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International Workshop on Active Inference

IWAI 2020: Active Inference pp 156–164Cite as

Confirmatory Evidence that Healthy Individuals Can Adaptively Adjust Prior Expectations and Interoceptive Precision Estimates

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1326))

Abstract

Theoretical proposals have previously been put forward regarding the computational basis of interoception. Following on this, we recently reported using an active inference approach to 1) quantitatively simulate interoceptive computation, and 2) fit the model to behavior on a cardiac awareness task. In the present work, we attempted to replicate our previous results in an independent group of healthy participants. We provide evidence confirming our previous finding that healthy individuals adaptively adjust prior expectations and interoceptive sensory precision estimates based on task context. This offers further support for the utility of computational approaches to characterizing the dynamics of interoceptive processing.

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Author information

Authors and Affiliations

  1. Laureate Institute for Brain Research, Tulsa, OK, USA

    Ryan Smith, Rayus Kuplicki, Adam Teed, Valerie Upshaw & Sahib S. Khalsa

Authors
  1. Ryan Smith
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  2. Rayus Kuplicki
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  3. Adam Teed
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  4. Valerie Upshaw
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  5. Sahib S. Khalsa
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Corresponding author

Correspondence to Ryan Smith .

Editor information

Editors and Affiliations

  1. Ghent University, Ghent, Belgium

    Tim Verbelen

  2. Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands

    Pablo Lanillos

  3. University of Sussex, Brighton, UK

    Christopher L. Buckley

  4. Ghent University, Ghent, Belgium

    Cedric De Boom

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Smith, R., Kuplicki, R., Teed, A., Upshaw, V., Khalsa, S.S. (2020). Confirmatory Evidence that Healthy Individuals Can Adaptively Adjust Prior Expectations and Interoceptive Precision Estimates. In: Verbelen, T., Lanillos, P., Buckley, C.L., De Boom, C. (eds) Active Inference. IWAI 2020. Communications in Computer and Information Science, vol 1326. Springer, Cham. https://doi.org/10.1007/978-3-030-64919-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-64919-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64918-0

  • Online ISBN: 978-3-030-64919-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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