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Unique features of stimulus-based probabilistic reversal learning

Carl Harris, Claudia Aguirre, Saisriya Kolli, Kanak Das, View ORCID ProfileAlicia Izquierdo, View ORCID ProfileAlireza Soltani
doi: https://doi.org/10.1101/2020.09.24.310771
Carl Harris
1Department of Psychological and Brain Science, Dartmouth College, Hanover, New Hampshire, United States 03755
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Claudia Aguirre
2Department of Psychology, University of California-Los Angeles, Los Angeles, California, United States 90095
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Saisriya Kolli
2Department of Psychology, University of California-Los Angeles, Los Angeles, California, United States 90095
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Kanak Das
2Department of Psychology, University of California-Los Angeles, Los Angeles, California, United States 90095
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Alicia Izquierdo
2Department of Psychology, University of California-Los Angeles, Los Angeles, California, United States 90095
3The Brain Research Institute, University of California-Los Angeles, Los Angeles, California, United States 90095
4Integrative Center for Learning and Memory, University of California-Los Angeles, Los Angeles, California, United States 90095
5Integrative Center for Addictions, University of California-Los Angeles, Los Angeles, California, United States 90095
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  • For correspondence: aizquie@psych.ucla.edu
Alireza Soltani
1Department of Psychological and Brain Science, Dartmouth College, Hanover, New Hampshire, United States 03755
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Abstract

Reversal learning paradigms are widely-used assays of behavioral flexibility with their probabilistic versions being more amenable to studying integration of reward outcomes over time. Prior research suggests differences between initial and reversal learning, including higher learning rates, a greater need for inhibitory control, and more perseveration after reversals. However, it is not well-understood what aspects of stimulus-based reversal learning are unique to reversals, and whether and how observed differences depend on reward probability. Here, we used a visual probabilistic discrimination and reversal learning paradigm where male and female rats selected between a pair of stimuli associated with different reward probabilities. We compared accuracy, rewards collected, omissions, latencies, win-stay/lose-shift strategies, and indices of perseveration across two different reward probability schedules. We found that discrimination and reversal learning are behaviorally more unique than similar: latencies to select the better option, win-stay strategies, and perseveration were of a different pattern in reversal learning compared to discrimination learning. Additionally, fit of choice behavior using reinforcement learning models revealed a lower sensitivity to the difference in subjective reward values (greater exploration) and higher learning rates for the reversal phase. Interestingly, a consistent reward probability group difference emerged with a richer environment associated with longer reward collection latencies than a leaner environment. We also replicated previous reports on sex differences in reversal learning. Future studies should systematically compare the neural correlates of fine-grained behavioral measures to reveal possible dissociations in how the circuitry is recruited in each phase.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵# co-first authors

  • ↵* co-senior authors

  • Funding This work was supported by R01 DA047870 (Soltani and Izquierdo). We acknowledge UCLA’s Graduate Division Graduate Summer Research Mentorship and Graduate Research Mentorship programs (Aguirre) and the NSF Graduate Research Fellowship (Aguirre).

  • Disclosure There is no conflict of interest or need for disclosure.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 22, 2021.
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Unique features of stimulus-based probabilistic reversal learning
Carl Harris, Claudia Aguirre, Saisriya Kolli, Kanak Das, Alicia Izquierdo, Alireza Soltani
bioRxiv 2020.09.24.310771; doi: https://doi.org/10.1101/2020.09.24.310771
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Unique features of stimulus-based probabilistic reversal learning
Carl Harris, Claudia Aguirre, Saisriya Kolli, Kanak Das, Alicia Izquierdo, Alireza Soltani
bioRxiv 2020.09.24.310771; doi: https://doi.org/10.1101/2020.09.24.310771

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