One hundred and twelve students (73 of whom were female) with a mean age of 23 (SD = 5) participated in Experiment 1 (Table 1). All participants gave written informed consent in accordance with the Declaration of Helsinki. The present experiments are part of a series of experiments that has been approved by the ethics committee of the Department of Experimental Psychology at Heinrich Heine University Düsseldorf.

The same sequential Prisoner’s Dilemma Game was used as in previous studies (Bell et al., 2012b, 2013). In this game, participants were required to invest money into a joint business with partners whose faces were shown on the screen. Participants played with 20 trustworthy-looking partners and with 20 untrustworthy-looking partners. The faces were randomly drawn from a set of 40 trustworthy-looking and 40 untrustworthy-looking frontal facial photographs of women¹ with a neutral expression (250 × 375 pixel) from the FERET database (Phillips et al., 1998). In a norming study, the untrustworthy-looking faces had received low trustworthiness ratings (M = 2.75, SD = 0.24) and the trustworthy-looking faces had received high trustworthiness ratings (M = 4.28, SD = 0.23) on a scale ranging from 1 to 6. Half of the partners in each condition cooperated and the other half cheated.

Participants could familiarize themselves with the game in two practice trials. At the start of the game, they were informed that they played for real money. In each trial, participants first saw a silhouette at the left side of the screen (representing the participant), and the partner’s face at the right side of the screen (Figure 1). Participants were required to decide whether to invest 15 cents or 30 cents (by pressing a left or right button of the response box, respectively). The decision was displayed on screen for 1 s. The investment was presented in an arrow for 500 ms before it moved to the center of the screen within 500 ms. Similarly, the partner’s decision was shown in an arrow for 500 ms, before it moved to the center of the screen within 500 ms. The sum of investments was then shown in the middle of the screen. After 500 ms a bonus of 1/3 of the sum of investments was added. After 500 ms, the total sum was shown. After a further 500 ms, this total sum was split up between the partners. Both the participant and the partner received half of the total sum, regardless of what they had invested. The partner’s share was shown in an arrow moving toward the partner’s face (500 ms). After 500 ms, the participant’s share was shown in an arrow moving to the participant’s silhouette (500 ms). After 1 s, the partner’s gain or loss was presented, followed by the participant’s gain or loss (after 500 ms). After a further 500 ms, the updated account balance of the participant was presented, and (again after 500 ms) a summary of the interaction was displayed. The next trial was initiated by the participant pressing the continue button.

A cooperating partner always reciprocated the participant’s investment (either 15 or 30 cents), which resulted in a gain for both players. A cheating partner invested nothing (0 cents), which resulted in a gain for the partner at the expense of the participant, who lost money.

The payoff (gain or loss) of each player can be determined by the formula:

where P_a is the payoff of Player A, I_a is the investment of Player A, and I_b is the investment of Player B. Applying this formula, it is obvious that interacting with a cooperating partner led to a gain, and interacting with a cheating partner led to a loss of the same magnitude for the participant.

After the game, participants received the instructions for the surprise source memory test. Eighty faces were presented. Half of the faces were old (presented during the sequential Prisoner’s Dilemma Game), and the other half were new. Participants were first required to rate the likability of the faces on a scale ranging from 1 (not likable at all) to 6 (very likable). After pressing the continue button, participants were asked whether or not they had seen the face during the game. If participants indicated that they had seen the face before, they were required to decide whether the face belonged to a cheater or to a cooperator. After pressing the continue button, the next face was shown. Before leaving, participants filled out a paper–pencil version of the justice sensitivity questionnaire (Schmitt et al., 2005), and were paid.

The design was a 2 × 2 repeated measures design with facial trustworthiness (trustworthy vs. untrustworthy) and behavior (cheating vs. cooperation) as independent variables. Dependent variables were game investments, likability ratings, and memory performance. A multinomial model was used to distinguish among old–new recognition, source memory, and guessing processes. Given α = 0.05, a sample size of N = 112, and 80 responses in the source memory test, it was possible to detect an effect of size w = 0.04 (comparable to the effect sizes observed by Buchner et al., 2009; Küppers and Bayen, 2014; Bell et al., 2015; Kroneisen et al., 2015) for the comparison between source memory for cheaters and cooperators with a statistical power (1 – β) of 0.97. The power calculation was performed using G^∗Power (Faul et al., 2007).

