Consciousness and Cognition 96:103218 (2021)

Daniel Burnston
Tulane University
Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture.
Keywords Bayesian Cognition  Optimal Feedback Control  Motor Control  Decision  Intention  Predictive Processing
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DOI 10.1016/j.concog.2021.103218
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Perceptual Pluralism.Jake Quilty‐Dunn - 2020 - Noûs 54 (4):807-838.
Intention and Motor Representation in Purposive Action.Stephen Andrew Butterfill & Corrado Sinigaglia - 2014 - Philosophy and Phenomenological Research 88 (1):119-145.

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