The Exhausted Brain Theory: An Energy-Based Framework for

Understanding Visually Induced Motion Sickness

Diego Monteiro

1 a

and Hai-Ning Liang

2 b

Immersion and Interaction Lab - LII, ESIEA, Laval, France

Computational Media and Arts Thrust, The Hong Kong University of Science and Technology (Guangzhou),

Guangzhou, China

Keywords:

Visually Induced Motion Sickness, Exhausted Brain Theory, Energy Metabolism, Sensory Conﬂict, Virtual

Reality, Predictive Coding, Computational Neuroscience.

Abstract:

Visually Induced Motion Sickness (VIMS) poses a persistent challenge in various scenarios, from virtual and

augmented reality (VR/AR) to transportation and simulation-based training. Existing theories, such as sensory

conﬂict and postural instability, offer partial insights but fail to fully explain the metabolic and cognitive

dynamics underlying VIMS. This paper introduces the Exhausted Brain Theory, which proposes that VIMS

arises from excessive energy demands on the brain as it recalibrates internal models to resolve conﬂicting

sensory inputs. Drawing from computational neuroscience, information theory, and energy metabolism, the

theory highlights how sensory conﬂicts overwhelm neural processing, deplete energy reserves, and disrupt

predictive coding mechanisms. We discuss implications for modeling, detection, and mitigation of VIMS,

including energy-efﬁcient VR design, targeted acclimatization protocols, and personalized interventions. By

integrating diverse perspectives, this theory provides a unifying framework to advance understanding of VIMS

and guide future research on its prevention and management.

1 INTRODUCTION

In 1835, Charles Darwin said “. . . I continue to suf-

fer so much from sea-sickness, that nothing,[. . . ], can

make up for the misery. . . ” (Dobie, 2019) and he is

not alone. Motion sickness is also a problem that af-

fects millions of people (Dobie, 2019). In this posi-

tion paper, we use the term Visually Induced Motion

Sickness (VIMS) as an umbrella term to encompass

motion sickness, cybersickness, and simulator sick-

ness. VIMS has been a persistent challenge since the

advent of transportation and, more recently, virtual re-

ality (VR) technologies, including even 2D videos.

Despite centuries of study, a comprehensive under-

standing of its mechanisms remains elusive. Current

theories, such as the sensory conﬂict theory (Reason,

1978b) and the postural instability theory (Riccio and

Stoffregen, 1991), while valuable, fail to fully explain

all aspects of VIMS, particularly its variability across

individuals and situations.

VIMS manifests across a wide range of scenarios.

In transportation, it affects passengers in cars, boats,

https://orcid.org/0000-0002-1570-3652

https://orcid.org/0000-0003-3600-8955

airplanes, and even space vehicles, with symptoms

ranging from mild discomfort to severe nausea, and

the advent of autonomous vehicles introduces new

challenges as passengers become more disconnected

from vehicle control. In VR, augmented reality (AR),

and mixed reality (MR) applications, users often ex-

perience it during immersive experiences, limiting

the technology’s potential in ﬁelds such as education,

healthcare, and entertainment. VIMS affects mili-

tary personnel and civilian trainees using high-ﬁdelity

simulators for aircraft, vehicles, and complex machin-

ery. Even in everyday scenarios, users of smartphones

and tablets can experience discomfort when viewing

motion-rich content or using navigation apps. As 3D

displays become more common in consumer electron-

ics, cinema, and gaming, a broader population is ex-

posed to potential visual discomfort and sickness.

Monteiro, D. and Liang, H.-N.

The Exhausted Brain Theory: An Energy-Based Framework for Understanding Visually Induced Motion Sickness.

DOI: 10.5220/0013317000003912

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2025) - Volume 1: GRAPP, HUCAPP

and IVAPP, pages 627-638

ISBN: 978-989-758-728-3; ISSN: 2184-4321

627

2 THEORETICAL BACKGROUND

2.1 Visually Induced Motion Sickness

Theories

The Multisensory Integration Perspective (Gallagher

and Ferr

e, 2018) is one of the most recent frame-

works for understanding VIMS. This perspective em-

phasizes the brain’s adaptive processes and how they

might be overwhelmed in environments with diver-

gent stimuli. It proposes VIMS as the nervous sys-

tem’s challenge to appropriately weigh and integrate

various sensory signals, and it is arguably an evolu-

tion of the Neural Mismatch model (Reason, 1978b;

Oman, 1989), which is currently one of the most

widely accepted theories for VIMS. Initially proposed

for “motion sickness” alone and later applied to other

forms of VIMS, the Neural Mismatch model posits

that symptoms arise when there is a mismatch be-

tween sensory inputs, particularly between visual,

vestibular, and expectations. For example, in virtual

reality, the visual system may perceive motion, while

the vestibular system detects no movement, leading

to conﬂict and subsequent sickness. While this the-

ory explains many instances of VIMS, it does not

fully account for individual differences in suscepti-

bility or why some sensory mismatches cause more

severe symptoms than others, and mainly why these

would inﬂict symptoms.

