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The presence of another person can influence task performance. What is, however, still unclear is whether performance also depends on what this other person is doing. In two experiments, two participants (A and B) jointly performed a Simon task, and we selectively manipulated the difficulty of the task for participant A only. This was achieved by presenting A with 90% congruent trials (creating an easy task requiring low effort investment) or 10% congruent trials (creating a difficult task requiring high effort investment). Although this manipulation is irrelevant for the task of participant B, we nevertheless observed that B exerted more mental effort when participant A performed the difficult version of the task, compared to the easy version. Crucially, in Experiment 2 this was found to be the case even when participants could not see each other's stimuli. These results provide a first compelling demonstration that the exertion of effort is contagious.
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BRIEF REPORT
Is mental effort exertion contagious?
Kobe Desender
1
&Sarah Beurms
2
&Eva Van den Bussche
1
#Psychonomic Society, Inc. 2015
Abstract The presence of another person can influence task
performance. What is, however, still unclear is whether per-
formance also depends on what this other person is doing. In
two experiments, two participants(A and B) jointly performed
a Simon task, and we selectively manipulated the difficulty of
the task for participant A only. This was achieved by present-
ing A with 90% congruent trials (creating an easy task requir-
ing low effort investment) or 10% congruent trials (creating a
difficult task requiring high effort investment). Although this
manipulation is irrelevant for the task of participant B, we
nevertheless observed that B exerted more mental effort when
participant A performed the difficult version of the task, com-
pared to the easy version. Crucially, in Experiment 2this was
found to be the case even when participants could not see each
others stimuli. These results provide a first compelling dem-
onstration that the exertion of effort is contagious.
Keywords Effort exertion .Cognitive control .Contagion .
Social facilitation .Joint Simon
Introduction
Nowadays, an increasing number of people perform their dai-
ly working duties in the presence of others, for example in
open landscape offices. The introduction of these landscape
desks is often met with criticism, arguing that the design ham-
pers efficient work, due to an overflow of potential sources of
distraction. This raises a straightforward empirical question:
What is the influence of co-workers on our task performance?
According to the Social Facilitation Theory (Zajonc, 1965),
the presence of another person facilitates the execution of
dominant responses, which are those behaviors that are highly
overlearned and executed without deliberate cognitive control
(Botvinick, Braver, Barch, Carter, & Cohen, 2001). The pres-
ence of another person thus makes it easier to execute a dom-
inant response when it is appropriate, but harder to overcome
it when this is not the case (see Baron, 1986, for a different
interpretation). More specifically, performance on a simple,
low-level motor task improves in the presence of observers
(Travis, 1925), whereas performance on a difficult test-battery
assessing executive functioning worsens in the presence of a
third-party observer (Horwitz & McCaffrey, 2008; for a sem-
inal meta-analysis, see Bond & Titus, 1983). In line with this,
recent studies have shown that performance on a conflict task
assessing executive functioning decreases in the presence of
others who are executing the same task (Huguet, Barbet,
Belletier, Monteil, & Fagot, 2014), suggesting that the pres-
ence of these others taxes our cognitive control capacity (see
also Conty, Gimmig, Belletier, George, & Huguet, 2010).
As described above, the Social Facilitation Theory only
deals with explaining how the presence of another person
influences performance,but it does not address action-specific
influences of others. As a result, most studies to date investi-
gating the influence of social presence on cognitive control
have compared the mere presence versus the absence of an-
other person. Contrarily, ideomotor theories (James, 1890;
Jeannerod, 1999) predict that our behavior is highly depen-
dent on actions that we observe in other people (for empirical
demonstrations, see e.g., Chartrand & Bargh, 1999;Iacoboni
*Kobe Desender
Kobe.Desender@vub.ac.be
1
Faculty of Psychology and Educational Sciences, Vrije Universiteit
Brussel, Brussels, Belgium
2
Department of Psychology, KU Leuven, Leuven, Belgium
Psychon Bull Rev
DOI 10.3758/s13423-015-0923-3
et al., 1999; Sebanz, Knoblich, & Prinz, 2003). Building on
this notion, we aimed to examine whether task performance
depends not simply on the presence of another person, but
rather on the degree of mental effort that this other person is
exerting; thus, whether the exertion of mental effort is conta-
gious. To accomplish this, participants performed a task to-
gether with another person, and task difficulty was selectively
manipulated for the other participant: the more difficult the
task, the more effort this other person needs to invest to obtain
good task performance (Botvinick et al., 2001; Shenhav,
Botvinick, & Cohen, 2013). By doing so, we can examine
whether task performance is influenced by the other persons
exerted degree of effort. If task performance improves when
the other person invests more effort, this would be indicative
of increased effort exertion and thus demonstrate the conta-
gious nature of effort exertion.
