systematically analyze the effects on the distinct fidelity components. As the
model holistically addresses input fidelity and output fidelity, a complete
reflection of the effect of, e.g., joystick vs. actual walking as different
input techniques for locomotion in VR not only addresses the fidelity of the
users’ actions, the detection and the transfer function on the input side. It
also directs to connected aspects of the output, such as perceptual fidelity,
which can broadly vary between setups with different input fidelity. The
model’s components can provide insight into where differences or similarities
between systems are, how decisive they are, what the underlying reasons are,
or what could be done to compensate for them.

Hypothesize & Guide: The IntFi Model can be used to generate research
questions around fidelity in VR to analyze and understand this design space
further. It reveals many research opportunities, such as design guidelines
regarding the combination of different fidelity levels in the model’s
components. For example, can components compensate for each other? Or should
the components rather have coherent levels of fidelity? How do the
quantitative components influence the overall experiential fidelity? The model
could also be used as a starting point for heuristic evaluations of the
fidelity of VR systems by systematically addressing the fidelity components.
For empirical evaluations of realism in VR, researchers can also use the IntFi
Model to formulate reasoned hypotheses as well as to explain and discuss the
findings in relation to the different stages of the loop. In the following
section, we will elaborate on the arising research opportunities based on our
model.

Teach & Convince  The model’s simple structure combined with the intelligible
visualization makes it easy for students to learn about VR interactions and
how different components must be considered for reproducing something
virtually. As the IntFi Model can be universally employed for any use case, it
works in various practical and scientific curricula. Teachers can use the
sequence of aspects to guide students stage-wise through the relevant
components of interactions while emphasizing the reciprocal nature of human-
centered simulations. Similarly, the model is suitable for convincing
stakeholders and managers with limited experience with HCI methods or VR
technology why a proposed strategy, system design, or research agenda would be
advisable. The complexity of seemingly trivial interactions can be
demonstrated just as effectively as the interdependence of the single
components.

###  6.3. Application Traps

We identified several common pitfalls that people applying the model fall into
and which we were also repeatedly caught in when developing it. The following
traps are partially based on experiences from the expert interviews presented
in Section 5. Be sure to avoid these common mistakes to get the most out of
the IntFi Model.

Better–Worse Trap The most frequent fallacy might be attributing a judgment
instead of objectively describing fidelity. People tend to use phrases such as
better, worse, nicer, more immersive, better UX, etc., instead of an impartial
assessment of the exactness of correspondence. The neutral, dispassionate view
in fidelity evaluations is not the most intuitive attitude. A possible
explanation is that fidelity and desirability correlate for many interactions,
especially when striving for natural interfaces. However, it can also be
beneficial to decrease fidelity deliberately to achieve a particular effect.
Therefore, it helps to avoid thinking of low fidelity as a shortcoming.

Time-Travel Trap In contrast to absolute assessments of how close an aspect is
compared to the reference, peoples’ assessment is sometimes linked to the
state of the art at a certain time. For example, some researchers evaluated a
system in the context of technical possibilities at the time of development.
Consequently, the low-poly visuals of an application were described as high-
fidelity because 15 years ago, when it was built, the system was considered
world-class, thus high-fidelity compared to anything else at the time. It can
be reasonable to make a time-dependent comparison, e.g., when selecting
hardware with the current technical limitations or tracing system capabilities
over time. In other instances, however, it can be misleading to make
assessments depending on the current state of the art as it shifts the assumed
upper limit from maximum fidelity to currently attainable fidelity.

Apples-and-Oranges Trap Another trap we experienced and observed frequently is
comparing the VR interactions to different references without noticing. For
example, in the expert interviews, one participant assessed a locomotion
technique as low- and high-fidelity at the same time and was confused about
the outcome. The reason was that the comparison was made to different
references: first to a teleportation technique, second to actual walking.
While both comparisons can result in valuable insights, it should always be
clear which reference is currently compared to. Another person fell into the
same trap and referred to it as “comparing apples and oranges.” Therefore, it
is vital to apply the IntFi Model in a meaningful way. When comparing, choose
a clear purpose and an adequate reference interaction. When in doubt, you can
ask: What do I want to reproduce?

Visual-Dominance Trap As there is a strong connotation of rendering and
display regarding vision, there is a risk of neglecting other modalities and
senses when using the model. In many experiences, the visual impression is the
most dominant and sophisticated modality. But it might be important to include
all senses, depending on the use case. On the other hand, it can also make
sense to narrow the focus to relevant modalities.

Feasibility Trap We also observed the risk of overestimating a system’s
fidelity and referring to it incorrectly as maximum fidelity, although it was
merely the highest fidelity possible to achieve. Increasing it further could
be restricted by limited resources, physical boundaries, personal abilities,
or one’s own imagination. However, it is irrelevant to assessing fidelity if
there are limits in feasibility. Describing the exactness of correspondence is
technology-agnostic, hence independent of the reasons behind a system design.
To achieve maximum fidelity, optimizing a system as much as possible is
insufficient unless there is a perfect match with the reference. We may never
accomplish maximum fidelity in some aspects.