The Postural Instability Theory (Riccio and Stof-

fregen, 1991) proposes that VIMS occurs when an

individual’s balance, which is the body’s main aim,

is disrupted as a virtual space discomfort follows.

Though some studies support this; with increased

postural sway in those experiencing sickness in vir-

tual environments, it does not explain all cases, espe-

cially in stationary, seated positions. Similarly, the

Rest Frame Hypothesis (Prothero, 1998a) suggests

the brain selects a “rest frame” or a set of stable vi-

sual references in the environment. It proposes that

sickness occurs when there are conﬂicting cues about

what should be considered stationary. This theory has

led to practical interventions in virtual reality, such

as adding ﬁxed visual references to reduce sickness.

Nevertheless, all these theories are only explanations

for what triggers VIMS, and not why there would be

a trigger in the ﬁrst place.

In terms of symptoms, several evolutionary ex-

planations have been proposed to explain how they

are triggered. The Poison Theory (Treisman, 1977)

suggests that the body interprets unusual sensory in-

puts as signs of poisoning, triggering nausea as a pro-

tective response. Another evolutionary perspective

proposes that sickness symptoms serve as a negative

reinforcement to discourage activities that create af-

tereffects harmful to locomotion and gaze stability

(Guedry et al., 1998). These theories offer interest-

ing perspectives on why sickness might occur; nev-

ertheless, they can struggle to explain the full range

of symptoms and individual variations and even the

counter-productivity of some strategies in critical sce-

narios.

Each of these theories contributes valuable in-

sights about VIMS, but none fully explains all aspects

of the condition. They often complement each other,

addressing different facets of the complex interplay

between human physiology and technological envi-

ronments. The limitations of these existing theories

highlight the need for a more comprehensive unifying

framework to understand and address VIMS across

various scenarios.

We propose The Exhausted Brain Theory (EBT).

A framework for understanding VIMS by integrating

concepts from most previous theories, neuroscience,

information theory, and energy metabolism. Our the-

ory posits that VIMS results from an excessive en-

ergy demand placed on the brain when it attempts to

rapidly recalibrate its internal models in response to

unfamiliar or conﬂicting sensory inputs.

2.2 Information Theory and

Computational Neuroscience

Information Theory, introduced by Claude Shannon

(Shannon and Weaver, 1949), provides a mathemati-

cal framework for quantifying the transmission, pro-

cessing, and storage of information, and evaluating

the efﬁciency of encoding schemes. It presents im-

portant concepts such as Entropy (H), Mutual Infor-

mation (I), and Channel Capacity (C), which quantify

the amount of information produced by a data source

and the reliable transmission capacity of this informa-

tion. These concepts provide the theoretical under-

pinnings for understanding information processing in

various systems, including the human brain.

The brain, as an intricate information-processing

system, can be analysed through the lens of Infor-

mation Theory. Neurons communicate via electrical

impulses, with synaptic connections facilitating the

transmission and transformation of information. Neu-

ral coding encompasses both rate coding and tempo-

ral coding, where either the ﬁring rate of a neuron or

the timing of spikes carry information. These mecha-

nisms allow the brain to represent and process diverse

stimuli efﬁciently.

In sensory processing, the Efﬁcient Coding Hy-

pothesis suggests that sensory systems are optimized

to represent information efﬁciently, minimizing re-

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628

dundancy, and maximizing information transmission.

This principle is evident in phenomena such as edge

detection in the retina, which reduces redundant vi-

sual information to conserve energy and processing

capacity.

The concepts of Predictive Coding and the Free

Energy Principle (Friston, 2010), introduced by Karl

Friston, present a possible explanation of how the

brain processes information. Predictive Coding posits

that the brain continually generates predictions about

sensory inputs and updates its internal models based

on prediction errors. The Free Energy Principle ex-

tends this idea, stating that the brain seeks to mini-

mize free energy (a measure related to surprise or pre-

diction error) to maintain a stable state. These princi-

ples highlight the brain’s constant effort to balance in-

formation processing demands with energy availabil-

ity, a crucial consideration given the high metabolic

cost of neuronal activity.

2.2.1 Biological Signal Processing

The free-energy principle (Friston, 2010) proposes

that self-organizing systems maximize information

between sensory and internal states by selectively

sampling expected sensory inputs. This principle uni-

ﬁes the Bayesian Brain Hypothesis, Efﬁcient Cod-

ing Principle, and Cell Assembly theory, suggest-

ing the brain optimizes energy efﬁciency by creating

and adjusting reality models through neuronal group

changes—aligning with observed brain topology (Ma

et al., 2021).

