Abstract
At present, the hierarchical predic-tive coding framework does not yet make stringent commitments as to the nature of the causal models that the brain can rep-resent. Hence, contrary to suggestions by Clark (in press) , the framework does not yet have the virtue that it effectively implements tractable Bayesian inference. At this point in time three mutually exclusive options remain open: either predictive coding does not implement Bayesian inference, or pre-dictive coding is not tractable, or the theory of hierarchical predictive coding is enriched by specific assumptions about the structure of the brain’s causal models.Assuming that one is committed to the Bayesian Brain Hypothesis, the first two options are out and the third is the only one remaining. Formal analyses expanding on this option are beyond the scope of this commentary (see e.g., Blokpoel et al., 2010; van Rooij et al., 2011), but
Highlights
It is a major virtue of the hierarchical predictive coding account that it effectively implements a computationally tractable version of the so-called Bayesian Brain Hypothesis. (Clark, in press)
Can predictive brains really be the same as Bayesian brains? Or is the claim merely an informal or imprecise shorthand for something which is formally and factually false? We address these questions by reconsidering the formal specifications of the theory of hierarchical predictive coding, as put forth by Friston (2002, 2005)
In the hierarchical predictive coding framework, it is assumed that the brain represents the statistical structure of the world at different levels of abstraction by maintaining different causal models that are organized on different levels of a hierarchy, where each level obtains input from its subordinate level
Summary
It is a major virtue of the hierarchical predictive coding account that it effectively implements a computationally tractable version of the so-called Bayesian Brain Hypothesis. (Clark, in press). It is a major virtue of the hierarchical predictive coding account that it effectively implements a computationally tractable version of the so-called Bayesian Brain Hypothesis. In the hierarchical predictive coding framework, it is assumed that the brain represents the statistical structure of the world at different levels of abstraction by maintaining different causal models that are organized on different levels of a hierarchy, where each level obtains input from its subordinate level.
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