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

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.Author Summary: A defining feature of human social cognition is our insight that others' behaviour is driven by their beliefs and preferences, rather than by what is objectively true or good for them. In fact, a great deal of our social interactions are concerned with guessing others' mental states. But is such "mentalizing" of any help for predicting others' behaviour? After all, most animal species seem to cope with this problem without appealing to any form of sophisticated "Theory of Mind". Here, sophistication refers to the depth of recursive beliefs, as in "I think that you think that I think…" Although we are likely to engage in such recursive beliefs whenever our interests are tied up with others' (e.g. in the aim of deceiving them), it is unclear how these beliefs are updated and whether this gives us any advantage when we learn. These are the questions we address in this work, by combining computational and experimental approaches.