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A challenge for predictive coding: Representational or experiential diversity?

Published online by Cambridge University Press:  19 June 2020

Martina G. Vilas Open the ORCID record for Martina G. Vilas [Opens in a new window]  and
Lucia Melloni
Martina G. Vilas
Affiliation:
Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, 60322 Frankfurt am Main, Germany. [email protected]@ae.mpg.de martinagvilas.github.io aesthetics.mpg.de/en/the-institute/people/lucia-melloni-en.html
Lucia Melloni
Affiliation:
Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, 60322 Frankfurt am Main, Germany. [email protected]@ae.mpg.de martinagvilas.github.io aesthetics.mpg.de/en/the-institute/people/lucia-melloni-en.html Department of Neurology, NYU Comprehensive Epilepsy Center, School of Medicine, New York University, New York, NY 10016
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Abstract

To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e150
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

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