Trujillo (Trujillo, 2019) found experimental evi-

dence that mental model adjustments increase energy

consumption and subjective exhaustion. The brain

develops multisensory integration through early-life

cross-integration training (Xu et al., 2012), enabling

mental maps crucial for spatial awareness (Hasselmo

and Stern, 2013; Hughes et al., 2014; Allen et al.,

2016). Honey et al. (Honey et al., 2012) re-

vealed complex sensory integration involving slow-

ﬁring ”information accumulator” neurons (0.1Hz),

while Kok et al. (Kok et al., 2017) demonstrated

that expectations preactivate sensory templates in the

brain.

Neural communication is metabolically expensive

(Laughlin, 2001; Attwell and Laughlin, 2001; Lennie,

2003), consuming 35 times more energy than infor-

mation processing (Levy and Calvert, 2021). Lower

ﬁring rates (Koch et al., 2006) and weakly active cells

(Sarpeshkar, 1998) reduce energy costs, although re-

dundancy increases the cost per bit (Laughlin, 2001).

Spatial awareness requires dense, narrow-ﬁeld cells

(Sterling, 2004; Wassle and Boycott, 1991) with low

information rates to minimize costs (Koch et al.,

2006).

Brain imaging reveals that vestibular stimula-

tion reduces visual cortex blood ﬂow (Gallagher and

Ferr

e, 2018; Deutschl

ander et al., 2002; Wenzel et al.,

1996), while optic ﬂow deactivates vestibular areas

(Bense et al., 2001). This apparent energy waste

(Christie and Schrater, 2015) may indicate neuronal

group decoupling for reconﬁguration.

The visual system is very energy expensive; neu-

ral activity between the brain and the retina creates a

high metabolic demand. Energy is necessary for ev-

ery signal sent, and densely packed neurons are con-

stantly active (Laughlin, 2001). For mammals, 50%

of the total energy consumed by the brain is from

signaling, and in the cortex area, it represents 80%

(Laughlin, 2001).

The outputs of two adjacent photoreceptors often

measure light coming from the same object and there-

fore send very correlated signals. Thus, simply trans-

mitting their redundant information further as the out-

put of the photoreceptors would be inefﬁcient, since

the same information would be sent multiple times

(Roland, 1999). However, in the presence of noise,

some redundancy can be helpful to (1) identify infor-

mation corruption and (2) correct errors. Thus, animal

structures such as the retina of vertebrates are made

up almost exclusively of non-spiking neurons, which

appear to be used to eliminate redundancies and noise

while boosting the remainder (Burton, 2000).

Because light varies widely in intensity and pho-

toreceptors are limited to the dynamic range, sensory

adaptation is a solution (Niven and Laughlin, 2008).

For instance, in insects, when receiving a constant in-

put, the photoreceptor will keep a tonic activity (con-

stant activation), but the neuron communication will

be phasic, allowing for the ampliﬁcation and ﬁltering

of noises. At least in insects, the output of the messag-

ing system among their neurons matches the probabil-

ity curve that maximizes information (Burton, 2000).

Moreover, humans have been observed to see signals

when pacemaker neurons are in speciﬁc phases and

miss in the opposite phase (Busch et al., 2009).

After the basics of signal reception, Field (Field,

1994) argues that natural images follow certain pat-

terns, which he describes as “sparse” (which are sim-

ilar to the ﬁlters in a CNN) and that our photorecep-

tors have arrays that activate speciﬁcally upon ﬁnding

these patterns, thus even though identifying all possi-

ble patterns requires a lot of cells, individual images

only activate a few. And then, through this method,

cognitive tasks such as learning would be facilitated

because there will be little ambiguity (Burton, 2000).

Naturally, this process can be changed because it has

been experimentally seen that the receptive ﬁelds of

The Exhausted Brain Theory: An Energy-Based Framework for Understanding Visually Induced Motion Sickness

629

cortical cells are dynamic.

To summarize, because the visual system de-

mands a lot of energy, it must come up with ways to

be robust, effective, and energy-efﬁcient; thus, it will

come up with representations of the world, which can

later be altered.

2.2.2 Biologically Inspired Computing

To delve deeper into the neural computations un-

derlying these principles, models such as Hopﬁeld

Networks and Boltzmann Machines offer valuable

insights. Hopﬁeld Networks are recurrent artiﬁcial

neural networks that function as content-addressable

memory systems. They store information in a dis-

tributed manner and retrieve it through an energy

minimization process. The network dynamics settle

into stable states (local minima of an energy func-

tion), representing stored patterns (Vallejo and Bayro-

Corrochano, 2008), (Kumar and Satsangi, 1992),

(Abubakar, 2021), (Murthy and Gabbouj, 2015).

