Conditional bribery: Insights from incentivized experiments across 18 nations.

Bribery, a grand global challenge, often occurs across national jurisdictions. Behavioral research studying bribery to inform anticorruption interventions, however, has merely examined bribery within single nations. Here, we report online experiments and provide insights into crossnational bribery. We ran a pilot study (across three nations) and a large, incentivized experiment using a bribery game played across 18 nations (N = 5,582, total number of incentivized decisions = 346,084). The results show that people offer disproportionally more bribes to interaction partners from nations with a high (vs. low) reputation for foreign bribery, measured by macrolevel indicators of corruption perceptions. People widely share nation-specific expectations about a nation's bribery acceptance levels. However, these nation-specific expectations negatively correlate with actual bribe acceptance levels, suggesting shared yet inaccurate stereotypes about bribery tendencies. Moreover, the interaction partner's national background (more than one's own national background) drives people's decision to offer or accept a bribe-a finding we label conditional bribery.

Money for microbes-Pathogen avoidance and out-group helping behaviour.

Humans have evolved various adaptations against pathogens, including the physiological immune system. However, not all of these adaptations are physiological: the cognitive mechanisms whereby we avoid potential sources of pathogens-for example, disgust elicited by uncleanliness-can be considered as parts of a behavioural immune system (BIS). The mechanisms of BIS extend also to inter-group relations: Pathogen cues have been shown to increase xenophobia/ethnocentrism, as people prefer to keep their societal in-group norms unaltered and "clean." Nonetheless, little is known how pathogen cues influence people's willingness to provide humanitarian aid to out-group members. We examined how pathogen cues affected decisions of providing humanitarian aid in either instrumental (sending money) or non-instrumental form (sending personnel to help, or accepting refugees), and whether these effects were moderated by individual differences in BIS sensitivity. Data were collected in two online studies (Ns: 188 and 210). When the hypothetical humanitarian crisis involved a clear risk of infection, participants with high BIS sensitivity preferred to send money rather than personnel or to accept refugees. The results suggest that pathogen cues influence BIS-sensitive individuals' willingness to provide humanitarian aid when there is a risk of contamination to in-group members.

The Road to Bribery and Corruption.

Major forms of corruption constitute a strong threat to the functioning of societies. The most frequent explanation of how severe corruption emerges is the slippery-slope metaphor-the notion that corruption occurs gradually. While having widespread theoretical and intuitive appeal, this notion has barely been tested empirically. We used a recently developed paradigm to test whether severely corrupt acts happen gradually or abruptly. The results of four experimental studies revealed a higher likelihood of severe corruption when participants were directly given the opportunity to engage in it (abrupt) compared with when they had previously engaged in minor forms of corruption (gradual). Neither the size of the payoffs, which we kept constant, nor evaluations of the actions could account for these differences. Contrary to widely shared beliefs, sometimes the route to corruption leads over a steep cliff rather than a slippery slope.

Lie detection algorithms disrupt the social dynamics of accusation behavior.

Humans, aware of the social costs associated with false accusations, are generally hesitant to accuse others of lying. Our study shows how lie detection algorithms disrupt this social dynamic. We develop a supervised machine-learning classifier that surpasses human accuracy and conduct a large-scale incentivized experiment manipulating the availability of this lie-detection algorithm. In the absence of algorithmic support, people are reluctant to accuse others of lying, but when the algorithm becomes available, a minority actively seeks its prediction and consistently relies on it for accusations. Although those who request machine predictions are not inherently more prone to accuse, they more willingly follow predictions that suggest accusation than those who receive such predictions without actively seeking them.

Social Preferences Toward Humans and Machines: A Systematic Experiment on the Role of Machine Payoffs.

There is growing interest in the field of cooperative artificial intelligence (AI), that is, settings in which humans and machines cooperate. By now, more than 160 studies from various disciplines have reported on how people cooperate with machines in behavioral experiments. Our systematic review of the experimental instructions reveals that the implementation of the machine payoffs and the information participants receive about them differ drastically across these studies. In an online experiment (N = 1,198), we compare how these different payoff implementations shape people's revealed social preferences toward machines. When matched with machine partners, people reveal substantially stronger social preferences and reciprocity when they know that a human beneficiary receives the machine payoffs than when they know that no such "human behind the machine" exists. When participants are not informed about machine payoffs, we found weak social preferences toward machines. Comparing survey answers with those from a follow-up study (N = 150), we conclude that people form their beliefs about machine payoffs in a self-serving way. Thus, our results suggest that the extent to which humans cooperate with machines depends on the implementation and information about the machine's earnings.

Love and affectionate touch toward romantic partners all over the world.

Touch is the primary way people communicate intimacy in romantic relationships, and affectionate touch behaviors such as stroking, hugging and kissing are universally observed in partnerships all over the world. Here, we explored the association of love and affectionate touch behaviors in romantic partnerships in two studies comprising 7880 participants. In the first study, we used a cross-cultural survey conducted in 37 countries to test whether love was universally associated with affectionate touch behaviors. In the second study, using a more fine-tuned touch behavior scale, we tested whether the frequency of affectionate touch behaviors was related to love in romantic partnerships. As hypothesized, love was significantly and positively associated with affectionate touch behaviors in both studies and this result was replicated regardless of the inclusion of potentially relevant factors as controls. Altogether, our data strongly suggest that affectionate touch is a relatively stable characteristic of human romantic relationships that is robustly and reliably related to the degree of reported love between partners.

Bad machines corrupt good morals.

