Individual differences through self-reinforcement of suboptimal strategies

Project Summary/Abstract What produces individual differences in behavior? This fundamental question has classically been given two answers: nature and nurture. Here, we suggest that those two answers, while both critical and correct, are insufficient to fully explain individual variability. Instead, we propose that the vast differences in behavior

between individuals arise in part from different individuals forming different reward associations within the same environment. This results from the fact that the world is complex and high-dimensional, in that there are almost always multiple possible actions or events that could be attributed to reward. Given the key role of

dopamine neurons as the brain’s positive feedback system for behavioral control, the specific hypothesis is that small differences across individuals in initial conditions ultimately produce large differences in which features of the environment that the individual attributes to reward. This hypothesis is inspired in part by complex systems

theory, which emphasizes the role of positive feedback in generating and amplifying small differences, creating outcomes that seem stochastic. To address this hypothesis, we will leverage our recent finding that different dopamine neurons calculate reward prediction error across different dimensions of the environment.

Specifically, we will use dopamine neuron recordings to infer the time-varying features of the environment that each animal uses to predict reward, and then build reinforcement learning models of each individual based on these features. Ultimately, this testable framework aims to explain both normal variation across individuals, as

well as the ubiquitous contribution of dopamine in mediating a disparate range of neuropsychiatric diseases.