# The Interaction Fidelity Model: A Taxonomy to Distinguish the Aspects of
Fidelity in Virtual Reality

Michael Bonfert<EMAIL_ADDRESS>0000-0002-3605-6693 University of
BremenBibliothekstraße 1Germany28359 ,  Thomas Muender<EMAIL_ADDRESS>0000-0002-0606-9258 University of BremenBibliothekstraße 1Germany28359 ,  Ryan
P. McMahan<EMAIL_ADDRESS>0000-0001-9357-9696 University of Central Florida4328
Scorpius StreetUSA32816-2362 ,  Frank Steinicke  frank.steinicke@uni-
hamburg.de 0000-0001-9879-7414 Universität HamburgVogt-Kölln-Str.
30Germany22527 ,  Doug Bowman<EMAIL_ADDRESS>0000-0003-0491-5067 Virginia
TechUSA24061 ,  Rainer Malaka<EMAIL_ADDRESS>0000-0001-6463-4828
University of BremenBibliothekstraße 1Germany28359 and  Tanja Döring
<EMAIL_ADDRESS>0000-0001-8648-340X University of
BremenBibliothekstraße 1Germany28359

(2024)

###### Abstract.

Fidelity describes how closely a replication resembles the original. It can be
helpful to analyze how faithful interactions in virtual reality (VR) are to a
reference interaction. In prior research, fidelity has been restricted to the
simulation of reality—also called realism. Our definition includes other
reference interactions, such as superpowers or fiction. Interaction fidelity
is a multilayered concept. Unfortunately, different aspects of fidelity have
either not been distinguished in scientific discourse or referred to with
inconsistent terminology. Therefore, we present the Interaction Fidelity Model
(IntFi Model). Based on the human-computer interaction loop, it systematically
covers all stages of VR interactions. The conceptual model establishes a clear
structure and precise definitions of eight distinct components. It was
reviewed through interviews with fourteen VR experts. We provide guidelines,
diverse examples, and educational material to universally apply the IntFi
Model to any VR experience. We identify common patterns and propose
foundational research opportunities.

VR, fidelity, realism, theory, framework, HCI, input, simulation, output

††copyright: acmcopyright††journalyear: 2024††doi:
XXXXXXX.XXXXXXX††conference: XXXX; 2024; XXXX††price: 15.00††isbn:
978-1-4503-XXXX-X/18/06††ccs: Human-centered computing HCI theory, concepts
and models††ccs: Human-centered computing Virtual reality††ccs: Computing
methodologies Virtual reality

This is a preprint that has not yet been published in a peer-reviewed journal.
The manuscript is currently under review.
Current version of figures, definitions, and supplemental material: v2.1

##  1\. Introduction

Realism in virtual reality (VR) is pursued intensely in research and
development (Rogers et al., 2022; Kapralos et al., 2014; Bowman et al., 2012;
van Gisbergen et al., 2019; Ragan et al., 2015; Grant, 2019; Witmer and
Singer, 1998). While the concept of realism—how closely a simulation resembles
reality—may initially seem straightforward, it is a complex, multi-faceted
construct. We quickly assess something as realistic or unrealistic, be it a
painting, the behavior of a movie character, a synthetic voice, or a virtual
world. However, this intuitive judgment is insufficient for a comprehensive
understanding of and reasoning why something is perceived as more or less
realistic, especially in such a complex domain as VR, where countless factors
might influence the outcome. To purposefully design virtual experiences that
are convincingly realistic, it is essential to untangle the different aspects
that impact the overall realism.

Figure 1. The Interaction Fidelity Model differentiates between eight fidelity
components that affect any VR interaction. The model is based on the
Human–Computer Interaction Loop (Reenskaug, 1979; Norman and Draper, 1986) and
extends a previous framework by McMahan et al. (2016).

An illustration of the Interaction Fidelity Model. It is a circular
arrangement of the following items in clockwise order starting at the top
right: User actions and action fidelity, input devices and detection fidelity,
transfer functions and transfer fidelity, the computer and simulation
fidelity, rendering software and rendering fidelity, output devices and
display fidelity, perception of sensory stimuli and perceptual fidelity, as
well as the user and experiential fidelity. The components on the right side
of the loop can be grouped as input fidelity and on the left side as output
fidelity.