To investigate this, we adopted a variant of the Simon task
in which two persons jointly perform the task. In a regular
Simon task, one participant responds to the color of patches
(e.g., blue or red) with either the left or the right hand, while
ignoring its location on the screen (i.e., left or right). Typically,
reaction times (RTs) are shorter and error rates lower on con-
gruent trials, where the (task-irrelevant) location triggers the
same response as the (task-relevant) color, compared to incon-
gruent trials, where both features trigger a different response
(i.e., the congruency effect). Here, two participants (A and B)
areseatednexttoeachotherandeachrespondstohalfofthe
stimuli. For example, A responds to blue stimuli, whereas B
responds to red stimuli. Although this is in essence a simple
(joint) Go/No-Go task, it nevertheless produces robust con-
gruency effects for both participants (Sebanz et al., 2003). The
typical approach is to compare the congruency effect in this
Joint Simon task to the congruency effect obtained in a con-
dition in which participants perform the same task without the
presence of another person (i.e., individual Go/No-Go). In the
current study, we adopted a novel approach and used a within-
participant comparison. We varied the difficulty of participant
Aby selectively manipulating his or her proportion of congru-
ent trials. More specifically, this participant either performs a
difficult version of the task, (i.e., 10% congruent trials) or an
easy version of the task (i.e., 90% congruent trials). In line
with the typical findings obtained with the regular Simon task,
we expect that the congruency effect of participant A will be
large in the 90% congruent condition, while it will be severely
reduced in the 10% congruent condition (Logan & Zbrodoff,
1979). This pattern is typically explained by assuming that
participants do not exert control in the 90% congruent condi-
tion and thus do not suppress the location information, be-
cause this information is helpful in the majority of trials.
This strategy is beneficial for congruent trials, but not for the
few incongruent trials, leading to large congruency effects. In
the 10% congruent condition, on the other hand, it is assumed
that participants increase their level of control in order to
handle the now interfering location information. In this case,
this strategy is beneficial for incongruent trials, but it reduces
the beneficial effect of location on the infrequent congruent
trials, leading to reduced congruency effects. The difference
between these two conditions is believed to reflect the larger
investment of cognitive control in the latter condition
(Botvinick et al., 2001). Crucially, our design allows us to
examine whether task performance of participant B, who al-
ways receives 50% congruent trials, is dependent on the task
difficulty and thus effort exertion of participant A. If the exer-
tion of mental effort is contagious, the results of participant B
should mimic those of participant A: the congruency effect of
participant B should be smaller when participant A performs a
difficult task compared to performing an easy task.
Below, we report the results of two experiments. In
Experiment 1, participants jointly perform half of a Simon task
(i.e., a joint Go/No-Go; Experiment 1; see Fig. 1a) on the same
screen. In Experiment 2, participants only had visual access to
their own stimuli and thus individually perform half of a
Simon task, while seated next to each other (i.e., individual
Go/No-Go, Experiment 2; see Fig. 1b).
Experiment 1
Method
Participants
Thirty-eight participants (20 females) participated on a volun-
tary basis or in return for course credit, and provided written
informed consent. The sample size was determined before-
hand (aimed at 40, with only 38 showing up) based on our
experience with conflict studies. All participants reported hav-
ing normal or corrected-to-normal vision, had normal color
vision, and were naïve with respect to the hypothesis. Mean
age of the sample was 21.6 years (range 1730, SD =2.1).
