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Computations of uncertainty mediate acute stress responses in humans

Author

Listed:
  • Archy O. de Berker

    (UCL Institute of Neurology, University College London
    Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London)

  • Robb B. Rutledge

    (Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London
    Max Planck University College London Centre for Computational Psychiatry and Ageing Research)

  • Christoph Mathys

    (Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London
    Max Planck University College London Centre for Computational Psychiatry and Ageing Research)

  • Louise Marshall

    (UCL Institute of Neurology, University College London)

  • Gemma F. Cross

    (Clinical Biochemistry, King’s College Hospital)

  • Raymond J. Dolan

    (Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London
    Max Planck University College London Centre for Computational Psychiatry and Ageing Research)

  • Sven Bestmann

    (UCL Institute of Neurology, University College London)

Abstract

The effects of stress are frequently studied, yet its proximal causes remain unclear. Here we demonstrate that subjective estimates of uncertainty predict the dynamics of subjective and physiological stress responses. Subjects learned a probabilistic mapping between visual stimuli and electric shocks. Salivary cortisol confirmed that our stressor elicited changes in endocrine activity. Using a hierarchical Bayesian learning model, we quantified the relationship between the different forms of subjective task uncertainty and acute stress responses. Subjective stress, pupil diameter and skin conductance all tracked the evolution of irreducible uncertainty. We observed a coupling between emotional and somatic state, with subjective and physiological tuning to uncertainty tightly correlated. Furthermore, the uncertainty tuning of subjective and physiological stress predicted individual task performance, consistent with an adaptive role for stress in learning under uncertain threat. Our finding that stress responses are tuned to environmental uncertainty provides new insight into their generation and likely adaptive function.

Suggested Citation

  • Archy O. de Berker & Robb B. Rutledge & Christoph Mathys & Louise Marshall & Gemma F. Cross & Raymond J. Dolan & Sven Bestmann, 2016. "Computations of uncertainty mediate acute stress responses in humans," Nature Communications, Nature, vol. 7(1), pages 1-11, April.
  • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10996
    DOI: 10.1038/ncomms10996
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    Cited by:

    1. Elizabeth Lomas & Julie McLeod, 2017. "Engaging with change: Information and communication technology professionals’ perspectives on change in the context of the ‘Brexit’ vote," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-26, November.
    2. Brice Corgnet & Simon Gaechter & Roberto Hernan Gonzalez, 2020. "Working Too Much for Too Little: Stochastic Rewards Cause Work Addiction," Discussion Papers 2020-03, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    3. Giovanni Leone & Charlotte Postel & Alison Mary & Florence Fraisse & Thomas Vallée & Fausto Viader & Vincent Sayette & Denis Peschanski & Jaques Dayan & Francis Eustache & Pierre Gagnepain, 2022. "Altered predictive control during memory suppression in PTSD," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    4. Corgnet, Brice & Hernán-González, Roberto & Kujal, Praveen, 2020. "On booms that never bust: Ambiguity in experimental asset markets with bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    5. Payam Piray & Nathaniel D. Daw, 2021. "A model for learning based on the joint estimation of stochasticity and volatility," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    6. Daniel S Kluger & Nico Broers & Marlen A Roehe & Moritz F Wurm & Niko A Busch & Ricarda I Schubotz, 2020. "Exploitation of local and global information in predictive processing," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-17, April.
    7. Payam Piray & Nathaniel D Daw, 2020. "A simple model for learning in volatile environments," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-26, July.
    8. Toby Wise & Jochen Michely & Peter Dayan & Raymond J Dolan, 2019. "A computational account of threat-related attentional bias," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-21, October.
    9. Candace M. Raio & Benjamin B. Lu & Michael Grubb & Grant S. Shields & George M. Slavich & Paul Glimcher, 2022. "Cumulative lifetime stressor exposure assessed by the STRAIN predicts economic ambiguity aversion," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    10. John Deke & Mariel Finucane & Daniel Thal, "undated". "The BASIE (BAyeSian Interpretation of Estimates) Framework for Interpreting Findings from Impact Evaluations: A Practical Guide for Education Researchers," Mathematica Policy Research Reports 5a0d5dff375d42048799878be, Mathematica Policy Research.
    11. Bae, Siye & Jo, Soojin & Shim, Myungkyu, 2023. "United States of Mind under Uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 102-127.

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