Review of Behavioral Economics, 2017, 4: 69–81
Cognitive Load and Cooperation
Felix Døssing1 , Marco Piovesan1 and Erik Wengström2∗
1
University of Copenhagen, Department of Economics, Denmark
Lund University, Department of Economics, Lund, Sweden and
University of Copenhagen, Department of Economics, Denmark;
[email protected]
2
ABSTRACT
We study the effect of intuitive and reflective processes on cooperation using cognitive load. Compared with time constraint, which
has been used in the previous literature, cognitive load is a more
direct way to block reflective processes, and thus a more suitable
way to study the link between intuition and cooperation. Using a
repeated public goods game, we study the effect of different levels
of cognitive load on contributions. We show that a higher cognitive load increases the initial level of cooperation. In particular,
subjects are significantly less likely to fully free ride under high
cognitive load.
Keywords: Public goods, Cooperation, Cognitive load, Experiment
JEL Codes: C70, C90, D03
1
Introduction
Are we automatically inclined to cooperate when facing a social dilemma, or
is prosocial cooperation the result of deliberate thinking? A recent stream of
research has investigated the cognitive and motivational mechanisms related to
prosocial behavior by focusing on the link between intuitive/deliberate cognitive processes and cooperative behavior (Rand et al., 2012; Rand et al., 2014).
These papers study cooperation within the framework of dual process theory
(Evans, 2008; Sloman, 1996; Miller and Cohen, 2001) in which autonomous
processes (Type 1) yield default responses unless hindered by higher order
∗ We are thankful for financial support from the Department of Economics of the
University of Copenhagen. Erik Wengström is also thankful for financial support from the
Ragnar Söderberg Foundation.
ISSN 2326-6198; DOI 10.1561/105.00000059
©2017 F. Døssing, M. Piovesan and E. Wengström
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Felix Døssing et al.
reasoning processes (Type 2). Specifically the social heuristics hypothesis
proposes that intuitive responses in a context involving cooperation are shaped
by what the individual has experienced as successful in the past (Rand et al.,
2012). The fact that there is a mismatch between what is optimal in an
anonymous one-shot cooperation decision and what is often optimal in real life
situations, which are repeated and subject to reputations effects, means that
the intuitive response will favor cooperation when processing is more intuitive.
Recent theoretical models have shown why one might expect to observe a
conflict between cooperation and non-cooperation within the same agent and
why manipulating intuition may affect the outcome of this conflict (Bear and
Rand, 2016; Dreber et al., 2016). Bear and Rand (2016) present an evolutionary
model, where agents participate in either a one-shot social dilemma or a social
dilemma with reciprocal consequences. Agents may either act intuitively –
cooperate or non-cooperate regardless of the game structure – or deliberate
at a cost and choose the optimal strategy for the current game. Interestingly,
depending on the probability of reciprocal consequences, selection favors either
intuitive defectors, or dual-process agents who intuitively cooperate, but use
deliberation to detect one-shot games, when the cost of deliberation is below
a certain threshold. Selection never favors agents who use deliberation to
override selfish impulses.
From an experimental point of view, the effect of manipulating cognitive
processing on cooperation remains controversial. While several studies have
documented that cooperation increases when people rely more on the intuitive
processes (Rand et al., 2012; Rand et al., 2014; Zaki and Mitchell, 2013;
Lotito et al., 2013; Nielsen et al., 2014), these results have proven difficult to
replicate (Verkoijen and Bouwmeester, 2014; Tinghög et al., 2013). In a recent
meta-analysis, however, manipulating processing type was shown to have an
overall and robust effect on cooperation (Rand, 2016).
One reason for the inconsistent results may be that previous studies have
primarily used time pressure and delay to manipulate cognitive processing.
Although it is true that Type 1 processing is often associated with fast responses,
response time is not viewed as a defining difference between the two processing
types (Evans and Stanovich, 2013). Speed is simply a “typical correlate” and
Evans and Stanovich (2013, pp. 226–229) even state that it is a common
fallacy to think, that fast processing is necessarily indicative of Type 1 intuitive
processing. The defining feature, which differentiates Type 1 from Type 2
processing, is rather, whether the process requires working-memory resources
in order to function. Working memory is a system, which provides temporary
storage of information and allows for different types of manipulations involved
with reasoning (Baddeley, 1992; Baddeley, 2003).
