Social decision-making in the brain: Input-state-output modelling reveals patterns of effective connectivity underlying reciprocal choices.

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SHAW Daniel Joel CZEKÓOVÁ Kristína GAJDOŠ Martin STANĚK Rostislav ŠPALEK Jiří BRÁZDIL Milan

Year of publication 2019
Type Article in Periodical
Magazine / Source Human Brain Mapping
MU Faculty or unit

Central European Institute of Technology

Keywords nterior insula; anterior (mid)cingulate cortex; behavioural Dynamic Causal Modelling; connectivity; iterated ultimatum game; reciprocity; social decision making
Description During social interactions, decision making involves mutual reciprocity, i.e. each individual s choices are simultaneously a consequence of, and antecedent to those of their interaction partner. Neuroeconomic research has begun to unveil the brain networks underpinning social decision making, but we know little about the patterns of neural connectivity within them that give rise to reciprocal choices. To investigate this, the present study measured the behaviour and brain function of pairs of individuals (N = 66) whilst they played multiple rounds of economic exchange comprising an iterated ultimatum game. During these exchanges, both players could attempt to maximise their overall monetary gain by reciprocating their opponent's prior behaviour: they could promote generosity by rewarding it, and/or discourage unfair play through retaliation. By adapting a model of reciprocity from experimental economics, we show that players' choices on each exchange are captured accurately by estimating their expected utility (EU) as a reciprocal reaction to their opponent's prior behaviour. We then demonstrate neural responses that map onto these reciprocal choices in two brain regions implicated in social decision making: right anterior insula (AI) and anterior/anterior mid cingulate cortex (aMCC). Finally, with behavioural Dynamic Causal Modelling, we identified player specific patterns of effective connectivity between these brain regions with which we estimated each player's choices with over 70% accuracy; namely, bidirectional connections between AI and aMCC that are modulated differentially by estimates of EU from our reciprocity model. This input state output modelling procedure therefore reveals systematic brain behaviour relationships associated with the reciprocal choices characterising interactive social decision making.
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