Tracking subject's strategies in behavioural choice experiments at trial resolution. Maggi, S., Hock, R. M., O'Neill, M., Buckley, M. J., Moran, P. M., Bast, T., Sami, M., & Humphries, M. D. September, 2022. Pages: 2022.08.30.505807 Section: New Results![Tracking subject's strategies in behavioural choice experiments at trial resolution [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Investigating the strategies engaged by subjects in decision making and learning requires tracking their choice strategies on a trial-by-trial basis. Here we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution, using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. We find learning occurs earlier and more often than estimated using classical approaches. Also, win-stay and lose-shift strategies, often considered as complementary, are consistently used independently, with the adoption of lose-shift preceding both learning new rules and switching away from old rules. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.
@misc{maggi_tracking_2022,
title = {Tracking subject's strategies in behavioural choice experiments at trial resolution},
copyright = {© 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
url = {https://www.biorxiv.org/content/10.1101/2022.08.30.505807v2},
doi = {10.1101/2022.08.30.505807},
abstract = {Investigating the strategies engaged by subjects in decision making and learning requires tracking their choice strategies on a trial-by-trial basis. Here we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution, using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. We find learning occurs earlier and more often than estimated using classical approaches. Also, win-stay and lose-shift strategies, often considered as complementary, are consistently used independently, with the adoption of lose-shift preceding both learning new rules and switching away from old rules. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.},
language = {en},
urldate = {2022-09-05},
publisher = {bioRxiv},
author = {Maggi, Silvia and Hock, Rebecca M. and O'Neill, Martin and Buckley, Mark J. and Moran, Paula M. and Bast, Tobia and Sami, Musa and Humphries, Mark D.},
month = sep,
year = {2022},
note = {Pages: 2022.08.30.505807
Section: New Results},
}
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