A novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing
Pain perception can be studied as an inferential process in which prior information influences the perception of nociceptive input. To date, there are no suitable psychophysical paradigms to measure this at an individual level. We developed a quantitative sensory testing paradigm allowing for quanti...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
23 February 2023
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| In: |
Scientific reports
Year: 2023, Jahrgang: 13, Pages: 1-14 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-023-29758-8 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41598-023-29758-8 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41598-023-29758-8 
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| Verfasserangaben: | Armin Drusko, David Baumeister, Megan McPhee Christensen, Sebastian Kold, Victoria Lynn Fisher, Rolf-Detlef Treede, Albert Powers, Thomas Graven-Nielsen & Jonas Tesarz |
| Zusammenfassung: | Pain perception can be studied as an inferential process in which prior information influences the perception of nociceptive input. To date, there are no suitable psychophysical paradigms to measure this at an individual level. We developed a quantitative sensory testing paradigm allowing for quantification of the influence of prior expectations versus current nociceptive input during perception. Using a Pavlovian-learning task, we investigated the influence of prior expectations on the belief about the varying strength of association between a painful electrical cutaneous stimulus and a visual cue in healthy subjects (N = 70). ... |
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| Beschreibung: | Gesehen am 19.12.2023 |
| Beschreibung: | Online Resource |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-023-29758-8 |