One Size Does Not Fit All: Idiographic Computational Models Reveal Individual Differences in Learning and Meta‐Learning Strategies

Abstract

Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long‐term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT‐R models of performance on the stimulus‐response learning, Reinforcement Learning Working Memory task. The task consisted of short 3‐image, and long 6‐image, feedback‐based learning blocks. A no‐feedback test phase was administered after learning, with an interfering task inserted between learning and test. Our four models included two single‐mechanism RL and LTM models, and two integrated RL‐LTM models: (a) RL‐based meta‐learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL‐LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta‐RL: 13.25%, bias‐RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus‐response task. Finally, long‐term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3‐ vs 6‐image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated‐models, owing to the strength of our idiographic approach.