##  7\. Discussion

We now turn to the broader context of the model, considering its general
meaning and implications. In the following, we discuss potential patterns of
how the fidelity components might be connected, considerations of optimizing
for realism, how the IntFi Model relates to similar constructs, how fidelity
can be described objectively, how it can be measured, the significance of
perceived fidelity, and how the model can be applied to reference frames other
than reality, such as fiction or mixed reality.

###  7.1. Patterns

Investigating numerous VR interaction techniques, systems, experiences, and
user studies, we found similar phenomena repeatedly. From this, we distilled
reoccurring connections, dependencies, and relationships between the fidelity
components. In this section, we propose potential patterns that might be
discoverable in various VR interfaces. Not all patterns necessarily appear in
every interaction. Please note that the patterns presented here are not
systematically studied and lack empirical evidence. They are merely based on
incomplete sets of examples and theoretical reasoning. Further systematic
research is needed to test these suggested patterns and reveal additional
ones.

Bottleneck Pattern — In some use cases, experiential fidelity cannot be higher
than a limiting key component, even if other aspects have much higher
fidelity. For example, when juggling in VR, it is irrelevant if visual
rendering fidelity is exceptionally high as long as action or simulation
fidelity is low. The bottleneck of these limitations will always impair
experiential fidelity. Consequently, it is necessary to identify the critical
components of an application and prioritize them for increasing fidelity.

Loss-Propagation Pattern — Certain aspects in the loop cannot be higher than
the previous one. In this case, the component inherits the constraints of the
preceding one. We can find such a conditional dependence in the pipeline
Simulation → Rendering → Display. For example, a display can depict something
at most with the resolution it was rendered at, and the rendering software
can, at best, match the quality of the simulation’s 3D model. Although we can
speculate and approximate to compensate, we cannot assure the compensation’s
authenticity. Thus, loss at an early stage of such sequences cannot reliably
be made up for and is propagated throughout the loop.

Similar dependencies can be in the sequence Action → Detection → Transfer. For
example, if a controller prevents a natural hand pose when juggling, no sensor
of an input device can make up for this, and if the sensors do not detect the
hand position, a transfer function cannot compensate for missing data. There
might be further such sequences, e.g., Display → Perceptual → Experiential; or
Experiential → Action. As a consequence, it is advisable to optimize at the
start of such sequences to avoid inheritance of early losses. However, a
component can also be limited by a component much earlier in the loop, e.g.,
Action → Perceptual due to the vestibular system. Therefore, searching for the
root of an issue in the preceding components can be helpful, as it might just
be a propagated problem.

Irrevocable-Loss Pattern — An extreme version of the Loss-Propagation Pattern
was proposed by E08 in our expert interviews: “It’s like a pipeline.
[Progressing through the pipeline], there are only losses.” In many use cases,
such a drastic error progression might occur. However, it can be prevented in
some cases. For example, if the hand pose is detected incompletely due to
occlusion, we can still compensate for this deficiency by reconstructing a
probable hand pose using anatomic models and inverse kinematics. Consequently,
there might be a viable solution to a component’s limitation later in the
pipeline, compensating for earlier losses.

Free-Upgrade Pattern — Maximum fidelity in some components automatically leads
to maximum fidelity for the subsequent component. For example, if the displays
reproduce the physical stimuli perfectly, they will also be perceived
indistinguishably from the original stimuli of the reference, as we cannot
sense the source of a physical signal but just the signal itself. Thus,
maximum display fidelity inevitably results in maximum perceptual fidelity.
Similarly, experiential fidelity will automatically be at maximum as inherited
from the previous component. Therefore, if you need to increase the fidelity
of a particular component, it might be helpful to optimize the preceding
components.

Uncanny-Valley Pattern — Comparing varying input fidelity with user
performance, McMahan et al. (2016) suspected an “uncanny valley of VR
interactions” that leads to poor performance for unfamiliar interfaces with
medium input fidelity. While the tested low-fidelity interfaces were known
from preexisting systems and the high-fidelity interfaces were intuitive, both
delivered higher performance than unknown, somewhat abstract medium-fidelity
alternatives. However, as the authors discuss, this non-linear relation cannot
be generally applied to any input system. Further studies identified similar
patterns in gaming (Lukosch et al., 2019), training (Bhargava et al., 2018),
and locomotion (Nabiyouni, 2017). Consequently, it might be beneficial to
avoid interaction techniques with medium fidelity.

###  7.2. Optimizing Realism

Reality is not the only reference frame to which the IntFi Model helps compare
VR, but it is undoubtedly a particularly relevant and common one. Therefore,
we discuss the ambivalent goal of striving for high realism. An interaction’s
level of realism is merely a descriptive, impartial attribute. High realism is
not per se superior. For some use cases, increasing interaction realism can be
beneficial (e.g., skill training and education) or fundamental (e.g.,
preservation of historical artifacts). For other use cases, enhancing realism
might be negligible (e.g., data visualization, art), detrimental (e.g.,
fictional entertainment), or even harmful (e.g., source memory confusion).
Supernatural abilities have massive potential for VR interactions (e.g.,
brain-computer interfaces for telekinesis or changing laws of physics), as
pointed out by Bowman et al. (2012) and supported through a design method by
Sadeghian and Hassenzahl (2021). Even in use cases originally meant for
reproducing real experiences, such as social interactions, deviating from high
realism can enrich the experience and introduce new possibilities (McVeigh-
Schultz and Isbister, 2021, 2022; Bonfert et al., 2023). Dewitz et al. (2023)
develop a framework on interaction techniques beyond realism, such as magic
techniques, superpowers, or hyper-natural augmentation. It locates interaction
techniques along the three orthogonal axes internalizability, congruence, and
enhancement.

