Biased belief updating and suboptimal choice in foraging decisions

Deciding which options to engage, and which to forego, requires developing accurate beliefs about the overall distribution of prospects. Here we adapt a classic prey selection task from foraging theory to examine how individuals keep track of an environment’s reward rate and adjust choices in response to its fluctuations. Preference shifts were most pronounced when the environment improved compared to when it deteriorated. This is best explained by a trial-by-trial learning model in which participants estimate the reward rate with upward vs. downward changes controlled by separate learning rates. A failure to adjust expectations sufficiently when an environment becomes worse leads to suboptimal choices: options that are valuable given the environmental conditions are rejected in the false expectation that better options will materialize. These findings offer a previously unappreciated parallel in the serial choice setting of observations of asymmetric updating and resulting biased (often overoptimistic) estimates in other domains

Sympathetic involvement in time-constrained sequential foraging

Appraising sequential offers relative to an unknown future opportunity and a time cost requires an optimization policy that draws on a learned estimate of an environment’s richness. Converging evidence points to a learning asymmetry, whereby estimates of this richness update with a bias toward integrating positive information. We replicate this bias in a sequential foraging (prey selection) task and probe associated activation within the sympathetic branch of the autonomic system, using trial-by-trial measures of simultaneously recorded cardiac autonomic physiology. We reveal a unique adaptive role for the sympathetic branch in learning. It was specifically associated with adaptation to a deteriorating environment: it correlated with both the rate of negative information integration in belief estimates and downward changes in moment-to-moment environmental richness, and was predictive of optimal performance on the task. The findings are consistent with a framework whereby autonomic function supports the learning demands of prey selection