Boltzmann Machines extend this concept by intro-

ducing stochasticity into neuron activation, allowing

the network to explore various states and escape local

minima. They are capable of learning internal rep-

resentations and modeling complex probability dis-

tributions, with an energy function (Liu and Chen,

2011), (Barra, 2012), (Agliari, 2013), (Fukai, 1992).

3 THE EXHAUSTED BRAIN

THEORY

These computational models reﬂect the brain’s efforts

to reach low-energy states through synaptic adjust-

ments, mirroring how neural networks adapt to mini-

mize prediction errors (Friston, 2010). In the context

of the EBT, they exemplify how processing conﬂict-

ing sensory inputs requires additional energy as the

brain strives to settle into a new stable state when con-

fronted with discrepancies. As such, in this case, we

will consider variations in signal that cause abnormal

activation as conﬂicting signals.

For example, in virtual reality or motion sim-

ulation, the usual correspondence between visual,

vestibular, and proprioceptive input is disrupted. This

mismatch increases the entropy of sensory input as

the brain faces greater uncertainty. Consequently, the

brain must process more information to resolve this

uncertainty, leading to higher entropy. This increased

entropy results in higher information processing de-

mands (Laughlin, 2001). The mutual information be-

tween sensory inputs and internal models decreases

due to the conﬂict, as the brain’s predictions no longer

match incoming data.

To minimize prediction errors, the brain attempts

to update its internal models, a process that requires

processing additional information and consumes sig-

niﬁcant energy (Christie and Schrater, 2015). The

metabolic cost of these processes can lead to energy

depletion. This processing can increase entropy and

updating internal models is a metabolically demand-

ing task, with neuronal ﬁring and synaptic plasticity

consuming energy in the form of adenosine triphos-

phate (ATP). If the energy demand exceeds supply,

the brain experiences a form of ”exhaustion,” man-

ifesting as symptoms associated with VIMS, such as

nausea and dizziness—akin to the fatigue experienced

during excessive physical exercise (see Figs 1 and 2).

Channel capacity limits also play a role in VIMS.

The neural pathways have limited capacity, and ex-

cessive sensory information can overwhelm these

channels, causing delays or errors in processing. Ad-

ditionally, conﬂicting inputs can reduce the effective

signal-to-noise ratio, making it harder for the brain

to extract meaningful information without expending

more energy (Trujillo, 2019).

From the perspective of Predictive Coding, VIMS

can be understood as a failure of the brain’s predictive

Figure 1: A visual representation of the EBT. The user in-

teracts with an unnatural environment, such as a VR head-

set, leading to changes in the activation patterns of neurons.

This disruption requires reconnections and neural recalibra-

tion to achieve a ’new normal.’ If this recalibration occurs

efﬁciently, symptoms of Visually Induced Motion Sickness

(VIMS) are avoided. However, when the neural readjust-

ment is excessive or prolonged, the resulting energy deple-

tion manifests as VIMS. Neuron activations are represented

by differently colored circles.

HUCAPP 2025 - 9th International Conference on Human Computer Interaction Theory and Applications

630

mechanisms. Its capacity is constrained by metabolic

resources, which is why prolonged exposure to sen-

sory conﬂicts without adequate energy supply can

lead to persistent symptoms. The discrepancy be-

tween the expected and actual sensory inputs leads to

higher prediction errors, which are computationally

and energetically costly to resolve (Friston, 2010).

The theory also accounts for individual susceptibil-

ity to VIMS, as variations in metabolic efﬁciency and

neural processing capacity can affect one’s ability to

manage increased informational entropy.

Further insights come from examining the brain’s

oscillatory activity and spatial representations. Theta

rhythms, neural oscillations in the 4–8 Hz frequency

range, are prominent during active behaviors like ex-

ploration and navigation. They are associated with

memory encoding, spatial navigation, and sensorimo-

tor integration (Ravassard, 2013), (Romani, 2011),

(Zielinski et al., 2019) . Place cells, neurons in the

hippocampus, become active when an individual is in

or moving toward a speciﬁc location, forming a cog-

nitive map of the environment.

In virtual environments, sensory conﬂicts can dis-

rupt normal theta rhythm patterns and place cell activ-

ity, impairing the synchronization of neural networks

involved in spatial cognition. This desynchronization

requires additional neural processing to resolve, in-

creasing energy consumption. Moreover, the brain’s

effort to recalibrate its spatial maps in response to in-

consistent cues aligns with the energy demands de-

scribed in the EBT—costly, as a parallel can be drawn

from a gradient descent, in which closer values are

easier to achieve.

The concept of Neural Manifolds and Latent

Spaces provides a framework for understanding how

the brain represents high-dimensional sensory inputs

in a low-dimensional space, capturing the essential

features while reducing complexity. When sensory

inputs are conﬂicting or novel, this mapping becomes

less efﬁcient, requiring more energy to process and

interpret the data. Adjusting these internal representa-

tions is metabolically demanding, contributing to the

symptoms of VIMS (Monaco, 2019), (Herweg and

Kahana, 2018), (Lu, 2020).

