Integrating Local Learning into the Two-Stage Markov Task to Separate Model-Based from Model-Free Learning
Peizhe Li, Jimmy Vineyard, Seungyeon Oh, Jack Maloney, Amy Louise Cochran, Haley Colgate Kottler · 2024
The two-stage Markov task, widely-used for measuring model-based relative to model-free learning in humans, has faced skepticism regarding its effectiveness. We suggest a modification to better distinguish the two learning approaches. Our revised task incorporates an additional phase for learning local contingencies, mirroring a strategy from machine learning for separating model-free from model-based algorithms. We evaluated the effectiveness of our revised task through simulations, employing model-free and model-based strategies.