Article

A new take on model-based and model-free influences on mental effort and striatal prediction errors

2022-11-04

Abstract excerpt

A standard assumption in neuroscience is that low-effort model-free learning is automatic and continuously employed, while more complex model-based strategies are only used when the rewards they generate are worth the additional effort. We present evidence refuting this assumption. First, we demonstrate flaws in previous reports of combined model-free and model-based reward prediction errors in the ventral striatu...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 9ecd8986-2f69-5214-aeff-8dd3a313766f
DOI: 10.1101/2022.11.04.515162

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A new take on model-based and model-free influences on mental effort and striatal prediction errorsDOI 10.1101/2022.11.04.515162Read

Model-based and model-free valuation signals in the human brain vary markedly in their relationship to individual differences in human behavioral controlSemantic proximity Read

The comparable strategic flexibility of model-free and model-based learningSemantic proximity Read

Explicit knowledge of task structure is the primary determinant of human model-based actionSemantic proximity Read

When models matter: Environmental demand guides the arbitration between model-based and model-free controlSemantic proximity Read

Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.Semantic proximity Read

Predicting human prediction error empowers reward learning task designSemantic proximity Read

Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-step TaskSemantic proximity Read

Higher Motivation and Pleasure Scores Predict More Reliance on Model-Free Decision-MakingSemantic proximity Read

Humans are resource-rational predictors in a sequence learning taskSemantic proximity Read

Characterizing heterogeneity in human reinforcement learning and the arbitration of behavioral controlSemantic proximity Read

Characterizing heterogeneity in human reinforcement learning and the arbitration of behavioral controlSemantic proximity Read

Emerging Insights into Cognitive Control from Computational Models of ChoiceSemantic proximity Read