Striving for high fidelity requires time and financial effort. Therefore, it
is important in research and development to critically reflect on how much
realism is desirable and expedient. The IntFi Model can help identify
components that should be optimized or can be less prioritized. We discuss
such considerations in Subsection 8.2. While we can do a lot of good with
highly realistic VR simulations, it can also be harmful and used maliciously.
A growing body of literature addresses problematic implications of progressing
XR technology, the ethics of increasingly attainable realism, and the risk of
hostile manipulations (Slater et al., 2020; Madary and Metzinger, 2016;
Wassom, 2015; Tseng et al., 2022; Rubo et al., 2021), which should be
considered when striving for high-fidelity applications.

Supporting the recommendation of careful tradeoffs by Jacob et al. (2008) as
part of their framework for reality-based interaction, we further suggest
reducing realism in return for other desired qualities that align with the
simulation’s purpose. Jacob et al. (2008) propose considering benefits in
expressive power, efficiency, versatility, ergonomics, accessibility, or
practicality for a tradeoff. For example, in a training simulator for learning
how to juggle, high action and simulation fidelity are essential for the
trainee to transfer the acquired skills to reality and apply the movements
with real balls. Nevertheless, offering a training mode in slow motion to
practice the movements without time pressure can be a beneficial deviation
from reality.

###  7.3. Related Constructs

In the literature, numerous concepts and ideas have been associated with the
fidelity of a simulation, such as the Place Illusion of “being there” and the
Plausibility Illusion (also referred to as presence) (Slater et al., 2022;
Skarbez et al., 2017a), coherence(Skarbez, 2016), immersion (Bowman and
McMahan, 2007), engagement (Lukosch et al., 2019), and others (Skarbez et al.,
2017a). We consider these as different from but correlated with fidelity.
Thus, a high-fidelity interaction could result in low presence but usually
leads to high presence. Conversely, a high sense of presence in a coherent,
highly engaging world can also be achieved with a low-fidelity system. Slater
(2009) argues that high levels of place and plausibility illusions lead to
realistic behavior of the user. Accordingly, the user’s reactions to the
virtual experience ought to correspond to how the user would react to the
reference interaction, i.e., high experiential and action fidelity.

In practice, these constructs have a strong link and correlate in countless
empirical studies. They also overlap in their typical assessment, e.g., some
presence questionnaires comprise items to self-report perceived or experienced
realism (Witmer and Singer, 1998; Schubert et al., 2001). Yet, the constructs
concern different theoretical questions. It is, therefore, important not to
confuse their claim. In particular, in empirical evaluations, the choice of
measurements and interpretation of evidence depends on the concept that the
research question revolves around. While there have been decades of discourse
on the conception of presence and similar concepts (Skarbez et al., 2017a),
fidelity as the objective degree of correspondence between simulation and
original is straightforward and with the distinct components of the IntFi
Model intuitive for the planning and analysis of VR systems. Although this
makes fidelity an unequivocally defined concept, it might not be the relevant
one to evaluate depending on the purpose of a system or study, just as
presence is not always the essential metric that should be sought after
(Latoschik and Wienrich, [n. d.]).

###  7.4. Describing Fidelity Objectively

Let’s assume there is an objective, indisputable ground truth of how exact the
correspondence between an original and its replication is. This truth could be
described objectively if it is known. The assessments of what is true,
however, can be subjective and might diverge. The more precisely we agree on
how to evaluate interaction fidelity in a systematic, replicable way, the more
objectively we can determine and agree on it. As discussed in the following
subsection, we can only approximate the ground truth and achieve consensus
through standardized measuring criteria. Technical parameters regarding system
fidelity are simpler to assess objectively, while we need to rely more on
subjective evaluation of user-related aspects. As various aspects determine
the multi-faceted concept of interaction fidelity, it is difficult to identify
its ground truth comprehensively.

Here is an example of a seemingly unambiguous and objective fidelity
assessment. Probably, most people would agree that compared to the reference
interaction of grasping an object, we can attribute higher interaction
fidelity when the user reaches out and encloses the virtual object with their
bare hand, than when the user points at the object with a hand controller and
presses the trigger button. The latter implementation relies on mappings and
seems less natural. However, primarily action fidelity is higher in the first
implementation, while display fidelity is lower due to the lack of haptic
feedback. The controller’s passive force feedback has a higher correspondence
to grasping a rigid object, providing higher display fidelity. Hence,
depending on the focus or context, evaluations can vary. Although there is an
irrefutable ground truth that we strive to ascertain, we do not necessarily
succeed in doing so objectively. As a result, it is essential in
communication—especially in scientific discourse—to clearly describe our
perspective and reasoning.

