Empirically Integrating the Evidence for Different Predictive Coding Components Using Auditory False Perception

TL;DR

This study explores how the brain's internal model and sensory input interact to shape perception, using illusions and brain responses to understand individual differences in predictive coding.

Contribution

The paper is the first to empirically integrate multiple components of the predictive coding system using behavioral and electrophysiological data.

Findings

High perceivers rely more on internal models and are more sensitive to context-driven prediction errors.

Behavioral illusion likelihood correlates with self-reported likelihood but not when controlling for perceptual threshold.

The study links predictive coding components to individual perceptual biases in sensory contexts.

Abstract

Perception is a probabilistic estimation of the sensory information we receive at any given time and is shaped by an internal model generated by the brain by assimilating information over the life course. This predictive system in the brain has several components–(i) the internal model, (ii) the model‐based prediction called priors, (iii) the weighted difference between the prior and sensory input called prediction error (PE) and (iv) the weighted sum of the prior and input called perceptual inference. Until now, different studies have explored the independent components of this predictive coding system, and we, for the first time to our knowledge, integrate them. To do this, we induce a conditioned hallucination (CH) illusion by means of a multisensory integration paradigm and use this as a model to study the behavioral and electrophysiological responses to this experience. Additionally, we also probe their predictive coding system using a well‐established local–global auditory oddball paradigm. By comparing the behavioral and electrophysiological components of people more and less likely to perceive an illusion in the two paradigms, we observed that high perceivers place more confidence in their internal model and low perceivers in the sensory information. Furthermore, high perceivers were more sensitive than low perceivers to PEs that were generated by a change in the context of the sensory information, which served as a measure of a change in the internal model itself. As an exploratory analysis, we also observed that the objective likelihood of perceiving an illusion was corrected to the self‐reported likelihood of perceiving an illusion in a day‐to‐day setting, which disappears when controlled for the perceptual threshold. These results taken together start to give us an idea as to how a person's innate bias—either towards a learned model or external information may—affect their perception in a sensory context.

This study integratively measured the independent components of the predictive coding system including priors, confidence, prediction errors (PEs) and perceptual inference. The results showed that subjects with higher confidence in priors are more likely to shape perception towards prior belief and are more sensitive to context‐driven PEs.

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