When examining source memory, it is important to use a measure that does not confound item recognition, source memory, and guessing (Bröder and Meiser, 2007). Therefore, we applied the widely used (Erdfelder et al., 2009) source monitoring model of Bayen et al. (1996) to measure source memory and source guessing separately.

To illustrate, the first model tree in Figure 2 represents the cognitive states that are assumed to underlie the classification of a cheater face. With probability D_Cheat, participants know that the face is old (remember that they have seen the face during the game). With probability d_Cheat, they also have source memory for the face (remember that the person is a cheater). The source memory parameter is expressed as a conditional probability that varies between 0 and 1. A probability of 0 represents the absence of source memory while a probability of 1 represents perfect source memory. If participants fail to remember the source, which occurs with the complementary probability 1 – d_Cheat, they may guess, with probability g, that the person was a cheater or, with probability 1 – g, that the person was a cooperator. If they fail to recognize the face as old, which occurs with probability 1 – D_Cheat, they may guess, with probability b, that the face is old, and may then guess that the person was a cheater with probability g, or that the person was a cooperator with probability 1 – g. With probability 1 – b, participants may guess that the face is new (has not been encountered during the game). The goodness-of-fit tests are based on the log-likelihood ratio statistic G² which is asymptotically chi-square distributed (Riefer and Batchelder, 1988; Stahl and Klauer, 2007; Singmann and Kellen, 2013). Parameter estimations and goodness-of-fit tests were calculated using multiTree (Moshagen, 2010). The observed response frequencies for Experiments 1–3 are reported in the Online Supplementary Material (Data Sheets 1–3).

Game investments were analyzed with a repeated measures MANOVA with facial trustworthiness (trustworthy-looking vs. untrustworthy-looking) as independent variable. Participants only interacted once with each partner and thus had no chance to anticipate the behavior of the partners before they decided whether to invest or not. Therefore, only the partners’ facial trustworthiness, but not their behavior could influence the investments. As expected, participants invested more money when playing with trustworthy-looking partners than when playing with untrustworthy-looking partners, F(1,111) = 136.83, p < 0.001, ηp2 = 0.55 (see left panel of Figure 3).

Likability ratings were analyzed with a 2 × 2 MANOVA with facial trustworthiness (trustworthy-looking vs. untrustworthy-looking) and partner behavior (cheating vs. cooperation) as independent variables. Trustworthy-looking faces were more likable than untrustworthy-looking faces, F(1,111) = 410.29, p < 0.001, ηp2 = 0.79. Cooperators received higher likability ratings than cheaters, F(1,111) = 12.94, p < 0.001, ηp2 = 0.10. There was no interaction between facial trustworthiness and behavior, F(1,111) = 1.75, p = 0.189, ηp2 = 0.01 (see left panel of Figure 4).

Old–new recognition in terms of P_r (the sensitivity measure of the two-high-threshold model of old–new recognition, often referred to as corrected hit rate and given by hit rate minus false alarm rate; Snodgrass and Corwin, 1988) is shown in the left panel of Figure 5. A 2 × 2 MANOVA was performed with facial trustworthiness (trustworthy-looking vs. untrustworthy-looking) and partner behavior (cheating vs. cooperation) as independent variables. There was no main effect of facial trustworthiness on face recognition, F(1,111) = 0.52, p = 0.472, ηp2 < 0.01, no main effect of partner behavior, F(1,111) = 1.11, p = 0.294, ηp2 = 0.01, and no interaction between facial trustworthiness and behavior, F(1,111) = 0.90, p = 0.346, ηp2 < 0.01.

To disentangle source guessing and memory, the multinomial source monitoring model mentioned above (Bayen et al., 1996) was used. For the present study, we needed two sets of the trees displayed in Figure 2, one for trustworthy faces and one for untrustworthy faces. To obtain an identifiable base model, we assumed that old–new recognition does not differ as a function of partner behavior (as evidenced by the analysis of old–new recognition reported above), and does not differ between old and new faces (D_Cheat = D_Coop = D_New), which is commonly assumed when using the two high threshold model (Snodgrass and Corwin, 1988; Bayen et al., 1996). This base model fit the data well, G²(2) = 1.84, p = 0.398.