In this paper, we study the effect of cognitive load on cooperative behavior.
We test the hypothesis that cognitive load increases cooperation, as would be
expected given validity of the results from experiments involving time pressure
Cognitive Load and Cooperation
71
and delay. Since the cognitive load task we used is designed to crowd out
working memory, we can test the link between cooperative behavior and dual
process theory in a way that targets the defining difference between the two
types of processes.
To the best of our knowledge there have been no other studies testing the
effect of cognitive load on contributions in a public goods game. There are a
number of studies that have tested the effect of cognitive load on generosity in
the dictator game and other non-strategic games (Cornelissen et al., 2011; Roch
et al., 2000; Schulz et al., 2014; Hauge et al., 2009; Kessler and Meier, 2014).
Most studies report that generosity increases with cognitive load (Cornelissen
et al., 2011; Schulz et al., 2014; Roch et al., 2000) while others do not find
significant effects (Hauge et al., 2009). However, there are several differences
between being generous in a dictator game and choosing to cooperate in the
context of a social dilemma. In the public goods game the choices of each
player reciprocally affects the outcome of the other players. As a consequence
the public goods game involves the possibility of conditioning contributions on
beliefs regarding other players’ contribution. Studies have documented a large
tendency towards “conditional cooperation” (Fischbacher, 2007; Martinsson
et al., 2013). Because of this the public goods game more accurately models the
concept of cooperation as opposed to altruism. Further, contributions in the
public goods game generally involve positive externalities and are efficiency enhancing, which is often not the case in dictator games.1 This means that in the
case of the public goods game there is a mismatch between what is optimal in
normal contexts involving repeated interactions, and the experimental context,
which involves a one-shot anonymous interaction. This means that according
to the social heuristics hypothesis we should expect increasing intuition to have
an effect in the public goods game specifically (Rand, 2016; Rand et al., 2016).
The effect of cognitive load in the context of social dilemma has so far
only been studied in three papers of which none reliably measure the specific
effect of cognitive load on coopration (Duffy and Smith, 2014; Liu and Hao,
2011; Milinski and Wedekind, 1998). Milinski and Wedekind (1998) study
aniterated prisoners’ dilemma game. They find that when working memory is
crowded, subjects switch from the “Pavlov strategy” (“win stay, lose shift”) to
the “generous tit-for-tat” strategy. While this suggests an effect of cognitive
load on cooperation, the study focuses on the use of different strategies
in a repeated setting, which confounds intuitive cooperation with issues of
complexity. Tit-for-tat is for example a more cooperative strategy, but also
1 Another difference is that there is strategic risk in the public goods game. Branas-Garza
et al. (2016) study the relationship between response time and strategic risk and find that
responses are slower in a game with high strategic risk compared to a game with low strategic
risk. Taking this result at face value, it would imply slower response times among free riders
in the public goods game as they have a dominant strategy that is independent of beliefs
about other players’ actions.
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Felix Døssing et al.
a simpler strategy. Duffy and Smith (2014) find that subjects under high
cognitive load behave less strategic than subjects under low cognitive load in
a finite four-player prisoner’s dilemma. More specifically high load subjects
exhibit more strategic defection near the end of the play and were more likely to
condition their play on previous outcomes. Whether cognitive load affects the
level of cooperation is not addressed by Duffy and Smith. Liu and Hao (2011)
study the interaction between framing (giving vs. taking) and a combination of
priming and cognitive load. Although they find no effect on cooperation, this
has little to say in regards to a general effect of cognitive load on cooperation.
In our experiment we focus on the initial level of cooperation in a public
goods game. We find that cooperation in the public goods game is significantly
higher in a treatment group exposed to high cognitive load compared to a treatment group with low cognitive load in the initial round. In the following rounds
the effect of cognitive load weakens but also becomes more difficult to interpret.
