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EXPRESS: Remembering the bad ones: Does the source memory advantage for cheaters influence our later actions positively?

Authors:

Abstract

Previous research has demonstrated that people remember information that is (emotionally) incongruent to their expectations, but it has left open the question if this memory enhancement has also an influence on our later actions. We investigated this question in one pilot study and two experiments. In all studies, participants first interacted with trustworthy and untrustworthy looking partners in an investment game. Facial trustworthiness was manipulated to stimulate social expectations about the behavior of the partners. In a later second investment game, participants played against old opponents from the first game and new opponents. Overall, willingness to cooperate in the second game was influenced by the formerly behavior of the opponent. However, facial trustworthiness affected economic decisions, too. Furthermore, we analyzed source memory data that indicated no differences in memory between cheaters and cooperators. Instead, source guessing was related to cooperation: The more participants guessed that an untrustworthy looking face belonged to a cheater, the less they cooperated with untrustworthy looking opponents. Interestingly, in experiment 2, we found a positive correlation between old-new recognition and later cooperation. In sum, the results demonstrate that memory and guessing processes can influence later decisions. However, economic decisions are also heavily affected by other social expectations like facial trustworthiness.
Peer Review Version
Remembering the bad ones: Does the source memory
advantage for cheaters influence our later actions
positively?
Journal:
Quarterly Journal of Experimental Psychology
Manuscript ID
QJE-STD-19-375.R2
Manuscript Type:
Standard Article
Date Submitted by the
Author:
28-Feb-2021
Complete List of Authors:
Kroneisen, Meike; University of Mannheim , School of Social Sciences,
Department of Psychology
Bott, Franziska; University of Mannheim, School of Social Sciences
Mayer, Maren; University of Mannheim, School of Social Sciences
Keywords:
cheater recognition, cooperation, source memory, trustworthiness,
evolution
Quarterly Journal of Experimental Psychology
Author Accepted Manuscript
DOI: 10.1177/17470218211007822
Peer Review Version
Remembering the bad ones: Does the source
memory advantage for cheaters influence our later
actions positively?
Meike Kroneisen12, Franziska M. Bott2 and Maren Mayer2
Author Note
1University of Koblenz-Landau
2University of Mannheim
Note: Franziska Bott and Maren Mayer contributed equally to this work.
Correspondence concerning this article should be addressed to Meike Kroneisen, Department of
Psychology, University of Koblenz-Landau, Fortstraße 7, D-76829 Landau, Germany, Email:
kroneisen@uni-landau.de
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DOI: 10.1177/17470218211007822
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Remembering the bad ones 2
ABSTRACT
Previous research has demonstrated that people remember information that is (emotionally)
incongruent to their expectations, but it has left open the question if this memory enhancement
has also an influence on our later actions. We investigated this question in one pilot study and
two experiments. In all studies, participants first interacted with trustworthy and untrustworthy
looking partners in an investment game. Facial trustworthiness was manipulated to stimulate
social expectations about the behavior of the partners. In a later second investment game,
participants played against old opponents from the first game and new opponents. Overall,
willingness to cooperate in the second game was influenced by the formerly behavior of the
opponent. However, facial trustworthiness affected economic decisions, too. Furthermore, we
analyzed source memory data that indicated no differences in memory between cheaters and
cooperators. Instead, source guessing was related to cooperation: The more participants guessed
that an untrustworthy looking face belonged to a cheater, the less they cooperated with
untrustworthy looking opponents. Interestingly, in experiment 2, we found a positive correlation
between old-new recognition and later cooperation. In sum, the results demonstrate that memory
and guessing processes can influence later decisions. However, economic decisions are also
heavily affected by other social expectations like facial trustworthiness.
Abstract: 204 words
Keywords: cheater recognition, cooperation, source memory, trustworthiness
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Remembering the bad ones 3
Remembering the bad ones: Does the source memory advantage for cheaters influence our later
actions positively?
From an evolutionary point of view, social cooperation among unrelated individuals is still
puzzling (Bell, Buchner, & Musch, 2010). Mutual cooperation is a common phenomenon among
humans (Fehr & Fischbacher, 2004), so it seems obvious that individuals benefit from it.
However, cooperation usually implies some kind of fitness costs for the acting individual and is
therefore risky (Trivers, 1971). When explaining this puzzle of human cooperative sentiment,
researchers have long relied on reciprocity (e.g. Axelrod, 1984; Trivers, 1971): Individuals
should not provide benefits to others unconditionally, but should only cooperate when there is the
possibility of a favor being returned later on (Axelrod, 1984; Axelrod & Hamilton, 1981; Tooby
& Cosmides, 1992; Trivers, 1971). This, however, also implies that cooperators must avoid
exploitation from cheating individuals. According to Barclay (2008) different cognitive abilities
are required to ensure that cooperation is a stable behavior. More precisely, humans should have
“cognitive abilities that can solve two tasks: (1) Detecting instances of non-cooperation (cheating
detection), and (2) remembering who has been cooperative and who has not (cheater recognition)
and interacting preferentially with other cooperators” (Barclay, 2008, p. 818).
Whereas there is a lot of research concerned with memory advantage for recognizing
cheaters and research interested in economic decisions, only few experimental studies investigate
the postulated effect of supposedly enhanced memory for cheaters on actual decisions in social
cooperation. In the following experiments, we thus investigated the influence of (source) memory
and appearance-based expectations on economic decisions in social cooperation games.
(Source) Memory for Cheaters
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Remembering the bad ones 4
According to social contract theory (Cosmides, 1989; Tooby & Cosmides, 1992, 2005), a
specialized cognitive module has evolved that comprises a cheater detection algorithm. This idea
is supported, for instance, by the finding that individuals are better able to solve the Wason
Selection Task (Wason, 1966) when it is presented as a cheater detection situation instead of an
abstract logical problem (Cosmides, 1989; Gigerenzer & Hug, 1998). Moreover, previous
research on source memory for faces of cheaters, that is memory for the cheating context in
which these faces were previously encountered, showed that source memory for faces of cheaters
is enhanced as compared with faces of people described as trustworthy or cooperative (e.g.,
Buchner, Bell, Mehl, & Musch, 2009; Bell et al., 2010). In these studies the moral status of these
faces (i.e., source of faces) was manipulated either by using short descriptions in which the
stimulus faces were associated with cheating behavior, trustworthy/cooperative behavior, or
neutral behavior, or by using a social-cooperation game in which the interactants cheated,
cooperated, or showed neutral behavior. The test phase included old photographs (targets) and
new photographs (distractors) and the participants were asked if they had already seen the face
(i.e. “old”) or not (i.e. “new”). Given the answer was old, participants were additionally required
to indicate whether each face belonged to a cheater, to a trustworthy person, or to a neutral person
(not every study included this last category).
Although there is evidence in favor of some kind of cheater detection module, more recent
research suggests that more general mechanisms are responsible for the effects found: better
performance in the Wason Selection Task when framed as a cheater detection situation can be
attributed to, for example, greater relevance as compared with its abstract version (Carlisle &
Shafir, 2005), or to the influence of world knowledge and syntactic cues (Ayala & Klar, 2014).
Regarding enhanced source memory for cheaters, Bell, Buchner, Kroneisen, and Giang (2012),
for example, demonstrated that source memory is not generally better for the context of cheating
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Remembering the bad ones 5
behavior, but that source memory is enhanced for a certain behavior if this behavior is somehow
unexpected. A more general memory mechanism which prioritizes the processing of
unanticipated information seems more sensible in light of further corroborative evidence (see, for
example, Barclay, 2008; Kroneisen & Bell, 2013; Kroneisen, Woehe & Rausch, 2015).
To summarize, these memory studies showed that even after a very short contact with
another person, individuals are able to memorize some of the past behavior of this person.
Cheating or cooperation in earlier encounters seems to have important informational value,
especially when it is unexpected. Yet, a detection module specifically for cheaters would fail to
take into account the importance of new information that violates expectancies, which people are
able to memorize especially well (Bell et al., 2012). In particular, there seems to be a sensitivity
for violations of positive expectations (Kroneisen et al., 2015).
Trustworthiness as Cue in Economic Decisions
Besides memory that is assumed to affect decisions, various factors can influence
economic decisions; for instance, when considering social interactions information on others’
trustworthiness may play an important role. Delgado, Frank, and Phelps (2005), for example,
showed that additional information about three fictional partners indicating good, bad or neutral
moral behavior led participants to make more risky choices with partners introduced as morally
good. Participants were also more likely to share money with them in comparison to the other
partners formerly introduced as bad or neutral even after the expectation of good behavior was
violated. Another study demonstrated that after the possibility to speak with the two other gaming
partners for 30 minutes, participants were more likely to cooperate in one-shot prisoner`s
dilemmas given they perceived the partners as cooperative (Frank, Gilovich, & Regan, 1993).
