---
title: "Brain-behavior generative model links individual connectomes to perceptual decision speed"
id: "biorxiv-5-a-brain-behavior-generative-model-links-individual-brain-dynamics-to-perceptual"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-5-a-brain-behavior-generative-model-links-individual-brain-dynamics-to-perceptual"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1?rss=1"
published_at: "2026-10-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Brain-behavior generative model links individual connectomes to perceptual decision speed
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-5-a-brain-behavior-generative-model-links-individual-brain-dynamics-to-perceptual
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1?rss=1)
- **Published At:** 2026-10-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors developed a whole-brain **brain-behavior generative model** that uses each participant's structural connectome to produce both neural signals and behavior. - The model generated single-trial **EEG** waveforms and participant **reaction times** simultaneously, thereby tying anatomy, dynamics, and behavior in one framework. - Models were fitted to data from 17 participants performing a motion-discrimination task; fitted models reproduced individual event-related potentials and reaction-time distributions. - Joint fitting allowed prediction of whether held-out trials within the same stimulus condition would be relatively fast or slow, according to the authors' report. - The team ran in silico silencing experiments on model nodes to predict where, how, and when each person's brain controls decision speed. - Results showed that **sensorimotor** regions had consistent effects on decision speed across most participants, while one parietal region produced opposite effects across individuals. - The authors reported that the opposing parietal effects depended on whether that region influenced network dynamics broadly or affected the decision readout directly. - Temporal analyses indicated that early neural activity primarily set decision speed, and later EEG signals largely tracked the unfolding decision process. - The paper is a preprint; the authors declared no competing interests and the work has not been peer reviewed. Some fine-grained methodological details beyond the abstract are not reported in the source document.
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Chaoming Wang 1 Guangdong Institute of Intelligence Science and Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Chaoming%2BWang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wang%20C&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AChaoming%2BWang%2B) * [ORCID record for Chaoming Wang](http://orcid.org/0000-0002-7986-3890 "Open in new tab") * For correspondence: chao.brain@qq.com Guochun Yang 1 Guangdong Institute of Intelligence Science and Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Guochun%2BYang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Yang%20G&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AGuochun%2BYang%2B) Shaonan Wang 2 Hong Kong Polytechnic University; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Shaonan%2BWang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wang%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AShaonan%2BWang%2B) Huan Luo 3 Peking University * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Huan%2BLuo%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Luo%20H&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AHuan%2BLuo%2B) * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5893740/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5893740/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5893740/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5893740/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5893740/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Perceptual decision speed varies across individuals and trials. Explaining this variability requires linking individual brain anatomy, neural dynamics, and behavior, yet whole-brain models typically reproduce brain signals without behavior, whereas behavioral models lack anatomical constraints. Here we introduce a brain-behavior generative model in which whole-brain dynamics on each participant's structural connectome generate both single-trial electroencephalography (EEG) and reaction times. Fitted to 17 participants performing motion discrimination, the models reproduced individual event-related potentials and reaction-time distributions. Joint fitting enabled the models to predict whether held-out trials were relatively fast or slow within each stimulus condition. In silico silencing further predicted where, how, and when each person's brain controls decision speed. Sensorimotor regions affected decision speed consistently across most participants, whereas the same parietal region had opposite effects in different participants. These opposite effects reflected whether the region acted mainly through network dynamics or the decision readout. Temporal analyses suggested that early activity largely set decision speed, whereas late EEG mainly tracked the unfolding decision. These results show that brain-behavior generative modeling can link individual brain dynamics to perceptual decisions and yield testable predictions of why decision speed varies across individuals and trials. ### Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/22/2026.10.16.759120.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1.supplementary-material) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) A brain-behavior generative model links individual brain dynamics to perceptual decisions Chaoming Wang, Guochun Yang, Shaonan Wang, Huan Luo bioRxiv 2026.10.16.759120; doi: https://doi.org/10.64898/2026.10.16.759120 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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