2
Materials and Methods
In our experiment, 166 subjects participated in a standard four-player public
goods game (PGG) repeated for 10 rounds with random partner matching. All
subjects where first year economics students with 65 female subjects, 101 male
subjects and an average age of 21 years. The experiment was conducted at
the Centre for Experimental Economics at the University of Copenhagen in
2014, during the Fall semester. Before the beginning of the experiment, we
distributed written instructions to participants and we ask them to answer
a number of comprehension questions correctly (for more details about the
experimental design, including screen shots and instructions, see the Online
Appendix). At the beginning of each period, they received an endowment of 20
points that they could allocate to a private or a public account. Points in the
private account remained unchanged whereas points in the public account were
doubled and shared equally between the four group members. After choosing
their contribution, subjects were informed of their personal earnings and the
average contribution of the other members of their group.
The public goods game was set up with each player i having an endowment
ei and choosing a contribution ci ≤ ei . If we have n players in each group,
and we denote the set of players by I, the payoff of player i is given by:
P
a j∈I cj
− ci
vi (ci , c−i ) =
n
As long as na < 1 player i maximizes her payoff by choosing ci = 0, while social
efficiency is achieved when ci = ei for all i. In the experiment the parameters
where set a = 2, n = 4 and such that ei = 20 for all i.
Cognitive Load and Cooperation
Low Load
73
High Load
Figure 1: Low and High Cognitive Load (red lines are added to the picture to illustrate
what constitutes separate shapes).
Before each period, subjects were presented with a dot pattern, which they
were asked to remember and replicate at the end of the period. This cognitive
load task is similar to the one developed by Bethell-Fox and Shepard (1988). In
the Low Load (LL) treatment (n = 82), subject were shown a 3 × 3 matrix containing a single-dotted object shape, whereas in the High Load (HL) treatment
(n = 84) the matrix contained either two or three dotted object shapes. This
method for inducing cognitive load was chosen over the more common practice
of having subjects remember a number, because we did not want to risk priming
subjects with specific numbers. The left column of Figure 1 shows two examples of the low cognitive load, while the right column shows examples of the
high load. The amount of load is determined by the amount of vertically and
horizontally connected shapes rather than the amount of dots. This subtle manipulation has been shown to limit Type 2 processing specifically (Neys, 2006).
The load task was incentivized, i.e. we pay subjects 10 point per correct entry.
At the end of the experiment, subjects filled in standard background questions about age, gender and did the cognitive reflection test (CRT) introduced
by Frederick (2005). The CRT was incentivized with subjects earning 5 points
for each correct answer. We also included an incentivized confusion test to
check if subjects understood the incentive structure of the public good game
(see Online Appendix D for details).
The total earnings (sum of all the points earned during the experiment)
for each subject were converted to Danish kroner (DKK) with the following
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Felix Døssing et al.
exchange rate: 3 points = 1 DKK. The experiment lasted about an hour and
subjects earned 147 DKK (∼EUR 20) on average (LL treatment: 137 DKK;
HL treatment: 160 DKK). The experiment was programmed and conducted
with the software z-Tree (Fischbacher, 2007) and subjects were recruited using
the online recruitment system ORSEE (Greiner, 2015).
3
Results
Since observations are only independent in the first round, our main focus is
on first round contributions. As is common practice (Rand, 2016) we restrict
our main analysis to subjects who answered the ex post confusion questions
correctly.2 The remaining sample used in the analysis consists of 82 subjects,
with 39 in LL and 43 in LL.3 Note that there is no significant difference in
confusion, i.e. understanding of incentive structure, between the two treatment
groups (Mann-Whitney test on the number of correct answers to the confusion
questions, p-value = 0.782). The average contribution in the first round was
6.2 points in LL and 9.6 points in HL. That is, subjects under high cognitive
load contributed on average 54 percent more than subject exposed to low
cognitive load. The difference in contributions is statistically significant using
the Mann-Whitney test (one-tailed p-value = 0.0064 ). Figure 2 shows the
distribution of contributions for the two treatments. It is evident that the
average treatment effect is driven by a higher fraction of subjects choosing to
free ride by contributing zero to the public good in the LL treatment as well
as a higher fraction of subjects choosing a contribution of 5, 10, and 20 in the
HL treatment. The difference in free riding is significant using the Chi2 test
(p-value = 0.006). The difference at 10 is borderline significant at the 5% level
(Chi2 test p-value = 0.054), while the difference at 20 is not significant (Chi2
test p-value = 0.285).