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Facial Appearance as Indicator of Trustworthiness
Besides direct experiences in a game, Chang, Doll, van`t Wout, Frank, and Sanfey (2010)
showed that facial trustworthiness as an implicit social signal for trust can also influence the
investment behavior in a trust game. In the first round of a cooperation game, participants
invested more, when the opponent looked trustworthy. In social-dilemma games, too, participants
rather defected against untrustworthy looking partners and cooperated with trustworthy looking
partners (van’t Wout & Sanfey, 2008; Rezlescu, Duchaine, Olivola, Chater, & Rustichini, 2012).
Facial trustworthiness thus seems to be used as a reliable cue for later behavior by triggering
expectations and stereotypes. When only seeing a face we already draw conclusions about the
trustworthiness of this person (Winston, Strange, O’Doherty, & Dolan, 2002). Impressions based
on appearance are formed quickly (Willis & Todorov, 2006; Todorov, Pakrashi, & Oosterhof,
2009) and automatically (Engell, Haxby, & Todorov, 2007). Apparently, there is also a high
consent about the question who looks trustworthy and who does not (Todorov, 2008). Moreover,
Chang et al. (2010) found that trustworthiness and experience interact: trustworthy looking
opponents that also showed trustworthy behavior were entrusted with the most money in a trust
game.
Overall, these studies suggest that impressions, reputation, and experience may influence
people`s economic decision making. Trust in a partner is determined by two things (1) an initial
trustworthiness rating influenced by social signals (e.g., facial trustworthiness) and (2)
subsequent experience or reputational information about this partner.
Interaction Effects of Memory and (Facial) Trustworthiness on Economic Decisions
According to the adaptive memory framework (e.g., Buchner et al., 2009; Kroneisen &
Bell, 2018; Nairne & Pandeirada, 2016), memory did not only evolve for the purpose of
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Remembering the bad ones 7
remembering, but to inform judgments and decisions based on the remembered information. Bell
et al. (2012), for instance, demonstrated that facial trustworthiness in combination with
reputational information influences memory: unexpected information was remembered especially
well. Participants showed very good memory for trustworthy looking cheaters and for
untrustworthy looking cooperators. Yet, it is still an open question how exactly memory interacts
with beliefs, for example, about the moral behavior of a partner and their influence on economic
decisions. As outlined above, people are better able to memorize cheaters in comparison to
trustworthy or neutral opponents even after very short contact and also among distractor faces in
a classical source-memory design. In the economic decision studies discussed, however,
participants only encountered a small number of players. Therefore, memorizing their faces is not
a difficult task. Initial findings by Suzuki, Honma and Suga (2013) demonstrated that formerly
learned bad lenders persist in memory even after verbal extinction in comparison to good
borrowers. In their experiments, however, participants only played against old opponents. Murty,
FeldmanHall, Hunter, Phelps, and Davachi (2016) also investigated if episodic memory can
influence decision-making. They found that participants had better memory for opponents
making less fair offers in a Dictator game. Furthermore, they were more likely to reengage with
old opponents in a later decision task if these opponents made more favorable offers during the
preceding Dictator game. Schaper, Mieth, and Bell (2019), too, found evidence that the
willingness to cooperate in a trust game may be determined by the memory for the previous
behavior of opponents. Both, the results from Murty et al (2016) and Schaper et al. (2019),
underline the importance of source memory in adaptive decision making.
Combining the different strands of research, in one pilot study and two main experiments,
we examined the interaction of memory and appearance–based expectations on economic
decisions. Mainly, we were interested in two things: (1) how much does the behavior in an
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investment game depend on memory for the previous experience with the partner and (2) what
influence do moral expectations like facial trustworthiness have on both, source memory and
economic decisions? Similar to Bell et al. (2012), we combined the two factors facial appearance
(trustworthy vs. untrustworthy) and behavior in a trust game in the encoding phase. According to
the incongruity effect (Bell et al., 2012; Kroneisen et al., 2015), unexpected combinations of
facial trustworthiness and actual behavior (e.g., a trustworthy looking cheater) should be
remembered especially well. With regard to economic decision, we expected source memory to
influence participants’ investments: participants should defect more when confronted with
formerly introduced cheaters as compared with cooperators. Based on the incongruity effect,
participants should especially defect more when confronted with trustworthy looking cheaters in
comparison to untrustworthy looking cheaters and invest more in formerly learned untrustworthy
looking cooperators as compared with trustworthy looking cooperators. Moreover, based on the
results discussed above, facial trustworthiness seems to be an important cue for trust. Therefore,
participants might invest more when confronted with trustworthy looking opponents as compared
with untrustworthy looking opponents.
Pilot study
The pilot study’s objective was to investigate the effects of facial trustworthiness and prior
behavior of fictitious opponents on economic decisions involving these opponents. We set the
stage for the two subsequent experiments that tested the role of source memory.
Participants
Thirty-nine students (27 female) of the University of Mannheim participated. They received
a participation credit and 1% of the money they earned during the two games. Their age ranged
from 18 to 35 years (M = 21.92; SD = 3.19).
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Apparatus and materials
Thirty-six colored portrait photographs (512 x 768 pixels) of male white adults were taken
from the FERET database (Phillips, Wechsler, Huang, & Rauss, 1998). The faces were selected
based on a norming study conducted at the Heinrich-Heine University in Düsseldorf1. The same
norming study was already used to select pictures in other experiments (see, for example, Bell et
al., 2012; Mieth, et al., 2016). All faces were unfamiliar to the participants. Only forward-facing
pictures with a neutral facial expression were selected. Mean a priori trustworthiness ratings for
the selected 36 faces were M = 4.18 (SD = 0.18) for faces with high a priori trustworthiness and
M = 2.20 (SD = 0.14) for faces with low a priori trustworthiness.
The faces were randomly assigned to two sets. One consisted of 24 photographs and was
presented in the debt game (see below for details). Here, participants saw 12 trustworthy looking
pictures and 12 untrustworthy looking pictures. Out of these, 6 of the trustworthy and 6 of the
untrustworthy looking pictures were combined with the behavior of a good lender or bad lender
in the debt game, each. The second set consisted of 12 photographs and served as new pictures in
the two-player Public Goods game (again, half of them received high facial trustworthiness
ratings and the other half received low ratings in the norming study).
Procedure
Debt game – Learning Phase
Our debt game used in the encoding phase was similar to the debt game used by Suzuki
and Suga (2010). Participants were first informed that they had to invest in a friend’s enterprise.
In order to do so, they had to borrow money from a lender. On each trial, participants were first
1 Twenty-one adults (mean age = 23.2, SD of age = 2.6) rated the a priori trustworthiness of 256 male faces.
Trustworthiness was rated on a scale ranging from 1 (hardly trustworthy) to 6 (trustworthy). We thank Raoul Bell for
providing us with the data.
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asked to rate the likeability of the lenders face. Each trial started with a headline (“How likeable
do you find this person?”) and a photograph shown for 2 seconds. Subsequently, the likeability
rating scale, ranging from 1 (not likeable) to 6 (extremely likeable), was shown. Participants
made their choice by using the computer mouse. After that, participants had to decide whether to
borrow 15 euros or 30 euros from a lender whose face was again presented on the monitor. The
participants were aware that after investing the borrowed money, they would temporarily earn a
20% profit. Then, the lender demanded repayment. The participants were told that there were
different lenders who had been classified as good or bad beforehand. Good lenders demanded
only the money they lent in repayment (i.e., no interest), yielding the 20% profit to the
participants. Bad lenders demanded the money they lent plus 40% (i.e., high interest), yielding a
20% net loss to the participants. After participants played 24 trials of the game and had a short
pause of 20 seconds, they were informed that they now had to complete the second part of the
experiment, a two-player Public Goods game.
Public Goods game – Testing Phase
Here, each participant played a two-player Public Goods game with 36 opponents. Out of
these, 24 had been presented during the encoding phase (12 bad lenders, 12 good lenders) and 12
were new persons. Participants were informed that they played for real money, and that their
decisions influenced the amount of money they would receive at the end of the study. They were
told that they would receive 1% of the money earned during the Public Goods game. They were
also informed that the old opponents would behave in line with their behavior in the debt game.
The game played was very similar to the game used in several other studies (see, for example,
Bell et al., 2010; Bell et al., 2012; Bell, Mieth, & Buchner, 2015). On each trial, participants were
first asked to rate the likeability of the presented face. Each trial started with a headline (“How
likeable do you find this person?”) and a photograph shown for two seconds. Subsequently, the
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likeability rating scale [ranging from 1 (not likeable) to 6 (extremely likeable)] was shown.