VR technology can create immersive experiences of being in and interacting
with simulated realities. By interacting with the VR system, a user can
perceive and affect the virtual environment (VE), while the system can sense
and react to user input. As in the real world, users and their environments
can mutually influence each other. For many VR applications, realism is a
decisive quality metric. The true-to-life resemblance is essential for skill
training (e.g., surgery (Chheang et al., 2019)), learning abilities (e.g.,
sports climbing (Schulz et al., 2019), vocational education like public
speaking (Poeschl, 2017), music (Serafin et al., 2017)), entertainment (e.g.,
traveling the world (Sarkady et al., 2021)), therapy (e.g., fear of heights
(Freeman et al., 2018)), or use cases that would be expensive or impossible
without VR (e.g., visiting Mars (Holt, 2023)). In these scenarios, the success
of the simulation depends on how closely the equivalent from reality can be
reproduced. Even in fictional scenarios, certain aspects of the interaction
might need to be grounded in reality (e.g., Euclidean geometry, spatial audio,
swarm behavior, gravity, or the color space perceptible by humans). However,
the concept of realism is limited to matching the real world and, therefore,
cannot be applied to VR use cases simulating aspects impossible in reality.

Figure 2. Two examples of reference interactions that are virtually replicated
in VR simulations. (top) Here, an interaction from the real world is
reproduced. Picking an apple from a tree is a complex activity to be simulated
virtually as the user’s actions, the provided feedback, and the physical
simulation are closely coupled. This is a recent implementation with a haptic
device by de Tinguy et al. (2020) which dynamically moves the sphere proxy
into the user’s hand when grasping. (bottom) Here, a fictional interaction is
reproduced. The mythological magic carpet from Middle Eastern literature can
be realized as a VR locomotion technique in many different ways (Medeiros et
al., 2020). This is an early implementation by Pausch et al. (1996). Copyright
of the top-right images by (de Tinguy et al., 2020) and of the bottom-right
images by (Pausch et al., 1996) (modified by the authors). The images for the
reference interactions have been generated with Midjourney.

In the first row, the reference interaction of picking an apple is compared
with a VR implementation. The first image is a photograph of a hand grasping
and pulling down an apple. The second image shows the input/output devices
where a device is mounted to the user’s wrist with a Vive tracker and a sphere
proxy approaching the palm. The third image of the virtual environment shows a
detailed rendering of a virtual hand grasping for an apple. In the second row,
the reference is a sketch of the concept of a flying rug with a person sitting
on it flying over a mountain landscape. The depicted I/O devices are a
bicycle-like construction on which a user with an HMD sits. The virtual
environment shows a 3D-rendered scene resembling a Middle Eastern cityscape
with dome-shaped towers, and in the foreground, there is a patterned flying
carpet with two cartoony white hands holding the sides.

###  1.1. The Concept of Fidelity

More generally speaking, the degree of how accurately an original is
reproduced is called fidelity (Merriam-Webster, 2024). Every VR simulation
recreates some reference, be it a real-life situation, a training scenario, a
fictional world, or a designer’s imagination. From the Latin term fidēlis for
“faithful,” fidelity describes how faithful something is to its original. When
simulating reality, this degree of correspondence is called realism. Thus,
realism is a specific form of fidelity. Therefore, a VR application can have
low realism yet high fidelity to a reference frame other than the real world.
For example, the swords in Beat Saber have high fidelity to lightsabers from
Star Wars but are unrealistic. Comparing simulations more generally to
reference systems than only to the real world has been suggested by Raser
(1969) in 1969, and we adopt this notion for more universal applicability.
Figure 2 compares reference interactions and their simulated VR interactions
in two examples. On the top, a real-life activity is reproduced virtually:
picking an apple from a tree. This has been investigated with haptic
interfaces of different realism (Kovacs et al., 2020; de Tinguy et al., 2020).
On the bottom, the fantasy concept of flying with a magic carpet is adapted as
a locomotion technique (Pausch et al., 1996). This metaphor affords a large
design space of possible realizations with varying interaction fidelity in VR
(Medeiros et al., 2020). The terms fidelity, realism, and naturalness are
often used universally in scientific literature without detailing which aspect
is referred to. If a specific aspect of fidelity is mentioned, a clear
definition is often missing (Rogers et al., 2022). As a result, the terms and
definitions within the VR literature have been inconsistent and contradictory,
as illustrated in Section 2.

