A Decision-Space Model Explains Context-Specific Decision Making

Authors

Dirk W. Beck, University of Texas at El Paso
Cory N. Heaton, University of Texas at El Paso
Luis D. Davila, University of Texas at El Paso
Lara I. Rakocevic, University of Texas at El Paso
Sabrina M. Drammis, Massachusetts Institute of Technology
Danil Tyulmankov, University of Southern California
Atanu Giri, University of Texas at El Paso
Shreeya Umashankar Beck, University of Texas at El Paso
Qingyang Zhang, Harvard Medical School
Michael Pokojovy, Old Dominion UniversityFollow
Kenichiro Negishi, National Institute on Drug Abuse, Baltimore MD
Alexis A. Salcido, University of Texas at El Paso
Neftali F. Reyes, University of Texas at El Paso
Andrea Y. Macias, University of Texas at El Paso
Serina A. Batson, University of Texas at El Paso
Paulina Vara, University of Texas at El Paso
Raquel J. Ibáñez Alcalá, University of Texas at El Paso
Safa B. Hossain, University of Texas at El Paso
Graham L. Waller, University of Texas at El Paso
Laura E. O'Dell, University of Texas at El Paso
Travis M. Moschak, University of Texas at El Paso
Ki A. Goosens, Icahn School of Medicine at Mount Sinai
Alexander Friedman, University of Texas at El Paso

Document Type

Article

Publication Date

2025

DOI

10.1038/s41467-025-61466-x

Publication Title

Nature Communications

Volume

16

Issue

1

Pages

7437 (1-30)

Abstract

Optimal decision-making requires consideration of internal and external contexts. Biased decision-making is a transdiagnostic symptom of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a context-dependent mathematical space for decision-making computations, and how the matrix compartment uses this space to define action value. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused by a necessary increase in dimensionality of the space. The model makes testable predictions about individual differences in disorder susceptibility, decision-making symptoms shared among neuropsychiatric disorders, and differences in neuropsychiatric disorder symptom presentation. The model provides evidence for the central role that striosomes play in neuroeconomic and disorder-affected decision-making.

Rights

© 2025 The Authors.

This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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.

Data Availability

Article states: "• Source data are provided with this paper. • This paper primarily analyzes existing, publicly available data in the Corticostriosomal Circuit Stress Experiment database69 (https://data.mendeley.com/datasets/z9jd8xhj84/1). • This paper additionally analyzes data published alongside the current paper in the Computational model of striosomal circuit in normal and disordered decision-making database70 (https://doi.org/10.7910/DVN/SMKW0I). • All original code has been deposited in the DM_space model repository71 https://zenodo.org/records/15433189 and is publicly available as of the date of publication. • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. • See Instructions to run code, Supplementary Information for instructions to run the code that analyzes the data. Source data are provided with this paper."

Original Publication Citation

Beck, D. W., Heaton, C. N., Davila, L. D., Rakocevic, L. I., Drammis, S. M., Tyulmankov, D., Giri, A., Umashankar Beck, S., Zhang, Q., Pokojovy, M., Negishi, K., Salcido, A. A., Reyes, N. F., Macias, A. Y., Batson, S. A., Vara, P., Ibáñez Alcalá, R. J., Hossain, S. B., Waller, G. L.,…Friedman, A. (2025). A decision-space model explains context-specific decision-making. Nature Communications, 16(1), 1-30, Article 7437. https://doi.org/10.1038/s41467-025-61466-x

ORCID

0000-0002-2122-2572 (Pokojovy)

Repository Citation

Beck, Dirk W.; Heaton, Cory N.; Davila, Luis D.; Rakocevic, Lara I.; Drammis, Sabrina M.; Tyulmankov, Danil; Giri, Atanu; Beck, Shreeya Umashankar; Zhang, Qingyang; Pokojovy, Michael; Negishi, Kenichiro; Salcido, Alexis A.; Reyes, Neftali F.; Macias, Andrea Y.; Batson, Serina A.; Vara, Paulina; Ibáñez Alcalá, Raquel J.; Hossain, Safa B.; Waller, Graham L.; O'Dell, Laura E.; Moschak, Travis M.; Goosens, Ki A.; and Friedman, Alexander, "A Decision-Space Model Explains Context-Specific Decision Making" (2025). Mathematics & Statistics Faculty Publications. 298.
https://digitalcommons.odu.edu/mathstat_fac_pubs/298