Participants made their choice by using the computer mouse. On the next screen, the participant
saw a black and white contour, representing themselves, on the left side of the screen. On the
right side of the screen, opposite to the participant’s contour, a facial photograph of the opponent
was shown. The current account balance of the participant was shown under the participant’s
contour. In every trial, participants had to decide whether to cooperate or not. More precisely,
they had to decide whether to invest either 0 euros or 30 euros. Once confirmed, the selected
amount was shown on the screen, at the same time the opponent’s investment appeared. An
opponent introduced as good lender during the encoding phase invested the same amount of
money as the participant. An opponent introduced as bad lender during the encoding phase
invested nothing. If the opponent was a new person, he randomly invested either 0 euros or 30
euros. On the next screen the sum of investments, a profit, and the total sum appeared. The profit
was always 20% of the sum of both investments. The sum of the investments and the profit were
added up and the resulting total sum was split up between the participant and the opponent. Both
received half of the total sum regardless of their investments. For example, if a participant
decides to invest 30 euros, an opponent induced as bad lender during the encoding phase would
invest 0 euros. Accordingly, the sum of investments would be 30 euros. The profit would be 6
Euros, and the total return would be 36 euros. Each opponent would receive 18 euros. Thus, the
participant would incur a loss of 12 euros, whereas the bad lender would gain 18 euros. Thus,
depending on the investment of the participant and the opponents’ behaviors, participants could
win or lose money. Finally, the participant’s current account balance was updated, and the
participant could initiate the next trial by pressing the space bar.
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Design
A 3 x 2 design was used with behavioral history (bad lender vs. good lender vs. new) and
trustworthiness (trustworthy vs. untrustworthy) as within-subject factors. Likeability ratings and
investments during the two-player Public Goods game were the dependent variables.
Results
Likeability Ratings
During the Debt Game (learning phase) trustworthy looking persons were rated more
likable than untrustworthy looking persons, a 2 (behavior: bad vs. good lender) x
2(trustworthiness) repeated measurement ANOVA revealed a significant main effect of
trustworthiness, F(1, 38) = 522.51, p < .001, 93. There was no difference in likeability
η
𝑝
=
.
depending on later behavior, F(1, 38) = 2.64, p = .11, 07. In addition, there was no
η
𝑝
=
.
significant interaction between facial trustworthiness and later behavior, F(1, 38) = .25, p = .62,
006.
η
𝑝
=
.
During the Public Goods game (test phase), trustworthy looking persons were rated more
likable than untrustworthy looking persons, a 3 (behavioral history: good lender vs. bad lender vs.
new) x 2 (trustworthiness) repeated measurement ANOVA revealed a significant main effect of
trustworthiness, F(1, 38) = 220.92, p < .001, 85. The previous association with specific
η
𝑝
=
.
behavior (bad vs. good lender vs. new) had an effect on likability, F (1.62, 61.352) = 20.12, p <
.001, .35. Descriptively, good lenders and new opponents were rated more likeable than
η
𝑝
=
bad lenders (Mbad;trustworthy = 3.37, SD = 0.68; Mgood;trustworthy = 3.67, SD = 0.64; Mnew;trustworthy =
3.75, SD = 0.66; Mbad;untrustworthy = 1.96, SD = 0.50; Mgood;untrustworthy = 2.21, SD = 0.46;
2 Greenhouse-Geisser correction
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Remembering the bad ones 13
Mnew;untrustworthy = 2.38, SD = 0.58). There was no significant interaction between the two factors,
F(2, 76) = 0.23, p = .79, .006.
η
𝑝
=
Investments in the Debt Game (learning phase)
In the learning phase, participants invested more in the game when the opponent looked
trustworthy than when he looked untrustworthy, . As the
𝐹
(
1, 38
)
=
306.11,
𝑝
<
.001,
η
𝑝
=
.89
individual opponent hasn’t been identified as good lender versus bad lender at the time of
investment, there was no effect of behavioral history, , and no
𝐹
(
1, 38
)
=
1.04,
𝑝
=
.32,
η
𝑝
=
.
03
interaction, .
𝐹
(
1, 38
)
=
1.46,
𝑝
=
.23,
η
𝑝
=
.
04
Public Goods game investments3 (testing phase)
The average investments depending on behavioral history (good lender vs. bad lender vs.
new) and facial trustworthiness (trustworthy vs. untrustworthy) in the testing phase can be seen in
the upper panel of Figure 1.
A 3 (behavioral history) x 2 (trustworthiness) repeated measurement ANOVA revealed a
significant main effect of behavioral history, Participants
𝐹
(
2, 76
)
=
21.50,
𝑝
<
.001,
η
𝑝
=
.36.
spent less money when playing against an opponent identified as bad lender in the learning phase
in comparison to a good lender, or a new opponent,
𝑡
(
38
)
=
4.75,
𝑝
<
.001,
d
=
0
.
76
,
𝑡
(
38
)
. There was no difference in investment between good lenders
=
6.29,
𝑝
<
.001,
d
=
1
.
01
and new opponents, .4 In addition, the ANOVA revealed that
𝑡
(
38
)
=
1.54,
𝑝
=
.13,
d
=
-
0
.
25
participants invested more in trustworthy looking opponents than in untrustworthy looking
3 For all reported experiments, data are available via the Open Science Framework (OSF) and can be accessed at
https://osf.io/qku28/?view_only=b1a3b0433b794e64bc57afd50d900d9a.
4 All post-hoc tests were conducted using JASP (JASP Team, 2020). We used the Holm method of adjustment to
control for multiple tests.
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Remembering the bad ones 14
opponents, 64. The two main effects were accompanied by a
𝐹
(
1, 38
)
=
68.51,
𝑝
<
.001,
η
𝑝
=
.
significant interaction, . Post-hoc tests showed that given
𝐹
(
2, 76
)
=
10.71,
𝑝
<
.001,
η
𝑝
=
.
22
trustworthy looking opponents participants spent less money to bad lenders than to good lenders (
) or new opponents (
𝑡
(
38
)
=
4.26,
𝑝
<
.001,
d
=
0
.
68
𝑡
(
38
)
=
7.64,
𝑝
<
.001,
d
=
1
.
). There was also a difference between good lenders and new opponents (
22
𝑡
(
38
)
=
3.39,
𝑝
. For untrustworthy looking opponents, there was an equivalent difference
=
.005,
d
=
0.
54
in investments between bad lenders and good lenders ( ), but
𝑡
(
38
)
=
2.84,
𝑝
=
.02,
d
=
0.45
no difference between bad lenders and new opponents ( ) or
𝑡
(
38
)
=
1.75,
𝑝
=
.17,
d
=
0
.
28
between good lenders and new opponents ).
(
𝑡
(
38
)
=
1.09,
𝑝
=
.28,
d
=
0
.
17
Discussion
Similar to other studies (Mieth et al., 2016; van`t Wout & Sanfey, 2008), our results
suggest that individuals are less likely to invest money when their opponent looks untrustworthy
than when he looks trustworthy. Moreover, the opponent’s previous behavior seems to influence
investments: participants invested more in the Public Goods game when the opponent was
experienced as good lender or new as compared with an opponent experienced as bad lender in a
previous debt game. Contrary to findings by Bell et al. (2012) and Kroneisen et al. (2015) on
source memory, however, we did not find an effect of expectation violation. Instead, participants
invested less money when a formerly learned bad lender looked untrustworthy as compared with
trustworthy and cooperated more often when a formerly learned good lender looked trustworthy
as compared with untrustworthy. Borrowing money represents a risky strategy that pays only
when one expects the opponent to be nice. Our finding suggests that participants had more
positive expectancies toward faces with high a priori trustworthiness.
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Still, the effect of the factor facial trustworthiness was reduced by behavioral history in
comparison to its effect when confronted with new opponents. For new opponents, only facial
appearance could be used as cue. Interestingly, trustworthy looking new opponents got the
highest cooperation rate, a result also found in other studies (Bell, Giang, Mund, & Buchner,
2013; Mieth et al., 2016). This might indicate bad source memory for old opponents resulting in a
more cautious investment strategy when facing old opponents as well as a high reliance on facial
trustworthiness.
