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

TL;DR

This study investigates whether humans utilize eligibility traces in reinforcement learning by developing a new experimental paradigm, revealing behavioral and physiological evidence supporting their use in sequential decision making.

Contribution

The paper introduces a novel paradigm to observe eligibility trace usage in humans, providing direct behavioral and physiological evidence for their role in reinforcement learning.

Findings

Behavioral signatures of eligibility traces observed

Physiological evidence (pupil dilation) supports eligibility trace use

Eligibility traces influence decision-making across sensory modalities

Abstract

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 asked whether humans use eligibility traces. 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.