Data from: One-shot learning and behavioral eligibility traces in sequential decision making

In many daily tasks we make multiple decisions before reaching a goal. In
order to learn such sequences of decisions, a mechanism to link earlier
actions to later reward is necessary. Reinforcement learning theory
suggests two classes of algorithms solving this credit assignment problem:
In classic temporal-difference learning, earlier actions receive reward
information only after multiple repetitions of the task, whereas models
with eligibility traces reinforce entire sequences of actions from a
single experience (one-shot). Here we show one-shot learning of sequences.
We developed a novel paradigm to directly observe which actions and states
along a multi-step sequence are reinforced after a single reward. By
focusing our analysis on those states for which RL with and without
eligibility trace make qualitatively distinct predictions, we find direct
behavioral (choice probability) and physiological (pupil dilation)
signatures of reinforcement learning with eligibility trace across
multiple sensory modalities.