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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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Volterra decomposition of participants' responses.Top: impulse response to participants' own action (x-axis: lag , y-axis: Volterra weight ) against each opponent (red: non-social framing, blue: social framing). Right: impulse response to participants' opponent's action. Errorbars depict one standard error on the mean. Black lines depict the "best k-ToM response" to each opponent type.
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pcbi-1003992-g004: Volterra decomposition of participants' responses.Top: impulse response to participants' own action (x-axis: lag , y-axis: Volterra weight ) against each opponent (red: non-social framing, blue: social framing). Right: impulse response to participants' opponent's action. Errorbars depict one standard error on the mean. Black lines depict the "best k-ToM response" to each opponent type.

Mentions: Fig. 4 depicts the group mean Volterra kernels against each opponent, in the social and in the non-social framing condition. For each opponent, we superimposed the Volterra kernel of the corresponding "best k-ToM response", i.e. one ToM sophistication level above participants' opponents. For completeness, results of a parametric Volterra decomposition are exposed in Figure 5 of Text S1. In the non-social framing, it seems that people have a strong tendency to imitate their opponent's last action (cf. positive Volterra weight ). They also tend to perseverate, i.e. to reproduce their last choice (cf. positive Volterra weight ). In the social condition, people rather seem to alternate their own actions (cf. negative Volterra kernels ) and to imitate their opponent's choices less often than in the non-social framing (cf. small Volterra kernels ). In addition, Volterra decompositions of peoples' choice sequences in the social framing seem much closer to the "best k-ToM response" than in the non-social framing (except maybe in the control condition).


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

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

Volterra decomposition of participants' responses.Top: impulse response to participants' own action (x-axis: lag , y-axis: Volterra weight ) against each opponent (red: non-social framing, blue: social framing). Right: impulse response to participants' opponent's action. Errorbars depict one standard error on the mean. Black lines depict the "best k-ToM response" to each opponent type.
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Related In: Results  -  Collection

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

pcbi-1003992-g004: Volterra decomposition of participants' responses.Top: impulse response to participants' own action (x-axis: lag , y-axis: Volterra weight ) against each opponent (red: non-social framing, blue: social framing). Right: impulse response to participants' opponent's action. Errorbars depict one standard error on the mean. Black lines depict the "best k-ToM response" to each opponent type.
Mentions: Fig. 4 depicts the group mean Volterra kernels against each opponent, in the social and in the non-social framing condition. For each opponent, we superimposed the Volterra kernel of the corresponding "best k-ToM response", i.e. one ToM sophistication level above participants' opponents. For completeness, results of a parametric Volterra decomposition are exposed in Figure 5 of Text S1. In the non-social framing, it seems that people have a strong tendency to imitate their opponent's last action (cf. positive Volterra weight ). They also tend to perseverate, i.e. to reproduce their last choice (cf. positive Volterra weight ). In the social condition, people rather seem to alternate their own actions (cf. negative Volterra kernels ) and to imitate their opponent's choices less often than in the non-social framing (cf. small Volterra kernels ). In addition, Volterra decompositions of peoples' choice sequences in the social framing seem much closer to the "best k-ToM response" than in the non-social framing (except maybe in the control condition).

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