Efficient coding of cognitive variables underlies dopamine response and choice behavior.

Autor: Motiwala A; Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal. amotiwala@cmu.edu.; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. amotiwala@cmu.edu., Soares S; Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal.; Harvard Medical School, Boston, MA, USA., Atallah BV; Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal., Paton JJ; Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal. joe.paton@neuro.fchampalimaud.org., Machens CK; Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal. christian.machens@neuro.fchampalimaud.org.
Jazyk: angličtina
Zdroj: Nature neuroscience [Nat Neurosci] 2022 Jun; Vol. 25 (6), pp. 738-748. Date of Electronic Publication: 2022 Jun 06.
DOI: 10.1038/s41593-022-01085-7
Abstrakt: Reward expectations based on internal knowledge of the external environment are a core component of adaptive behavior. However, internal knowledge may be inaccurate or incomplete due to errors in sensory measurements. Some features of the environment may also be encoded inaccurately to minimize representational costs associated with their processing. In this study, we investigated how reward expectations are affected by features of internal representations by studying behavior and dopaminergic activity while mice make time-based decisions. We show that several possible representations allow a reinforcement learning agent to model animals' overall performance during the task. However, only a small subset of highly compressed representations simultaneously reproduced the co-variability in animals' choice behavior and dopaminergic activity. Strikingly, these representations predict an unusual distribution of response times that closely match animals' behavior. These results inform how constraints of representational efficiency may be expressed in encoding representations of dynamic cognitive variables used for reward-based computations.
(© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Databáze: MEDLINE
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