The computation of social behavior

I point out this article by Behrens et al. on the quest to compute social behavior, mainly to pass on their nice summary graphic, preceded by their abstract. They review the recent application of formal behavioral models in the area of social cognitive neuroscience, and the challenge of identifying which behaviors are causes, which are effects, and which are epiphenomena.

Neuroscientists are beginning to advance explanations of social behavior in terms of underlying brain mechanisms. Two distinct networks of brain regions have come to the fore. The first involves brain regions that are concerned with learning about reward and reinforcement. These same reward-related brain areas also mediate preferences that are social in nature even when no direct reward is expected. The second network focuses on regions active when a person must make estimates of another person’s intentions. However, it has been difficult to determine the precise roles of individual brain regions within these networks or how activities in the two networks relate to one another. Some recent studies of reward-guided behavior have described brain activity in terms of formal mathematical models; these models can be extended to describe mechanisms that underlie complex social exchange. Such a mathematical formalism defines explicit mechanistic hypotheses about internal computations underlying regional brain activity, provides a framework in which to relate different types of activity and understand their contributions to behavior, and prescribes strategies for performing experiments under strong control.


Fig. 1 (A) The functional neuroanatomy of social behavior. Primary colors denote brain regions activated by reward and valuation, frequently identified in studies of social interaction within the frame of reference of the subject’s own actions: anterior cingulate cortex sulcus (ACCs), ventromedial prefrontal cortex (VMPFC), amygdala, and ventral striatum (VStr). Pastels denote brain regions activated by considering the intentions of another individual: anterior cingulate cortex gyrus (ACCg), dorsomedial prefrontal cortex (DMPFC), temporoparietal junction (TPJ), and superior temporal sulcus (STS). (B) Schematic of an approach that combines mathematical models of behavior with neural recordings. The model contains parameters that represent specific computations underlying behavior. As the subject/model undergoes different experiences, these parameters will fluctuate. The fluctuation in these parameters is used to find neural correlates of the specific underlying computations. Separately, the same parameter fluctuations come together to predict changes in behavior.