Biology & Philosophy

, Volume 29, Issue 1, pp 71–88 | Cite as

Two neurocomputational building blocks of social norm compliance

  • Matteo Colombo


Current explanatory frameworks for social norms pay little attention to why and how brains might carry out computational functions that generate norm compliance behavior. This paper expands on existing literature by laying out the beginnings of a neurocomputational framework for social norms and social cognition, which can be the basis for advancing our understanding of the nature and mechanisms of social norms. Two neurocomputational building blocks are identified that might constitute the core of the mechanism of norm compliance. They consist of Bayesian and reinforcement learning systems. It is sketched why and how the concerted activity of these systems can generate norm compliance by minimization of three specific kinds of prediction-errors.


Social norms Bayesian brain Reinforcement learning Uncertainty minimization 



I am sincerely grateful to Andy Clark, Peggy Seriès, Paul Churchland, and Mark Sprevak for their encouragement, criticisms, and feedback. A special thank you goes to Kim Sterelny and an anonymous reviewer for detailed comments and suggestions. This work was supported by a grant from the Deutsche Forschungsgemeinschaft (DFG) as part of the priority program New Frameworks of Rationality (SPP 1516).


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Tilburg Center for Logic and Philosophy of ScienceTilburg UniversityTilburgThe Netherlands

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