Jean-François Bonnefon

The Modular Nature of Trustworthiness Detection.

The capacity to trust wisely is a critical facilitator of success and prosperity, and it has been conjectured that people of higher intelligence are better able to detect signs of untrustworthiness from potential partners. In contrast, this article reports five trust game studies suggesting that reading trustworthiness of the faces of strangers is a modular process. Trustworthiness detection from faces is independent of general intelligence (Study 1) and effortless (Study 2). Pictures that include nonfacial features such as hair and clothing impair trustworthiness detection (Study 3) by increasing reliance on conscious judgments (Study 4), but people largely prefer to make decisions from this sort of pictures (Study 5). In sum, trustworthiness detection in an economic interaction is a genuine and effortless ability, possessed in equal amount by people of all cognitive capacities, but whose impenetrability leads to inaccurate conscious judgments and inappropriate informational preferences.

Analytical reasoning task reveals limits of social learning in networks

Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

Active involvement, not illusory control, increases risk taking in a gambling game

The research considers the influence of choice (the possibility for the player to choose a gamble or another) and involvement (the physical interaction with the gambling device) on risk taking in gambling games and whether this influence is mediated by illusory control over the outcome of the gamble. Results of a laboratory experiment (n = 100) show that (a) although choice does increase illusory control, this influence does not translate in increased risk taking, and (b) whilst involvement does increase risk taking, this effect is not mediated by illusory control. These results are discussed in relation to problem gambling, beliefs in the deployability of personal luck, and arousal approaches to risk taking.

The Causal Structure of Utility Conditionals.

The psychology of reasoning is increasingly considering agents values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, if p then q statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure.

The psychology of reasoning about preferences and unconsequential decisions

People can reason about the preferences of other agents, and predict their behavior based on these preferences. Surprisingly, the psychology of reasoning has long neglected this fact, and focused instead on disinterested inferences, of which preferences are neither an input nor an output. This exclusive focus is untenable, though, as there is mounting evidence that reasoners take into account the preferences of others, at the expense of logic when logic and preferences point to different conclusions. This article summarizes the most recent account of how reasoners predict the behavior and attitude of other agents based on conditional rules describing actions and their consequences, and reports new experimental data about which assumptions reasoners retract when their predictions based on preferences turn out to be false.

Is the Above-Average Effect Measurable at All? The Validity of the Self-Reported Happiness Minus Others' Perceived Happiness Construct.

Individuals routinely rate themselves higher than their peers on a number of attributes and capabilities, including their satisfaction with life. However, the construct validity of this above-average effect requires specific psychometric properties of ratings of ones contentment and ratings of others perceived contentment. This article tests these properties with respect to the popular Satisfaction With Life Scale through a multivariate measurement model with latent change and method effects. The model is fitted to two independent data sets (N = 597 and N = 964), and it is found twice that four items are suitable to compute a meaningful composite difference score. It is concluded that the above-average effect is a systematic multivariate phenomenon that can be assessed by the difference of two manifest, absolute evaluation scores.

Non-Reflective Thinkers Are Predisposed to Attribute Supernatural Causation to Uncanny Experiences

For unknown reasons, individuals who are confident in their intuitions are more likely to hold supernatural beliefs. How does an intuitive cognitive style lead one to believe in faith healing, astrology, or extrasensory perception (ESP)? We hypothesize that cognitive style is critically important after one experiences an uncanny event that seems to invite a supernatural explanation. In three studies, we show that irrespective of their prior beliefs in the supernatural, non-reflective thinkers are more likely than reflective thinkers to accept supernatural causation after an uncanny encounter with astrology and ESP. This is the first time that controlled experiments demonstrate the negative dynamics of reflection and supernatural causality attribution. We consider the possible generalization of our findings to religious beliefs and their implications for the social vulnerability of non-reflective individuals.

The grim reasoner: Analytical reasoning under mortality salience

The human species enjoys uniquely developed capacities for analytical reasoning and rational decision making, but these capacities come with a price: They make us aware of our inevitable physical death. Drawing on terror management theory and dual-process theories of cognition, we investigate the impact of mortality awareness on analytical reasoning. Two experiments show that experimentally induced thoughts of death impair analytical reasoning performance, just as cognitive load would. When made aware of their own mortality, reasoners allocate their executive resources to the suppression of this disturbing thought, therefore impairing their performance on syllogisms that require analytic thought. This finding has consequences for all aspects of rational thinking that draw on executive resources, and calls for an integrated approach to existential psychology and the psychology of rational thought.

Modus Tollens, Modus Shmollens: Contrapositive reasoning and the pragmatics of negation

The utterance of a negative statement invites the pragmatic inference that some reason exists for the proposition it negates to be true; this pragmatic inference paves the way for the logically unexpected Modus Shmollens inference: “If p then q; not-q; therefore, p.” Experiment 1 shows that a majority of reasoners endorse Modus Shmollens from an explicit major conditional premise and a negative utterance as a minor premise: e.g., reasoners conclude that “the soup tastes like garlic” from the premises “If a soup tastes like garlic, then there is garlic in the soup; Carole tells Didier that there is no garlic in the soup they are eating.” Experiment 2 shows that this effect is mediated by the derivation of a pragmatic inference from negation. We discuss how theories of conditional reasoning can integrate such a pragmatic effect.

Cooperating with Machines

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human–machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithmxa0with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human–machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.Artificial intelligence is now superior to humans in many fully competitive games, such as Chess, Go, and Poker. Here the authors develop a machine-learning algorithm that can cooperate effectively with humans when cooperation is beneficial but nontrivial, something humans are remarkably good at.