Incorporating uncertainty within dynamic interoceptive learning
IntroductionInteroception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making home...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1254564/full |
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| author | Katja Brand Katja Brand Toby Wise Alexander J. Hess Bruce R. Russell Klaas E. Stephan Klaas E. Stephan Olivia K. Harrison Olivia K. Harrison Olivia K. Harrison |
| author_facet | Katja Brand Katja Brand Toby Wise Alexander J. Hess Bruce R. Russell Klaas E. Stephan Klaas E. Stephan Olivia K. Harrison Olivia K. Harrison Olivia K. Harrison |
| author_sort | Katja Brand |
| collection | DOAJ |
| description | IntroductionInteroception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty.MethodsSixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures.ResultsOur results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty.DiscussionThese results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health. |
| first_indexed | 2024-04-24T13:09:45Z |
| format | Article |
| id | doaj.art-8fce3319068a4cb0823aaf9699543051 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-24T13:09:45Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-8fce3319068a4cb0823aaf96995430512024-04-05T04:58:00ZengFrontiers Media S.A.Frontiers in Psychology1664-10782024-04-011510.3389/fpsyg.2024.12545641254564Incorporating uncertainty within dynamic interoceptive learningKatja Brand0Katja Brand1Toby Wise2Alexander J. Hess3Bruce R. Russell4Klaas E. Stephan5Klaas E. Stephan6Olivia K. Harrison7Olivia K. Harrison8Olivia K. Harrison9Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, SwitzerlandDepartment of Psychology, University of Otago, Dunedin, New ZealandKing's College London, Institute of Psychiatry, Psychology and Neuroscience, London, United KingdomTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, SwitzerlandSchool of Pharmacy, University of Otago, Dunedin, New ZealandTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, SwitzerlandMax Planck Institute for Metabolism Research, Cologne, GermanyTranslational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, SwitzerlandDepartment of Psychology, University of Otago, Dunedin, New ZealandNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomIntroductionInteroception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty.MethodsSixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures.ResultsOur results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty.DiscussionThese results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1254564/fullinteroceptionlearningbreathinginspiratory resistancemental health |
| spellingShingle | Katja Brand Katja Brand Toby Wise Alexander J. Hess Bruce R. Russell Klaas E. Stephan Klaas E. Stephan Olivia K. Harrison Olivia K. Harrison Olivia K. Harrison Incorporating uncertainty within dynamic interoceptive learning Frontiers in Psychology interoception learning breathing inspiratory resistance mental health |
| title | Incorporating uncertainty within dynamic interoceptive learning |
| title_full | Incorporating uncertainty within dynamic interoceptive learning |
| title_fullStr | Incorporating uncertainty within dynamic interoceptive learning |
| title_full_unstemmed | Incorporating uncertainty within dynamic interoceptive learning |
| title_short | Incorporating uncertainty within dynamic interoceptive learning |
| title_sort | incorporating uncertainty within dynamic interoceptive learning |
| topic | interoception learning breathing inspiratory resistance mental health |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1254564/full |
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