Bayesian model comparison.

<p>Left: exceedance probabilities of the no-ToM (T-) and ToM (T+) model families (red: non-social framing, blue: social framing). Right: exceedance probabilities of the no-ToM/non-Bayesian (T-B-), no-ToM/Bayesian (T-B+), ToM/bayesian (T+B+) and Tom/non-Bayesian (T+B-) model families.</p

Accuracy of behavioural predictions in competitive and cooperative contexts: example of 0-ToM playing against 1-ToM.

<p>The behavioural prediction of ToM players (y-axis) is plotted against her opponent’s true behavioural tendency (x-axis) for each trial of a simulated repeated game with trials. The grey line indicates the best-fitting straight line in the data. Upper half: “Hide and Seek”. Lower half: “Battle of the Sexes”. Left: accuracy of 1-ToM predictions when playing against 0-ToM. Right: accuracy of 0-ToM predictions when playing against 1-ToM.</p

The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn?

<div><p>When it comes to interpreting others' behaviour, we almost irrepressibly engage in the attribution of mental states (beliefs, emotions…). Such "mentalizing" can become very sophisticated, eventually endowing us with highly adaptive skills such as convincing, teaching or deceiving. Here, sophistication can be captured in terms of the depth of our recursive beliefs, as in "I think that you think that I think…" In this work, we test whether such sophisticated recursive beliefs subtend learning in the context of social interaction. We asked participants to play repeated games against artificial (Bayesian) mentalizing agents, which differ in their sophistication. Critically, we made people believe either that they were playing against each other, or that they were gambling like in a casino. Although both framings are similarly deceiving, participants win against the artificial (sophisticated) mentalizing agents in the social framing of the task, and lose in the non-social framing. Moreover, we find that participants' choice sequences are best explained by sophisticated mentalizing Bayesian learning models only in the social framing. This study is the first demonstration of the added-value of mentalizing on learning in the context of repeated social interactions. Importantly, our results show that we would not be able to decipher intentional behaviour without a priori attributing mental states to others.</p></div

Phase diagram of ToM evolution.

<p>Each pie chart depict the evolutionary stable state that is induced by a particular combination of amount of learning τ (x-axis) and proportion ω of cooperative interactions (y-axis).</p

Dual Process for Intentional and Reactive Decisions

<div><p>Efficient cognitive decisions should be adjustable to incoming novel information. However, most current models of decision making have so far neglected any potential interaction between intentional and stimulus-driven decisions. We report here behavioral results and a new model on the interaction between a perceptual decision and non-predictable novel information. We asked participants to anticipate their response to an external stimulus and presented this stimulus with variable delay. Participants were clearly able to adjust their initial decision to the new stimulus if this latter appeared sufficiently early. To account for these results, we present a two-stage model in which two systems, an intentional and a stimulus-driven, interact only in the second stage. In the first stage of the model, the intentional and stimulus-driven processes race independently to reach a transition threshold between the two stages. The model can also account for results of a second experiment where a response bias is introduced. Our model is consistent with some physiological results that indicate that both parallel and interactive processing take place between intentional and stimulus-driven information. It emphasizes that in natural conditions, both types of processing are important and it helps pinpoint the transition between parallel and interactive processing.</p> </div

Sequence of trial events.

<p>The fixation dot indicates the target color. As soon as the participant hears a sound, the Go Signal, she is required to press one of two Go Keys (‘2’ or ‘8’ on a numeric keypad). Which Go Key to press is indicated by the pitch of the Go signal. After a variable time Gap (0–330ms) that starts when the participant presses the Go Key, a target and a distracter dots appear on either side of the fixation dot. The participant has to initiate her motor response (pressing the key on the left or right of the Go Key to indicate her choice of target location) immediately after pressing the Go Key, even though the actual location of the target is available only after the Gap.</p