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The social Bayesian brain: does mentalizing make a difference when we learn?

Devaine M, Hollard G, Daunizeau J - PLoS Comput. Biol. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Brain and Spine Institute, Paris, France; INSERM, Paris, France.

ABSTRACT
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.

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Group-level performance results.Top-left: average cumulative earnings ūt (y-axis) in the social (blue) and non-social (red) framings, as a function of trials t in the game (x-axis), overlaid on the chance 5% false positive rate threshold (grey shaded area). Top-right: average difference in cumulative earnings ūt (social minus non-social) as a function of trials t in the game, overlaid on the chance 5% false positive rate threshold. Bottom-left: group average cumulated earnings against the four different opponents (red: non-social framing, blue: social framing). Errorbars depict one standard error. Bottom-right: group average difference (social minus non-social) in cumulated earnings against the four different opponents.
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pcbi-1003992-g003: Group-level performance results.Top-left: average cumulative earnings ūt (y-axis) in the social (blue) and non-social (red) framings, as a function of trials t in the game (x-axis), overlaid on the chance 5% false positive rate threshold (grey shaded area). Top-right: average difference in cumulative earnings ūt (social minus non-social) as a function of trials t in the game, overlaid on the chance 5% false positive rate threshold. Bottom-left: group average cumulated earnings against the four different opponents (red: non-social framing, blue: social framing). Errorbars depict one standard error. Bottom-right: group average difference (social minus non-social) in cumulated earnings against the four different opponents.

Mentions: Fig. 3 summarizes the group results on the behavioural performance in the main task. Overall, the pattern of mean performances follows our predictions.


The social Bayesian brain: does mentalizing make a difference when we learn?

Devaine M, Hollard G, Daunizeau J - PLoS Comput. Biol. (2014)

Group-level performance results.Top-left: average cumulative earnings ūt (y-axis) in the social (blue) and non-social (red) framings, as a function of trials t in the game (x-axis), overlaid on the chance 5% false positive rate threshold (grey shaded area). Top-right: average difference in cumulative earnings ūt (social minus non-social) as a function of trials t in the game, overlaid on the chance 5% false positive rate threshold. Bottom-left: group average cumulated earnings against the four different opponents (red: non-social framing, blue: social framing). Errorbars depict one standard error. Bottom-right: group average difference (social minus non-social) in cumulated earnings against the four different opponents.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256068&req=5

pcbi-1003992-g003: Group-level performance results.Top-left: average cumulative earnings ūt (y-axis) in the social (blue) and non-social (red) framings, as a function of trials t in the game (x-axis), overlaid on the chance 5% false positive rate threshold (grey shaded area). Top-right: average difference in cumulative earnings ūt (social minus non-social) as a function of trials t in the game, overlaid on the chance 5% false positive rate threshold. Bottom-left: group average cumulated earnings against the four different opponents (red: non-social framing, blue: social framing). Errorbars depict one standard error. Bottom-right: group average difference (social minus non-social) in cumulated earnings against the four different opponents.
Mentions: Fig. 3 summarizes the group results on the behavioural performance in the main task. Overall, the pattern of mean performances follows our predictions.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Brain and Spine Institute, Paris, France; INSERM, Paris, France.

ABSTRACT
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.

Show MeSH