One of the advantages of this framework is that it

does not require the brain to have an area dedicated

to deciding when VIMS should appear and does not

impose new systems or differentiated systems to inter-

pret different inputs and detect poisoning. Moreover,

it is well accepted that “neurons that ﬁre together wire

together”, and the ones that do not lose their connec-

tions.

This framework also highlights the importance of

energy efﬁciency in neural processing. It underscores

the need for virtual environments and technologies

to account for the brain’s capacity limitations and

metabolic constraints, potentially guiding the devel-

opment of interventions and design principles to mit-

igate VIMS.

In summary, the EBT synthesizes concepts from

Information Theory, computational neuroscience, and

physiological observations to explain how VIMS

arises from the brain’s overexertion due to conﬂicting

sensory information and the consequent energy deple-

tion. It provides a unifying framework that accounts

for individual variability and offers pathways for fu-

ture research and practical solutions.

3.1 Evidences for the Theory in

Literature

Empirical observations and research ﬁndings provide

evidence supporting the EBT’s proposition that VIMS

results from brain overexertion and energy depletion

during conﬂicting sensory processing. Studies have

shown that performing activities with VR headsets

leads to higher heart rates and increased calorie burn

compared to the same activities without VR, suggest-

ing increased physiological energy expenditure (Xu

et al., 2020). EEG studies indicate that task complex-

ity correlates with cybersickness (Sepich et al., 2022).

The correlation between sensory conﬂict and neu-

ral effort is supported by EEG research that demon-

strates higher P3 amplitudes in susceptible individuals

responding to sensory mismatches (Ahn et al., 2020).

This suggests increased cognitive demand and energy

expenditure as the brain attempts to resolve conﬂict-

ing sensory information. Participants who experience

symptoms also show notable changes in autonomic

responses, indicating heightened energy demand and

stress responses.

Babies and the elderly appear less susceptible to

traditional motion sickness, possibly due to differ-

ences in neural pathways or sensory reliance, ba-

bies for not having the pathways deﬁned and the el-

derly for having a lower dependency on vestibular

cues (Schm

al, 2013), thus neither needing readjust-

ment. Susceptibility factors linked to metabolic pro-

cesses further support the theory. Genetic factors are

often associated with glucose imbalance rather than

vestibular dysfunction, suggesting that efﬁcient en-

ergy utilization is crucial to managing sensory con-

ﬂicts (Hromatka et al., 2015).

The impact of nutrition and sleep reinforces the

EBT. Studies have shown that maintaining stable

blood sugar can mitigate symptoms, while sleep de-

privation, which impairs glucose metabolism and

cognitive function, has been linked to increased sus-

The Exhausted Brain Theory: An Energy-Based Framework for Understanding Visually Induced Motion Sickness

631

Figure 2: Top - Natural processing: Environmental signals (A) are captured by sensors (B), sampled (C,D), processed (E),

compared against predictions (G,H) based on previous states (F), and weighted (I) for continuous model updating. Bottom

- VR processing: Similar pathway but with artiﬁcial signals leading to sampling mismatches and corrupted observations,

requiring additional energy for recalibration and processing.

ceptibility (Kaplan et al., 2017). The sopite syn-

drome, characterized by drowsiness and fatigue fol-

lowing motion exposure, aligns with the theory, sug-

gesting the brain requires rest to recover and recali-

brate its energy balance (Matsangas and McCauley,

2014).

Cognitive load and learning limitations provide

further evidence. Participants using VR headsets

with higher sickness incidence showed lower rates

of knowledge acquisition, suggesting that the brain’s

resources are diverted to manage sensory conﬂicts

(Makransky et al., 2019). The effectiveness of miti-

gation strategies, such as gradual acclimatization and

nutritional interventions, supports the energy-based

explanation (Graybiel and Wood, 1969; Graybiel

et al., 1969).

Physiological measurements during exposure, in-

cluding altered blood ﬂow in brain regions and elec-

trogastrographic changes, indicate metabolic activity

changes and systemic responses to neural overexer-

tion (Gavgani et al., 2018). These ﬁndings collec-

tively support the EBT, demonstrating that VIMS is

closely linked to the brain’s energy dynamics dur-

ing sensory conﬂicts. This comprehensive explana-

tion offers a foundation for developing targeted miti-

gation strategies focused on managing cognitive load

and supporting neural energy requirements.

