Neural organization of sensory stimuli into decision-relevant variables

Determine how the human brain structures sensory stimuli into decision-relevant variables that enable efficient control and adaptation.

Background

The paper motivates its contributions by highlighting a central uncertainty in cognitive neuroscience: despite advances in reinforcement learning and representation learning, the mechanisms by which the brain efficiently organizes multimodal sensory input for planning and control are not well understood.

This unresolved question motivates the authors’ pursuit of an algorithmic framework that leverages active inference principles within distributional RL to better exploit efficient information organization without explicit transition-model learning.

References

Yet it remains unclear how the brain structures sensory stimuli into decision-relevant variables to solve control and adaptation problems so efficiently.

— Distributional Active Inference  (2601.20985 - Akgül et al., 28 Jan 2026) in Section 1: Introduction

This restriction reduces the dimensionality of the predictive-content calculation, while the mechanism by which the system selects or adapts policies to increase information enthalpy is left to future work.

— If the brain were so simple: Information-based drivers of intelligence and their interaction through an n-body-inspired framework  (2610.11142 - Kagan et al., 8 Oct 2026) in Section 2, subsection “Predictive Content”