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Bayesian reinforcement learning models reveal how great-tailed grackles improve their behavioral flexibility in serial reversal learning experimentsuse asterix (*) to get italics
Dieter Lukas, Kelsey B. McCune, Aaron P. Blaisdell, Zoe Johnson-Ulrich, Maggie MacPherson, Benjamin M. Seitz, Augustus Sevchik, Corina J. LoganPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2024
<p>Environments can change suddenly and unpredictably and animals might benefit from being able to flexibly adapt their behavior through learning new associations. Serial (repeated) reversal learning experiments have long been used to investigate differences in behavioral flexibility among individuals and species. In these experiments, individuals initially learn that a reward is associated with a specific cue before the reward is reversed back and forth between cues, forcing individuals to reverse their learned associations. Cues are reliably associated with a reward, but the association between the reward and the cue frequently changes. &nbsp;Here, we apply and expand newly developed Bayesian reinforcement learning models to gain additional insights into how individuals might dynamically modulate their behavioral flexibility if they experience serial reversals. We derive mathematical predictions that, during serial reversal learning experiments, individuals will gain the most rewards if they 1) increase their *rate of updating associations* between cues and the reward to quickly change to a new option after a reversal, and 2) decrease their *sensitivity* to their learned association to explore the alternative option after a reversal. We reanalyzed reversal learning data from 19 wild-caught great-tailed grackles (*Quiscalus mexicanus*), eight of whom participated in serial reversal learning experiment, and found that these predictions were supported. Their estimated association-updating rate was more than twice as high at the end of the serial reversal learning experiment than at the beginning, and their estimated sensitivities to their learned associations declined by about a third. The changes in behavioral flexibility that grackles showed in their experience of the serial reversals also influenced their behavior in a subsequent experiment, where individuals with more extreme rates or sensitivities solved more options on a multi-option puzzle box. Our findings offer new insights into how individuals react to uncertainty and changes in their environment, in particular, showing how they can modulate their behavioral flexibility in response to their past experiences.&nbsp;</p>
https://doi.org/10.5063/F1H41PWSYou should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
https://github.com/corinalogan/grackles/blob/master/Files/Preregistrations/g_flexmanip2post.RmdYou should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
https://You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
Behavioral flexibility, comparative cognition, grackle, innovativeness, multi-access box, problem solving, reversal learning
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Behaviour & Ethology, Phenotypic plasticity, Preregistrations, Zoology
Benjamin Robira suggested: Sofia Forss No need for them to be recommenders of PCIEcology. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe [john@doe.com]
2022-08-15 21:04:14
Aurélie Coulon