While the community’s research efforts have led to useful definitions, models,
and frameworks (see, e.g., Table 2), these mainly focus on dedicated aspects
of fidelity, differ in their use of terms, and therefore do not provide a
comprehensive understanding. This makes it harder to establish links between
individual discoveries, generalize the results, and synthesize fundamental
principles. Thus, this research aims to provide an umbrella framework that
conflates existing findings on different aspects of fidelity into one
comprehensive and consistent model.

###  1.2. Introducing the Interaction Fidelity Model

Therefore, we present the Interaction Fidelity Model (IntFi Model). This
conceptual model distinguishes the different aspects of fidelity inherent in
all VR interactions. The IntFi Model considers not only the system’s fidelity
but also the fidelity of interactions between the user and the system because
of their reciprocal relationship. Beyond physical and functional simulation of
a virtual environment in the form of bits and bytes, VR technology requires
accounting for how the user’s body affects the virtual world and how output
devices can generate physical stimuli. This makes a holistic integration of
all elements of embodied 3D interactions imperative. Therefore, the IntFi
Model is based on the human-computer interaction (HCI) loop (Norman and
Draper, 1986), a well-established design principle that breaks down how a user
and a system perceive and influence each other. For this, one aspect of
fidelity is assigned to each of the eight stages of the HCI loop, as
illustrated in Figure 1. As a result, the IntFi Model consists of eight
distinct fidelity components: (i) action, (ii) detection, (iii) transfer, (iv)
simulation, (v) rendering, (vi) display, (vii) perceptual, and (viii)
experiential fidelity.

Building on prior work, the proposed model establishes a clear structure of
the fidelity components with consistent terminology, precise definitions,
detailed explanations, and illustrative examples in Section 3. This paper
serves as a signpost by referring to more specialized frameworks and models
detailing single components beyond the scope of this work. The IntFi Model can
also help set a rigorous research agenda to advance purposeful measurement
methods, determine factors contributing to fidelity, and understand the
interdependence of the individual components. The IntFi Model can inform the
VR community on how to focus its efforts to achieve a broad comprehension of
realistic interactions. Beyond demonstrating how realistic a simulation is or
how its fidelity differs from another, the model’s theoretical foundation
allows us to understand why (Wobbrock and Kientz, 2016). Hence, theory-driven
study designs facilitate more generalizable evaluation results and make
linking them to other research easier.

The next section summarizes previous approaches, terms, and conceptualizations
of fidelity and related constructs. We then present the IntFi Model in detail
in Section 3. The subsequent section demonstrates the model’s application with
three example use cases. In Section 5, we describe the validation process and
report the findings from 14 semi-structured, one-hour interviews with VR
experts from research and the industry. In Section 6.1, we share best
practices for applying the model, explain how it can serve as different lenses
through which VR interactions can be viewed, and caution against common
application traps. Section 7 provides a discussion of typical fidelity
patterns, practical and theoretical implications, and limitations of the
proposed model. Lastly, we propose an abundant research agenda that the IntFi
Model opens up and can inspire future work in our field in Section 8.

##  2\. Related Work

As soon as users enter a VE, they interact with the simulation. Within the
scope of this paper, we consider interaction as a reciprocal exchange of a
user and a computer system observing and reacting to each other through
actions and states. The actions taken by users and the output generated by a
computer depend on each other and together form the interaction. Therefore,
interactions with a computer system cannot be attributed solely to humans or
computers. The two must be considered together (Hornbæk and Oulasvirta, 2017).
The two-sided behavior happens simultaneously, continuously, inseparably, and
inevitably, similar to a person affecting and being affected by the world
around them in reality.

