How to Detect Altruists: Experiments Using a Zero-Acquaintance Video Presentation Paradigm

Cognitive mechanisms for detecting altruists are thought to have evolved in humans because those who cheat (i.e., receive benefits without contributing) pose a serious problem for the maintenance of reciprocal exchanges. According to Trivers (1971), cheating manifests in two forms: gross and subtle. Subtle cheating occurs in reciprocal relationships when one party attempts to give less than the other, whereas gross cheating occurs when a cheater does not reciprocate at all. Subtle cheating poses a major problem for the maintenance of reciprocal, altruistic relationships in division-of-labor situations that characterize complex human societies. One can escape the exploitation of subtle cheating by selectively engaging in exchanges with genuine altruists who behave altruistically irrespective of their own benefits and costs. Thus, cognitive mechanisms for detecting altruists can confer an evolutionary advantage. Indeed, some studies have shown that people can discern altruists from non-altruists. For example, using a zero-acquaintance video presentation paradigm, Brown et al. (2003) found that participants could detect altruists based on nonverbal cues. Oda et al. (2009b) reported similar results using 30-s video clips of natural conversations between Japanese individuals. After viewing the video clips without sound, participants correctly estimated the altruism levels of target individuals. Oda et al. (2009a) found that people tended to trust altruists more than non-altruists in an economic game using real money. The detailed mechanisms of altruist detection, however, were not extensively studied, prompting us to explore the factors that could enhance or hinder the detection of altruism. Specifically, we examined the effects of cognitive load (Experiment 1), mood induction (Experiment 2), and hiding upper and lower parts of facial stimuli (Experiment 3) on altruist detection.

Several points should be addressed before we explain the details of each experiment. First, it should be noted that genuine altruism is somehow different from other prosocial characteristics, such as cooperativeness and trustworthiness. Quite often, prosociality is defined by behaviors in economic games like the one-shot prisoner’s dilemma game (which is related to cooperativeness) or trust game (which is related to trustworthiness). Those behaviors, however, are not necessarily products of genuine altruism because a player’s outcome rests on the behavior of their counterpart. For example, a manipulative player may behave cooperatively to elicit cooperation from their counterpart. Thus, cooperativeness and trustworthiness in these games entail strategic altruism, which is different from genuine altruism (e.g., blood donation) expressed spontaneously and habitually. Genuine altruism could reflect the actors’ combination of underlying personality traits.

How, then, can we assess genuine altruism? Two major methods have been proposed. One uses questionnaires that ask participants to self-report the frequency of helpful behaviors they have performed in the past (Johnson et al. 1989; Oda et al. 2013). The other utilizes the dictator game (Camerer 2003) in which one of the players, “the dictator”, determines the distribution of “pie” (e.g., $10) to their counterpart (recipient) without any influence from the latter. The altruistic behavior of the dictator, such as even distribution of the pie among the two, can thus be considered a manifestation of genuine altruism. In this study, we employed the questionnaire method to classify target individuals into altruist and non-altruist categories (see the “Method” section for more detail).

There is then the issue of how to measure the ability to detect altruism in participants. We employed the Faith game (Kiyonari and Yamagishi 1999; Mifune and Li 2018), which is an extension of the dictator game. In the dictator game, the recipient can do nothing but accept the decision made by the dictator. In the Faith game, recipients have two options. Option A is to accept the decision made by the allocator, as in the dictator game (trust choice). Option B is to have a fixed amount of money provided by the experimenter (sure choice). There are three tricks. First, the fixed amount of money is always less than half of the pie. Second, the allocator believes that the recipient has no option but to accept their decision, and the recipient is informed of the dictator’s misbelief. Third, the recipient has to decide before they know the decision by the allocator. If a recipient thinks the allocator is genuinely altruistic and will distribute the pie evenly (or a large share to the recipient), then they should choose Option A. Otherwise, they should take Option B to get the sure reward. That is, the trust choice is selected solely based on the recipient’s estimation of the allocator’s altruism. In this sense, the trust choice in the Faith game is different to the trust choice in the trust game. We asked our participants to play the Faith game with the videotaped individuals as allocators. This gave us the participants’ dichotomous decisions as to whether they trusted each allocator. There could be four cases. First, the participant entrusts an altruistic allocator (Hit). Second, the participant entrusts a non-altruist (false alarm or type I error). Third, the participant fails to entrust an altruist (miss or type II error). Fourth, the participant does not entrust a non-altruist (correct rejection). The relative frequencies of the four cases enabled us to employ the signal detection theory to evaluate the accuracy of altruist detection.

Finally, there is the concern of using videos as stimuli. Recent studies using still pictures have shown that people attribute social and personality characteristics to facial appearances at a cost of little time and effort (Olivola et al. 2014). However, the accuracy of trustworthiness scores attributed to still pictures is thought to be only slightly higher than that of random guessing (Bonnefon et al. 2015; Todorov et al. 2015). In addition, different pictures of the same individual can induce different guesses (Todorov and Porter 2014). The detection of trustworthy partners from still pictures may also depend on the moment when the picture was taken (Verplaetse et al. 2007; Yamagishi et al. 2003). Given that portrait photographs are evolutionarily novel stimuli for humans, it is perhaps unsurprising that people cannot make reliable trustworthiness judgments based on them. Videos of target individuals, on the other hand, would contain more ecologically valid stimuli. However, only a few studies have employed video stimuli (e.g., Centorrino et al. 2015) and the factors that work to enhance or hinder the ability to detect altruism are not well known. Therefore, we explored three factors that may influence altruist detection.

In the first experiment, we focused on the process by which altruists are detected. If the cognitive mechanisms for detecting altruists are adaptations that prevent subtle cheating, it is plausible that these mechanisms may be domain-specific, working rapidly and implicitly like the cheater detection mechanism (Cosmides and Tooby 1992). We hypothesized that people make fast and frugal decisions using specific cues when they try to detect altruists. Evolved, domain-specific modules, such as cheater detection, are expected to exclude the contribution of common domain-general resources, such as working memory, attention, and inhibition. Indeed, Van Lier et al. (2013) tested the independency of the cheater detection module from general cognitive capacity and reported that a secondary task requiring working memory resources had no impact on the performance of the cheater detection task (Van Lier et al. 2013). We conjectured that an examination of the effects of cognitive load on altruism detection would clarify the process. If participants under cognitive load detect altruists as accurately as those under controlled conditions, then altruism detection via a domain-specific module would be supported. It should be noted, however, that the impact of cognitive load on performance depends on the complexity and difficulty of the task in hand (i.e., the focal task and the load task; Schmid 2016). This point will be addressed in the “Discussion” section.

In Experiment 1, we asked participants to decide whether they trusted targets in a video clip. Following the methods of Oda et al. (2009a), participants played the Faith game, in which they were recipients of videotaped altruist and non-altruist allocators. During the game, participants were asked to perform mental calculations (thus, dual tasks were required). Single-digit numbers (1–9) were orally presented in a pseudo-random sequence during the video, and participants were requested to provide the sum of these numbers at the end of each video clip. Similar methods have been applied in studies on the effects of cell phone use and conversation on driver performance (e.g., Caird et al. 2008). Signal detection analysis was then performed to compare detection performance between conditions with and without calculation.