As machines powered by artificial intelligence (AI) influence humans' behaviour in ways that are both like and unlike the ways humans influence each other, worry emerges about the corrupting power of AI agents. To estimate the empirical validity of these fears, we review the available evidence from behavioural science, human-computer interaction and AI research. We propose four main social roles through which both humans and machines can influence ethical behaviour. These are: role model, advisor, partner and delegate. When AI agents become influencers (role models or advisors), their corrupting power may not exceed the corrupting power of humans (yet). However, AI agents acting as enablers of unethical behaviour (partners or delegates) have many characteristics that may let people reap unethical benefits while feeling good about themselves, a potentially perilous interaction. On the basis of these insights, we outline a research agenda to gain behavioural insights for better AI oversight.

Fooled twice: People cannot detect deepfakes but think they can.

Hyper-realistic manipulations of audio-visual content, i.e., deepfakes, present new challenges for establishing the veracity of online content. Research on the human impact of deepfakes remains sparse. In a pre-registered behavioral experiment (N = 210), we show that (1) people cannot reliably detect deepfakes and (2) neither raising awareness nor introducing financial incentives improves their detection accuracy. Zeroing in on the underlying cognitive processes, we find that (3) people are biased toward mistaking deepfakes as authentic videos (rather than vice versa) and (4) they overestimate their own detection abilities. Together, these results suggest that people adopt a "seeing-is-believing" heuristic for deepfake detection while being overconfident in their (low) detection abilities. The combination renders people particularly susceptible to be influenced by deepfake content.

Collaborative dishonesty: A meta-analytic review.

Although dishonesty is often a social phenomenon, it is primarily studied in individual settings. However, people frequently collaborate and engage in mutual dishonest acts. We report the first meta-analysis on collaborative dishonesty, analyzing 87,771 decisions (21 behavioral tasks; k = 123; nparticipants = 10,923). We provide an overview of all tasks used to measure collaborative dishonesty, and inform theory by conducting moderation analyses. Results reveal that collaborative dishonesty is higher (a) when financial incentives are high, (b) in lab than field studies, (c) when third parties experience no negative consequences, (d) in the absence of experimental deception, and (e) when groups consist of more males and (f) younger individuals. Further, in repeated interactions, group members' behavior is correlated-participants lie more when their partners lie-and lying increases as the task progresses. These findings are in line with the justified ethicality theoretical perspective, suggesting prosocial concerns increase collaborative dishonesty, whereas honest-image concerns attenuate it. We discuss how findings inform theory, setting an agenda for future research on the collaborative roots of dishonesty. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Affective Interpersonal Touch in Close Relationships: A Cross-Cultural Perspective.

Interpersonal touch behavior differs across cultures, yet no study to date has systematically tested for cultural variation in affective touch, nor examined the factors that might account for this variability. Here, over 14,000 individuals from 45 countries were asked whether they embraced, stroked, kissed, or hugged their partner, friends, and youngest child during the week preceding the study. We then examined a range of hypothesized individual-level factors (sex, age, parasitic history, conservatism, religiosity, and preferred interpersonal distance) and cultural-level factors (regional temperature, parasite stress, regional conservatism, collectivism, and religiosity) in predicting these affective-touching behaviors. Our results indicate that affective touch was most prevalent in relationships with partners and children, and its diversity was relatively higher in warmer, less conservative, and religious countries, and among younger, female, and liberal people. This research allows for a broad and integrated view of the bases of cross-cultural variability in affective touch.

Intuitive Honesty Versus Dishonesty: Meta-Analytic Evidence.

Is self-serving lying intuitive? Or does honesty come naturally? Many experiments have manipulated reliance on intuition in behavioral-dishonesty tasks, with mixed results. We present two meta-analyses (with evidential value) testing whether an intuitive mind-set affects the proportion of liars (k = 73; n = 12,711) and the magnitude of lying (k = 50; n = 6,473). The results indicate that when dishonesty harms abstract others, promoting intuition causes more people to lie, log odds ratio = 0.38, p = .0004, and people to lie more, Hedges's g = 0.26, p < .0001. However, when dishonesty inflicts harm on concrete others, promoting intuition has no significant effect on dishonesty (p > .63). We propose one potential explanation: The intuitive appeal of prosociality may cancel out the intuitive selfish appeal of dishonesty, suggesting that the social consequences of lying could be a promising key to the riddle of intuition's role in honesty. We discuss limitations such as the relatively unbalanced distribution of studies using concrete versus abstract victims and the overall large interstudy heterogeneity.

"Who Doesn't?"--The Impact of Descriptive Norms on Corruption.

Corruption poses one of the major societal challenges of our time. Considerable advances have been made in understanding corruption on a macro level, yet the psychological antecedents of corrupt behavior remain largely unknown. In order to explain why some people engage in corruption while others do not, we explored the impact of descriptive social norms on corrupt behavior by using a novel behavioral measure of corruption. We conducted three studies to test whether perceived descriptive norms of corruption (i.e. the belief about the prevalence of corruption in a specific context) influence corrupt behavior. The results indicated that descriptive norms highly correlate with corrupt behavior--both when measured before (Study 1) or after (Study 2) the behavioral measure of corruption. Finally, we adopted an experimental design to investigate the causal effect of descriptive norms on corruption (Study 3). Corrupt behavior in the corruption game significantly drops when participants receive short anti-corruption descriptive norm primes prior to the game. These findings indicate that perceived descriptive norms can impact corrupt behavior and, possibly, could offer an explanation for inter-personal and inter-cultural variation in corrupt behavior in the real world. We discuss implications of these findings and draw avenues for future research.