3.2 Relation with Previous Theories

The EBT can encompass other theories of technol-

ogy sickness because it posits that the brain’s adap-

tation to new sensory inputs requires signiﬁcant en-

ergy, leading to an ”exhausted” state when demand

exceeds supply (see table 1). The Cue Conﬂict The-

ory (Irwin, 1952; Bonato et al., 1990) suggests sick-

ness arises from mismatches between sensory inputs

(visual, vestibular, proprioceptive). This aligns with

the EBT because resolving these conﬂicts necessitates

neural rewiring, which is energy-intensive. Similarly,

the Rest Frame Hypothesis (Chang et al., 2013; Lin

et al., 2017) and Postural Instability Theory (Riccio

and Stoffregen, 1991; Villard and Flanagan, 2008) fo-

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632

cus on the brain’s reliance on stable reference frames,

which can be integrated because recalibrating these

frames in response to conﬂicting cues in virtual envi-

ronments demands additional energy.

The Negative Reinforcement and Poison Theories

also ﬁt within this framework. The former proposes

that sickness is a deterrent against potentially harm-

ful activities (Bowins, 2010), while the latter sug-

gests that it is an evolved response to perceived toxins

(Treisman, 1977; Nalivaiko et al., 2004). Both align

with the EBT because avoiding energy-depleting sit-

uations would be evolutionarily advantageous and the

residues of energy-intensive activity can be harm-

ful. Lastly, Sensory Rearrangement Theory (Reason,

1978a; Oman, 1982), which posits that the brain up-

dates paired sensory information during conﬂicts, and

Multisensory Integration perspectives (Gallagher and

Ferr

e, 2019; Kaliuzhna et al., 2015), which empha-

size the brain’s weighting of different sensory cues,

are encompassed because these processes require en-

ergy to modify neural pathways and synaptic connec-

tions. Therefore, the EBT provides a unifying frame-

work by explaining the energy demands underlying

these various theoretical perspectives.

3.3 Mitigation Through the Theory

Lenses

Several techniques can be applied to reduce VIMS,

especially in VR. For example, techniques such as the

VRCockpit (Chen et al., 2022), PlaneFrame (Mon-

teiro et al., 2020), and Rest Frames (Monteiro et al.,

2018b; Monteiro et al., 2018a; Prothero, 1998b; Shi

et al., 2021) offer consistent visual or physical cues

that help the brain establish stable reference frames,

easing the energy-intensive process of recalibration

(Wienrich et al., 2018).

Techniques that minimize sensory discrepancies

directly address the root cause of energy drain. For

example, ﬁeld-of-view reduction (FOV) (Fernandes

and Feiner, 2016) and blurring (Kobayashi et al.,

2015) limit visual information, particularly in periph-

eral vision, which is the main way the brain detects

speed visually. As a result, the brain has to process

fewer conﬂicting cues, thereby reducing energy ex-

penditure (Lin et al., 2002).

Increasing Fidelity is an approach that can ei-

ther do wonders or have the opposite desired out-

comes. Enhancing the realism of virtual environments

by adding congruent vestibular, such as higher resolu-

tion and frame rate (Wang et al., 2023b; Wang et al.,

2022a) or proprioceptive feedback, such as vibration

or wind (Wang et al., 2022b; Zhao et al., 2024), re-

duces sensory mismatches, when done well. This can

minimize the need for the brain to reconcile discrep-

ancies, thus conserving energy (Suzuki et al., 2019).

However, presenting even more discrepant informa-

tion can exacerbate the symptoms (D’Amour et al.,

2017).

Techniques that improve the brain’s efﬁciency in

handling sensory input can indirectly reduce energy

consumption by optimizing information processing.

For instance, gradual exposure and adaptation by and

incremental introduction of users to virtual environ-

ments or motion stimuli allows their brain to gradu-

ally adapt and rewire neural pathways (Graybiel et al.,

1969). This staged process avoids a sudden surge in

energy demand, enabling more efﬁcient learning and

reducing sickness over time. Even cognitive training,

with targeted exercises, can enhance the brain’s abil-

ity to process conﬂicting sensory information. By im-

proving efﬁciency, the brain can require less energy

to handle sensory discrepancies, mitigating sickness

(Nalivaiko et al., 2018).

3.4 Implications, Predictions, and

Future Work

The EBT provides a foundation for advancing the

modeling, research, treatment, detection, and mitiga-

tion of VIMS.

The brain’s energy demands during sensory con-

ﬂicts and the resulting metabolic imbalances can be

explored using mathematical models and simulations.

These models could predict susceptibility to VIMS by

analyzing brain connectivity patterns and metabolic

rates. By focusing on speciﬁc regions and behaviors,

the accuracy of these predictions has the potential to

Table 1: Summary of what each theory accounts for. OK symbolizes that the theory accounts for that component at least

partially.

What the theories account for Sickness Triggers Individual Susceptibility Signal Intensity Why Triggers Cause Sickness Adaptation After-Effects

Cue Conﬂict Theory OK OK

Rest Frame Hypothesis OK OK

Postural Instability OK OK OK OK

Negative Reinforcement OK OK OK OK

Poison Theory OK OK OK OK

Sensory Rearrangement OK OK OK

Multisensory Integration OK OK OK OK OK

Exhausted Brain Theory OK OK OK OK OK OK

The Exhausted Brain Theory: An Energy-Based Framework for Understanding Visually Induced Motion Sickness

633

improve signiﬁcantly.