Especially the IntFi Model’s components Perceptual and Experiential Fidelity
are difficult to determine objectively as the subjective nature of a person’s
qualia is individual and might be intrinsically subjective. Depending on the
perspective on the philosophical mind–body problem, it might not even be
possible to deduce mental events of the unobservable mind from the physical
events in the observable brain (Georgiev, 2020). Therefore, an objective,
holistic description of interaction fidelity might be unattainable. Regardless
of metaphysics and seen from a pragmatic point of view, it is more expedient
to assess experiential fidelity subjectively on a user-by-user basis: Does a
specific person experience the VR simulation exactly how this person would
experience the reference interaction? This perspective takes all personal
characteristics and biological features into account. Usually, the diversity
of people’s individuality must be considered because most systems are aimed at
large user groups.

###  7.5. Measuring Fidelity

Beyond a qualitative understanding of interaction fidelity, it can be helpful
to express the degree of correspondence quantitatively. Some fidelity aspects
can already be assessed with high objectivity. For example, characteristics of
input and output devices can be technically gauged, e.g., regarding pixel
density, sensory noise, or degrees of freedom. Some of these quantifiable
parameters allow a direct interpretation of how they affect interaction
fidelity in direct comparisons. For example, a screen with a higher resolution
than an otherwise identical screen provides higher display fidelity. For other
aspects, it is harder to infer an uncontroversial effect on interaction
fidelity from technical parameters. For example, regarding rendering fidelity,
the influence of shaders treating light reflections differently might depend
on various circumstances and is more intricate to interpret.

Some aspects are commonly assessed subjectively with self-reports, such as in
questionnaires or interviews. Due to their subjective nature, experiential and
perceptual fidelity are usually measured through user reports. But also some
system-related components might only make sense to be assessed subjectively by
large numbers of evaluators, for example, the credibility and human likeness
of virtual human animations. Unfortunately, there is a limit to how much we
can ask users to share their pmpressions in studies, making a holistic
assessment of all subjective parameters impossible. However, we claim that it
is possible to predict experiential fidelity sufficiently if enough about the
other fidelity components is known.

In this work, we described the level of fidelity with the coarse categories
low, medium, high, and maximum fidelity. This gives us an approximate location
on the continuum and allows the rough comparison of a few systems. However, we
should strive as a community for detailed, theory-based, technology-agnostic,
and unambiguous metrics for all fidelity components, as outlined in the
research opportunities in Section 8.

###  7.6. The Normative Power of Subjective Truth

There is an additional challenge when comparing VR interactions to the real
world. In the case of assessing realism, we need to agree not only on the
nature of the simulated interaction but also on the reality-based interaction.
From a philosophical perspective, it is hard enough to agree on what “reality”
objectively is. Anybody discussing the manifestation of the real world can
only do so from their subjective point of view informed by their individual
perceptions. While we can assess the exactness of the correspondence between a
simulation and what is considered a broad consensus about the real world, the
judge will ultimately be the users with their impressions. Depending on the
simulation’s purpose, their subjective judgment may not be decisive for how
the interactions are designed, but often, experiential fidelity is the only
outcome that matters and will be optimized for. In this case, only the user-
related components regarding what the user perceives, experiences, and does
seem important. Why should we then care for system fidelity at all? The
system-related components primarily determine the levels of the user fidelity
aspects. For designers and developers of VR systems and interactions, system
fidelity is the only way to influence the user’s perception, experience, and
actions. The better we understand the components’ mutual influence and
interdependencies, the more effective our endeavors can be.

Interestingly, increasing the fidelity of single aspects does not necessarily
increase the experiential fidelity. Previous research has suggested that
reducing fidelity aspects for some interactions elicits higher experiential
fidelity (Nilsson et al., 2017; McMahan et al., 2016). For example, when
moving through a VE using a treadmill as an input device, the walking speed is
experienced as more realistic by the user when the virtual pace is exaggerated
relative to the originally slower pace in the real world (Banton et al., 2005;
Kassler et al., 2010; Powell et al., 2011). Here, the transfer function is
mapped unrealistically to compensate for other limitations in interaction
fidelity, such as the missing kinetic feedback from staying in place (i.e.,
low perceptual fidelity). The required amount of exaggeration has also been
found to depend on the visual display fidelity: The smaller the field of view
is, the stronger the speed must be exaggerated for the user to feel realistic
(Nilsson et al., 2014). Alternatively, the visual projection can be distorted
to display more peripheral information (Nilsson et al., 2015). Hence, reducing
transfer or rendering fidelity can increase experiential fidelity. Similarly,
Bowman et al. (2012) argue that high fidelity in a certain component (e.g.,
action fidelity by rotating a Wii controller to steer a vehicle) might result
in lower perceived realism because of the shortcomings in other components (in
this example, missing force feedback and latency). As a consequence, the
purpose of a system must be considered for prioritizing the different
components’ targeted level of fidelity.

