Heterogeneity in strategy use during arbitration between experiential and observational learning

Creators: Charpentier, Caroline J.; Wu, Qianying; Min, Seokyoung; Ding, Weilun; Cockburn, Jeffrey; O'Doherty, John P.¹

1. California Institute of Technology

Abstract

To navigate our complex social world, it is crucial to deploy multiple learning strategies, such as learning from directly experiencing action outcomes or from observing other people’s behavior. Despite the prevalence of experiential and observational learning in humans and other social animals, it remains unclear how people favor one strategy over the other depending on the environment, and how individuals vary in their strategy use. Here, we describe an arbitration mechanism in which the prediction errors associated with each learning strategy influence their weight over behavior. We designed an online behavioral task to test our computational model, and found that while a substantial proportion of participants relied on the proposed arbitration mechanism, there was some meaningful heterogeneity in how people solved this task. Four other groups were identified: those who used a fixed mixture between the two strategies, those who relied on a single strategy and non-learners with irrelevant strategies. Furthermore, groups were found to differ on key behavioral signatures, and on transdiagnostic symptom dimensions, in particular autism traits and anxiety. Together, these results demonstrate how large heterogeneous datasets and computational methods can be leveraged to better characterize individual differences.

Copyright and License

© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Acknowledgement

This work was funded by the NIMH Caltech Conte Center grant on the neurobiology of social decision-making (P50MH094258) to J.P.O.D., as well as a Wellcome Trust Sir Henry Wellcome Postdoctoral Fellowship (218642/Z/19/Z) and NIMH K99/R00 award (K99MH123669) to C.J.C.

Contributions

C.J.C. and J.P.O.D. conceptualized the study. C.J.C. designed the experiment, collected and curated data. C.J.C. conceptualized the computational models with support from Q.W., W.D., J.C., and J.P.O.D. C.J.C. analyzed the data, with support from Q.W. and S.M. C.J.C. wrote the original draft of the manuscript, with support from J.P.O.D. All authors reviewed and edited the subsequent versions of the manuscript. J.P.O.D. supervised the work, with support from C.J.C. and J.C. J.P.O.D. and C.J.C. acquired funding.

Data Availability

The raw (trial-by-trial) and summary (participant-level) data generated in this study are available at: https://github.com/ccharpen/OL_EL_behavior; and https://doi.org/10.5281/zenodo.10695037⁷⁶.

Code Availability

All code used in this study to run the experiment online, analyze the data and generate the figures, tables and results reported in this manuscript is available on the following repository: https://github.com/ccharpen/OL_EL_behavior; and https://doi.org/10.5281/zenodo.10695037⁷⁶.

Conflict of Interest

The authors declare no competing interests.

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