Computational investigations of learning and decision making suggest that systematic deviations to adaptive behavior may be the incidental outcome of biological constraints imposed on neural information processing. In particular, recent studies indicate that range adaptation, i.e., the mechanism by which neurons dynamically tune their output firing properties to match the changing statistics of their inputs, may drive plastic changes in the brain’s decision system that induce systematic deviations to rationality. Here, we ask whether behaviorally-relevant neural information processing may be distorted by other incidental, hard-wired, biological constraints, in particular: Hebbian plasticity. One of our main contributions is to propose a simple computational method for identifying (and comparing) the neural signature of such biological mechanisms or constraints. Using ANNs (i.e., artificial neural network models) and RSA (i.e., representational similarity analysis), we compare the neural signatures of two types of hard-wired biological mechanisms/constraints: namely, range adaptation and Hebbian plasticity. We apply the approach to two different open fMRI datasets acquired when people make decisions under risk. In both cases, we show that although peoples’ apparent indifferent choices are well explained by biologically-constrained ANNs, choice data alone does not discriminate between range adaptation and Hebbian plasticity. However, RSA shows that neural activity patterns in bilateral Striatum and Amygdala are more compatible with Hebbian plasticity. Finally, the strength of evidence for Hebbian plasticity in these structures predicts inter-individual differences in choice inconsistency.
bioRxiv Subject Collection: Neuroscience