Title: Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions
Primary area: applications to neuroscience & cognitive science
Keywords: cognitive modeling, verbal theory, risky choice, group-relative policy optimization, supervised fine-tuning, large language model

Abstract: A central goal of cognitive modeling is to develop models that not only predict human behavior but also provide insight into the underlying cognitive mechanisms. While neural network models trained on large-scale behavioral data often achieve strong predictive performance, they typically fall short in offering interpretable explanations of the cognitive processes they capture. In this work, we explore the potential of pretrained large language models (LLMs) to serve as dual-purpose cognitive models--capable of both accurate prediction and interpretable explanation in natural language. Specifically, we employ reinforcement learning with outcome-based rewards to guide LLMs toward generating explicit reasoning traces for explaining human risky choices. Our findings demonstrate that this approach produces high-quality explanations alongside strong quantitative predictions of human decisions.

TL;DR: We applied reinforcement learning with outcome-based rewards to post-train large language models to elicit explanations and predictions for human risky choice.