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Related Experiment Video

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Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

Published on: July 22, 2025

Active Inference, epistemic value, and vicarious trial and error.

Giovanni Pezzulo¹, Emilio Cartoni², Francesco Rigoli³

¹Institute of Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy giovanni.pezzulo@istc.cnr.it.

Learning & Memory (Cold Spring Harbor, N.Y.)

|June 19, 2016

Summary

Active Inference models explain the balance between fast, habitual choices and slower, deliberate decisions. This research shows how rodents transition from deliberation to habit through optimal policy selection.

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Behavioral Economics

Background:

Balancing habitual (fast, inflexible) and deliberate (slow, versatile) choice is crucial for decision-making.
Rodent spatial decisions in T-mazes often involve vicarious trial and error (VTE) and hippocampal place cell forward sweeps, indicative of deliberation.

Purpose of the Study:

To demonstrate how Active Inference can model the arbitration between habitual and deliberate control.
To explain the emergence of VTE and forward sweeps as optimal strategies in decision-making.

Main Methods:

Utilized Active Inference framework to model choice arbitration.
Developed simulations replicating rodent spatial decisions in T-mazes.
Analyzed the trade-off between maximizing extrinsic value and epistemic value.

Main Results:

Simulations reproduced the transition from deliberative to habitual choice observed in rodents.
Forward sweeps and VTE emerged as optimal strategies for incorporating epistemic value.
The transition is explained as an optimal solution to balancing reward maximization and exploration.

Conclusions:

Active Inference provides a principled account for the interplay between habitual and deliberate control.
Forward sweeps and VTE are optimal mechanisms for epistemic exploration during decision-making.
This framework offers novel insights into the optimality principles underlying deliberate choice and learning transitions.