First, we analyzed whether participants would show an expectancy-congruent guessing bias. When the behavior of a recognized person is not remembered, participants have to guess whether the face was associated with cheating or cooperation. In previous studies (Bell et al., 2012b), participants guessed that trustworthy-looking persons were cooperators and that untrustworthy-looking persons were cheaters. That pattern was replicated here. If source memory was not available at test, participants showed a strong bias toward guessing that trustworthy-looking faces were previously associated with cooperation and that untrustworthy-looking faces were previously associated with cheating, ΔG²(1) = 43.01, p < 0.001, w = 0.07 (see left panel of Figure 6).

The left panel of Figure 7 displays the estimates for source memory parameter d representing the conditional probability of remembering the behaviors of cheaters and cooperators given that their faces were recognized as old. Source memory was better for cheaters than for cooperators when the faces looked trustworthy, ΔG²(1) = 4.82, p = 0.028, w = 0.02, but there was no corresponding memory advantage for cooperators over cheaters when the faces looked untrustworthy, ΔG²(1) = 0.14, p = 0.704, w < 0.01. Thus, we replicated the finding of an asymmetrical expectancy-violation effect (Suzuki and Suga, 2010; Bell et al., 2012b).

One hundred and nine students (67 of whom were female) with a mean age of 24 (SD = 5) participated in Experiment 2. Participants in Experiment 2 did not differ from those in Experiment 1 in terms of age, gender, and justice sensitivity (Table 1). All participants gave written informed consent.

Experiment 2 was identical to Experiment 1 except that participants were required to perform a secondary CRT task during the sequential Prisoner’s Dilemma Game. The task was to continuously classify three piano tones (C1, F3, and B6) by pressing a black left, gray middle, or white right button on a response box, respectively. Each tone was repeated once every second until participants made a CRT response by pressing a CRT button. Participants received no reminder of the CRT task and no explicit warning when they failed to respond to the CRT stimuli (but the repeated presentation of the same tone can be seen as an implicit warning). Before the start of the sequential Prisoner’s Dilemma Game, participants received a training of the CRT task. During this training, participants received immediate feedback about their responses (“correct” in green font color or “false” or “miss” in red font color). This training continued until participants had 20 correct responses in a row.

Given that participants were not pressured to perform the secondary CRT task, it was necessary to exclude participants who did not respond to the CRT stimuli properly. As an inclusion criterion, we required a minimum of one response per trial in the Prisoner’s Dilemma Game on average. Based on this criterion, datasets of 13 participants were excluded from analyses because of too few CRT responses. With the remaining sample consisting of 96 participants, it was possible to detect an effect of size w = 0.04 for the comparison of source memory between cheaters and cooperators with a statistical power (1 – β) of 0.94.

As in Experiment 1, participants invested more when playing with trustworthy-looking partners than when playing with untrustworthy-looking partners, F(1,95) = 160.64, p < 0.001, ηp2 = 0.63 (see middle panel of Figure 3).

There was a main effect of facial trustworthiness on likability, F(1,95) = 433.80, p < 0.001, ηp2 = 0.82. The effect of partner behavior was not significant, F(1,95) = 1.13, p = 0.290, ηp2 = 0.01. There was no interaction between facial trustworthiness and behavior, F(1,95) = 0.07, p = 0.794, ηp2 < 0.01 (see middle panel of Figure 4).

Old–new recognition was lower than in Experiment 1, but the same pattern of results was obtained (see middle panel of Figure 5). There was neither a main effect of facial trustworthiness, F(1,95) = 0.34, p = 0.563, ηp2 < 0.01, nor a main effect of partner behavior, F(1,95) = 0.02, p = 0.897, ηp2 < 0.01. The two-way interaction was not significant, F(1,95) = 0.34, p = 0.562, ηp2 < 0.01.

The base model fit the data well, G²(2) = 0.32, p = 0.852. As in Experiment 1, participants were more likely to guess that untrustworthy-looking faces were associated with cheating than that trustworthy-looking faces were associated with cheating, ΔG²(1) = 48.32, p < 0.001, w = 0.08 (see middle panel of Figure 6).

Again, source memory was better for cheating than for cooperation when the faces looked trustworthy, ΔG²(1) = 5.22, p = 0.022, w = 0.03, and source memory did not differ between cheating and cooperation when the faces looked untrustworthy, ΔG²(1) = 0.67, p = 0.414, w < 0.01 (see middle panel of Figure 7).