###  7.7. Applicability to Fiction, Mixed Reality, and Other Reference Frames

The IntFi Model is designed to help assess the correspondence of interactions
in virtual reality with interactions in any other reference frame. While the
reference can be the real world, the model can also guide the analysis of
fictional and other scenarios as long as the element to be reproduced is
explicitly specified. This applies to any fictional media, imaginary
narrative, dream, or fantasy, but also other VR systems, setups of previous
user studies, or different levels of blended realities along the
reality–virtuality continuum (Skarbez et al., 2021).

As an example of fictional references, the characteristics, behavior, and
appearance of a lightsaber from Star Wars are extensively defined by the
creators of the fictional artifact and, therefore, can be virtually
reproduced. By contrasting the simulation with the original descriptions and
depictions, we can evaluate its fidelity, as demonstrated in Example 3 of
Section 4. The less clear the original to be simulated is, the more
controversial the fidelity assessment might be. To stick to the Star Wars
example, when simulating interactions using “the force” for telekinesis, it is
less obvious how this might be realized virtually—arguably not only with a
hand gesture but rather a brain-computer interface. The experiential fidelity
will broadly differ between users as they have differing conceptualizations
and expectations depending on what Star Wars media they have seen or read.

While developing the IntFi Model, we encountered scenarios in which no
reference would be obvious to compare to, which always led us to the question:
What are we trying to (re)create here? As an example from our expert
interviews, participant E09 brought up an application to teach chemical
processes. However, the equivalent process from reality on an atomic level
made no sense to reproduce virtually for teaching. Instead, it had to be
magnified to a human scale. But what could it sound like if two atoms bond on
a human scale? The most suitable reference interaction we could come up with
was the educator’s idea of what an upscaled version of the virtualized school
book model might look, sound, and feel like. This is where the model reaches
its limits. A comparison using the IntFi Model provides little insight if the
reference is only vaguely defined.

Another use case for applying the model is to compare two interactive systems.
For example, we could compare the realism of playing baseball on a Nintendo
Wii with an implementation in VR. While aspects of input fidelity might be
similar using 6-DoF controllers, the VR version might show high fidelity in
other components and explain outcomes of comparative user studies.

Similarly, the IntFi Model can be used to evaluate mixed reality (MR)
applications blending virtual worlds with physical reality. There are many
forms of incorporating more or less portions of different realities (Skarbez
et al., 2021). The interaction can be based in the real world with virtual
elements integrated, or VR can be the foundation comprising elements from
reality—any combination is conceivable. To apply the model meaningfully in MR
contexts, it is even more important to clarify what is being compared. We
advise treating the blended realities as unity and comparing it to a non-mixed
equivalent. Consider, for example, an MR meeting in which co-located users
participate in the real world and virtual users join remotely. To design the
interactions in this blended setting, comparing them jointly to the purely
real or a purely virtual equivalent can be helpful. Obviously, you can achieve
high levels of fidelity when simply augmenting reality with virtual elements
compared to the challenge of building a system that recreates everything
virtually from scratch. Automatically, some aspects are maximum fidelity as
they equal the real world. But as Lindeman and Beckhaus (2009b) proposed, why
should we not leverage parts of the real world and augment it to create
overall high-fidelity interactions if reality affords it, such as using
passive haptics for teleoperation? On the other hand, we encounter limitations
that are more difficult to resolve in an MR context. For example, a remote
user cannot manipulate a physical object. Because the object is integrated
into the interaction but not necessarily part of the system, maximum fidelity
cannot be reached without virtually modifying the real world.

Another special case we would like to address is multi-user systems. Here, we
find another added complexity when comparing interactions because the same
encounter or activity might be experienced differently. We recommend splitting
every comparison per person to avoid entangling the actions and perceptions of
users or the differing hardware available to the users. Each user’s
interaction with the system must be considered individually for insightful
analysis. This is especially important in asymmetric settings, such as in MR,
where users have different possibilities and restrictions in perceiving and
influencing the simulation.

###  7.8. Limitations

Inductively built on established HCI theory, no empirical evidence confirms
the structure of the IntFi Model. The model was reviewed, practically tested,
and critically discussed in interviews with 14 experts from the field, but it
has not been systematically evaluated with large numbers of users in the wild.
The most conclusive validation will be the community’s application of the
model in everyday research and development, which is yet to be seen. To
provide a universal structure, our model deliberately does not include media-
specific fidelity focuses such as narrative realism as described by Rogers et
al. (2022) or fidelity addressing the single senses (e.g., olfactory fidelity)
but rather provides a generic framework, in which all of these can be further
detailed. Depending on the use case, specific aspects can influence more than
one component, such as visual fidelity, which can affect all three components
of output fidelity. Another current limitation concerns the quantification of
fidelity, which would help assess and compare approaches. We currently apply
the approximate ranges of low, medium, high, and maximum fidelity similar to
previous research (McMahan et al., 2016; Gilbert, 2016; Lukosch et al., 2019)
and we regard numeric assessments with standardized metrics as an opportunity
for future research.

The IntFi Model is designed for examining interactions in the context of VR.
This does not necessarily involve a graphical 3D environment, multimodal
interfaces, a head-mounted display, or a self-representation of the user. The
model can be helpful in better understanding other human-computer interactions
or even non-computer-assisted technology that involves some simulation, e.g.,
ship navigation simulators or telemedicine interfaces. However, we emphasize
that not all definitions and concepts will fit perfectly. We encourage using
the IntFi Model wherever it can provide structure and guidance but advise
awareness of blurred lines of systems and realities that make identifying
correspondences difficult.

