scholarly journals Mismatch of Visual-Vestibular Information in Virtual Reality: Is Motion Sickness Part of the Brains Attempt to Reduce the Prediction Error?

2021 ◽  
Vol 15 ◽  
Author(s):  
Matthias Nürnberger ◽  
Carsten Klingner ◽  
Otto W. Witte ◽  
Stefan Brodoehl
Keyword(s):  
Virtual Reality ◽  
Motion Sickness ◽  
Information Flow ◽  
Prediction Error ◽  
The Body ◽  
Brain State ◽  
Stable Model ◽  
Brain Areas ◽  
Vestibular Information ◽  
Frequency Power

Visually induced motion sickness (VIMS) is a relevant limiting factor in the use of virtual reality (VR) devices. Understanding the origin of this problem might help to develop strategies to circumvent this limitation. Previous studies have attributed VIMS to a mismatch between visual, and vestibular information, causing ambiguity of the position of the body in relation to its surrounding. Studies using EEG have shown a shift of the power spectrum to lower frequencies while VIMS is experienced. However, little is known about the relationship between the intensity of the VIMS and the changes in these power spectra. Moreover, the effect of different varieties of VIMS on the causal relationship between brain areas is largely unknown. Here, we used EEG to study 14 healthy subjects in a VR environment who were exposed to increasing levels of mismatch between vestibular and visual information. The frequency power and the bivariate transfer entropy as a measure for the information transfer were calculated. We found a direct association between increasing mismatch levels and subjective VIMS. With increasing VIMS, the proportion of slow EEG waves (especially 1–10 Hz) increases, especially in temporo-occipital regions. Furthermore, we found a general decrease in the information flow in most brain areas but especially in brain areas involved in the processing of vestibular signals and the detection of self-motion. We hypothesize that the general shift of frequency power and the decrease in information flow while experiencing high intensity VIMS represent a brain state of a reduced ability to receive, transmit and process information. We further hypothesize that the mechanism of reduced information flow is a general reaction of the brain to an unresolvable mismatch of information. This reaction aims on transforming a currently unstable model with a high prediction error into a stable model in an environment of minimal contradictory information.

A Deep Motion Sickness Predictor Induced by Visual Stimuli in Virtual Reality

2020 ◽  
pp. 1-13
Author(s):  
Jinwoo Kim ◽  
Heeseok Oh ◽  
Woojae Kim ◽  
Seonghwa Choi ◽  
Wookho Son ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Motion Sickness ◽  
Visual Stimuli

scholarly journals Limbic Expression of mRNA Coding for Chemoreceptors in Human Brain—Lessons from Brain Atlases

2021 ◽  
Vol 22 (13) ◽  
pp. 6858
Author(s):  
Fanny Gaudel ◽  
Gaëlle Guiraudie-Capraz ◽  
François Féron
Keyword(s):  
G Proteins ◽  
The Body ◽  
Trace Amines ◽  
Chemical Senses ◽  
Brain Areas ◽  
Genes Encoding ◽  
The Central Nervous System ◽  
Human Brains ◽  
The Brain ◽  
Brain Atlases

Animals strongly rely on chemical senses to uncover the outside world and adjust their behaviour. Chemical signals are perceived by facial sensitive chemosensors that can be clustered into three families, namely the gustatory (TASR), olfactory (OR, TAAR) and pheromonal (VNR, FPR) receptors. Over recent decades, chemoreceptors were identified in non-facial parts of the body, including the brain. In order to map chemoreceptors within the encephalon, we performed a study based on four brain atlases. The transcript expression of selected members of the three chemoreceptor families and their canonical partners was analysed in major areas of healthy and demented human brains. Genes encoding all studied chemoreceptors are transcribed in the central nervous system, particularly in the limbic system. RNA of their canonical transduction partners (G proteins, ion channels) are also observed in all studied brain areas, reinforcing the suggestion that cerebral chemoreceptors are functional. In addition, we noticed that: (i) bitterness-associated receptors display an enriched expression, (ii) the brain is equipped to sense trace amines and pheromonal cues and (iii) chemoreceptor RNA expression varies with age, but not dementia or brain trauma. Extensive studies are now required to further understand how the brain makes sense of endogenous chemicals.

scholarly journals The effect of water immersion on vection in virtual reality

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Géraldine Fauville ◽  
Anna C. M. Queiroz ◽  
Erika S. Woolsey ◽  
Jonathan W. Kelly ◽  
Jeremy N. Bailenson
Keyword(s):  
Virtual Reality ◽  
Motion Sickness ◽  
Water Immersion ◽  
Sensory Information ◽  
Future Research ◽  
Visually Induced Motion Sickness ◽  
Wide Range ◽  
Induced Motion ◽  
Ground Condition ◽  
The Impact

AbstractResearch about vection (illusory self-motion) has investigated a wide range of sensory cues and employed various methods and equipment, including use of virtual reality (VR). However, there is currently no research in the field of vection on the impact of floating in water while experiencing VR. Aquatic immersion presents a new and interesting method to potentially enhance vection by reducing conflicting sensory information that is usually experienced when standing or sitting on a stable surface. This study compares vection, visually induced motion sickness, and presence among participants experiencing VR while standing on the ground or floating in water. Results show that vection was significantly enhanced for the participants in the Water condition, whose judgments of self-displacement were larger than those of participants in the Ground condition. No differences in visually induced motion sickness or presence were found between conditions. We discuss the implication of this new type of VR experience for the fields of VR and vection while also discussing future research questions that emerge from our findings.