Apparatus and stimuli
The experiment was programmed in E-prime for Windows
(Psychology Software Tools, Pittsburgh, PA, USA) and run
on Intel Pentium 4 computers with 17-in LCD screens. The
refresh rate was set to 60 Hz. Targets were four color patches
(3.5° wide and 3.5° high) in blue (RGB 0, 0, 255), yellow
(RGB 255, 242, 0) green (RGB 34, 177, 76), or orange
(RGB 255, 127, 39).
Procedure
Participants performed the experiment in pairs, seated next to
each other in front of a computer screen (see Fig. 1a). They
were instructed that they were to perform a task on the same
Psychon Bull Rev
AZERTY keyboard and that each of them had to respond with
one hand to two of the four colors. Participants were not in-
formed that the difficulty of the task would vary over the
different blocks. The left participant responded to two colors
with the left hand by pressing the Bd^key, the right participant
responded to the two other colors with the right hand by
pressing the Bk^key (with all color combinations
counterbalanced across participants). Each trial started with a
centrally presented fixation cross lasting 800 ms, followed by
a color patch, which was presented either on the left or right
side of the screen (at 25% or 75% of the screen border) and
disappeared when a response was made or after 3000 ms. The
inter-trial interval lasted for 1000 ms. The experiment started
with 16 practice trials, where feedback was presented when an
error was committed, followed by four blocks of 160 trials
each in which feedback was no longer provided, with self-
paced breaks after every 80 trials.
Design
Figure 1c shows a graphic representation of the design. Within
the first two blocks, the proportion of congruent trials of one
participant was manipulated. This participant received 90%
congruent trials (i.e., low effort condition) in the first block
and 10% congruent trials (i.e., high effort condition) in the
second block (order counterbalanced across participants),
while the other participant received an equal proportion of
congruent and incongruent trials. Within the last two blocks,
the proportion of congruent trials was manipulated for the
other participant (i.e., 90% congruent trials in block 3 and
10% congruent trials in block four). Note that the manipulated
proportion of congruent trials in block 2 was always different
from the proportion of congruent trials of the other participant
in block 3 (e.g., 1090 % or vice versa), in order to rule out
any possible carry-over effects from ones own manipulation
to the next block. This design allowed us to test whether the
task difficult of one participant (90% vs. 10% congruent trials)
influences the congruency effect of the other participant, who
received an equal proportion of congruent and incongruent
trials.
Results
To examine performance when the proportion of congruent
trials was manipulated in the stimulus list of the other partic-
ipant (see Fig. 2), we submitted the median RTs of correct
Fig. 1 Panel ashows the set-up of Experiment 1,inwhichtwo
participants jointly perform a Simon task. Panel bshows the set-up of
Experiment 2, in which a cardboard was placed in the middle of the
screen, so participants could fully see each other but not each others
stimuli. Note that the black dotted squares are used here to indicate all
possible positions of the squares, but were not presented in the actual
experiment. Panel cshows the design of both experiments. Within the
first two blocks, the proportion of congruent trials was manipulated in the
stimulus list of the left participant, whereas the right participant received
an equal proportion of congruent and incongruent trials. In blocks 3 and 4,
the manipulation was swapped. Note that the order of the congruency
manipulation was counterbalanced; see text for the exact details
Psychon Bull Rev
trials (98.7% of all trials) and mean error rates to a 2 (congru-
ency: congruent or incongruent) x 2 (proportion congruency
of the other participant:90% or 10% congruent trials) repeated
measures analysis of variance.
1
RTs showed a main effect of congruency, F(1,37) = 25.85,
p< .001, reflecting that on average responses were shorter on
congruent trials (369 ms) than on incongruent trials (381 ms).
Crucially, this main effect of congruency was modulated by
the proportion congruency of the other participant, F(1,37) =
27.25, p< .001. The congruency effect of the participant re-
ceiving an equal amount of congruent and incongruent trials
was 23 ms when the other participant received 90% congruent
trials, t(37) = 7.75, p< .001. This effect dropped to a non-
significant 1 ms, t(37) = 0.2, p= .81, when the other partici-
pant received only 10% congruent trials. This shows that par-
ticipants were better in suppressing irrelevant location infor-
mation (i.e., exerted more effort) when the participant next to
him/her also exerted more effort. The main effect of propor-
tion congruency of the other participant did not reach signif-
icance, F(1,37) = 1.45, p= .23, showing that these results do
not reflect a speed-accuracy trade-off.
