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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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Main task's experimental paradigm.Left: social framing ("hide and seek" game). Right: non-social framing (Casino game). At each trial, participants have 1300 msec to pick one of the two options (social framing: wall or tree, non-social framing: left or right slot machine). Feedback is displayed for 1 sec, for both framings this feedback includes if the subject won or lost and the actual winning option by showing a character picture (social framing) or three identical coins (non-social framing).
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pcbi-1003992-g002: Main task's experimental paradigm.Left: social framing ("hide and seek" game). Right: non-social framing (Casino game). At each trial, participants have 1300 msec to pick one of the two options (social framing: wall or tree, non-social framing: left or right slot machine). Feedback is displayed for 1 sec, for both framings this feedback includes if the subject won or lost and the actual winning option by showing a character picture (social framing) or three identical coins (non-social framing).

Mentions: In fact, each game/session was played against a specific algorithm (2×4 factorial design, cf. Fig. 2), namely: a random sequence with a 65% bias for one option (bias was counterbalanced between the two framings within participants), a 0-ToM agent, a 1-ToM agent and a 2-ToM agent. Critically, 0-ToM, 1-ToM and 2-ToM algorithms are all learning agents (i.e. they adapt to the participant's choices), but only 1-ToM and 2-ToM engaged in (artificial) mentalizing. Note that the random biased opponent (RB) serves as a control condition for non-specific motivational or attentional confounds on the performance difference between the two framings (e.g., people being more willing to engage in a game with other human players). The order of opponents was randomized for each participant.


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

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

Main task's experimental paradigm.Left: social framing ("hide and seek" game). Right: non-social framing (Casino game). At each trial, participants have 1300 msec to pick one of the two options (social framing: wall or tree, non-social framing: left or right slot machine). Feedback is displayed for 1 sec, for both framings this feedback includes if the subject won or lost and the actual winning option by showing a character picture (social framing) or three identical coins (non-social framing).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003992-g002: Main task's experimental paradigm.Left: social framing ("hide and seek" game). Right: non-social framing (Casino game). At each trial, participants have 1300 msec to pick one of the two options (social framing: wall or tree, non-social framing: left or right slot machine). Feedback is displayed for 1 sec, for both framings this feedback includes if the subject won or lost and the actual winning option by showing a character picture (social framing) or three identical coins (non-social framing).
Mentions: In fact, each game/session was played against a specific algorithm (2×4 factorial design, cf. Fig. 2), namely: a random sequence with a 65% bias for one option (bias was counterbalanced between the two framings within participants), a 0-ToM agent, a 1-ToM agent and a 2-ToM agent. Critically, 0-ToM, 1-ToM and 2-ToM algorithms are all learning agents (i.e. they adapt to the participant's choices), but only 1-ToM and 2-ToM engaged in (artificial) mentalizing. Note that the random biased opponent (RB) serves as a control condition for non-specific motivational or attentional confounds on the performance difference between the two framings (e.g., people being more willing to engage in a game with other human players). The order of opponents was randomized for each participant.

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