The description of the results is incomplete without an analysis of the performance in the CRT task because it is important to test whether or not the enhanced memory for appearance-incongruent cheating is due to a performance trade-off between the encoding of the faces and the CRT task. Therefore, we performed two 2 × 2 MANOVAs with the partner trustworthiness (trustworthy-looking vs. untrustworthy-looking) and partner behavior (cheating vs. cooperation) as independent variables and the proportion of correct responses and the response times (including only correct responses that occurred after > 100 ms) in the CRT task as dependent variables (Table 2). Proportion correct did not differ as a function of facial trustworthiness, F(1,95) = 2.43, p = 0.122, ηp2 = 0.02. However, CRT performance was less accurate in the cheater condition in comparison to the cooperator condition, F(1,95) = 5.76, p = 0.018, ηp2 = 0.06. There was no interaction between facial trustworthiness and partner behavior, F(1,95) = 0.14, p = 0.704, ηp2 < 0.01. Response times showed a similar pattern. Response time did not differ as a function of facial trustworthiness, F(1,95) = 0.31, p = 0.578, ηp2 < 0.01. Responses were slower in the cheater condition in comparison to the cooperator condition, F(1,95) = 5.09, p = 0.026, ηp2 = 0.05. However, there was no interaction between facial trustworthiness and partner behavior, F(1,95) = 0.15, p = 0.697, ηp2 < 0.01. Given that this attentional disruption did not translate into better memory for cheaters (as shown by the analyses above), this result does not seem to reflect a reallocation of cognitive resources to the cheater faces and, therefore, does not seem to reflect a performance trade-off between the memory task and the CRT task. It seems possible to speculate that experiencing cheating may result in a negative emotional response that may distract from the secondary task, but does not seem to cause a direct memory enhancement.

One hundred three students (69 of whom were female) with a mean age of 22 (SD = 5) participated in Experiment 3. The sample was similar to those in Experiments 1 and 2 (Table 1). All participants gave written informed consent.

Experiment 3 was identical to Experiment 2 with the exception that the CRT task required participants to respond to each tone within 2 s, after which the next tone was presented. If participants failed to respond to a tone during a trial of the sequential Prisoner’s Dilemma Game, they received a warning after the trial that reminded them of the CRT task. In contrast to Experiment 2—in which the sequential Prisoner’s Dilemma Game was self-paced—the next round of the game was automatically initiated 10 s after the summary of the interaction had been displayed. Justice sensitivity was not assessed.

The data of two outliers were excluded from the analyses because these participants produced >20% CRT misses on average. The remaining sample responded to 98% of the CRT stimuli on average. With a remaining sample of 101 participants, it was possible to detect an effect of size w = 0.04 for the comparison between source memory for cheaters and cooperators with a statistical power (1 – β) of 0.95.

As in Experiments 1 and 2, participants invested more when playing with trustworthy-looking partners than when playing with untrustworthy-looking partners, F(1,100) = 157.95, p < 0.001, ηp2 = 0.61 (see right panel of Figure 3).

There was a main effect of facial trustworthiness on likability with higher likability ratings for trustworthy-looking compared to untrustworthy-looking partners, F(1,100) = 504.95, p < 0.001, ηp2 = 0.83. Cheaters were judged to be less likable than cooperators, F(1,100) = 15.08, p < 0.001, ηp2 = 0.13. The interaction between facial trustworthiness and behavior was not significant, F(1,100) = 0.05, p = 0.822, ηp2 < 0.01 (see right panel of Figure 4).

There was neither a main effect of facial trustworthiness on old–new recognition, F(1,100) = 1.49, p = 0.225, ηp2 = 0.01, nor a main effect of partner behavior, F(1,100) = 0.21, p = 0.651, ηp2 < 0.01. The two-way interaction was also not significant, F(1,100) = 0.57, p = 0.452, ηp2 < 0.01 (see right panel of Figure 5).

The base model fit the data well, G²(2) = 0.87, p = 0.647. As in Experiments 1 and 2, participants were significantly more likely to guess that untrustworthy-looking faces were associated with cheating than that trustworthy-looking faces were associated with cheating, ΔG²(1) = 55.78, p < 0.001, w = 0.08 (see right panel of Figure 6).