Due to these partially unexpected results, in Experiment 1, we tested if these remained
stable when using a larger sample size. Additionally, we addressed limitations in the pilot study:
First, in the Public Goods game of the pilot study’s test phase, participants received feedback on
their opponents’ behavior after each trial. This may have functioned as additional learning
potentially influencing further decisions. Second, we did not measure whether the investment
decisions participants made were actually connected to source memory (supposedly enhanced for
cheaters). Tackling these aspects, we adjusted our procedure for Experiment 1: Our participants
first played a game with cheating opponents and cooperating opponents. This was followed by a
decision task (participants could decide if they wanted to cooperate or to cheat) including old and
new opponents without feedback to ensure that this task did not function as an additional learning
phase. Similar to Murty et al. (2016), a classical source-memory test followed after this.
Experiment 1
Method
Participants
Sixty-nine students of the University of Koblenz-Landau participated. One participant had
to be excluded because of low task performance. The final sample comprised 68 students (54
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Remembering the bad ones 16
females). Participants’ age ranged from 18 to 45 years (M = 21.67, SD = 4.29). They received a
participation credit and 2 euros at the end of the experiment. Given N = 68, α=.05, and 36
responses in the source-memory test, effects of size w = 0.06 (Buchner et al., 2009) of the
behavioral history variable on memory could be detected with a probability of 1−β= .84. All
power calculations were conducted using G*Power (Faul, Erdfelder, Lang, & Buchner, 2007).
Apparatus and materials
Forty-eight colored portrait photographs (640 × 480 pixels) of male white adults were taken
from the lifespan database of adult facial stimuli (Minear & Park, 2004) which has been
extensively used in face research (e.g., Stahl, Wiese, & Schweinberger, 2008; Wiese,
Schweinberger, & Hansen, 2008; Wiese, Schweinberger, & Neumann, 2008). Only forward-
facing pictures with a neutral facial expression were selected. All faces were unfamiliar to the
participants. The faces were selected on the basis of a norming study conducted at the Heinrich-
Heine University in Düsseldorf. The same norming study was already used to select pictures in
the pilot study. Mean a priori trustworthiness ratings for the selected 48 faces were 3.72 (SD =
0.40) for faces with high a priori trustworthiness and 2.50 (SD = 0.35) for faces with low a priori
trustworthiness5. The faces were randomly assigned to a learning set (24 photographs; 12 faces
low in facial trustworthiness and 12 faces high in facial trustworthiness) and two distractor sets
(12 photographs; 6 faces low in facial trustworthiness and 6 faces high in facial trustworthiness,
each).
Procedure
Public Goods game - Learning phase
5 As mentioned in Footnote 1, trustworthiness was rated on a scale ranging from 1 (hardly trustworthy) to 6
(trustworthy).
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During the first phase of the experiment, 12 trustworthy looking and 12 untrustworthy
looking facial photographs were presented in the context of a Public Goods game (learning set).
During the game, subjects were supposed to learn from their own experience whether the
respective target person showed either cheating behavior or cooperative behavior. Thus, the
experimental design comprised two independent variables: a priori expectation (trustworthy
looking vs. untrustworthy looking) and behavior in the game (cheating vs. cooperative). Half of
the trustworthy looking opponents and half of the untrustworthy looking opponents were
randomly assigned to either the cheater condition or the cooperator condition. Consequently, the
learning phase comprised 24 trials, with six trials for trustworthy cheaters, trustworthy
cooperators, untrustworthy cheaters, and untrustworthy cooperators, each.
The game was similar to that used in previous studies on memory for reputations (Bell,
Buchner, & Musch, 2010; Giang, Bell, & Buchner, 2012). Each participant started with a deposit
of 450 euros. The participant was represented by a black and white shape that was presented on
the left side of the screen. On the right side of the screen, opposite to the participant’s contour, a
facial photograph of the opponent was shown. The current account balance of the participant was
shown under the participant’s contour. First, the participants had to decide whether to invest 10
euros, 20 euros, or 30 euros. On the next screen the investment of the participant was shown on
the left side, the opponent`s investment appeared on the right side of the screen. A cooperator
invested the same amount of money as the participant. A cheater invested 0 euros. Then, a bonus
was added to the sum of both investments, consisting of 20% of the total sum. At the end of each
round, the total money was split by two, with each player receiving half of the total money. Thus,
depending on the investment of the participant and the opponents’ behaviors, participants could
win or lose money.
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After participants played 24 trials of the game and had a short pause of 20 seconds, they
were informed that they now had to complete the second part of the experiment, a decision task.
Decision Task – Testing Phase
Each participant played a game with 36 opponents. Out of these, 24 had been presented
before (i.e., target items from the first Public Goods game) and twelve were new (distractor set 1,
no previous encounters, 6 trustworthy and 6 untrustworthy looking faces).
First, participants were instructed that a new game would start. They would play against old
and new opponents. Old opponents would behave in the exact same way as in the Public Goods
game before. In each round, participants could decide if they wanted to invest 0 euros or 30
euros. In contrast to the test phase of the pilot study, participants could only select the amount of
money they would invest, feedback about the behavior of the opponent or their current account
was never given. Participants were told that they would have the opportunity to be rewarded at
the end of the experiment if they showed good performance during this game. They were also
explicitly told, that, therefore, a good memory for the behavior of the old opponents would be
beneficial.
Memory Test
After a short pause of 20 seconds, the third part of the experiment started, a surprise
memory test. During the memory test, we presented 36 faces, 24 of them old (i.e., target items
from the first Public Goods game, learning phase) and 12 of them new (distractor set 2, no
previous encounters, 6 trustworthy and 6 untrustworthy looking faces). For each target,
participants first had to choose whether they “know this person from the first part of the study”
via mouse click on one of two buttons differentiating old and new items (= item memory).
Whenever participants endorsed that they recognized the target, they subsequently had to indicate
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Remembering the bad ones 19
whether this target had behaved “fair” or “unfair” during the Public Goods game (remembering
behavior of the target = source memory).
Design
A 3 x 2 design was used with behavioral history (cheater vs. cooperative vs. new) and
trustworthiness (trustworthy vs. untrustworthy) as within-subject factors. Investments during the
decision tasks, old-new recognition, and source memory were the dependent variables.
Results
Investments in the Learning Phase
In the learning phase, participants invested more in the game when the opponent looked
trustworthy than when he looked untrustworthy. . Again,
𝐹
(
1, 67
)
=
54.35,
𝑝
<
.001,
η
𝑝
=
.45
our findings suggest that participants had more positive expectancies toward faces with high a
priori trustworthiness. As there weren’t any previous encounters yet, there was no effect of
behavioral history, , and no interaction,
𝐹
(
1, 67
)
=
0.75,
𝑝
=
.39,
η
𝑝
=
.
01
𝐹
(
1, 67
)
=
0.03,
𝑝
.
=
.86,
η
𝑝
<
.
001
Investments in the Testing Phase
A 3 (behavioral history: cheater vs. cooperative vs. new) x 2 (trustworthiness) repeated
measurement ANOVA revealed a significant main effect of behavioral history,
𝐹
As can be seen in the
(
1.77,118.54
Greenhouse
-
Geisser correction
)
=
18.60,
𝑝
<
.001,
η
𝑝
=
.22.
middle panel of Figure 1, participants spent less money when playing against a cheater in
comparison to a cooperator, or a new opponent,
𝑡
(
67
)
=
5.43,
𝑝
<
.001,
d
=
0
.
66,
𝑡
(
67
)
. There was no difference in investment between cooperators
=
5.12,
𝑝
<
.001,
d
=
0
.
62
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and new opponents, .6 In addition, participants invested more in
𝑡
(
67
)
=
0.31,
𝑝
=
.76,
d
=
0
.
04
trustworthy looking opponents than in untrustworthy looking opponents,
𝐹
(
1, 67
)
=
38.80,
𝑝
37. The two main effects were accompanied by a significant interaction,
<
.001,
η
𝑝
=
.
𝐹
. Post-hoc tests showed that
(
1.83, 122.62
Greenhouse
-
Geisser correction
)
=
3.33,
𝑝
=
.04,
η
p
2
=
.
05
given trustworthy looking opponents, participants spent less money to cheaters than to
cooperators ( ) or new opponents (
𝑡
(
67
)
=
5.90,
𝑝
<
.001,
d
=
0
.
72
𝑡
(
67
)
=
5.11,
𝑝
), but no difference between cooperators and new opponents
<
.001,
d
=
0
.62
(
𝑡
(
67
)
=
0.79,
). For untrustworthy looking opponents, there was a difference in investments
𝑝
=
.99,
d
=
0.095
between cheaters and cooperators ( , cheaters and new
𝑡
(67)
=
2.95,
𝑝
=
.02,
d
=
0.36)
opponents ( ), but no difference between cooperators and
𝑡
(
67
)
=
3.24,
𝑝
=
.008,
d
=
0
.
39
new opponents ).
(
𝑡
(
67
)
=
0.29,
𝑝
=
.99,
d
=
0
.
