Temporal-difference valence-partitioned Bayesian brains work out whether others are caring or uncaring

Abstract

Working out whether others care for us is crucial in personal relationships and when seeking professional help. It is often most difficult for those most in need, e.g. following interpersonal traumata. Here, we introduce a simple Caring Attributions task and elucidate key computational mechanisms involved and, using EEG, the cortical activity representing the degree of belief that another is beneficent or maleficent. We find evidence for a new type of neurocomputational processing: valence-partitioned temporal-difference inference (TD-Bayes). This employs primary processing about latent causes, but also separate channels to represent these different valenced attributions, inspired by value-partitioned associative learning (VPAL). TD-Bayes uses slow propagation of beliefs using temporal-difference updating. These models gave a very good account of behaviour, slightly better than VPAL, but crucially, their partitioned representations have stronger, distinct representations in ERP signals. They provide a promising inroad into the understanding of how people may jump to atttibutions about caring vs. uncaring others.