A computational model of motion sickness dynamics during passive self-motion in the dark.
Orientation perception
Predictive modeling
Sensory conflict
Spatial disorientation
Vestibular
Journal
Experimental brain research
ISSN: 1432-1106
Titre abrégé: Exp Brain Res
Pays: Germany
ID NLM: 0043312
Informations de publication
Date de publication:
15 Mar 2024
15 Mar 2024
Historique:
received:
15
12
2023
accepted:
08
02
2024
medline:
15
3
2024
pubmed:
15
3
2024
entrez:
15
3
2024
Statut:
aheadofprint
Résumé
Predicting the time course of motion sickness symptoms enables the evaluation of provocative stimuli and the development of countermeasures for reducing symptom severity. In pursuit of this goal, we present an Observer-driven model of motion sickness for passive motions in the dark. Constructed in two stages, this model predicts motion sickness symptoms by bridging sensory conflict (i.e., differences between actual and expected sensory signals) arising from the Observer model of spatial orientation perception (stage 1) to Oman's model of motion sickness symptom dynamics (stage 2; presented in 1982 and 1990) through a proposed "Normalized Innovation Squared" statistic. The model outputs the expected temporal development of human motion sickness symptom magnitudes (mapped to the Misery Scale) at a population level, due to arbitrary, 6-degree-of-freedom, self-motion stimuli. We trained model parameters using individual subject responses collected during fore-aft translations and off-vertical axis of rotation motions. Improving on prior efforts, we only used datasets with experimental conditions congruent with the perceptual stage (i.e., adequately provided passive motions without visual cues) to inform the model. We assessed model performance by predicting an unseen validation dataset, producing a Q
Identifiants
pubmed: 38489025
doi: 10.1007/s00221-024-06804-z
pii: 10.1007/s00221-024-06804-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Space Technology Mission Directorate
ID : 80NSSC20K1202
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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