Literature DB >> 32846162

Trial-by-trial dynamics of reward prediction error-associated signals during extinction learning and renewal.

Julian Packheiser1, José R Donoso2, Sen Cheng2, Onur Güntürkün1, Roland Pusch3.   

Abstract

Reward prediction errors (RPEs) have been suggested to drive associative learning processes, but their precise temporal dynamics at the single-neuron level remain elusive. Here, we studied the neural correlates of RPEs, focusing on their trial-by-trial dynamics during an operant extinction learning paradigm. Within a single behavioral session, pigeons went through acquisition, extinction and renewal - the context-dependent response recovery after extinction. We recorded single units from the avian prefrontal cortex analogue, the nidopallium caudolaterale (NCL) and found that the omission of reward during extinction led to a peak of population activity that moved backwards in time as trials progressed. The chronological order of these signal changes during the progress of learning was indicative of temporal shifts of RPE signals that started during reward omission and then moved backwards to the presentation of the conditioned stimulus. Switches from operant choices to avoidance behavior (and vice versa) coincided with changes in population activity during the animals' decision-making. On the single unit level, we found more diverse patterns where some neurons' activity correlated with RPE signals whereas others correlated with the absolute value during the outcome period. Finally, we demonstrated that mere sensory contextual changes during the renewal test were sufficient to elicit signals likely associated with RPEs. Thus, RPEs are truly expectancy-driven since they can be elicited by changes in reward expectation, without an actual change in the quality or quantity of reward.
Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Electrophysiology; Extinction learning; Renewal; Reward prediction error; Trial-by-trial learning

Mesh:

Year:  2020        PMID: 32846162     DOI: 10.1016/j.pneurobio.2020.101901

Source DB:  PubMed          Journal:  Prog Neurobiol        ISSN: 0301-0082            Impact factor:   11.685


  5 in total

1.  Supervised machine learning aided behavior classification in pigeons.

Authors:  Neslihan Wittek; Kevin Wittek; Christopher Keibel; Onur Güntürkün
Journal:  Behav Res Methods       Date:  2022-06-14

2.  The Acute Pharmacological Manipulation of Dopamine Receptors Modulates Judgment Bias in Japanese Quail.

Authors:  Katarína Pichová; Ľubica Kubíková; Ľubor Košťál
Journal:  Front Physiol       Date:  2022-05-11       Impact factor: 4.755

3.  "Prefrontal" Neuronal Foundations of Visual Asymmetries in Pigeons.

Authors:  Qian Xiao; Onur Güntürkün
Journal:  Front Physiol       Date:  2022-05-02       Impact factor: 4.755

4.  Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning.

Authors:  Roland Pusch; Julian Packheiser; Charlotte Koenen; Fabrizio Iovine; Onur Güntürkün
Journal:  Anim Cogn       Date:  2022-01-06       Impact factor: 2.899

5.  Emergence of complex dynamics of choice due to repeated exposures to extinction learning.

Authors:  José R Donoso; Julian Packheiser; Roland Pusch; Zhiyin Lederer; Thomas Walther; Metin Uengoer; Harald Lachnit; Onur Güntürkün; Sen Cheng
Journal:  Anim Cogn       Date:  2021-05-12       Impact factor: 3.084

  5 in total

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