Error rates also showed a significant main effect of congru-
ency, F(1,37) = 7.91, p= .008, reflecting on average lower
error rates on congruent trials (0.9%) compared to incongruent
trials (1.6%). Mirroring the RTs, this congruency effect was
modulated by the proportion congruency of the other
participant, F(1,37) = 15.93, p< .001. When the other partic-
ipant received 90% congruent trials, the congruency effect
was 2.1%, t(37) = 4.41, p< .001, whereas it dropped to a
non-significant 0.6%, t(37) = 1.5, p= .13, when the other
participant received 10% congruent trials. The main effect of
proportion congruency of the other participant did not reach
significance, F<1.
For completeness, we also report the results when the pro-
portion of congruent trials was manipulated in participants
own stimulus list (see Table 1). RTs on correct trials (98.6%
of all trials) showed a main effect of congruency, F(1,37) =
17.34, p< .001, which was modulated by the proportion of
congruent trials, F(1,37) = 38.67, p< .001. Congruency ef-
fects were positive when 90% of the trials were congruent,
44 ms, t(37) = 7.76, p< .001, and negative when 10% of the
trials were congruent, 14 ms, t(37) = 3.5, p=.001.The
main effect of proportion congruency did not reach signifi-
cance, p>.31, showing that overall response speed did not
differ between both contexts. This rules out the possibility that
the other participant simply adapted his or her behavior to the
openly observable response speed. The error rates did not
show differences between conditions, all p>.10.
Interim discussion
In Experiment 1, we observed that participants showed re-
duced congruency effects (reflecting increased effort exertion
to ignore the irrelevant location information) when the partic-
ipant next to them performed a difficult task (i.e., 10% con-
gruent trials, requiring much effort), compared to an easy task
(i.e., 90% congruent trials, requiring little effort). Although
promising, one issue deserves further attention. Participants
performed the task in the presence of another person, while
they could clearly see both their own stimuli as well as the
others stimuli. Therefore, it is unclear whether they adapted
their response strategy based on the degree of effort exerted by
the person next to them, or simply based on the total number
of congruent and incongruent trials.
2
This difference is of
crucial importance, because the latter explanation implies that
the presence of the other person is not an important factor. In
order to rule out this interpretation, we created an experimen-
tal set-up that allowed us to unequivocally show that partici-
pants change their degree of effort exertion dependent on that
of the participant next to them. Therefore, in Experiment 2
participants could fully see each other, but had no visual
1
None of the variables that were counterbalanced had an impact on the
results of either Experiment 1or Experiment 2. Most importantly, the
effect was not different for participants who first performed the 50/50
proportion condition and participants who first performed the condition
in which the congruency proportion was manipulated, Experiment 1:F<
1; Experiment 2:F< 1, thus, our results cannot be explained by potential
carry-over effects. For both groups, we observed a significant interaction
between congruency and the proportion congruency of the other partici-
pant, both for Experiment 1: 50/50 condition first: 18 ms, t(18) = 3.24,
p
one-sided
= .002, 50/50 condition second: 26 ms, t(18) = 4.08, p
one-sided
<
.001, and Experiment 2: 50/50 condition first: 12 ms, t(18) = 1.94, p
one-
sided
= .034; 50/50 condition second: 8 ms, t(18) = 1.61, p
one-sided
= .06.
Fig. 2 Results of Experiment 1for the blocks in which participants
received an equal proportion of congruent and incongruent trials,
dependent on the proportion congruency of the other participant. Error
bars reflect 95% within-subjects confidence intervals
2
Another factor which might have added to the findings of Experiment 1
is that the location of the stimulus was predictive of the response. For
example, when participant A performs a block with 90 % congruent trials,
participant B has to respond to 83.33 % of the stimuli appearing on his or
her side (because 50 % of the stimuli of participant B and only 10 % of the
stimuli of participant A appeared on that side). Note, however, that this
confound is completely eliminated in Experiment 2.