In Experiment 2, we investigated mood-congruent effects on altruism detection by manipulating the affective state of the detector. Several studies have suggested that people are inclined to perceive faces in a mood-congruent manner (e.g., Bouhuys et al. 1995; Terwogt et al. 1991). Forgas and East (2008) reported that positive affect increased, and negative affect decreased, the perceived genuineness of videotaped facial expressions. Experiment 2 explored the effects of participants’ moods on altruism detection and their skepticism about facial expressions. We experimentally manipulated the affective state of participants by asking them to listen to music that induced a positive or negative mood during the Faith game. Signal detection analysis was conducted to compare detection performance under the two mood conditions. There were four possible outcomes of this experiment. (1) If positive music increased, and negative music decreased, the perceived genuineness of facial expressions, then “false alarms” (i.e., trusting non-altruists) would increase under positive mood conditions and “misses” (i.e., failing to trust altruists) would increase under negative mood conditions, resulting in reduced detection accuracy under both mood conditions; (2) Misses would occur less frequently under positive mood conditions, whereas false alarms would occur less frequently under negative mood conditions, resulting in an increase in detection accuracy under both mood conditions; (3) The effect of mood on skepticism about the genuineness of facial expressions could be asymmetrical, resulting in a significant difference in detection accuracy between the two mood conditions (4) Detection accuracy would be robust, i.e., not affected by skepticism about the genuineness of facial expressions.

In Experiment 3, we investigated which parts of the face participants examined to detect altruism. Brown et al. (2003) reported that the degree of felt smile (i.e., orbicularis oculi muscle activity), head nods, time per smile, and smile symmetry were correlated with the altruism score of target persons. Oda et al. (2009b) replicated Brown et al.’s (2003) study and found that only the degree of orbicularis oculi muscle activity showed a significantly high correlation with the altruism score. That is, orbicularis oculi muscle activity (AU6 of the Facial Action Coding System) is a candidate cue employed to detect altruists. Voluntary control of the orbicularis oculi muscle was said to be much more difficult, making it a better cue for the genuineness of a smile because any cues should be difficult to mimic, to function as an honest signal (Brown et al. 2003; Zahavi 1975). Recent studies, however, have reported that a substantial number of people can deliberately manipulate the orbicularis oculi muscle (e.g., Gunnery et al. 2013), which suggests that its movement cannot be an honest signal. Despite uncertainty surrounding the genuineness of orbicularis oculi muscle activity, it would be a cue for detecting altruists if it correlates with the degree of altruism. Experiment 3 investigated altruism cues by restricting eye or mouth stimuli in the video clips. Participants in the “upper mosaic” condition were asked to play the Faith game with videotaped targets whose faces were blurred from the tip of the nose upward by mosaic processing. Participants in the “lower mosaic” condition were asked to play against targets whose faces were blurred from the tip of the nose downward. Participants were expected to detect altruists more accurately in the lower mosaic condition than in the upper mosaic condition if orbicularis oculi muscle movement was the primary judgment cue.

In all experiments, we performed signal detection analysis (Gescheider 1997) to evaluate detection accuracy and criteria. The sensitivity parameter (d′) and criteria parameter (c) were calculated using the hit rate (how often each participant trusted altruistic targets) and false alarm rate (how often each participant trusted non-altruistic targets).

Participants engaged in two simultaneous tasks: the Faith game and mental arithmetic. In the Faith game, participants were required to decide whether they would entrust JPY 300 to videotaped target individuals. While the participants watched video clips of the target individuals, numbers from one to nine were orally presented in a pseudo-random sequence and participants were asked to sum the numbers after watching the clip. In the control condition, participants were not asked to calculate a sum. It was expected that participants in the control condition could trust altruist targets more than non-altruist targets (d′ became significantly positive), while participants under cognitive load would detect altruists less accurately than those in the control condition if the cognitive process demanded effort. Participants were expected to be hesitant about trusting targets (c became significantly positive) under the cognitive load because of the stressful situation.

Next, we investigated possible cues of altruism. We asked participants to play the Faith game with the upper or lower half of targets’ faces blurred. It was expected that only d′ in the lower mosaic condition would be significantly positive if orbicularis oculi muscle movement was the primary judgment cue. With regard to detection criteria, participants in both conditions would be hesitant about trusting the targets (c became significantly positive) because the mosaic would evoke anonymity, as half of the detection cues were lost.

Neither cognitive load nor mood manipulation greatly reduced the accuracy of altruism detection. A meta-analysis of the results for the five conditions revealed that participants were able to detect altruists despite interferences. However, participants could not recognize altruists when half of the targets’ faces were blurred. The effect was more apparent when the upper half was blurred, suggesting there is a crucial judgment cue around the eyes.

