ArticlebioRxiv : the preprint server for biology2025
Human Strategy Adaptation in Reinforcement Learning Resembles Policy Gradient Ascent.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
A hallmark of intelligence is the ability to adapt behavior to changing environments, which requires adapting one's own learning strategies. This phenomenon is known as learning to learn or meta-learning. Although well established in humans and animals, a computational framework that characterizes how biological agents adapt their learning strategies through experience remains elusive. Here we posit that humans update their learning strategies online through a gradient-based meta-learning process, effectively optimizing how they learn. However, estimating how these strategies evolve over time remains a significant challenge since traditional cognitive models, such as reinforcement learning (RL), typically assume that agents use static strategies. To address this, we introduce
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