Psychon Bull Rev
access to each others stimuli. As a result, any transfer effect of
our proportion congruency manipulation can only be attribut-
ed to participants being sensitive to the amount of effort
exerted by the person next to them.
Experiment 2
Method
Participants
Thirty-eight participants (30 females) participated in return for
course credit or 6, and provided written informed consent.
Sample size was based on that of Experiment 1. All partici-
pants reported having normal or corrected-to-normal vision,
had normal color vision, and were naïve with respect to the
hypothesis. Mean age of the sample was 20.8 years (range 18
27, SD = 2.1). None of them participated in Experiment 1.
Apparatus, stimuli, and design
The experiment was run on 21-in LCD screens divided into
two parts by means of a handcrafted cardboard screen. Target
patches were 2.4° degrees wide and 1.9° high. Apart from that,
apparatus, stimuli, and design were identical to Experiment 1.
Procedure
In Experiment 2, we used a set-up in which participants could
fully see each other, but only their own stimuli. As can be seen
Fig. 1b, this was achieved by means of a cardboard screen
which separated the monitor in two parts. As in Experiment
1, participants performed their task on the same keyboard,
which was clearly visible for both of them. In contrast to
Experiment 1, however, the stimuli were now presented for
each participant separately on his or her space of the screen.
On each trial, a fixation cross was presented centrally on each
half of the screen, which was followed by a color patch that
appeared only on the (left or right) side of the space on the
screen of the participant who was assigned to that color (i.e., at
10% or 40% of the entire screen border for the participant on
the left and at 60% or 90% of the entire screen border for the
participant on the right). For example, if the person on the left
responds to blue and orange, all blue and orange stimuli ap-
peared only on his or her space of the screen. For the 50%
condition, 50% of the time on the left side of their space (i.e.,
congruent) and 50% of the time on the right side of their space
(i.e., incongruent). On these trials, the other participant is pre-
sented with a blank screen. For the 10% condition, the blue
and orange stimuli appear in the following proportions: 10%
of the time on the left side of their space and 90% of the time
on the right side of their space. For the 90% condition, the
reverse is true: 90% of the time on the right and 10% of the
time on the left. Apart from this, the procedure was identical to
Experiment 1.
Results
The same analysis as in Experiment 1showed that when the
proportion of congruent trials was manipulated in the stimulus
list of the other participant, RTs on correct trials (99.4% of all
trials) again showed a main effect of congruency, F(1,37) =
12.15, p= .001, reflecting shorter average responses on con-
gruent trials (360 ms) than on incongruent trials (371 ms).
Crucially, this main effect of congruency was modulated by
the proportion congruency of the other participant, F(1,37) =
6.42, p=.015(seeFig.3). The congruency effect of the
Tabl e 1 Median reaction times (errorrates) as a function of congruency
proportion and congruency, when the proportion of congruent trials was
manipulated in the participantsown stimulus list. Note that for
Experiment 2, response errors are trials on which participants see a
blank screen but nevertheless respond, so these cannot be classified as
congruent/incongruent and are therefore not reported
Congruency proportion Incongruent Congruent Difference
Experiment 190% congruent 413 (2.63) 368 (1.13) 44.7 (1.50)
10% congruent 377 (1.42) 392 (1.64) 14.9 (0.21)
Experiment 290% congruent 397 362 35.2
10% congruent 364 375 11.2
Fig. 3 Results of Experiment 2for the blocks in which participants
received an equal proportion of congruent and incongruent trials,
dependent on the proportion congruency of the other participant. Error
bars reflect 95% within-subjects confidence intervals
Psychon Bull Rev
participant receiving an equal proportion of congruent and
incongruent trials was 16 ms, t(37) = 3.93, p<.001,when
the other participant received 90% congruent trials. This effect
dropped to 6 ms when the other participant received 10%
congruent trials, t(37) = 1.70, p= .09. Confirming
Experiment 1, this suggests that participants are better at sup-
pressing the interfering location information when the partic-
ipant next to them exerts more effort. The main effect of pro-
portion congruency of the other participant did not reach sig-
nificance, p= .09, again showing that these results do not
reflect a speed-accuracy trade-off.