Apart from the upper and lower mosaic conditions in Experiment 3, our meta-analysis results revealed that people were able to detect altruists through nonverbal cues that were available through video clips of target individuals. Even though the effects were not very large in size, they were observed and robust even when participants were distracted by cognitive tasks (Experiment 1) or emotion-inducing music (Experiment 2). This finding is remarkable given that the information available to participants lacked several important factors: the stimuli were two-dimensional, only the shoulders and above could be seen; there was no sound and the image was presented for only 30 s. In addition, there was no prior interaction between the participants and the target individuals.

There may be some possible limitations to this study. One is that it is not necessarily clear what the participants “detected” in the current experiments. The target individuals were classified either as an altruist or a non-altruist based on their responses on an altruism scale. Put differently, “altruism” was operationally defined as the scores of the scale. Therefore, the possibility remains that what the participants detected was not the altruistic traits per se but the “the scores of altruism scale.” Although we cannot entirely disregard the possibility, it is notable that the behavioral validity of the scale has been guaranteed to a certain degree by Brown et al. (2003) who reported positive association between the scale scores and altruistic behavior on a dictator game. Another related point is that participants might not have find an altruistic target as “altruistic” but agreeable or nice. We would argue that, from a functional point of view, what counts is whether participants could discriminately trust those who were more likely to behave generously. The psychological labels attached to the person such as “altruistic”, “agreeable”, or “nice” have little relevance. For instance, there is an argument that male humans could detect mate value of a female human based on her physical appearances (Buss and Schmitt 2019). The same phenomenon can be described as that the men find the woman attractive, beautiful, or charming. It is apparent that those labels have little importance from a functional point of view. Therefore, we should emphasize that we defined “altruism” operationally and functionally in this study and that the concept should not be taken as a psychological construct.

Second, our sample was composed of participants from a single cultural background (i.e., Japanese culture). The generalizability of the results should be considered carefully, in the same way as knowledge from the US undergraduates or the Western, Educated, Industrialized, Rich, and Democratic (WEIRD) sample should not be naively applied to samples from other cultures (Cheon et al. 2020; Henrich et al. 2010). In fact, preceding studies have indicated that Japanese participants were more likely to rely on upper facial features, such as the eyes, while participants from other cultures (e.g., the U.S.) relied on lower facial features (Ozono et al. 2010; Yuki et al. 2007). Yuki et al. (2007) argued that this was because people in Japanese culture tend to restrict emotional expression. This means that Japanese people need to pay more attention to subtle, difficult-to-control movements around the eyes. Indeed, one study revealed that French participants were unable to correctly estimate the altruism level of videotaped Japanese targets using the same stimuli in this study (Tognetti et al. 2018). This may be explained by the French participants, who relied more on lower facial parts, not having enough altruistic cues in the Japanese targets. To be precise, Tognetti et al. (2018) did not use the Faith game to measure participants’ evaluation of altruism in the targets. Therefore, it is possible that it was methodological, not cultural, differences that brought about the differences in results. Still, we believe caution should be used in generalizing the results of our study.

A third limitation is that we videotaped only natural and spontaneous facial appearances of altruists. Even if people can detect altruism from the upper part of the face, it does not necessarily mean that movement of the orbicularis oculi muscle is the sole, reliable signal of altruism. In fact, recent studies suggest that a substantial number of people can manipulate the orbicularis oculi muscle deliberately (e.g., Gunnery et al. 2013). To escape the exploitation of such deceptive “smiles”, one has to use other signals. For example, Namba et al. (2017b) reported that dynamic sequences of facial movements were different between spontaneous and posed smiles and that observers were sensitive to such differences (Namba et al. 2017a). Unfortunately, our study did not address whether people can discriminate between those who pretended to be an altruist and those who are genuine. Future studies are needed to address how people evaluate intentional deceivers.