Furthermore, artiﬁcial neural networks could sim-

ulate VR scenarios to identify those most likely

to induce VIMS. By tracking the number of it-

erations required for the network to ”readapt” to

conﬂicting inputs, designers could optimize envi-

ronments to reduce VIMS triggers. For example,

a bio-inspired Spiking Neural Network trained for

self-location and mapping—using accelerometer and

video data—could later be exposed to new VIMS-like

”noisy” data. The time required for readaptation in re-

sponse to this interference could serve as a guideline

for detecting VIMS-triggering environments.

The development of a standard for movement pa-

rameters and dimensions in XR environments could

also facilitate broader adoption. Mental training

acquired in one environment might transfer to an-

other application. However, applications with un-

even movement patterns or inconsistent frame opti-

mization could still cause users to struggle. For in-

stance, in a single application, ﬂuctuating frame rates

are more likely to induce sickness. This theory high-

lights that individual differences in sensory integra-

tion and metabolic efﬁciency inﬂuence susceptibility

to VIMS. It also underscores that adaptation to one

type of VIMS-inducing environment may not guar-

antee immunity in others, emphasizing the need for

tailored acclimatization.

Given this context, we can expect users who

present better spatial acuity and use several cues for

self-location to suffer more from VIMS than those

with poor self-location.

Using EEG to synchronize refresh rates with neu-

ronal activation frequencies—-potentially even vary-

ing by screen area—-may reduce VIMS. This ap-

proach could allow for better resource allocation, as

not all screen regions may need simultaneous updates.

Developing speciﬁc tools to measure energy ex-

penditure and adaptation processes could improve

VIMS detection. For example, functional near-

infrared spectroscopy (fNIRS) or similar non-invasive

methods could help analyze neural and metabolic re-

sponses. Recording blood glucose levels before and

after VR exposure, where ethically permissible, could

reveal metabolic changes linked to VIMS. Partici-

pants could also be asked about conditions affecting

glucose processing, such as diabetes, pancreatic dis-

orders, or body mass, to reﬁne the collected data.

Given the nature of the theory, it would theoret-

ically be possible to assess an individual’s suscepti-

bility by analyzing information beyond the exposure

itself. This could involve determining the strength of

the coupling between their visual and vestibular sys-

tems, as well as evaluating the amount of energy re-

quired to readjust when faced with a different form of

conﬂict. In a longitudinal study, as the degradation

of the vestibular system slowly causes the decoupling

of the two information systems, VIMS should be less

present (considering all other metabolic aspects re-

main constant).

For long-term studies or repeated exposure, intro-

ducing a ”Day-0” adaptation protocol for users and

participants would be ideal. This protocol would al-

low individuals to acclimate to new simulators and

HMDs, ensuring that they experience applications

as intended. Structured acclimatization frameworks

might include 1) Exercises to ”decouple” visual and

vestibular senses. 2) Gradual exposure to VIMS-

inducing content, starting with simpler environments

(e.g., ﬁgure outlines) and increasing complexity in-

crementally. 3) Training strategies to optimize brain

energy usage, akin to stepwise progression in athletic

training. 4) Adequate nutritional intake.

Detecting when users are ready to engage in learn-

ing activities within VIMS-inducing environments

could enhance the effectiveness of these settings. En-

suring habituation before introducing cognitive tasks

could prevent overload and maximize learning out-

comes. Short-term studies should consider using par-

ticipants who are already acclimated to the environ-

ment to avoid skewing the results. Moreover, studies

showing minimal learning differences in VR may un-

derestimate its potential, as new exposure to VR could

hinder outcomes. This suggests that some ﬁndings

that consider VR ineffective for learning might stem

from studies that are too short (Monteiro et al., 2024;

Barrett et al., 2023; Makransky et al., 2019).

The EBT could also inform new health and safety

guidelines for prolonged XR and simulator use, par-

ticularly in professional contexts where extended use

is required. Additionally, it raises important con-

siderations for children’s use of XR and simulators.

As ”virtual natives,” children exposed to XR envi-

ronments may develop long-term advantages, such

as more adaptable movement pathways and increased

neural connections, akin to the beneﬁts of early lan-

guage acquisition, once thought to be a disadvantage.

This also means that it is possible that some people

could never adapt when exposed to just ”normal” use.

3.5 Testable Hypotheses

To validate the EBT, several testable hypotheses can

be proposed. These hypotheses aim to explore the

relationship between energy dynamics, sensory con-

ﬂicts, and individual variability in the context of Vi-

sually Induced Motion Sickness.

