Confidence and second-order errors in cortical circuits

TL;DR

This paper develops a theoretical framework for cortical circuits that incorporates confidence and second-order errors into prediction error minimization, revealing how the brain dynamically integrates information and learns from confidence-related signals.

Contribution

It introduces a novel neural dynamic model that accounts for confidence and second-order errors, advancing understanding of cortical processing and learning mechanisms.

Findings

Predicts existence of cortical second-order errors.

Shows confidence modulates cortical information integration.

Provides a detailed mapping to cortical circuitry.

Abstract

Minimization of cortical prediction errors has been considered a key computational goal of the cerebral cortex underlying perception, action and learning. However, it is still unclear how the cortex should form and use information about uncertainty in this process. Here, we formally derive neural dynamics that minimize prediction errors under the assumption that cortical areas must not only predict the activity in other areas and sensory streams but also jointly project their confidence (inverse expected uncertainty) in their predictions. In the resulting neuronal dynamics, the integration of bottom-up and top-down cortical streams is dynamically modulated based on confidence in accordance with the Bayesian principle. Moreover, the theory predicts the existence of cortical second-order errors, comparing confidence and actual performance. These errors are propagated through the cortical hierarchy alongside classical prediction errors and are used to learn the weights of synapses responsible for formulating confidence. We propose a detailed mapping of the theory to cortical circuitry, discuss entailed functional interpretations and provide potential directions for experimental work.