Since the confusion test was conducted after the end of the public goods
game, we cannot fully control for the possibility that load induced uneven
confusion, which disappeared at the end of the experiment. As for the cognitive
load, we note that the number of incorrect answers across all ten periods was
significantly larger for subjects under high load compared with subjects under
low load using a Mann-Whitney test (p-value = 0.004). This evidence together
with the facts that the cognitive load task was incentivized and that there
2 Strømland et al. (2016) conduct a time pressure experiment similar to that of Rand
et al. (2012) and find that the effect on contribution is contingent on subjects answering
similar ex post confusion questions correctly.
3 The level of confusion is similar to earlier studies using the same test (see for example
Fosgaard et al., 2016).
4 Since we are specifically testing the hypothesis that cognitive load increases cooperation,
we use a one-tailed test. The results are also significant using the Mann-Whitney test with
the full sample (confused and non-confused subjects; one-tailed p-value = 0.046).
Cognitive Load and Cooperation
75
0.4
Relative frequency
0.3
Low Load
0.2
High Load
0.1
0.0
0
0-5
5
5-10
10
10-15
15
15-20
20
Contribution
Figure 2: The Distribution of Contributions in the First Round by Treatment.
were generally few incorrect answers, suggest that the cognitive load succeeded
in crowding the working memory of the subjects in the first round. We cannot
know for sure whether subjects, who did not answer the cognitive load task
correctly, where actually using working memory resources on the task. However,
we note that the difference in first round contribution is still significant when
we exclude subjects who failed the cognitive load task (Mann-Whitney test,
one-tailed p-value = 0.010).
Table 1 reports a series of regression estimates, which also indicate that
a higher cognitive load induces more contributions. The HL (High Load)
treatment dummy is positive and significant in column (1) which displays
OLS estimates with contribution in the first period as the dependent variable.
Column (2) shows that the positive association between HL and contributions
is robust to the inclusion of a set of control variables (age, gender, number of
incorrect answers in the cognitive load task, number of incorrect answers to
the CRT and an interaction between CRT and HL).
Up to now, we have focused on the behavior in the first period of the PGG.
After the first period, individual contributions can no longer be viewed as
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Felix Døssing et al.
Table 1: OLS Regressions of Contributions.
OLS
Period 1
Variables
High Load
Female
Age > 22
Age < 20
Incorrect CL
Incorrect CRT
Incorrect CRT*High
Load
Period
Constant
Observations
Number of id
Panel Random
Effects
Periods 1–5
Panel Random
Effects
All Periods
(1)
(2)
(3)
(4)
(5)
(6)
3.428∗∗
6.638∗∗
2.614∗∗
5.056∗∗∗
1.380
(1.092)
3.606∗∗
(1.765)
−2.569∗∗
(1.209)
−3.524∗∗∗
(1.192)
−4.017∗
(2.157)
1.070
(1.184)
1.606∗
(0.853)
−1.539
(1.503)
(2.550)
−2.418
(1.746)
−3.124∗
(1.721)
−4.712
(3.115)
0.582
(1.711)
1.861
(1.233)
−2.061
(1.455)
6.154∗∗∗ 5.428∗∗∗
(1.088)
(1.827)
82
82
82
82
(1.157)
−0.356∗∗
(0.151)
6.822∗∗∗
(0.953)
410
82
(1.877)
−2.723∗∗
(1.285)
−3.477∗∗∗
(1.267)
−4.443∗
(2.293)
1.312
(1.259)
1.631∗
(0.907)
−1.683
(1.071)
−0.356∗∗
(0.151)
6.498∗∗∗
(1.419)
410
82
(1.007)
−0.352∗∗∗ −0.352∗∗∗
(0.0510)
(0.0510)
7.472∗∗∗
7.170∗∗∗
(0.839)
(1.295)
820
820
82
82
Notes: Specifications (1) and (2) report OLS estimates with the contribution in period 1 as the
dependent variable. Specifications (3) to (6) report Random Effect Panel estimates. High Load
is a dummy variable for the HL treatment. The Female and Age variables are dummy variables.
