This title appears in the Scientific Report :
2020
Please use the identifier:
http://hdl.handle.net/2128/26134 in citations.
Please use the identifier: http://dx.doi.org/10.1016/j.cortex.2020.02.024 in citations.
Bayesian modelling captures inter-individual differences in social belief computations in the putamen and insula
Bayesian modelling captures inter-individual differences in social belief computations in the putamen and insula
Computational models of social learning and decision-making provide mechanistic tools toinvestigate the neural mechanisms that are involved in understanding other people. Whilemost studies employ explicit instructions to learn from social cues, everyday life is characterizedby the spontaneous use of...
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Personal Name(s): | Henco, Lara (Corresponding author) |
---|---|
Brandi, Marie-Luise / Lahnakoski, Juha M. / Diaconescu, Andreea O. / Mathys, Christoph / Schilbach, Leonhard | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Published in: | Cortex, 131 (2020) S. 221 - 236 |
Imprint: |
New York, NY
Elsevier
2020
|
PubMed ID: |
32571519 |
DOI: |
10.1016/j.cortex.2020.02.024 |
Document Type: |
Journal Article |
Research Program: |
Theory, modelling and simulation Connectivity and Activity |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1016/j.cortex.2020.02.024 in citations.
Computational models of social learning and decision-making provide mechanistic tools toinvestigate the neural mechanisms that are involved in understanding other people. Whilemost studies employ explicit instructions to learn from social cues, everyday life is characterizedby the spontaneous use of such signals (e.g., the gaze of others) to infer on internalstates such as intentions. To investigate the neural mechanisms of the impact of gaze cues on learning and decision-making, we acquired behavioural and fMRI data from50 participants performing a probabilistic task, in which cards with varying winningprobabilities had to be chosen. In addition, the task included a computer-generated facethat gazed towards one of these cards providing implicit advice. Participants’ individualbelief trajectories were inferred using a hierarchical Gaussian filter (HGF) and used aspredictors in a linear model of neuronal activation. During learning, social prediction errorswere correlated with activity in inferior frontal gyrus and insula. During decision-making,the belief about the accuracy of the social cue was correlated with activity in inferiortemporal gyrus, putamen and pallidum while the putamen and insula showed activity as afunction of individual differences in weighting the social cue during decision-making. Ourfindings demonstrate that model-based fMRI can give insight into the behavioural andneural aspects of spontaneous social cue integration in learning and decision-making.They provide evidence for a mechanistic involvement of specific components of thebasal ganglia in subserving these processes. |