Bayes or determinables? What does the bidirectional hierarchical model of brain functions tell us about the nature of perceptual representation?

The focus of this commentary is what Andy Clark takes to be the most groundbreaking of the philosophical import of the ‘bidirectional hierarchical model of brain functions’, namely, the claim that perceptual representations represent probabilities. This is what makes his account Bayesian and this is a philosophical or theoretical conclusion that neuroscientists and psychologists are also quick and happy to draw. My claim is that nothing in the ‘bidirectional hierarchical models of brain functions’ implies that perceptual representations are probabilistic, or that they represent or ‘encode probability density distributions’. There is a much more parsimonious way of describing the representations in the bidirectional hierarchical model of brain functions: they attribute properties to objects (or to the perceived scene) that are not fully determinate.
Keywords Predictive coding  Bayesian neuroscience  Determinable properties  Perceptual representation
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DOI 10.3389/fpsyg.2012.00500
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