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Brain mechanisms engaged in social network interactions| title | Brain mechanisms engaged in social network interactions |
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| start_date | 2024/03/19 |
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| schedule | 14h30 |
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| online | no |
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| location_info | Salle 1.1 |
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| summary | Social networks play a crucial role in creating links between individuals and in informal transmission of information across society. Although the brain computations engaged in social learning have started to be investigated in dyadic interactions and in very small groups1-6, little is known about the mechanisms used by the brain when individuals interact in social networks. First, I will present a taxonomy of different types of computations used by the brain for learning and inferences made during social interactions. I will illustrate how this taxonomy is useful to understand the computations underlying social interactions. In particular, I will present recent model-based functional MRI results showing how the human brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled.
Second, I will present a new study revealing the cognitive mechanisms underpinning the assessment of information veracity. The ambiguous nature of news contents fosters misinformation and makes news veracity judgments harder. Yet, the mechanisms by which individuals assess the veracity of ambiguous news and decide whether to acquire extra information to resolve uncertainty remain unclear. Using a controlled experiment, I will show that two characteristics of news ambiguity lure individuals into mistaking true news as false: the higher the news content imprecision and propensity to divide opinions, the greater the likelihood that news are assessed as false. Individuals’ accuracy in estimating veracity is independent from their confidence in their estimation, showing limited metacognitive ability when facing ambiguous news. Yet, the level of confidence in one’s judgment is what drives the demand for extra information about the news.
Third, I will present recent findings showing how the brain decides whether to share extra information with others, depending upon one’s own confidence about the reliability of information and upon our beliefs concerning the preferences of receivers.
Fourth, I will show that a variant of the classical DeGroot learning rule, captures transmission of information in social networks. This rule, which states that an agent updates beliefs by making weighted averages of neighbors’ opinions as an integrated snapshot, accounts for information propagation in an experimental game played in network, better than a sequential error-driven process using successive weighted update of one’s neighbors’ opinions.
Finally, I will show how Agent-Based modeling can be used to account for the dynamic formation of a social network in a behavioral economic experiment called the linking game. In such game, self-interested agents aim to balance maximizing their connectivity with minimizing the number of links they maintain. Our model accounts for the temporal dynamics (frequency of actions) observed in this game better than other models (eg. best response model).
Together, these results pave the way to develop a mechanistic understanding of how the brain makes inferences in social networks and decide to spread information through them, providing a multilevel comprehension of information transmission, integrating the brain system-level and the levels of individual and collective behavior. |
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| responsibles | NC |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2024/03/22 15:35 UTC |
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