Abstract
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.
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Notes
Although moral norms do not seem to be sharply distinct from social norms or, say, norms of disgust, there is a spectrum of social behaviors, some of which tend to be more readily called ‘moral.’ Specifically, behavioral patterns that typically involve a victim who has been harmed, whose rights have been violated, or who has been subject to some injustice seem to be more readily qualified as ‘moral’ norms.
A nice and important example of rational reconstruction is Bicchieri’s (2006) account of norms. For Bicchieri, social norms should be understood in game-theoretical terms as Nash equilibria that result from transforming a mixed-motive game such as the prisoner’s dilemma into a coordination game. The idea is that social norms solve social problems, in which each of the agents has a selfish interest to defect from the strategy that would provide the socially superior outcome if everybody followed it. When a social norm exists in problems of this sort, agents’ preferences and beliefs will reflect the existence of this norm. Accordingly, the payoffs of the problem will change in such a way that agents playing the socially superior strategy will now play an optimal equilibrium.
One of the consequences of my proposal is that agents can comply with “irrational” or even “immoral” norms indeed. If evolutionary pressure does not operate primarily over what is learned (the object of learning and decision-making), but over the learning and decision-making systems themselves (how such systems learn and make decisions), it is plausible that agents sometimes can learn and comply with norms that, in some sense, are “irrational” or even “immoral” (cf. e.g. Seymour et al. 2009). Consistent with this consequence is the view that there may well be no genetically-based special purpose neural network for social/moral learning and decision-making. The acquisition and implementation of specific norms would rather depend on “downstream ecological and epistemic engineering” (Sterelny 2003). The idea is that parental, upstream generations structure the downstream informational environment where the next generation develops so that the specific social norms embedded in that environment are more easily learnt and followed.
In what follows the shorthand ‘Bayesian’ refers to these types of tractable schemes.
An experimental task of this type has been used by Colombo, Stankevicius and Seriès (ms) to address how social rewards (e.g. facial expressions), in comparison to non-social rewards (e.g. conventional feedback marks such as ticks and crosses), impact learning performance and decision-making.
Doll et al. (2009) have developed two neurocomputational models that could explain the precise effect of verbal information on reward learning: an ‘override’ and a ‘bias model’. In the first, the striatum—a subcortical brain region and major target of dopaminergic neurons—learns cue-reward probabilities as experienced, but is overridden by the prefrontal cortex—where instructed information would be encoded—at the level of the decision output. In the bias model, action selection and learning supported by the striatum are biased by rules and instructions encoded in the prefrontal cortex.
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Acknowledgments
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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Colombo, M. Two neurocomputational building blocks of social norm compliance. Biol Philos 29, 71–88 (2014). https://doi.org/10.1007/s10539-013-9385-z
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DOI: https://doi.org/10.1007/s10539-013-9385-z