0
4
Old-New recognition
Old-new recognition is reported in terms of Pr, a sensitivity measure of the two-high
threshold model. It is calculated by subtracting the false alarm rate from the hit rate. Pr as a
sensitivity measure was evaluated in validation studies (Snodgrass & Corwin, 1988) and avoids
the problem of undefined values arising from the use of d’.
A 2 (behavioral history: cheater vs. cooperative) x 2 (trustworthiness) repeated
measurement ANOVA revealed no significant main effect of behavioral history,
𝐹
(
1, 67
)
=
0.40,
Trustworthiness did not affect old–new discrimination,
𝑝
=
.53,
η
𝑝
=
.006.
𝐹
(
1, 67
)
<
0.001,
𝑝
6 Again, all post-hoc tests were conducted using JASP. We used the Holm method of adjustment to control for
multiple tests.
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The interaction between both variables was also not significant,
=
.96,
η
𝑝
<
.001.
𝐹
(
1, 67
)
(see Figure 2, top panel).
=
2.23,
𝑝
=
.14,
η
𝑝
=
.0
3
Source Memory & Behavior
There are different ways to measure source memory. However, many ad hoc measures are
known to confound source memory with guessing biases (Bayen & Murnane, 1996; Bröder &
Meiser, 2007). Therefore, we analyzed our data using the multinomial source monitoring model
of Bayen, Murnane, and Erdfelder (1996). Using this kind of models, it is possible to measure
probabilities of underlying cognitive processes assumed to be involved in source monitoring, and
to estimate independent parameters of item memory, source memory, and guessing from the
observed response frequencies in the memory test (Erdfelder et al., 2009; Moshagen, 2010).
Multinomial models have been used in many studies examining memory for defectors (e.g., Bell
et al., 2012; Buchner et al., 2009; Kroneisen et al., 2015). We used the same model as in Bell et
al. (2012, Experiment 1; submodel 5d in the classification of identifiable source-memory
submodels of Bayen et al., 1996, which incorporates the assumptions DcheatTrustworthy =
DcoopTrustworthy = DNewTrustworthy = DcheatUntrustworthy = DcoopUntrustworthy = DNewUntrustworthy; a = g).
Furthermore, similar to Schaper et al (2019), instead of aggregating response frequencies across
participants, we fitted a Bayesian hierarchical extension of the MPT model (Klauer, 2010;
Matzke, Dolan, Batchelder, & Wagenmakers, 2015). This method allows to estimate individual
parameters from an overarching group distribution (Heck, Arnold, & Arnold, 2018; Klauer,
2010) and can therefore account for heterogeneity of the participants’ memory and guessing
tendencies. Furthermore, this allows us to investigate the correlation between individual
parameters and participants’ decisions in the decision task directly.
Hierarchical multinomial modeling is an increasingly popular approach in memory
research (Marevic, Arnold, & Rummel, 2018; Schaper et al., 2019) but is also used in other
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Remembering the bad ones 22
research fields (Kroneisen & Heck, 2020; Kroneisen & Steghaus, in press). In line with Schaper
et al. (2019), we used a two-step strategy to test (1) whether source memory and guessing
processes differed between trustworthy and untrustworthy looking cheaters and cooperators and
(2) whether there is a relationship between these source-monitoring processes and decision
making during the decision-task. For all analyses, we used the R package TreeBUGS (Heck et al.,
2018), which fits Bayesian hierarchical MPT models using Markov-chain Monte-Carlo methods
(Plummer, 2003).
Model fit was assessed with posterior-predicted p values and indicated a satisfactory fit
both with respect to the mean (p = .41) and the covariance structure (p = .47) of the observed
individual frequencies as tested by the T1 and T2 statistics proposed by Klauer (2010, see also
Heck et al., 2018).
To test for differences in source monitoring depending on behavioral history and
trustworthiness, we have to look at the parameter differences of interest. A statistically reliable
difference is indicated if the 95% BCI for the difference estimate does not contain zero. The
corresponding parameter estimates and 95% BCIs are reported in Table 1. Source memory is
reflected in the parameter d7. To test for differences in source memory between trustworthy
looking cheaters and trustworthy looking cooperators, we sampled the parameter difference
Δdtrustworthy = dCheatTrustworthy - dCoopTrustworthy. Source memory did not differ between trustworthy
looking cheaters and trustworthy looking cooperators, Δdtrustworthy = .03, 95% BCI = [-0.16, 0.22].
To test for differences in source memory between untrustworthy looking cheaters and
cooperators, we sampled the parameter difference Δduntrustworthy = dCheatUntrustworthy - dCoopUntrustworthy.
Source memory did not differ between untrustworthy looking cheaters and untrustworthy looking
7 Parameter d represents the conditional probability of remembering the context (cheater vs. cooperator, separately
for trustworthy and untrustworthy looking faces) in which the item has been encoded.
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cooperators, Δduntrustworthy = .19, 95% BCI= [-0.08, 0.61]. There was also no difference in source
memory between trustworthy looking cheaters and untrustworthy looking cheaters (Δdcheat = -.15,
95% BCI= [-0.56, 0.14]) or trustworthy looking cooperators and untrustworthy looking
cooperators (Δdcoop = .01, 95% BCI= [-0.11, 0.17]).
We also analyzed the guessing parameter g which represents the conditional probability of
guessing that a face belonged to a cheater given that the face and/or its context is not
remembered. Parameter g can be seen as a measure of participants' expectancies toward the
stimulus faces. Parameter g clearly differed between untrustworthy looking faces and trustworthy
looking faces, (Δg=.25, 95% BCI= [0.13, 0.37]). This finding suggests expectancy-congruent
guessing, such that participants had a tendency toward guessing that trustworthy looking faces
belonged to cooperators and untrustworthy looking faces belonged to cheaters. We also tested if
there was a difference in guessing between trustworthy and untrustworthy unrecognized faces.
Parameter b represents the conditional probability of guessing that an unrecognized face was
shown before. Parameter b differed between untrustworthy looking and trustworthy looking
faces, (Δb=.25, 95% BCI= [0.06, 0.44]). For untrustworthy looking faces, participants had a
greater tendency to guess that they had seen it during the learning phase.
As mentioned above, we also wanted to test whether source-monitoring processes were
correlated with adaptive decision making in the later decision task. Similar to Schaper et al.
(2019), we correlated the individual posterior parameter estimates with the proportion of
cooperation with trustworthy and untrustworthy looking cooperators, cheaters, and new partners
in the decision task8, within the hierarchical model. Table 2 summarizes the results.
8 To calculate the proportion of cooperation during the decision task, we recoded the dichotomous outcome variable
into 0 (= invested nothing) and 1 (= invested 30 euros). Using this, the proportion of cooperation for each subject
could be estimated.
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We found no relationship between old-new recognition or source memory9 and behavior
in the decision task. None of the memory parameters were associated to cooperation, irrespective
of a priori trustworthiness. However, source guessing (parameter g) was related to cooperation:
The more participants guessed that an untrustworthy looking face belonged to a cheater, the less
they cooperated with untrustworthy looking opponents. There was no reliable relationship
between old-new guessing (parameter b) and cooperation, irrespective of trustworthiness.
Discussion
In this experiment, expectations were formed by two aspects: facial trustworthiness of the
opponent and memory for former behavior of this opponent. Based on the incongruity effect (Bell
et al., 2012; Kroneisen et al., 2015), both factors, trustworthiness and behavioral history, should
interact: participants should tend to defect even more often when a formerly learned negative
opponent looked trustworthy. Moreover, participants should tend to cooperate more often when a
formerly learned good opponent looked untrustworthy. However, replicating the results of our
pilot study, in Experiment 1, again, participants invested more money when playing against
trustworthy looking formerly learned cooperators and invested the least when playing against
untrustworthy looking formerly learned cheaters.
The source-memory data showed no differences in memory between cheaters and
cooperators. Instead, participants showed a bias towards guessing that an untrustworthy looking
person had cheated in the Public Good game and a trustworthy looking person had cooperated.
This challenges the stability of the formerly found source-memory advantage for trustworthy
looking cheaters (incongruenty effect; e.g., Bell et al., 2012).
9 In line with Schaper et al. (2019), we also repeated the analysis with the restriction that source memory did not
differ between cheaters and cooperators. However, there was still no relationship between parameter d and behavior.
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Furthermore, Experiment 1 was designed to test if the investment decisions of participants
are connected to source memory. Based on the idea that memory evolved to inform decisions
(adaptive memory framework and recent results from Schaper et al., 2019), a correlation between
source memory (parameter d) and behavior in the decision game should be detected. Our
experiment showed no influence of source memory, but a clear relationship between source
guessing, a priori facial trustworthiness and decisions: For untrustworthy looking opponents, the
more participants guessed that a face belonged to a cheater, the less they cooperated with this
person.
