Insights from the application of computational neuroimaging to social neuroscience

Abstract

A recent approach in social neuroscience has been the application of formal computational models for a particular social-cognitive process to neuroimaging data. Here we review preliminary findings from this nascent subfield, focusing on observational learning and strategic interactions. We present evidence consistent with the existence of three distinct learning systems that may contribute to social cognition: an observational-reward-learning system involved in updating expectations of future reward based on observing rewards obtained by others, an action-observational learning system involved in learning about the action tendencies of others, and a third system engaged when it is necessary to learn about the hidden mental-states or traits of another. These three systems appear to map onto distinct neuroanatomical substrates, and depend on unique computational signals.

Figure 1

Illustration of three distinct social learning systems. The observation of reinforcing feedback delivered to others elicits reward prediction error signaling in the striatum and vmPFC (blue). Action observation prediction errors, representing deviations by others from predicted actions, have been reported in posterior dmPFC, bilateral dlPFC, and bilateral inferior parietal lobule (yellow). In contrast, anterior dmPFC and TPJ/posterior STS (red) are implicated in the updating of hidden mental state representations during strategic interactions, as opposed to observable action prediction representations.

Figure 2

Strategic game used by Hampton et al. [17]. (a) In this task, one participant acted as an “employer” and the other as an “employee”. Each player could perform one of two actions: the employer could “inspect” or “not inspect” her employee while the employee could “work” or “shirk”. The interests of the two players are divergent in that the employee prefers to shirk, while the employer would prefer the employee to work, and the employer would prefer to not inspect her employer than to inspect – yet for an employee it is disadvantageous to be caught shirking if the employer inspects, and disadvantageous for the employer if the employee shirks while she doesn’t inspect. (b) In such a context it is crucial to form an accurate prediction of an opponent’s next action. This could involve simply tracking the probability with which they choose each of the two strategies available to them. However, if an opponent is tracking ones own actions in this way, then it is advantageous for a player to update their prediction of their opponent’s action to incorporate the effect of the player’s own previous action on their opponent. Hampton et al. found that the effect of this update at mPFC (−3, 51, 24 mm) was modulated by the model-predicted degree to which a subject behaves as if his actions influence his opponent.