Increased ventromedial prefrontal cortex activity in adolescence benefits prosocial reinforcement learning

Bianca Westhoff*, Neeltje E. Blankenstein, Elisabeth Schreuders, Eveline A. Crone, Anna C.K. van Duijvenvoorde

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
30 Downloads (Pure)

Abstract

Learning which of our behaviors benefit others contributes to forming social relationships. An important period for the development of (pro)social behavior is adolescence, which is characterized by transitions in social connections. It is, however, unknown how learning to benefit others develops across adolescence and what the underlying cognitive and neural mechanisms are. In this functional neuroimaging study, we assessed learning for self and others (i.e., prosocial learning) and the concurring neural tracking of prediction errors across adolescence (ages 9–21, N = 74). Participants performed a two-choice probabilistic reinforcement learning task in which outcomes resulted in monetary consequences for themselves, an unknown other, or no one. Participants from all ages were able to learn for themselves and others, but learning for others showed a more protracted developmental trajectory. Prediction errors for self were observed in the ventral striatum and showed no age-related differences. However, prediction error coding for others showed an age-related increase in the ventromedial prefrontal cortex. These results reveal insights into the computational mechanisms of learning for others across adolescence, and highlight that learning for self and others show different age-related patterns.

Original languageEnglish
Article number101018
Number of pages10
JournalDevelopmental Cognitive Neuroscience
Volume52
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Funding Information:
We would like to thank all participants and their parents for their cooperation. We thank Myrna van den Berg, Sander de Heer, and Iris Koele for all their efforts during data collection. We thank Kiki Zanolie and Jochem Spaans for help with programming the task, and Ili Ma for help with the model simulations and parameter recovery. This work was supported by the European Research Council ( ERC ) under the European Union’s Horizon 2020 research and innovation programme [grant number 681632 , to E.A.C]; and by the Netherlands Organization for Scientific Research ( NWO ) [Open Research Area grant, grant number 464-15-176 , to A.C.K.D]. The funder had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2021 The Authors

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