##  8\. Research Opportunities

Understanding the intricacies of building systems closely resembling the real
world or other reference frames requires systematic research. The conceptual
nature of the IntFi Model opens up foundational research avenues as it
considers the full scope of people’s interactions with virtual worlds and
illustrates relations of the underlying processes. While it can be interesting
to examine a component individually (e.g., action fidelity of a controller-
based system), it inevitably depends on others (e.g., on display fidelity due
to contact forces from holding the controller). The complexity of
interdependencies, impacts on other components, and methodological challenges
raise novel research questions for future work. Further, the model can inspire
new perspectives on optimization and methodology.

###  8.1. Relationships

##### Which components depend on others?

On the system side, there are close relations between the hardware components
and the software transmitting between them, e.g., for an output device, the
rendering software calculating the simulation output for a display to show
must be precisely matched with both attached components. The same applies to
the transfer functions translating between input devices and the computer. But
also, on the user side, we find a close connection between the sensory
information from the receptors and the brain processing and interpreting it.
Although it is helpful for scientific discourse and system development to
abstract the individual components, they cannot be regarded as independent.
Even components that are not consecutive in the loop show dependencies. For
instance, when assessing a system with eye tracking, the action fidelity of a
user’s gaze inevitably depends on rendering and display fidelity. It can only
be planned or evaluated together. But how do the components generally depend
on and influence each other?

##### Can one component compensate for another?

If one aspect of realism is constrained, can an increase of another fidelity
aspect make up for it? If so, can any other aspect or only a specific one? For
example, in a system without a haptic display for rendering forces, modifying
the control/display ratio can still induce a sense of kinesthetic forces
(Rietzler et al., 2018; Samad et al., 2019). As a consequence, there is higher
perceptual fidelity despite low display fidelity by deliberately lowering
transfer fidelity. Are there similar compensations that allow us to build
cost-effective and universal systems? Another example was given in subsection
7.6 concerning the exaggerated virtual walking speed when using a treadmill.
Due to the lack of kinetic cues from not moving forward, the limitations in
perceptual fidelity can be compensated by reducing transfer fidelity with a
higher speed gain (Nilsson et al., 2017). Similarly, if the head-mounted
display’s field of view is small, hence low display fidelity, a decrease in
rendering fidelity due to minifying the visual output can result in higher
experiential fidelity (Steinicke et al., 2011). Further, when using redirected
walking as a locomotion technique, action fidelity is reduced as the virtual
and real paths do not match due to physical restrictions in space. We can
compensate by adjusting transfer fidelity (Razzaque et al., 2001), simulation
fidelity (Suma et al., 2012), display fidelity (Auda et al., 2019), and
rendering fidelity (Langbehn et al., 2018) all to maintain perceptual
fidelity. Nilsson et al. (2017) suggest “that when limitations to a given
component of fidelity reduce or distort perceptual information, then sometimes
it may be possible to compensate by adjusting another component of
fidelity—even if the adjustment on the surface constitutes decrease in the
fidelity of the second component.” Future research might investigate what
other compensations should be considered in systems with restricted fidelity.

##### What components constitute experiential fidelity?

Every component ultimately influences experiential fidelity. Some components
are already well-understood through years of research, such as the aspects of
input fidelity. Other components still need more scholarly attention. Above
all, it requires further research to understand how strong the components’
impact on experiential fidelity is. We hypothesize that the influence varies
between components and cannot be reduced to a simple weighted sum of the
single components. Various neural phenomena will increase complexity, such as
superadditivity in multisensory integration, i.e., the effect that, for
example, visual and auditory stimuli give a stronger sensory impression
combined than when just adding up the individual impressions (Stanford and
Stein, 2007). It is still unclear whether the model’s components are
sufficient predictors for experiential fidelity. Other influences not
represented in the IntFi Model might affect perceived fidelity. For example,
Witmer and Singer (1998) integrate the meaningfulness of the experience in
their questionnaire factor realism. Consequently, a dilemma would arise about
how a meaningless experience from reality would be effectively simulated.

The interdisciplinary nature of this component calls for joint research,
especially including psychology and cognitive science. Unraveling how
experiential fidelity relates, depends, and affects related constructs, such
as presence, coherence, or user experience research, is a complex endeavor
that has already been embraced (Skarbez et al., 2017a), but must be pursued in
further detail—both theoretically and empirically. We suggest systematic
analyses with a study design similar to the experiment by Skarbez et al.
(2017b) on the influence of components of the plausibility illusion.

##### Which further patterns can be identified?

Beyond the potential patterns that we proposed in Subsection 7.1, we can seek
further patterns by systematically analyzing and linking empirical evidence
based on the IntFi Model’s structure. Similarly, most of our proposed patterns
need empirical validation.

