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Predictive coding: Neuroscience and art.

¹Arts and Sciences Department, UCL, Bloomsbury, London, United Kingdom.

Progress in Brain Research

|August 11, 2020

Summary

This summary is machine-generated.

Predictive coding offers insights into perception, but its explanatory power varies with ambiguous stimuli and affective states. Further research is needed to clarify its role in cognition and emotion.

Keywords:

Ambiguity Art perception Bayesian brain Cortex Generative models Hierarchical Inference Predictive coding

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Area of Science:

Neuroscience
Cognitive Psychology
Philosophy of Mind

Background:

Predictive coding is a prominent theory explaining perception through hierarchical Bayesian inference.
Understanding the brain's perceptual mechanisms requires evaluating theoretical models against empirical evidence.

Purpose of the Study:

To assess the validity of predictive coding as a model for perception.
To investigate the implications of predictive coding for understanding ambiguous stimuli, art, and affective states.

Main Methods:

Re-examination of predictive coding assumptions using neuroscientific evidence.
Analysis of inferential processes in predictive coding with ambiguous stimuli.
Exploration of psychological and cognitive impacts on affective states using ambiguous art.

Main Results:

Predictive coding accurately explains some perceptual experiences.
The model's applicability is limited in cases involving ambiguous stimuli.
Ambiguous art highlights the complex interplay between predictive coding, cognition, and emotion.

Conclusions:

Predictive coding provides a partial framework for perception.
Its utility is constrained by stimulus ambiguity and emotional context.
Further research can refine the model's scope in explaining perception and affect.