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Brain network dynamics predict moments of surprise across contexts

Nature Human Behaviour volume 9, pages 554–568 (2025)Cite this article

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Abstract

We experience surprise when reality conflicts with our expectations. When we encounter such expectation violations in psychological tasks and daily life, are we experiencing completely different forms of surprise? Or is surprise a fundamental psychological process with shared neural bases across contexts? To address this question, we identified a brain network model, the surprise edge-fluctuation-based predictive model (EFPM), whose regional interaction dynamics measured with functional magnetic resonance imaging (fMRI) predicted surprise in an adaptive learning task. The same model generalized to predict surprise as a separate group of individuals watched suspenseful basketball games and as a third group watched videos violating psychological expectations. The surprise EFPM also uniquely predicts surprise, capturing expectation violations better than models built from other brain networks, fMRI measures and behavioural metrics. These results suggest that shared neurocognitive processes underlie surprise across contexts and that distinct experiences can be translated into the common space of brain dynamics.

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Fig. 1: Task overview.
Fig. 2: Surprise EFPM. Results from training the edge-fluctuation-based predictive model of surprise.
Fig. 3: Edges that predict high or low belief-inconsistent surprise in both the task and video datasets.
Fig. 4: Surprise EFPM uniquely generalized to novel data.

Similar content being viewed by others

Data availability

Helicopter learning task data are available at https://openneuro.org/datasets/ds003772/versions/1.0.1 (refs. 2,72). NCAA viewing data are available at https://openneuro.org/datasets/ds003338/versions/1.1.0 (refs. 20,73) and https://dataspace.princeton.edu/handle/88435/dsp019k41zh56d ref. 20. VoE data are available at https://openneuro.org/datasets/ds004934/versions/1.0.0 (refs. 39,40).

Code availability

Analysis code associated with this paper, including code for training and testing new edge-fluctuation-based predictive models, is available at https://github.com/ZiweiZhang0304/Surprise_EFPM.git ref. 79. We used the following versions of the softwares: MATLAB (R2020a), AFNI (v.19.0), R (v.4.0.5) and Python (3.12.2).

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Acknowledgements

We thank J. T. McGuire, J. W. Kable and C.-H. Kao for sharing and addressing questions about the MRI and behavioural data associated with refs. 2,5; J. W. Antony for sharing and addressing questions about the MRI data, behavioural data and analysis scripts associated with ref. 20; S. Liu for sharing and addressing questions about the MRI data associated with ref. 39; J. Faskowitz and colleagues for making the code associated with ref. 9 publicly available, which we adapted and used in this work; Veritone and the NCAA for permission to use basketball game screenshots; Y. Chang Leong, K. Yoo, H. Song and W. X. Q. Ngiam for helpful feedback and suggestions on this work. The research was supported by National Science Foundation BCS-2043740 and Alfred P. Sloan Research Fellowship in Neuroscience FG-2022-19032 to M.D.R. and resources provided by the University of Chicago Research Computing Center. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychology, The University of Chicago, Chicago, IL, USA

    Ziwei Zhang & Monica D. Rosenberg

  2. Institute for Mind and Biology, The University of Chicago, Chicago, IL, USA

    Ziwei Zhang & Monica D. Rosenberg

  3. Neuroscience Institute, The University of Chicago, Chicago, IL, USA

    Monica D. Rosenberg

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  1. Ziwei Zhang
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  2. Monica D. Rosenberg
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Z.Z. and M.D.R. conceptualized the study. Z.Z. curated and analysed the data. M.D.R. acquired funding and supervised the research. Z.Z. and M.D.R. wrote the paper.

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Correspondence to Ziwei Zhang or Monica D. Rosenberg.

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Zhang, Z., Rosenberg, M.D. Brain network dynamics predict moments of surprise across contexts. Nat Hum Behav 9, 554–568 (2025). https://doi.org/10.1038/s41562-024-02017-0

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