Although this exchange simultaneously occurs in both directions, it can be
helpful to think about the interaction as a circular sequence of steps for
conceptually distinguishing them. A software design framework that describes
this circular process is the model-view-controller (MVC) pattern introduced by
Reenskaug (1979) in 1979. This software architecture pattern became one of the
most influential for describing and developing user interfaces. We illustrate
the process in Figure 3. A design principle that describes a similar process
with HCI-specific labels is what we call the human-computer interaction loop.
Forming the inner circle in Figure 1, the HCI loop links the user, the input
devices, the computer, and the output devices with transitions between these
elements. This model can be traced back to a chapter by David Owen in “User
Centered System Design” (p. 368) edited by Don Norman and Stephen Draper
(Norman and Draper, 1986). In the same book, Norman describes a similar
process from a cognitive perspective of how users must overcome the Gulfs of
Execution and Evaluation. He argues that the user continuously evaluates the
current system state and plans actions to accomplish a specific goal—input and
feedback. Numerous frameworks and textbooks adapted the loop for different
purposes and specializations, making it an established tool in HCI research
(Abowd and Beale, 1991; McMahan et al., 2016; Palanque et al., 2020; Jr.
LaViola et al., 2017; Williamson et al., 2009; McMahan, 2018). Combined, the
states and transitions of the HCI loop form the eight components of the
proposed IntFi Model, and the two gulfs correspond to the grouping of input
into and output from the system.

Figure 3. The Model-View-Controller pattern (Reenskaug, 1979) from 1979 on
which the IntFi Model is conceptually based: A user uses a controller to
manipulate the model, which updates the view and is then seen by the user.

An illustration of the Model-View-Controller pattern. It is a circular
arrangement that presents the following relations: A user uses a controller to
manipulate the model, which updates the view and is then seen by the user. The
items model, view, and controller represent the system.

###  2.1. Understanding Interaction Fidelity

In the context of VR, the interaction with a system has the purpose for the
user to experience and influence a simulated reality. As outlined in the
introduction, the level of fidelity plays a vital role in many VR simulations.
Several frameworks, models, and evaluations have investigated various aspects
of interaction fidelity. Please note that we refer to the original terms from
cited works instead of the IntFi Model’s terminology in this subsection. As a
result, they might appear confusing and contradictory.

Most prominently, McMahan (2011) proposed the Framework for Interaction
Fidelity Analysis (FIFA) that was released in an updated version in 2016
(McMahan et al., 2016). The revised FIFA considers the three categories
biomechanical symmetry describing the reproduction of body movements from the
real world, input veracity, which considers the exactness of input devices
capturing movements, and control symmetry, which covers the exactness of
control in the virtual world compared to the real world. Each category
comprises further detailed components. The framework is designed to compare
the user’s motions during virtual activities that involve body movements, such
as techniques for locomotion or object manipulation, to their counterpart in
reality. Results from their user studies suggest an uncanny valley of VR
interactions. They found good user performance with low- and high-fidelity
systems but a drop in performance with medium-fidelity systems. Similar
findings have been presented in further studies (Bhargava et al., 2018;
Nabiyouni et al., 2015). In other investigations, Bowman et al. (2012) also
found that high-fidelity interactions can enhance performance and the overall
user experience, but medium levels of fidelity can be unfamiliar and
detrimental to performance.

The FIFA framework, however, only considers the fidelity of user actions and,
therefore, only the input side of the two-way interaction. The system output
is neglected in this framework even though it is an inseparable part of the
interaction and can heavily influence the realism of a system. McMahan et al.
(2012) acknowledge the missing output component by also analyzing the display
fidelity and finding a similar negative effect for medium display fidelity as
for their interaction fidelity. Nilsson et al. (2017) build on this finding
and argue that when some fidelity components are limited, maximizing the
fidelity of other components may be detrimental to the perceived realism.
Therefore, decreased fidelity might positively influence perceived realism in
some instances. In addition, Abtahi et al. (2022) show that even for
interactions that go beyond reality, some aspects of the interaction should
still be grounded in the real world to avoid sensory conflicts in the user.