Incorrect CL denotes the number of incorrect answers in the cognitive load task. Incorrect CRT
denotes number of incorrect answers in the Cognitive Reflection Test and Incorrect CRT*High
Load is an interaction variable. Period indicate the period variable in the panel estimations.
Robust standard errors in parentheses. ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1
independent observations. Nevertheless, it is interesting to explore whether the
effect of cognitive ability persists as the game is repeated. Previous studies using
response time manipulations suggest that the effects may decline over time.
For example, Sutter et al. (2003) report a time-pressure effect on rejection rates
in the ultimate game, but the effect vanishes quickly as the game is repeated.
Figure 3 shows the evolution of average contributions over time. The
average contribution in the HL treatment is higher than in the LL treatment
for the first rounds of the experiment but the difference vanishes as the game
is repeated. The same pattern is present in the regression estimates of Table 1.
Columns (3) and (4) display estimates from random effects panel estimations
for periods 1–5 with and without controls. The HL dummy is again positive
and significant. However, in line with a declining effect of cognitive load over
Cognitive Load and Cooperation
77
Contribution
10
Low Load
High Load
5
0
1
2
3
4
5
6
7
8
9
10
Figure 3: Average Contributions by Treatment. Error Bars Denote the Standard Errors of
the Means.
time, the HL coefficient is much smaller in columns (5) and (6) where we use
data from all 10 periods. Regarding the effect of the covariates, we note a
lower CRT score is positively related to cooperation in LL but not in HL. One
potential explanation might be that low cognitive load is enough for those
with low CRT scores to generate the effects we observe more generally under
high cognitive load.
One potential explanation behind the convergence in contributions is that
the difference in cognitive load between treatments becomes smaller as subjects
gain experience with the cognitive load task. Thus, we may not have been
able to induce a working memory load in subsequent rounds. This view is
supported by looking at the fraction of subjects that give an incorrect answer
to the cognitive load task. In the first period, there is a dramatic difference
with almost three times as many subjects providing an incorrect answer in
the HL treatment compared with the LL treatment. But from period 3 an
onwards, there is no apparent difference in the fraction of incorrect answers
between treatments. Whether the load task was successful in rounds following
the first is, however, difficult to distinguish from possible interaction effects.
78
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Felix Døssing et al.
Discussion
Our results indicate that cognitive load causes an increase in cooperation in
the first round, while the dynamic effects are more difficult to untangle. This
result seems to be consistent with the hypothesis that relying on intuition
increases cooperative behavior. One potential explanation is that cooperation
is a heuristic response that subjects bring with them into the lab (Rand et al.,
2014). When resources are not available to sufficiently compute costs and
benefits they increasingly rely on this heuristic. As noted, the distribution of
contributions in the first round of the two treatments show that the increase in
cooperation is due to a higher frequency of zero donations in the LL treatment,
as well as a higher amount of intermediate and maximum contributions in
the HL treatment (Figure 2). While the effect on free riding and maximum
contributions is perfectly in line with the social heuristics hypothesis, it does not
explain why we observe an increase in 50% contributions in the HL treatment.
Previous studies have looked at the tendency for choosing an intermediate
contribution (Capraro et al., 2014) or an “equal-division” (Roch et al., 2000) and
by analyzing subsequent statements have tied this to a preference for fairness.
Therefore, a possible explanation is that cognitive load causes players to apply
an “equal is fair” heuristic. We cannot, however, rule out the possibility that
cognitive load affects different types of subject differently. Lacking cognitive
resource may cause more selfish subjects to apply a more cooperative heuristic
or it may cause them to apply a heuristic, which states that an intermediate
choice is safe.
In future work, we hope to better understand the mechanisms behind
the treatment effect we observe. In particular, we hope to address how the
treatment effect relates to the complexity of the situation, the framing of the
game and decision noise.
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