Cognitive Effort in the Two-Step Task: An Active Inference Drift-Diffusion Model Approach

TL;DR

This paper introduces a novel combination of Active Inference and Drift-Diffusion Models to better understand cognitive effort, demonstrating improved parameter recovery and insights into effort related to habit violation and value discriminability.

Contribution

First integration of Active Inference with an Evidence Accumulator Model, providing a mechanistic account of cognitive effort in the two-step task.

Findings

Successfully modeled second-stage reaction times

Improved parameter recovery with DDM integration

Identified experimental design limitations affecting effort measurement

Abstract

High-level theories rooted in the Bayesian Brain Hypothesis often frame cognitive effort as the cost of resolving the conflict between habits and optimal policies. In parallel, evidence accumulator models (EAMs) provide a mechanistic account of how effort arises from competition between the subjective values of available options. Although EAMs have been combined with frameworks like Reinforcement Learning to bridge the gap between high-level theories and process-level mechanisms, relatively less attention has been paid to their implications for a unified notion of cognitive effort. Here, we combine Active Inference (AIF) with the Drift-Diffusion Model (DDM) to investigate whether the resulting AIF-DDM can simultaneously account for effort arising from both habit violation and value discriminability. To our knowledge, this is the first time AIF has been combined with an EAM. We tested the AIF-DDM on a behavioral dataset from the two-step task and compared its predictions to an information-theoretic definition of cognitive effort based on AIF. The model's predictions successfully accounted for second-stage reaction times but failed to capture the dynamics of the first stage. We argue the latter discrepancy likely stems from the experimental design rather than a fundamental flaw in the model's assumptions about cognitive effort. Accordingly, we propose several modifications of the two-step task to better measure and isolate cognitive effort. Finally, we found that integrating the DDM significantly improved parameter recovery, which could help future studies to obtain more reliable parameter estimates.