H1: Individuals performing cognitively demand-

HUCAPP 2025 - 9th International Conference on Human Computer Interaction Theory and Applications

634

ing tasks during exposure to a sensory conﬂict-

inducing VR environment will experience higher lev-

els of VIMS compared to those in less demanding

tasks. A controlled experiment could compare VIMS

to physiological markers of energy expenditure.

H2: Gradual acclimatization to VR environments

will reduce VIMS symptoms over time compared to

abrupt exposure to complex environments. This hy-

pothesis could be explored in a longitudinal study by

comparing symptoms between participants exposed

to incremental versus sudden increases in the com-

plexity of the VR session.

H3: Individuals with a higher baseline efﬁciency

in predictive coding will experience less severe VIMS

in sensory-conﬂicting VR environments. Predictive

coding efﬁciency could be measured using EEG pat-

terns or behavioral tests of sensory integration and

correlated with VIMS symptoms.

H4: Increasing the sensory ﬁdelity of VR environ-

ments, such as by adding congruent vestibular feed-

back in an extremely precise way, will reduce VIMS

symptoms compared to conditions with sensory mis-

matches; otherwise, it will cause worse symptoms.

This could be tested by comparing symptoms and

EEG markers such as theta rhythm desynchronization

in conditions with and without synchronized vestibu-

lar feedback.

H5: Participants with higher aerobic ﬁtness lev-

els will exhibit lower VIMS susceptibility, as better

metabolic efﬁciency could buffer against the energy

depletion associated with sensory conﬂicts.

H6: Participants with more stable theta rhythms

during VR exposure will experience fewer VIMS

symptoms. EEG data could be used to examine theta

rhythm stability and its relationship with VIMS sever-

ity across varying sensory conﬂict levels.

These hypotheses provide a framework for empir-

ical validation of the EBT and should be tested us-

ing techniques that detect VIMS within the experi-

ment (Wang et al., 2020; Wang et al., 2023a; Mon-

teiro et al., 2021).

3.6 Limitations

One limitation of this work is its correlational nature,

as it does not include empirical evidence derived from

original experiments. While correlations cannot es-

tablish causation, this does not diminish the valid-

ity of the framework presented. Similar research in

this ﬁeld also relies on the existing literature and ob-

servational studies, and other recognized theories are

untestable. Furthermore, the correlations drawn here

are supported by multiple reliable sources, lending

credibility to the insights. Importantly, this work is

intended as a foundation for future research, provid-

ing a framework that can guide experiments designed

to either support or challenge this theory.

Another limitation of this paper is that we do not

delve into the speciﬁcs of how glucose is processed

or explore the broader metabolic processes in detail.

Consequently, the root cause of the issue might be

related to the energy demand process rather than the

energy demand itself. However, this lies beyond the

scope of the paper and the technical expertise of the

researchers. Despite this limitation, related studies

still support the overall association between energy

and VIMS. The authors remain open to alternative

interpretations, e.g., such as the possibility that the

problem might stem from residual effects of the read-

justment process rather than the readjustment itself.

4 CONCLUSION

The EBT provides a comprehensive and unifying

framework to understand the phenomenon of VIMS.

By emphasizing the brain’s metabolic demands and

energy limitations during sensory conﬂict resolution,

the theory integrates insights from established per-

spectives, such as sensory conﬂict, postural instabil-

ity, and multisensory integration. It highlights how

the brain’s effort to recalibrate internal models in re-

sponse to conﬂicting sensory inputs can lead to energy

depletion and, consequently, VIMS symptoms.

This energy-based approach not only explains in-

dividual susceptibilities to VIMS but also accounts

for the variability in symptoms across scenarios and

technologies. By framing VIMS as a product of the

brain’s effort to minimize prediction errors and main-

tain internal stability, the theory bridges the gap be-

tween neurophysiology, computational neuroscience,

and energy metabolism. It underscores the impor-

tance of designing energy-efﬁcient virtual environ-

ments and personalized interventions to reduce the

cognitive and metabolic strain associated with immer-

sive technologies.

Furthermore, the EBT lays the groundwork for fu-

ture research aimed at modeling, detecting, and miti-

gating VIMS. From developing tools to measure neu-

ral and metabolic responses to designing acclimatiza-

tion protocols and energy-aware XR systems, the the-

ory inspires practical solutions to a critical challenge

in the adoption of emerging technologies. As immer-

sive environments become increasingly integral to ed-

ucation, healthcare, and entertainment, understanding

and addressing the underlying causes of VIMS is es-

sential for their safe and effective use.

In conclusion, the EBT not only enriches our un-

The Exhausted Brain Theory: An Energy-Based Framework for Understanding Visually Induced Motion Sickness

635

derstanding of VIMS but also opens new avenues for

research and innovation, offering a pathway toward

enhancing the accessibility and usability of virtual

and augmented reality technologies.

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