2,184) = 165.76,
p < 0.00001, MSE = 163.793, η2p = 0.64, and DBP,
F(2,184) = 29.18, p < 0.001, MSE = 104.509, η2p = 0.24. Participants showed increased HR, SBP, and DBP in the moderate (95,
130, 76) and intense (133, 150, 85) exercise condition as compared
to the rest condition (75, 116, 74). No other significant interaction
involving group was found, p > 0.14.
Replicating earlier findings (Steptoe and Bolton, 1988), arousal,
F(2,184) = 768.01, p < 0.00001, MSE = 109.063, η2p = 0.89, but
not mood, F(2,184) = 43.71, p < 0.0001, MSE = 1.077, η2p = 0.32,
was elevated after intense exercise (1.9, 1.1) as compared to normal
exercise (1.1, 1.3) and rest (0.6, 1.2), respectively. As in the case
of physiological measurements, no other significant interaction
involving group was found, F < 1.
CREATIVITY TASKS
In general, performance in the AUT and RAT was good and
comparable to performance in other studies without exercise
manipulations (e.g., Akbari Chermahini and Hommel, 2010); see
Table 1.
Convergent thinking: As expected, we found a significant interaction between group and session on RAT scores, F(2,184) = 5.16,
p < 0.01, MSE = 2.838, η2p = 0.05. Post-hoc multiple comparisons
tests revealed that, even if not quite significant, athletes tended to
perform better in convergent thinking in the moderate (4.1) and
intense (4.2) exercise conditions than in the rest condition (3.5),
p = 0.072, 0.095. This effect was reversed in non-athletes, where
intense exercise (3.6) impaired convergent thinking compared to
moderate exercise (4.4), p = 0.002 and rest (4.6), p = 0.029. The
interaction was not modified by testing moment, as the insignificant three-way interaction indicated, F(2,184) = 1.01, p = 0.364,
MSE = 2.838, η2p = 0.01.
Divergent thinking: From the four scores of the AUT, only flexibility yielded a significant main effect of session, F(2,184) = 3.69,
p < 0.05, MSE = 3.169, η2p = 0.03; post-hoc tests revealed that
participants showed greater flexibility in the rest condition (7.4)
than with intense (6.7) exercise, p = 0.011, while the difference
between rest and moderate exercise (7.0) only approached significance, p = 0.150. Numerically similar, but statistically insignificant
trends were obtained for originality, F(2,184) = 0.42, p = 0.66,
MSE = 0.320, η2p = 0.05, fluency, F(2,184) = 2.47, p = 0.09,
MSE = 5.420, η2p = 0.03, and elaboration, F(2,184) = 2.19,
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Colzato et al.
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FIGURE 1 | Sequence of events for the participants who performed the creativity tasks during exercise (A) or after exercise (B).
p = 0.11, MSE = 0.247, η2p = 0.02. In contrast to the RAT findings, the flexibility effect was not modulated by group, F < 1, and
the same was true for originality, F(2,184) = 1.20, p = 0.302,
MSE = 0.320, η2p = 0.01, fluency, F < 1, and elaboration,
F(2,184) = 1.07, p = 0.346, MSE = 2.838, η2p = 0.01. There
was also no indication of any three-way interaction, p’s > 0.21.
Frontiers in Human Neuroscience
DISCUSSION
In this study we investigated whether creativity in convergentand divergent-thinking tasks is affected by acute physical exercise. The results provide some preliminary evidence for a link between exercise and creativity, but they
also suggest that the nature and the consequences of this
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Colzato et al.
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Table 1 | Means for the number of correct items from the remote associates task (RAT), for flexibility, originality, fluency, and elaboration scores
from the alternate uses task (AUT), and perceived mood ratings as a function of group (athletes vs. non-athletes), session (rest vs. normal vs.
intense) and moment in which participants carried out the creativity tasks (during vs. after exercise).
Group
Athletes
Moment
During
After
Non-athletes
During
After
Session
RAT
AUT-
AUT-
AUT-
AUT-
flexibility
originality
fluency
elaboration
HR
BPS
BPD
Mood
Arousal
77.0
113.5
74.4
1.5
0.7
Rest
3.6
7.3
0.50
11.0
0.83
Normal
3.9
6.7
0.79
11.0
0.67
94.4
127.9
74.8
1.9
1.3
Intense
4.0
6.2
0.75
10.5
0.62
126.1
148.6
83.1
1.8
2.0
Rest
3.5
6.9
0.83
11.1
0.96
71.9
116.9
71.5
1.2
0.2
Normal
4.3
6.7
0.79
10.8
0.87
91.0
134.8
74.6
1.1
1.1
Intense
4.3
6.8
0.70
10.8
0.96
134.8
151.5
83.1
0.8
1.7
Rest
4.7
7.2
0.46
10.4
0.92
75.5
117.5
77.2
1.2
0.6
Normal
4.8
6.4
0.50
9.2
0.79
93.2
130.6
76.4
0.9
1.1
Intense
3.4
6.7
0.37
8.6
0.62
131.6
150.8
88.1
0.9
2.0
Rest
4.5
7.9
0.54
10.6
1.04
76.0
117.2
74.3
0.9
0.8
Normal
4.0
7.9
0.46
11.0
1.00
102.8
127.3
79.2
1.5
0.8
Intense
3.9
7.1
0.42
10.2
0.96
140.7
148.0
85.5
0.9
1.8
link depend on the particular task and the fitness of the
individual.
First, non-athletes did not benefit from acute exercise; in fact,
exercise caused their performance to drop in both creativity tasks.
The fact that this drop was not modified by the moment of testing suggests that it was not due to dual-tasking or related online
demands. Rather, in this group acute exercise seems to lead to
ego-depletion, hence, exhaust limited cognitive-control resources
(Baumeister et al., 1998) that are then no longer available for the
control of processes involved in convergent and divergent thinking....
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