Because errors reflect responses to trials on which no stim-
ulus was visible, the factor congruency and the interaction
between congruency and irrelevant proportion are meaning-
less. Therefore, we only examined whether the number of
errors was dependent upon on the proportion of congruent
trials of the other participant, which was not the case, t(37) =
0.56, p=.57.
For completeness, we also report the results when the pro-
portion of congruent trials was manipulated in participants
own stimulus list (see Table 1). RTs on correct trials (99.7%
of all trials) showed a main effect of congruency, F(1,37) =
7.22, p= .01, which was modulated by the proportion of
congruent trials, F(1,29) = 31.05, p< .001. Congruency ef-
fects were positive when 90% of the trials were congruent,
35 ms, t(37) = 4.96, p< .001, and negative when 10% of the
trials were congruent, 11 ms, t(37) = 2.28, p= .028. The
main effect of proportion congruency did not reach signifi-
cance, p> .10, showing that overall response speed was not
different between both contexts. The error rates did not show
differences between conditions, all p>.44.
General discussion
In the current study, we started from the notion that the pres-
ence of another person can have a large influence on task
performance. We extended this line of inquiry by showing that
what the other person is doing can be of critical importance. In
two experiments, two participants jointly performed a Simon
task in which we selectively manipulated task difficulty for
participant A only, by presenting this participant with either
90% congruent trials (creating an easy ask) or 10% congruent
trials (creating a difficult task). Although this manipulation is
irrelevant for the task performance of participant B, we nev-
ertheless observed that participant B exerted more effort to
ignore the irrelevant location information when participant A
performed a difficult task (i.e., requiring much effort), com-
pared to an easy task (i.e., requiring little effort). Crucially, in
Experiment 2this was found to be the case even when partic-
ipants could see each other, but not each othersstimuli.This
result provides a straightforward demonstration that the exer-
tion of mental effort can be contagious. Here, we elaborate on
the theoretical significance and underlying mechanisms of this
phenomenon.
Hitherto, the exertion of mental effort has repeatedly been
linked to conflicts in information processing (Botvinick et al.,
2001; Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis,
2004). For example, in the connectionist model of Botvinick
and colleagues (2001), the anterior cingulate cortex (ACC) is
ascribed the function of a conflict monitor, which constantly
monitors the information processing stream for conflicts.
When two conflicting responses are simultaneously triggered,
this will lead to a high level of conflict in the ACC. This, in
turn, is believed to stimulate the cognitive control system,
presumably located in the dorsolateral prefrontal cortex
(PFC), to increase effort exertion in order to increase perfor-
mance (Kerns et al., 2004). While this mechanistic explana-
tion provides a comprehensive yet compact account of effort
exertion (e.g., Shenhav et al., 2013), at current it does not
allow effort exertion to be dependent on the behavior of an-
other persons presence. Because the degree of effort exerted
by the person next to you does in no sense conflict with the
task you are doing, the model would not predict that this
would influence your own level of effort exertion. More recent
theoretic developments, on the other hand, increasingly aim to
broaden the scope and antecedents of effort exertion,
highlighting the role of motivation (Botvinick & Braver,
2014) and emotion (Inzlicht, Bartholow, & Hirsh, 2015)in
cognitive control. As such, our findings add to these theoretic
developments, by putting forth another personsbehavioras
an important antecedent of cognitive control. Supporting the
idea that we monitor the performance of other people, Sebanz,
Knoblich, & Prinz (2005) indeed found that participants ac-
tively represent the task rules of another person with whom
they are performing a task. Two participants responded to
different dimensions of the same stimulus (e.g., either color
or direction), and the results showed that performance was
reduced when a stimulus required a response from both par-
ticipants. Taken together with the current results, we can thus
conclude that there is clear evidence that participants both
monitor what the other participant should do and how the
other participant is doing.
