Proceedings of the Annual Meeting of the Cognitive Science Society

Procedural learning---the implicit acquisition of motor and cognitive skills through repeated practice---has been proposed to support abilities from typing to language acquisition. Traditional serial reaction time paradigms study procedural learning using stimulus sequences with simple underlying statistical structures: deterministic or with binary probability levels, involving only 4 possible states. We introduce a novel paradigm where participants learn sequences with graded probability levels, generated from transition matrices with a continuous gradient of transition probabilities. We found robust effects of both surprisal (transition probability from previous state) and entropy (uncertainty of next state) on reaction times across 5 state space sizes (4-8), with no interaction between surprisal and entropy, indicating learners tracked specific transition probabilities even at high-uncertainty states. These findings demonstrate that procedural learning is sensitive to fine-grained statistical structure and scales beyond traditional 4-state paradigms, supporting theoretical proposals linking procedural learning mechanisms to skill acquisition in complex domains like language.