Prefrontal Goal Codes Emerge as Latent States in Probabilistic Value Learning.

The prefrontal cortex (PFC) supports goal-directed actions and exerts cognitive control over behavior, but the underlying coding and mechanism are heavily debated. We present evidence for the role of goal coding in PFC from two converging perspectives: computational modeling and neuronal-level analysis of monkey data. We show that neural representations of prospective goals emerge by combining a categorization process that extracts relevant behavioral abstractions from the input data and a reward-driven process that selects candidate categories depending on their adaptive value; both forms of learning have a plausible neural implementation in PFC. Our analyses demonstrate a fundamental principle: goal coding represents an efficient solution to cognitive control problems, analogous to efficient coding principles in other (e.g., visual) brain areas. The novel analytical-computational approach is of general interest because it applies to a variety of neurophysiological studies.

Publication type: 
Articolo
Author or Creator: 
Stoianov, Ivilin
Genovesio, Aldo
Pezzulo, Giovanni
Publisher: 
Published by the MIT Press with the Cognitive Neuroscience Institute,, Cambridge, Mass. , Stati Uniti d'America
Source: 
Journal of cognitive neuroscience 28 (2016): 140–57. doi:10.1162/jocn_a_00886
Date: 
2016
Resource Identifier: 
http://www.cnr.it/prodotto/i/343328
https://dx.doi.org/10.1162/jocn_a_00886
info:doi:10.1162/jocn_a_00886
Language: 
Eng
ISTC Author: 
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