In recent years, the view has emerged that the exertion of
effort is computationally costly, and therefore humans tend to
avoid mental effort when possible (Botvinick, 2007). When
given the option, participants will avoid environments that
demand high levels of effort, and instead prefer a low demand-
ing alternative (Kool, McGuire, Rosen, & Botvinick, 2010;
Westbrook, Kester, & Braver, 2013). Interestingly, the cur-
rent results suggest that this default mode of avoiding mental
effort is bound to certain preconditions. In our study, there
were no incentives for exerting high or low levels of effort,
but nevertheless participants exert more mental effort when
the person next to them was doing so. Thus, subtle effects of
effort contagion, as observed in the current study, expose at
Psychon Bull Rev
least one of the boundaries within which people tend to
avoid high effort.
An undeniable prerequisite for contagious effort exertion is
that people are capable of detecting differences in the amount
of effort exerted by another person. Note that this was not
done by simply observing the openly observable response
speed of the other participant (as observed in previous re-
search, see Huguet et al., 1999). In both experiments, when
the congruency proportion was manipulated in the partici-
pantsown stimulus list, congruency effects were markedly
different between the condition with 10% versus 90% congru-
ent trials, whereas overall RTs did not differ between them
(i.e., there was no main effect of proportion of congruent tri-
als). This shows that response speed of the other participant is
not the source of the effect, but rather the actual degree of
exerted effort. When exerting effort yourself, this is associated
with increased heart rate, increased blood pressure, decreased
heart-rate variability, and a perceived increase in arousal
(Howells, Stein, & Russell, 2010; Peters et al., 1998; Smit,
Eling, Hopman, & Coenen, 2005). However, to our knowl-
edge, no study so far has examined how participants can per-
ceive (physiologic markers related to) the effort exerted by
another person. One likely possibility is that participants infer
the degree of effort based on subtle differences in the body
posture. Effort exertion is linked to a more tense body posture
and the adoption of such a posture also leads to an increased
level of effort exertion (Friedman & Elliot, 2008). However,
expanding the limits, more radical hypotheses should also be
considered, such as the possibility that effort exertion is influ-
enced by a difference in scent of someone else exerting high or
low effort (see e.g., Holland, Hendriks, & Aarts, 2005).
Finally, apart from how we detect an increase in effort in
another person, the question remains why we subsequently
match our own degree of effort. After all, participants were
always motivated to respond fast and accurately, so there is
no incentive to match their degree of effort with that of the
person next to them. As such, it could be that this does not
reflect a truly deliberate decision, but instead a more auto-
matic tendency to imitate people, as is the case with yawn-
ing (Senju et al., 2007), rubbing your face, or shaking your
foot (Chartrand & Bargh, 1999), and facial expressions
(Meltzoff & Moore, 1989). Neurons that code for both the
execution and the observation of actions (i.e., mirror neu-
rons) have been proposed to underlie these forms of imita-
tion (Iacoboni et al., 1999). However, it is unclear whether
mirror neurons are able to fully account for the contagious
nature of more high-level cognition. Therefore, more com-
plex dynamics, beyond low-level motor imitation, might be
at play. For example, people are very sensitive to how they
are perceived by others, and perceiving that the person next
to them exerts a lot of effort might motivate them to do the
same (i.e., impression management; Leary & Kowalski,
1990). Manipulating the task context, the task requirements,
and the instructions given are promising fruitful avenues for
further empirical inquiry.
Conclusion
In the current study, we showed for the first time that the exer-
tion of mental effort is contagious. Simply performing a task
next to a person who exerts a lot of effort in a task will make you
do the same. Our results extend literature on social facilitation,
and raise several promising avenues for future investigation.
Acknowledgments We would like to thank Florian Rothenbucher and
Christophe Bievelez for their help in data collection.
Funding This work was supported by grants from the Research
Foundation Flanders, Belgium (FWO-Vlaanderen) awarded to K.D.
(grant number 11H3415N), S.B. (grant number 11N8115N), and
E.V.D.B. (grant number G023213N).
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Psychon Bull Rev
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