Humans can uniquely assign value to novel, abstract outcomes to support reinforcement learning. However, this flexibility is cognitively costly and reduces learning efficiency. We propose that goal-dependent learning initially relies on capacity-limited working memory.
With consistent experience, learners create a compressed reward function — a simplified rule — that transfers to long-term memory for automatic evaluation upon receiving feedback. This automaticity frees working memory resources, thereby boosting learning efficiency. Across six experiments, we demonstrate that learning is impaired by the size of the goal space but improves when this space allows for compression.
Additionally, faster reward processing correlates with better learning. Although the algorithmic details remain to be established, computational modeling revealed that individual differences in compression efficiency, proxied by reward processing speed, led to higher choice accuracy. Together, our behavioral results and computational models suggest that efficient goal-directed learning relies on compressing complex goals into stable reward functions.
We are grateful to Samuel McDougle, Beth Baribault, and Amy Zou for support on previous iterations of this work. This work was funded by NSF Grants 2020844 and 2336466 awarded to A.G.E.C. University of California, Berkeley, Berkeley, CA, USA The authors declare no competing interests.
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