scholarly journals Muscular Movements in Man, and their Evolution in the Infant: a Study of Movement in Man, and its Evolution, together with Inferences as to the Properties of Nerve-Centres and their Modes of Action in expressing Thought

1889 ◽  
Vol 35 (149) ◽  
pp. 23-44 ◽  
Author(s):  
Francis Warner
Keyword(s):  
Brain Area ◽  
Muscular Contraction ◽  
The Body ◽  
Brain Areas ◽  
Modes Of Action ◽  
Central Nerve System ◽  
Nerve System ◽  
The Brain ◽  
Compound Series ◽  
Stimulation Of

(1) Movement in mau has long been a subject of profitable study. Visible movement in the body is produced by muscular contraction following upon stimulation of the muscles by efferent currents passing from the central nerve-system. Modern physiological experiments have demonstrated that when a special brain-area discharges nerve-currents, these are followed by certain visible movements or contraction of certain muscles corresponding. So exact are such reactions, as obtained by experiment upon the brain-areas, that movements similar to those produced by experimental excitation of a certain brain-area may be taken as evidence of action in that area, or as commencing in discharge from that area (see Reinforcement of Movements, 35; Compound Series of Movements, 34).

scholarly journals Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals

2021 ◽  
Vol 9 ◽  
Author(s):  
Marco Recenti ◽  
Carlo Ricciardi ◽  
Romain Aubonnet ◽  
Ilaria Picone ◽  
Deborah Jacob ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Motion Sickness ◽  
Muscle Activation ◽  
Brain Activity ◽  
Conflict Theory ◽  
Visual Signals ◽  
Physiological Factors ◽  
Biomedical Sensors ◽  
Dry Electrode ◽  
Importance Analysis

Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (IMS). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for IMS). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.

scholarly journals Toward the unity of pathological and exertional fatigue: A predictive processing model

2021 ◽  
Author(s):  
A. Greenhouse-Tucknott ◽  
J. B. Butterworth ◽  
J. G. Wrightson ◽  
N. J. Smeeton ◽  
H. D. Critchley ◽  
...  
Keyword(s):  
Prediction Error ◽  
Conscious Experience ◽  
Physical Exertion ◽  
Generative Models ◽  
The Body ◽  
Predictive Processing ◽  
Research Fields ◽  
Common Foundation ◽  
Health And Disease ◽  
Sensory Evidence

AbstractFatigue is a common experience in both health and disease. Yet, pathological (i.e., prolonged or chronic) and transient (i.e., exertional) fatigue symptoms are traditionally considered distinct, compounding a separation between interested research fields within the study of fatigue. Within the clinical neurosciences, nascent frameworks position pathological fatigue as a product of inference derived through hierarchical predictive processing. The metacognitive theory of dyshomeostasis (Stephan et al., 2016) states that pathological fatigue emerges from the metacognitive mechanism in which the detection of persistent mismatches between prior interoceptive predictions and ascending sensory evidence (i.e., prediction error) signals low evidence for internal generative models, which undermine an agent’s feeling of mastery over the body and is thus experienced phenomenologically as fatigue. Although acute, transient subjective symptoms of exertional fatigue have also been associated with increasing interoceptive prediction error, the dynamic computations that underlie its development have not been clearly defined. Here, drawing on the metacognitive theory of dyshomeostasis, we extend this account to offer an explicit description of the development of fatigue during extended periods of (physical) exertion. Accordingly, it is proposed that a loss of certainty or confidence in control predictions in response to persistent detection of prediction error features as a common foundation for the conscious experience of both pathological and nonpathological fatigue.

scholarly journals Why should any body have a self?

2017 ◽  
Author(s):  
Jakob Hohwy ◽  
John Michael
Keyword(s):  
Brain Function ◽  
Prediction Error ◽  
The Body ◽  
The Self ◽  
Error Minimization ◽  
Computational Framework ◽  
Key Characteristics ◽  
Prediction Error Minimization ◽  
Hierarchical Bayesian Inference

We use a general computational framework for brain function to develop a theory of the self. The theory is that the self is an inferred model of endogenous, deeply hidden causes of behavior. The general framework for brain function on which we base this theory is that the brain is fundamentally an organ for prediction error minimization.There are three related parts to this project. In the first part (Sections 2-3), we explain how prediction error minimization must lead to the inference of a network of deeply hidden endogenous causes. The key concept here is that prediction error minimization in the long term approximates hierarchical Bayesian inference, where the hierarchy is critical to understand the place of the self, and the body, in the world.In the second part (Sections 4-5), we discuss why such a set of hidden endogenous causes should qualify as a self. We show how a comprehensive prediction error minimization account can accommodate key characteristics of the self. It turns out that, though the modelled endogenous causes are just some among other inferred causes of sensory input, the model is special in being, in a special sense, a model of itself.The third part (Sections 6-7) identifies a threat from such self-modelling: how can a self-model be accurate if it represents itself? We propose that we learn to be who we are through a positive feedback loop: from infancy onward, humans apply agent-models to understand what other agents are up to in their environment, and actively align themselves with those models. Accurate self-models arise and are sustained as a natural consequence of humans’ skill in modeling and interacting with each other. The concluding section situates this inferentialist yet realist theory of the self with respect to narrative conceptions of the self.

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