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Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform

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Toward a Dynamic Probabilistic Model for Vestibular Cognition.

Andrew W Ellis¹, Fred W Mast¹

¹Department of Psychology, University of Bern Bern, Switzerland.

Frontiers in Psychology

|February 17, 2017

Summary

This summary is machine-generated.

Probabilistic models offer a new theoretical framework for vestibular cognition research. This approach can help understand how cognitive processes and sensory input interact, integrating mental simulation and prediction in the vestibular system.

Keywords:

computational modeling mental imagery mental simulation particle filters self-motion perception spatial cognition spatial perspective taking

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Area of Science:

Cognitive Neuroscience
Computational Psychiatry
Sensory Processing

Background:

Vestibular cognition research lacks systematic computational modeling.
Interactions between high-level cognition and low-level sensory processing are experimentally shown but not computationally modeled in the vestibular domain.
The link between mental simulation, prediction, and perception is established in other domains but overlooked for vestibular cognition.

Purpose of the Study:

To propose probabilistic models as a theoretical framework for vestibular cognition.
To explore the computational modeling of vestibular cognition.
To integrate the understanding of mental simulation and prediction within the vestibular system.

Main Methods:

Theoretical framework development using probabilistic models.
Computational modeling approaches.
Review of existing experimental findings on cognitive-sensory interactions.

Main Results:

Probabilistic models provide a framework for understanding vestibular cognition.
Computational modeling can systematically explore vestibular cognition.
The connection between mental simulation, prediction, and vestibular processing can be investigated.

Conclusions:

Future research in vestibular cognition should adopt probabilistic modeling.
Computational approaches are essential for a deeper understanding of vestibular cognition.
Integrating predictive coding principles can advance the study of vestibular cognitive functions.