Published in: Trends in Cognitive Sciences, Volume 22, Issue 3, 201-212
Abstract
“We propose a multilayered framework consisting of two hidden layers – traits and states –and one observable layer – actions. This framework address two key challenges in social cognition: organizing social knowledge efficiently, and using it for social prediction.
fMRI, combined with advanced analytic techniques such as representational similarity analysis and encoding models, offers a way to reveal the dimensional structure organizing each layer of social cognition.
Three dimensions – rationality, social impact, and valence – organize the layer of mental states, while three dimensions – power, sociality, and valence – organize the domain of traits. Cross-encoding analysis suggests that these layers may be partially overlapping.
Proximity within state space predicts perceived and actual transitional probabilities between emotions, and mediates the accuracy of one’s perceptions. Thus, the organizational dimensions of social content may scaffold social prediction.
The social mind is tailored to the problem of predicting the mental states and actions of other people. However, social cognition researchers have only scratched the surface of the predictive social mind. We discuss here a new framework for explaining how people organize social knowledge and use it for social prediction. Specifically, we propose a multilayered framework of social cognition in which two hidden layers – the mental states and traits of others – support predictions about the observable layer – the actions of others. A parsimonious set of psychological dimensions structures each layer, and proximity within and across layers guides social prediction. This simple framework formalizes longstanding intuitions from social cognition, and in doing so offers a generative model for deriving new hypotheses about predictive social cognition.”
Written by: Diana I. Tamir, Mark A. Thornton
For full text: http://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(17)30267-X