NCLGSep 8, 2025

Reward function compression facilitates goal-dependent reinforcement learning

arXiv:2509.06810v22 citationsh-index: 3
Originality Incremental advance
AI Analysis

This work provides insights into cognitive mechanisms of human motivation, potentially improving behavioral techniques for goal achievement, but it is incremental in building on existing theories of working memory and learning.

The study tackled how humans efficiently learn from rewards by compressing complex goal information into simplified rules, finding that learning improves when goal spaces allow compression and is impaired by larger goal spaces, with faster reward processing correlating with better performance.

Reinforcement learning agents learn from rewards, but humans can uniquely assign value to novel, abstract outcomes in a goal-dependent manner. However, this flexibility is cognitively costly, making learning less efficient. Here, we propose that goal-dependent learning is initially supported by a capacity-limited working memory system. With consistent experience, learners create a "compressed" reward function (a simplified rule defining the goal) which is then transferred to long-term memory and applied automatically upon receiving feedback. This process frees up working memory resources, boosting learning efficiency. We test this theory across six experiments. Consistent with our predictions, our findings demonstrate that learning is parametrically impaired by the size of the goal space, but improves when the goal space structure allows for compression. We also find faster reward processing to correlate with better learning performance, supporting the idea that as goal valuation becomes more automatic, more resources are available for learning. We leverage computational modeling to support this interpretation. Our work suggests that efficient goal-directed learning relies on compressing complex goal information into a stable reward function, shedding light on the cognitive mechanisms of human motivation. These findings generate new insights into the neuroscience of intrinsic motivation and could help improve behavioral techniques that support people in achieving their goals.

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