###  8.2. Optimization

##### What benefits result from improving each fidelity component?

While it seems safe to assume that maximum fidelity has advantages for various
user experience metrics, several studies demonstrate how less-than-perfect
fidelity systems have considerable limitations in performance and preference
(Bowman et al., 2012). Since it is an immensely long way to the “ultimate
display” (Sutherland, 1965) indistinguishable from the real world, we
currently ought to focus our research efforts on interactions with medium to
high fidelity. Striving for maximum fidelity is costly and must be justified.
As outlined in the Uncanny-Valley Pattern in Subsection 7.1, medium-fidelity
interfaces can even result in a worse outcome than a low-fidelity
implementation. Bowman et al. (2012) hypothesized that hyper-natural
interaction techniques (or “magic” interactions) could potentially even exceed
the best possible performance of natural approaches. Yet, we need to recognize
where to deviate from faithful imitation profitably.

After decades of empirical research on VR interactions, a large body of
literature informs us about the effects of different levels of fidelity
(Rogers et al., 2022; Bowman et al., 2012; van Gisbergen et al., 2019; Ragan
et al., 2015; Grant, 2019; Bhargava et al., 2018; Lukosch et al., 2019).
Unfortunately, it is often not further differentiated what fidelity component
varies or how we can relate the findings in a broader picture. With the
model’s systematic and theoretical foundation to understand interaction
fidelity as an overarching concept, we can better connect the dots and
systematically design follow-up studies.

##### What are the natural limits for expedient system optimization?

Technically, we could indefinitely increase interaction fidelity by
reproducing reference interactions in ever greater detail, converging closer
and closer to maximum fidelity. However, human perceptual sensitivity and body
control are naturally limited, making further optimization futile. For
example, although screen resolution could be increased to the point where we
can display a grain of sand at the horizon, no user could ever tell the
difference as the retina’s resolution is limited. Similarly, the limited
precision of performing manual tasks makes more precise tracking obsolete.
Studies on such constraints in perception or body control can inform expedient
system design when systematically assessed, e.g., as has been demonstrated for
thresholds of redirected walking (Langbehn et al., 2018; Steinicke et al.,
2010), virtual hand offset (Benda et al., 2020; Zenner and Kruger, 2019),
latency for foveated rendering (Albert et al., 2017), or shape dissimilarity
for passive haptics (de Tinguy et al., 2019).

##### Should all components have a coherent level of fidelity?

The discrepancy in the levels of fidelity between different components can
result in low experiential fidelity, as discussed above, or bring
disadvantages in performance, preference, or other user experience aspects
(Bowman et al., 2012; McMahan et al., 2016). One could hypothesize that the
lower fidelity is in one component of the IntFi Model, the more other
components need to be enhanced to compensate. But it would also be reasonable
to assume that other components must be matched at the same level to give the
user a consistent impression. For example, in Mario Kart with cartoony visuals
and a comical setting, high-fidelity vehicle control and physics would seem
inappropriate, might decrease the perceived overall fidelity, and limit
performance. Instead, the transfer functions and car behavior that make
driving simple and error-tolerant lead to a coherent experience and arguably
higher experiential fidelity. Similarly, we hypothesize that a driving
simulator with sophisticated car physics and true-to-life input devices will
be experienced as most realistic if the sensory feedback matches the high
faithfulness and does not rely on cartoony visuals, funny sounds, or lacks
haptics. Systematic experimental comparisons might reveal how uniform
interactions should be designed.

###  8.3. Methodology

##### How can we quantify fidelity?

Ideally, every component of the IntFi Model would come with means of
quantification or a theoretically founded metric. Due to the scope of this
universal model and the depth of its conceivable subcomponents, achieving a
set of methods for quantifying all fidelity components comprehensively is a
considerable endeavor that the research community has worked on and will
arguably need to continue working on for decades. Al-Jundi and Tanbour (2022)
proposed five categories in their framework for evaluating some fidelity
aspects. The limitations of human sensory capabilities partially define the
maximum. The other classifications are not delimited clearly. It is
challenging to evaluate the moving target of rapidly evolving technology as it
requires either dynamic adjustment of the classification or prospective
universality. Ideally, a comprehensive framework would allow assigning numeric
values objectively, reproducibly, and universally. Looking at haptic fidelity
as an example, the Haptic Fidelity Framework by Muender et al. (2022) shows
how complex and manifold it can be to specify even one of the subcomponents of
display fidelity. This specialized framework identifies 14 factors defining
haptic fidelity along the categories sensing, hardware, and software. The
publication includes an expert tool to quantify each factor and calculate an
aggregated haptic fidelity score on a five-point Likert scale for technology-
agnostic comparison. Considering that this covers only one modality in one out
of eight fidelity components, it poses a significant research opportunity to
provide such sophisticated frameworks for all aspects of interaction fidelity.
Meanwhile, a validated questionnaire for users’ self-reports in studies on
haptic fidelity is still missing—leading to the following research
opportunity.

