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Related Experiment Video

Updated: Mar 18, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Active inference and learning.

Karl Friston¹, Thomas FitzGerald², Francesco Rigoli¹

¹The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, United Kingdom.

Neuroscience and Biobehavioral Reviews

|July 5, 2016

Summary

This summary is machine-generated.

This study presents an active inference model for choice behavior and learning, explaining how habits emerge from optimizing policies. It highlights how exploration and exploitation in active inference enable reward-seeking and habit formation.

Keywords:

Active inference Bayesian inference Bayesian surprise Epistemic value Exploitation Exploration Free energy Goal-directed Habit learning Information gain

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Area of Science:

Cognitive Science
Neuroscience
Machine Learning

Background:

Distinguishing goal-directed and habitual behavior is crucial for understanding learning.
Active inference provides a framework for understanding decision-making under uncertainty.

Purpose of the Study:

To propose an active inference account of choice behavior and learning.
To explain the emergence of habitual behavior from sequential policy optimization.
To differentiate between belief-based and belief-free schemes in learning.

Main Methods:

Utilizing active inference principles to model behavior.
Implementing sequential policy optimization with state-action policies.
Analyzing the roles of epistemic (ambiguity-resolving) and pragmatic (reward-seeking) behaviors.

Main Results:

Habits emerge naturally from autodidactic sequential policy optimization.
Explorative behavior resolves ambiguity, enabling pragmatic behavior and habit formation.
Active inference offers a process theory for phenomena like dopamine transfer and reversal learning.

Conclusions:

Active inference provides a unified account of goal-directed and habitual behaviors.
The distinction between belief-based and belief-free schemes is more critical than model-based vs. model-free.
Active inference reduces to classical schemes (e.g., Bellman) when ambiguity is absent.