However, Experiment 1 differed from other experiments (Bell et al., 2012, Schaper et al.,
2019) in so far that we included an additional task between learning and memory phase. It is
unclear if the time between learning and testing is a critical moderator of the source-memory
advantage for unexpected information and of an effect of memory on decisions. To test the
hypothesis that the order of memory test and decision task is critical, we replicated Experiment 1,
but using the reversed order of memory test before decision task.
Experiment 2
Method
Participants
Participants were recruited via electronic mailing-lists of the University of Mannheim and
the University of Koblenz-Landau. Eighty-nine students (69 females) participated. Participants’
age ranged from 18 to 42 years (M = 22.60, SD = 4.10). They received a participation credit at
the end of the experiment.
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Apparatus and Materials, Procedure, and Design were as in Experiment 1, except that the order
of the memory task and the decision task was changed with memory being tested before the
decision task. Given N = 89, α = .05, and 36 responses in the source-memory test, w = .06 of the
behavioral history variable could be detected with a probability of 1−β = .92.
Results
Investments in the Learning Phase
In line with the pilot study and experiment 1, the 2 (behavioral history) x 2
(trustworthiness) repeated measurement ANOVA revealed a main effect of trustworthiness:
Participants invested more in the game when the opponent looked trustworthy than when he
looked untrustworthy. . There was also a significant main
𝐹
(
1, 88
)
=
80.39,
𝑝
<
.001,
η
𝑝
=
.4
8
effect of behavioral history, ( ). Participants spend more money
𝐹
(
1, 88
)
=
4.51,
𝑝
=
.04,
η
𝑝
=
.
05
to opponents that cooperated later. There was no significant interaction,
𝐹
(
1, 88
)
=
0.72,
𝑝
.
=
.40,
η
𝑝
=
.
00
8
Investments in the Testing Phase
In line with Experiment 1, a 3 (behavioral history: cheater vs. cooperative vs. new) x 2
(trustworthiness) repeated measurement ANOVA showed a significant main effect of behavioral
history, Participants spent less money when playing
𝐹
(
2, 176
)
=
40.38,
𝑝
<
.001,
η
𝑝
=
.32.
against a cheater in comparison to a cooperator, or a new
𝑡
(
88
)
=
6.93,
𝑝
<
.001,
d
=
0
.
74,
opponent, (see Figure 1, lower panel). There was no
𝑡
(
88
)
=
8.42,
𝑝
<
.001,
d
=
0
.
89
difference in investment between cooperators and new opponents,
𝑡
(
88
)
=
1.49,
𝑝
=
.14,
d
=
.10 In addition, participants invested more in trustworthy looking opponents than in
-
0
.
16
10 Again, all post hoc tests were conducted using JASP. We used the Holm method of adjustment to control for
multiple tests.
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untrustworthy looking opponents, 51. The two main effects
𝐹
(1, 88)
=
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𝑝
<
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η
𝑝
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.
were accompanied by a significant interaction, . Post-hoc
𝐹
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η
p
2
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.
09
tests revealed that given trustworthy looking opponents, participants spent less money to cheaters
than to cooperators, or new opponents,
𝑡
(
88
)
=
4.33,
𝑝
<
.001,
d
=
0
.
46,
𝑡
(
88
)
=
8.11,
𝑝
, and spent less to cooperators than new opponents,
<
.001,
d
=
0
.86
𝑡
(
88
)
=
3.78,
𝑝
. For untrustworthy looking opponents, there was a difference in investments
<
.001,
d
=
-
0.40
between cheaters and cooperators, , between cheaters and
𝑡
(
88
)
=
5.91,
𝑝
<
.001,
d
=
0.
63
new opponents, , but no difference between cooperators
𝑡
(
88
)
=
4.33,
𝑝
<
.001,
d
=
0
.
46
and new opponents, .
𝑡
(
88
)
=
1.58,
𝑝
=
.29,
d
=
0
.
17
Old-New recognition
A 2 (behavioral history: cheater vs. cooperative) x 2 (trustworthiness) repeated
measurement ANOVA revealed no significant main effect of behavioral history on Pr,
𝐹
(
1, 88
)
Trustworthiness did not affect old–new discrimination,
=
0.01,
𝑝
=
.92,
η
𝑝
<
.001.
𝐹
(
1, 88
)
The interaction between both variables was also not significant,
=
1.43,
𝑝
=
.24,
η
𝑝
=
.0
2
.
𝐹
(see Figure 2, lower panel).
(
1, 88
)
=
1.02,
𝑝
=
.32,
η
𝑝
=
.0
1
Source Memory & Behavior
Similar to Experiment 1, we again used a two-step strategy to analyze our data: First, we
tested whether source memory and guessing were affected by trustworthiness and behavioral
history. Second, we tested whether memory and guessing processes influenced later decision
making during the decision task.
Model fit was again assessed with posterior-predicted p-values and indicated a
satisfactory fit, pT1 = .34 and pT2 = .79. The parameter estimates and 95% BCIs are reported in
Table 1. To test for differences in source memory between trustworthy looking cheaters and
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trustworthy looking cooperators, we sampled the parameter difference Δdtrustworthy = dCheatTrustworthy
- dCoopTrustworthy. Source memory did not differ between trustworthy looking cheaters and
trustworthy looking cooperators, Δdtrustworthy = .13, 95% BCI= [-0.35, 0.49]. To test for
differences in source memory between untrustworthy looking cheaters and untrustworthy looking
cooperators, we sampled the parameter difference Δduntrustworthy = dCheatUntrustworthy - dCoopUntrustworthy.
Source memory did differ between untrustworthy looking cheaters and untrustworthy looking
cooperators, Δduntrustworthy = .51, 95% BCI= [0.005, 0.91]. Participants had a better source memory
for untrustworthy looking cheaters in comparison to untrustworthy looking cooperators. There
was no difference in source memory between trustworthy looking cheaters and untrustworthy
looking cheaters (Δdcheat = -.20, 95% BCI= [-0.67, 0.31]) or trustworthy looking and
untrustworthy looking cooperators (Δdcoop = .18, 95% BCI= [-0.06, 0.45]). Again, the guessing
parameter g clearly differed between untrustworthy looking and trustworthy looking faces, (Δg =
.215, 95% BCI= [0.07, 0.21]) which suggests expectancy-congruent guessing. Parameter b (old-
new guessing) did not differ between untrustworthy looking and trustworthy looking faces,
(Δb=.05, 95% BCI= [-0.04, 0.15]). There was no difference in guessing that an unrecognized face
was shown before.
As in Experiment 1, we computed the correlation between the individual posterior
parameter estimates and the proportion of cooperation with trustworthy and untrustworthy
looking cooperators, cheaters, and new opponents in the decision task. Table 3 summarizes the
results. There was a positive correlation between old-new recognition (D) and cooperation given
trustworthy looking cooperators and untrustworthy looking cooperators, but not given cheaters.
Similar to Experiment 1, we found no relationship between source memory (d) and behavior in
the decision task, irrespective of a priori trustworthiness. Source guessing (g) was related to
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cooperation: The more participants guessed that an untrustworthy looking face belonged to a
cheater, the less they cooperated with untrustworthy looking opponents.
Discussion
In line with the pilot study and Experiment 1, participants invested more money when playing
against trustworthy looking formerly learned cooperators and invested the least when playing
against untrustworthy looking formerly learned cheaters. The source-memory data showed a
difference in memory between untrustworthy looking cheaters and untrustworthy looking
cooperators: Participants remembered more untrustworthy looking cheaters. There was no such
difference given trustworthy old opponents. Furthermore, facial trustworthiness again strongly
biased guessing in the source-memory test replicating other studies (Bell et al., 2012).
Consistent with Experiment 1, there was a reliable relationship between source guessing, a
priori facial trustworthiness, and decisions: When opponents looked untrustworthy, participants
guessed that this face belonged to a cheater and cooperated less with this person. However, again,
no correlation between source memory and later behavior could be found. In line with Schaper et
al (2019), we found a relationship between old-new recognition and cooperation in the cooperator
condition. The better old-new recognition was, the more our participants cooperated with
previously encountered cooperative opponents. As Schaper et al. (2019) already pointed out, that
the recognition of a face seems to be useful to approach cooperators, but not to avoid cheaters.