##### How can we measure each aspect?

Given we have means to describe all fidelity factors quantitatively, we need
to establish methods, standards, and instruments to measure it objectively and
subjectively. Gonçalves et al. (2021) recently presented a systematic
literature review on the methodology of 79 studies on VR realism. For
heuristic expert analysis, specialized frameworks, such as the FIFA (McMahan
et al., 2016), the Haptic Fidelity Framework(Muender et al., 2022), or the
Simulation Fidelity Rating Scale for flight simulators (Perfect et al., 2014)
can help investigate single (sub)components, as outlined before. Furthermore,
standards for technical evaluations are needed to compare devices, for
instance, regarding the physical similarity of generated sensory stimuli or
the accuracy of tracking devices. Validated instruments for psychometric
evaluations allow comparison between studies on perceptual fidelity. Case-
specific behavioral measures can improve our assessment of how realistically
users react but are difficult to standardize. For subjective assessments,
specialized and validated possibilities for self-reporting are essential
beyond broad subscales of presence questionnaires. We propose developing
dedicated tools to understand the perceived fidelity of the distinct interface
components instead of generalizing overall realism.

##### Which specialized tools are out there?

Gathering the already available resources on fidelity research would
significantly support the community. However, comprehensively collecting and
arranging all frameworks, instruments, questionnaires, etc., is challenging.
This work provides a first step, particularly with the overview in Table 2.
Beyond that, a systematic review is needed.

##### What should reporting guidelines include?

Another crucial challenge for interaction fidelity research is finding
standard reporting guidelines for fostering comparability and
generalizability. Currently, relevant information about the system design is
often missing to understand evaluation results and apply meta-analyses
comprehensively. The IntFi Model might serve as a starting point to agree on
reporting guidelines ensuring all system components that define interaction
fidelity as a holistic concept are being reported. Because of the
interdependencies of the single aspects, we encourage researchers to report
details about the interactions beyond the element of interest.

##  9\. Conclusion

To understand what makes interactions in VR simulations more or less faithful
to the real world or any other reference frame, we must distinguish between
various aspects of fidelity. In this article, we proposed the Interaction
Fidelity Model (IntFi Model) that allows analyzing how closely a virtual
interaction corresponds to the original along various factors. We define eight
fidelity components along the HCI loop: action, detection, transfer,
simulation, rendering, display, perceptual, and experiential fidelity.

The consequent terminology offered in this work supports precise communication
and consistency across publications in the field. With a clear structure,
rigorous explanations, practical examples, and a guideline with best
practices, we demonstrate how VR professionals in research and development can
use the conceptual model to describe, understand, compare, hypothesize, and
teach. With its theoretically grounded simplicity, the IntFi Model can be
universally applied to any VR experience. Therefore, our taxonomy defines only
twelve general fidelity terms, as listed in Table 1. Beyond that, this article
serves as a signpost referring readers to previous publications with
specialized frameworks or concepts, as each component can be further
distinguished in more detail.

The presented model underwent rigorous, critical discussions and was refined
iteratively. For validation, we conducted 14 extensive interviews with experts
from academia and the industry to review and test the model from different
perspectives. The thematic analysis showed criticism of the concept, the
terminology, and applications, identified application strategies, and outlined
various benefits and use cases of the model. All experts found it interesting
and helpful. As suggested in the interviews, we provide educational material
as part of the supplemental material, including modern posters and a slide
deck, which are free to use and adapt.

From our practical experiences with the model, we identified common patterns
that might be prevalent in various use cases and interaction techniques. By
connecting the dots in such a way, the IntFi Model will support finding
similarities in study results and see their findings in a bigger picture.
Using the model to think about the fidelity of VR interactions opens up
promising opportunities for systematic and targeted research. We hope to
inspire new directions in research for a better understanding of interactions
in VR.

###### Acknowledgements.

We are grateful to the experts for sharing their perspectives and criticism in
the interviews. We thank Yvonne Rogers, Don Norman, and Katja Rogers for
helping shape this work, and Laura Canil for realizing the visual designs of
Figures 1 and 3.

### Funding

This research was partially funded by the Klaus Tschira Stiftung.

### Conflicts of Interest

The authors report there are no competing interests to declare.

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##  Appendix A Appendix: Educational Material

As part of this publication, we will provide educational material as
supplemental material. It will be distributed under the Creative Commons BY
4.0 license (https://creativecommons.org/licenses/by/4.0). It can then be
shared, adapted, and printed as long as the original publication and authors
are appropriately cited, and any modifications are indicated. Figures 8, 9,
10, and 11 preview the files that will be made available upon acceptance of
the peer-reviewed article.

We will further share a template of the Correspondence Figure as used in
Figures 2, 5, 6, and 7, which compares a reference interaction to an
implementation in VR. It can be adapted in other publications and will be
available as PNG and PSD (Adobe Photoshop) files.

Figure 8. A preview of the large ISO A0 portrait poster. It will be
distributed in the highest printing quality after peer reviewing.

A dark poster conveying the central concepts of this work.

Figure 9. A preview of the ISO A1 landscape poster. It will be distributed in
the highest printing quality after peer reviewing.

A dark poster with the central loop from Figure 1.

Figure 10. A preview of the slide deck outlining the central concepts of this
work. The editable PPTX file will be distributed after peer reviewing.

The title slide and a preview of the upcoming slides on the side.

Figure 11. The template of the Correspondence Figure in which other systems
can be inserted. The high-resolution PNG and an editable PSD file will be
distributed after peer reviewing.

Boxes with space for the reference interaction, input/output devices, and the
virtual environment with an arrow in between.