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.

评估相对于未知的未来机会和时间成本的顺序报价需要一种优化策略，该策略利用对环境丰富度的学习估计。汇聚的证据表明学习不对称，由此对这种丰富度更新的估计偏向于整合积极信息。我们在连续觅食（猎物选择）任务中复制这种偏差，并使用同时记录的心脏自主生理学的逐个试验测量来探测自主系统交感神经分支内的相关激活。我们揭示了交感神经分支在学习中的独特适应性作用。它特别与适应不断恶化的环境有关：它与信念估计中的负面信息整合率和瞬间环境丰富度的向下变化相关，并且可以预测任务的最佳表现。这些发现与自主功能支持猎物选择的学习需求的框架一致。