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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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Distribution of ToM sophistication.Top: Estimated model frequencies in the social framing (dark grey: having restricted the models to the winning T+B+ family). Errorbars depict one posterior standard error. Bottom: Estimated model frequencies in the non-social framing (dark grey: having restricted the models to the winning T-B- family).
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pcbi-1003992-g007: Distribution of ToM sophistication.Top: Estimated model frequencies in the social framing (dark grey: having restricted the models to the winning T+B+ family). Errorbars depict one posterior standard error. Bottom: Estimated model frequencies in the non-social framing (dark grey: having restricted the models to the winning T-B- family).

Mentions: Let us now focus on the estimated models' frequency distribution in the social condition (cf. upper panel of Fig. 7). First, one can see that 2-ToM is the most prevalent model (well above reference models such as Nash or RL). Second, we restricted the model comparison to the T+B+ family, in the aim of deriving efficient estimates of the distribution of ToM sophistication in the human population. We found that 2-ToM agents are about two times more frequent than 1-ToM agents (3-ToM being almost negligible). This suggests that the natural inter-individual variability of ToM sophistication exists but is rather narrow. In addition, it is likely to be upper-bounded.


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

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

Distribution of ToM sophistication.Top: Estimated model frequencies in the social framing (dark grey: having restricted the models to the winning T+B+ family). Errorbars depict one posterior standard error. Bottom: Estimated model frequencies in the non-social framing (dark grey: having restricted the models to the winning T-B- family).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003992-g007: Distribution of ToM sophistication.Top: Estimated model frequencies in the social framing (dark grey: having restricted the models to the winning T+B+ family). Errorbars depict one posterior standard error. Bottom: Estimated model frequencies in the non-social framing (dark grey: having restricted the models to the winning T-B- family).
Mentions: Let us now focus on the estimated models' frequency distribution in the social condition (cf. upper panel of Fig. 7). First, one can see that 2-ToM is the most prevalent model (well above reference models such as Nash or RL). Second, we restricted the model comparison to the T+B+ family, in the aim of deriving efficient estimates of the distribution of ToM sophistication in the human population. We found that 2-ToM agents are about two times more frequent than 1-ToM agents (3-ToM being almost negligible). This suggests that the natural inter-individual variability of ToM sophistication exists but is rather narrow. In addition, it is likely to be upper-bounded.

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