As in the previous experiments, there was a source memory advantage for cheaters over cooperators when the faces looked trustworthy, ΔG²(1) = 12.60, p < 0.001, w = 0.04, but source memory did not differ between cheaters and cooperators when the faces looked untrustworthy, ΔG²(1) = 0.42, p = 0.519, w < 0.01 (see right panel of Figure 7).

As in Experiment 2 we performed analyses of the proportion of correct responses and response times (including only correct responses that occurred after >100 ms) in the CRT task. CRT responses were faster than they were in Experiment 2, but the same pattern of results was observed (Table 2). Proportion correct did not differ as a function of facial trustworthiness, F(1,100) = 0.42, p = 0.520, ηp2 < 0.01. CRT performance was less accurate in the cheater condition in comparison to the cooperator condition, F(1,100) = 21.82, p < 0.001, ηp2 = 0.18. There was no interaction between facial trustworthiness and partner behavior, F(1,100) = 0.55, p = 0.460, ηp2 < 0.01. Response times showed a similar pattern. Response time did not differ as a function of facial trustworthiness, F(1,100) = 0.09, p = 0.764, ηp2 < 0.01. However, responses were slower in the cheater condition in comparison to the cooperator condition, F(1,100) = 33.29, p < 0.001, ηp2 = 0.25. There was no interaction between facial trustworthiness and partner behavior, F(1,100) = 0.04, p = 0.845, ηp2 < 0.01. Again, the previous analyses suggest that this attentional disruption is not associated with enhanced encoding of the cheater faces.

Forty students (27 of whom were female) with a mean age of 24 (SD = 4) participated in Experiment 4. Participants were consecutively assigned to either the cognitive load group or the control group (i.e., Participant 1 was assigned to the cognitive load condition, Participant 2 was assigned to the control condition, and so on). All participants gave written informed consent.

Participants performed a verbal working memory task and a spatial working memory task. Task order was counterbalanced between groups (cognitive load vs. control).

In the verbal working memory task, participants were required to remember sequences with varying sequence lengths of four to nine items. The items were randomly drawn from the set {1, 2, … 9}. Each trial started with a visual warning that participants were required to remember the digits. The digits were presented one after another in 24 pt Arial font at the center of a computer screen for 800 ms with a 200 ms inter-stimulus interval. After a retention interval of 2 s, a number pad with the previously presented digits was shown, and participants were required to select the numbers in the correct (forward) order, using the computer mouse. Selected digits were grayed out, and could not be selected again. After all digits were selected, the number pad disappeared, and a continue button was shown. Upon clicking this button, the next trial started. The task started with a sequence length of four digits. Digit length gradually increased during the task. Participants completed three trials of each sequence length.

The spatial working memory task was identical to the verbal working memory task except that participants were required to remember the spatial locations of four to nine black dots instead of four to nine digits. The locations of the dots were not aligned (but instead randomly distributed across the screen) to make a verbal coding strategy extremely difficult. In each trial, the spatial positions were randomly drawn from a set of nine different spatial positions. The dots appeared one after another at their designated positions (800 ms on, 200 ms off). After a retention interval of 2 s, the previously presented dots were presented again at their corresponding spatial locations. The participants’ task was to select the spatial locations of the dots in the order of their appearance. Selected locations were grayed out, and could not be selected again.

The working memory tasks were either completed alongside the secondary CRT task (in the cognitive load condition) or without the secondary CRT task (in the control condition). The CRT task was identical to the one used in Experiment 3. Participants were reminded of the tone classification task before each trial. Tones were presented only during visual item presentation and the retention interval of the working memory task, but not during recall. If participants did not give a response to all CRT tones, they received a warning when the recall of the items was completed.

The design was a mixed 2 × 2 design with working memory task (verbal vs. spatial) as a within-subject variable and cognitive load (cognitive load vs. control) as a between-subjects variable. The dependent variable was working memory performance according to a strict scoring criterion (only items remembered in their correct serial position were scored as correct). Given α = 0.05, a total sample size of N = 40 participants, and an assumed correlation between the levels of the within-subject variable of ρ = 0.50, an effect of size f = 0.50 could be detected for the cognitive load variable with a statistical power (1 – β) of 0.95.