General Discussion
Several studies were able to show that source memory was better for cheaters than other
types of behavior (e.g., Barclay, 2008; Bell & Buchner, 2010; Bell et al., 2010; Buchner et al.,
2009; Kroneisen, 2018; Kroneisen & Bell, 2013; Suzuki & Suga, 2010). This has led to the
hypothesis of a cheater detection module that is a highly specific cognitive mechanism assumed
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to support social exchange by facilitating the detection of cheaters. The results of more recent
studies, however, showed a more general influence of social expectations on memory (e.g., Bell
et al., 2012; Kroneisen & Bell, 2013; Kroneisen et al., 2015). Cheating persons were not per se
remembered better than cooperative persons, but source memory for expectancy-incongruent
information was superior in comparison to expectancy-congruent information. These findings are
inconsistent with the assumption of a specialized cognitive module. A mechanism, however, that
is sensitive to expectancy violations, independent of the expectations’ origin (e.g., stereotypes,
social rules, facial appearance, …), may be an efficient way of optimal information processing in
social exchange situations (Bell et al., 2012).
Beside the effects on memory, moral beliefs about persons also influence people`s decision
making (see, for example, Delgado et al., 2005). First impressions, reputational information about
a person, and personal experience influence economic decision making (Chang et al., 2010;
Delgado et al., 2005; Frank et al., 1993). It can be concluded that social cooperation depends
fundamentally on expectations about other people’s behaviors (Mieth et al., 2016).
But how are these expectations about the trustworthiness of a person formed? Several
studies have shown that (a) initial impressions and (b) previous interactions influence the amount
of trust people place in a partner (Chang et al, 2010). Trustworthiness, for example, is often
rapidly inferred from several first impressions. People can detect very subtle signals of
trustworthiness by simply viewing faces (Winston et al., 2002). Facial trustworthiness as an
implicit social signal also influences initial judgments of trustworthiness and therefore economic
decision making. Van‘t Wout and Sanfey (2008), for example, showed that judgments of facial
trustworthiness predict the financial risk someone is willing to take. Combining these different
strands of research, in one pilot study and two main experiments, we examined the interaction of
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appearance–based expectations and memory on economic decisions. More precisely, we were
interested in (1) how much the behavior in a two-player Public Goods game depends on memory
for previous experiences with the opponents and (2) what influence moral expectations like facial
trustworthiness have on both, source memory and economic decisions.
Interestingly, the source-memory results from both main experiments did not replicate the
often-found source-memory advantage for unexpected behavior. Our data showed no differences
in source memory between cheaters and cooperators (independently of facial trustworthiness) in
Experiment 1, and only a source-memory advantage for untrustworthy looking cheaters in
comparison to cooperators in Experiment 2, challenging previous results. However, when looking
at other studies, these findings may not be completely inconsistent with the literature. So far, a
clear source-memory advantage for cheaters was found when using third-hand information in the
learning phase. This is usually done by presenting participants with pictures of faces paired with
short statements describing the pictured persons as cheating, cooperative, or neutral. Other
researcher used game-theoretical paradigms in the learning phase (Bell et al., 2010, Bell et al.,
2016; Giang, Bell, & Buchner, 2012, Schaper et al., 2019). When using this paradigm, no reliable
evidence for a source memory advantage of cheaters over cooperators could be found. One
possible explanation for this effect is that people focus on the information that is relevant in a
given situation. When giving third-hand information about cooperative behavior, there is no need
to reciprocate it. This changes when playing a “game” with different partners. Here, a cooperative
opponent must be rewarded. Furthermore, an explicit interaction with a person suggests that
future encounters are likely. Therefore, it is important to remember the cooperators as well as the
cheaters to give an appropriate reaction to every opponent (Bell et al., 2010; Bell et al., 2016;
Schaper et al., 2019). Furthermore, also the prioritization of the processing of unexpected
information and its association to better source memory was often found in third-hand
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information paradigms (Bell et al., 2012; Bell et al., 2015; Kroneisen & Bell, 2013; Kroneisen et
al., 2015). But only a few experiments used game-theoretical paradigms to test this (Bell et al.,
2012). There is the possibility that a similar argumentation also holds for the lack of the
incongruity effect in our experiments: When using a paradigm that encourages a direct
involvement of the participants, it is important to remember cooperators as well as cheaters.
Furthermore, in such situations other cues that influence moral expectations about other people,
like facial trustworthiness, may become increasingly important for the participants. Apparently,
behavioral relevance determines the processing of social information. Kroneisen (2018), for
example, found that behavioral relevance impacted source memory resulting in better source
memory for cheaters who showed a behavior with high probability of occurrence for a student
population (e.g., “F.A. secures a spot in the library early in the morning during the busy exam
prep period, but then shows up three hours later to study.”) in contrast to cheaters who showed a
behavior with only low behavioral relevance for a student population (e.g., “P.O. fulfills his
military service. Because he has free access to the armory, he steals ammunition and sells it on
the black market.”). In the current experiments, explicit interaction and the need to remember
cooperators as well as cheaters might have triggered participants to use other cues and to overuse
facial trustworthiness as an important diagnostic cue for later behavior, increasing its behavioral
relevance.
Interestingly, in line with Schaper et al. (2019), we found a clear positive relationship
between old-new recognition and cooperation with previously encountered cooperative partners
in Experiment 2. Participants cooperated more often with formerly learned cooperators, the better
old-new recognition was. Schaper et al. (2019) already pointed out that face recognition seemed
to be useful to approach cooperators only. No negative relationship between old-new recognition
and cooperation with previously encountered cheating partners could be found.
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In contrast to Schaper et al. (2019), we did not find a relationship between source memory
and the decision to cooperate. Instead, we found that the correlation between cooperation and the
classifications in the memory test was based on strategic guessing, but only when the opponent
looked untrustworthy. The more participants guessed that an untrustworthy looking face
belonged to a cheater, the less they cooperated with untrustworthy looking opponents. However,
there are some differences between the study reported by Schaper and colleagues and our studies.
First, instead of two learning rounds, we only had one. Second, we used pictures from
trustworthy and untrustworthy looking opponents. Why are these differences important? Previous
research showed that expectations about the trustworthiness of other people are often formed
automatically on the basis of appearance, for example based on facial cues (Todorov et al., 2009,
2015). The results from our decision tasks indicate that both, facial trustworthiness as well as
experienced behavior of the opponent can influence later actions. Similar to other studies (Mieth
et al., 2016; van‘t Wout & Sanfey, 2008), participants invested less money in the two-player
Public Goods game when their opponent looked untrustworthy than when he looked trustworthy.
Contrary to our expectations, our participants invested more when playing against trustworthy
looking formerly learned cooperators and least when playing against untrustworthy looking
formerly learned cheaters. A finding that was consistently found in all our experiments. This
confirms that facial cues have a strong effect on trust, social expectations, and thereby decision
making. In line with this notion, the effect of facial trustworthiness was strongest when playing
against new partners. This is not a completely unreasonable behavior: As Sparks, Burleigh, and
Barclay (2016) showed, people can predict each other's Prisoner's Dilemma decisions after
talking with later opponents for only a short time. Our experiments extend these results. Facial
trustworthiness seems to be a very important factor for investment behavior. Our results indicate
that this cue can even overwrite previous encounters with a person. Given facial trustworthiness
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as cue is available, people seem to overweight it in their investment decisions. This is also
consistent with our finding that source guessing is associated with investment behavior.
However, memory of the formerly shown behavior can also influence decisions.
Knowledge about one’s opponent reduces the influence of facial trustworthiness. Chang et al.
(2010) showed that trustworthiness and experience interact: trustworthy looking opponents who
also showed trustworthy behavior were entrusted with the most money. However, contrary to our
experiments, in the experiment from Chang et al. participants played repeatedly with their
opponents and in Schaper et al (2019) there were two learning rounds. Thus, the more people
know about their opponents, the less influence their facial appearance has on investment
strategies.
To summarize, our present experiments extend the current research on memory and
guessing on adaptive decision making. We were able to demonstrate that besides previous
interactions and the memory for these interactions, there are other factors influencing
cooperation. Facial trustworthiness seems to be a very important diagnostic cue for people to
decide whether they can trust someone or not. This cue is so important that it may even overwrite
previous experiences with the same person. However, one caveat in this research field remained
also in our study. Psychological studies often ignore the possibility of within-person variations in
appearance (Jenkins, White, Van Montfort, Mike Burton, 2011). We used the same image of a
person in the learning phase as well as in the memory test and the decision task. From an
ecological validity perspective, this is an unrealistic choice. Different pictures from a person can
vary substantially and it is unclear if the recognition of one image of a person can be seen as
proof that the identity of this person was remembered (Jenkins et al., 2011). Therefore, future
research should try to increase the ecological validity of their experiments.
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FIGURE CAPTIONS
Figure 1. Average game investments [in euros] as a function of behavioral history (cheating vs.
cooperative vs. new opponent) and facial trustworthiness (trustworthy vs. untrustworthy) in the
pilot study (upper panel), Experiment 1 (middle panel), and Experiment 2 (lower panel). The
error bars represent standard errors of the means.
Figure 2. Old-new recognition as a function of behavioral history (cheater vs. cooperative) and
trustworthiness (trustworthy vs. untrustworthy) in Experiment 1 (upper panel) and Experiment 2
(lower panel). The error bars represent standard errors of the means.
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Figure 1. Average game investments [in euros] as a function of behavioral history (cheating vs. cooperative vs. new opponent) and
facial trustworthiness (trustworthy vs. untrustworthy) in the pilot study (upper panel), Experiment 1 (middle panel), and Experiment 2
(lower panel). The error bars represent standard errors of the means.
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Figure 2. Old-new recognition as a function of behavioral history (cheater vs. cooperative) and trustworthiness
(trustworthy vs. untrustworthy) in Experiment 1 (upper panel) and Experiment 2 (lower panel). The error bars
represent standard errors of the means.
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Table 1
Parameter estimates (D=probability of recognizing a face as old or new, d = conditional probability of
source memory in the sense of remembering the context in which a face was encountered;, g = conditional
probability of guessing that the face belonged to a cheater rather than to a cooperator, b = conditional
probability of guessing that an unrecognized face is old) for a priori trustworthiness of the faces
(trustworthy (TW) vs. untrustworthy (UTW)) in Experiment 1 and 2. Bayesian Credibility Intervals are
reported in the brackets.
Parameter
Parameter estimate
[BCI]
Experiment 1
D
0.78
[.74, .81]
dCheatTW
0.09
[.003, .25]
dCheatUTW
0.24
[.01, .64]
dCoopTW
0.06
[.001, .20]
dCoopUTW
0.05
[.001, .14]
gTW
0.47
[.39, .55]
gUTW
0.72
[.63, .82]
bTW
0.28
[.13, .43]
bUTW
0.53
[.38, .66]
Experiment 2
DTW
0.61
[.56, .66]
dCheatTW
0.35
[.06, .54]
dCheatUTW
0.56
[.10, .94]
dCoopTW
0.22
[.01, .49]
dCoopUTW
0.05
[.001, .15]
gTW
0.42
[.32, .53]
gUTW
0.62
[.55, .70]
bTW
0.27
[.19, .33]
bUTW
0.32
[.24, .40]
Page 46 of 48
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Table 2. Correlations between parameter estimates (D=probability of recognizing a face as old or new, d = conditional probability of source memory in the sense of remembering
the context in which a face was encountered;, g = conditional probability of guessing that the face belonged to a cheater rather than to a cooperator, b = conditional probability of
guessing that an unrecognized face is old) and the proportion of cooperative decisions as a function of partner type (cooperator, cheater, new) and a priori facial trustworthiness
(trustworthy vs. untrustworthy) in Experiment 1. The values in brackets correspond to the Bayesian Credibility Intervals.
Probability of cooperation with…
Cheaters
Cooperators
New
partners
trustworthy
untrustworthy
trustworthy
untrustworthy
trustworthy
untrustworthy
corr
[BCI]
corr
[BCI]
corr
[BCI]
corr
[BCI]
corr
[BCI]
corr
[BCI]
D
-0,04
[-.22, .14]
-0,03
[-.25, .19]
0,09
[-.09, .27]
0,02
[-.21, .24]
0,08
[-.09, .25]
0,02
[-.18, .21]
dCheatNTW
-0,09
[-.30, .15]
-0,32
[-.59, .13]
-0,01
[-.21, .22]
-0,30
[-.58, .15]
-0,02
[-.22, .19]
-0,18
[-.42, .15]
dCheatTW
-0,05
[-.29, .20]
0,01
[-.41, .40]
-0,01
[-.25, .23]
0,07
[-.41, .47]
0,02
[-.20, .24]
0,01
[-.31, .31]
dCoopNTW
-0,01
[-.26, .24]
0,09
[-.39, .49]
-0,01
[-.25, .24]
0,12
[-.39, .53]
0,01
[-.21, .22]
0,06
[-.28, .36]
dCoopTW
-0,01
[-.24, .22]
-0,03
[-.41, .35]
0,05
[-.20, .30]
0,00
[-.44, .40]
0,02
[-.20, .24]
0,00
[-.30, .29]
gNTW
-0,13
[-.27, .02]
-0,56
[-.67, -.43]*
-0,08
[-.24, .07]
-0,62
[-.72, -.50]*
-0,10
[-.24, .05]
-0,37
[-.49, -.23]*
gTW
-0,12
[-.32, .09]
0,17
[-.10, .42]
-0,17
[-.37, .05]
0,17
[-.12, .43]
-0,04
[-.23, .16]
0,04
[-.19, .27]
bNTW
0,08
[-.10, .26]
0,12
[-.13, .35]
0,06
[-.12, .23]
0,21
[-.04, .43]
0,09
[-.08, .26]
0,13
[-.05, .31]
bTW
-0,02
[-.20, .17]
0,09
[-.15, .33]
0,02
[-.16, .21]
0,19
[-.05, .42]
0,02
[-.15, .20]
0,06
[-.13, .26]
Page 47 of 48
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Table 3. Correlations between parameter estimates (D=probability of recognizing a face as old or new, d = conditional probability of source memory in the sense of remembering
the context in which a face was encountered;, g = conditional probability of guessing that the face belonged to a cheater rather than to a cooperator, b = conditional probability of
guessing that an unrecognized face is old) and the proportion of cooperative decisions as a function of partner type (cooperator, cheater, new) and a priori facial trustworthiness
(trustworthy vs. untrustworthy) in Experiment 2. The values in brackets correspond to the Bayesian Credibility Intervals.
Probability of cooperation with…
Cheaters
Cooperators
New
partners
trustworthy
untrustworthy
trustworthy
untrustworthy
trustworthy
untrustworthy
corr
[BCI]
corr
[BCI]
corr
[BCI]
corr
[BCI]
corr
[BCI]
corr
[BCI]
D
-0,05
[-.22, .14]
-0,05
[-.18, .07]
0,18
[.06, .29]*
0,14
[.02, .26]*
0,11
[-.01, .22]
0,12
[-.003,.23]
dCheatNTW
-0,02
[-.23, .20]
-0,16
[-.17, .12]
-0,12
[-.31, .10]
-0,08
[-.29, .16]
-0,03
[-.23, .18]
-0,05
[-.25, .16]
dCheatTW
-0,09
[-.34, .19]
-0,10
[-.38, .27]
-0,08
[-.33, .22]
-0,05
[-.32, .23]
-0,04
[-.22, .28]
0,00
[-.26, .24]
dCoopNTW
0,02
[-.25, .26]
0,05
[-.28, .35
0,02
[-.26, .29]
0,05
[-.324 .32]
0,03
[-.28, .22]
0,04
[-.21, .28]
dCoopTW
0,04
[-.22, .29]
0,04
[-.27, .33]
0,06
[-.23, .31]
0,01
[-.27, .28]
0,04
[-.20, .24]
0,00
[-.30, .29]
gNTW
-0,10
[-.26, .06]
-0,35
[-.48, -.18]*
-0,06
[-.23, .11]
-0,26
[-.41, -.10]*
-0,11
[-.26, .05]
-0,25
[-.39, -.10]*
gTW
-0,17
[-.35, .07]
-0,17
[-.38, .10]
-0,23
[-.42, .05]
-0,07
[-.287, .17]
-0,17
[-.35, .06]
-0,01
[-.21, .19]
bNTW
0,14
[-.04, .32]
0,11
[-.10, .32]
0,06
[-.14, .26]
0,16
[-.04, .34]
0,15
[-.04, .32]
0,15
[-.04, .32]
bTW
0,15
[-.08, .34]
0,07
[-.18, .30]
0,09
[-.13, .30]
0,18
[-.05, .38]
0,13
[-.09, .32]
0,08
[-.14, .28]
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... Individuals invest more money in trust games when their opponents look trustworthy (vs. untrustworthy) (Chang et al., 2010;Kroneisen et al., 2021) and cooperate less with untrustworthy-looking opponents (Kroneisen et al., 2021). Duarte et al. (2012) also showed that more trustworthy-looking individuals had higher loan approval rates and better credit scores. ...
... Individuals invest more money in trust games when their opponents look trustworthy (vs. untrustworthy) (Chang et al., 2010;Kroneisen et al., 2021) and cooperate less with untrustworthy-looking opponents (Kroneisen et al., 2021). Duarte et al. (2012) also showed that more trustworthy-looking individuals had higher loan approval rates and better credit scores. ...
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