Pitfalls in quantifying exploration in reward-based motor learning and how to avoid them.

ABSTRACT: When learning a movement based on binary success information, one is more variable following failure than following success. Theoretically, the additional variability post-failure might reflect exploration of possibilities to obtain success. When average behavior is changing (as in learning), variability can be estimated from differences between subsequent movements. Can one estimate exploration reliably from such trial-to-trial changes when studying reward-based motor learning? To answer this question, we tried to reconstruct the exploration underlying learning as described by four existing reward-based motor learning models. We simulated learning for various learner and task characteristics. If we simply determined the additional change post-failure, estimates of exploration were sensiti

SUBMITTER: van Mastrigt NM 

PROVIDER: S-EPMC8382626 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature