Work

Integrating Local Learning into the Two-Stage Markov Task to Separate Model-Based from Model-Free Learning

Year: 2025

Type: article

Abstract: 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.

Source: SIAM Undergraduate Research Online

DOI: 10.1137/24s1703392

Authors: Peizhe Li, Jimmy Vineyard, Seungyeon Oh, Jack Maloney, Amy L. Cochran, Haley Colgate Kottler

Institution: University of Wisconsin–Madison

Language: English

Topic: Neural and Behavioral Psychology Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W4412442403

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