For evaluating the fidelity of a VR simulation, Stoffregen et al. (2003)
consider the action fidelity as the relationship between performance in the
simulator and performance in the simulated system, the system’s output in the
form of optic, acoustic, mechanical, and inertial arrays, as well as the
experiential fidelity in the form of perceived presence (Skarbez et al.,
2017a). With a focus on more practical aspects of current VR systems, Al-Jundi
and Tanbour (2022) present a framework for evaluating fidelity concerning four
interrelated elements: digital sensory system fidelity, interaction system
fidelity, simulation system fidelity, and integration among these aspects to
produce high-fidelity virtual experiences. They identify various factors for
evaluating VR hardware regarding visual, auditory, and haptic feedback, the
tracking system, and graphic quality. Conversely to the FIFA framework, this
framework focuses on the output side of interactions and neglects the user
actions as part of the interaction with the VR system. The fidelity of haptic
feedback can also be assessed in more detail with the Haptic Fidelity
Framework by Muender et al. (2022), providing detailed factors to analyze and
quantify aspects of sensing, hardware, and software.

In the context of gaming, Rogers et al. (2019) evaluated interaction fidelity
for object manipulation and whole-body movements and found that high fidelity
is preferred for object manipulation. Still, moderate fidelity can suffice for
whole-body movements as there is a trade-off between fidelity, usability, and
social factors. Further, Rogers et al. (2022) provide an in-depth analysis of
realism in digital games, including a focus on VR. The authors present a two-
part framework of realism dimensions consisting of a hierarchical taxonomy of
realism dimensions and the mapping of realism dimensions within Adams’ game
model (Adams, 2014). Alexander et al. (2005) investigated the effect of
fidelity on the transfer of knowledge from games and simulations to the real
world. They argue that the fidelity of a simulation is a significant factor in
enabling skill transfer and define three categories of fidelity: Physical
fidelity is the degree to which the simulation looks, sounds, and feels like
the real world; functional fidelity is the degree to which the simulation acts
like the real world; and psychological fidelity is the degree to which the
simulation replicates the psychological factors (e.g., stress, fear)
experienced in the real world.

The Reality-Based Interaction framework by Jacob et al. (2008) provides four
themes to enable high-fidelity interactions on a more general level with
interfaces such as touchscreens, tangibles, and VR. Interaction designers
should consider naïve physics, body awareness and skills, environment
awareness and skills, and social awareness and skills. The work outlines
trade-offs between realism and expressiveness, efficiency, versatility,
ergonomics, accessibility, and practicality. In contrast to most other
frameworks that cover input and output components of the interaction, Lindeman
and Beckhaus (2009a) focus on experiential fidelity, enhancing the realism of
the user experience by guiding the user’s frame of mind in a way that their
expectations, attitude, and attention are aligned with the VR experience.

###  2.2. Inconsistent Fidelity Terminology

To this point, we have adhered to the terminology originally used in the
mentioned works. The literature established a patchwork of different but
similar terms based on different interpretations and assumptions. This is why
the wording of the above explanations might sound inconsistent and
contradictory. It demonstrates how critical uniform designations are for
research communication.

The terms fidelity, realism, and naturalness were often used synonymously in
previous literature. Researchers often investigated only a specific part of
interaction fidelity but referred to it universally as (interaction) fidelity.
For example, the term interaction fidelity has been used to refer to visual
render quality (Mania et al., 2006), camera views and gravity (Bhargava et
al., 2018), or dialogue capabilities (Carnell et al., 2022). Some publications
refer only to the user’s system input with it (McMahan et al., 2016; Bowman et
al., 2012). This neglects half of the two-way interaction between the user and
the system, which can only be considered in its reciprocal dependence, as
outlined at the beginning of this section. Also, the literature generally
refers to other individual aspects of the interaction as fidelity. For
example, some fidelity conceptions focus on the simulated virtual environment,
such as in game research (Lukosch et al., 2019; Alexander et al., 2005), or
are reduced to the simulation’s physical and functional dimensions (Hays and
Singer, 1989; Harteveld, 2011), while it is crucial for VR and 3D interfaces
also to consider the means of input and output as well as the user’s role. A
recent framework classified the fidelity of mixed-reality prototyping (Cox et
al., 2022). Furthermore, outside computer science, fidelity has been narrowly
defined within the fields’ contexts, such as in health and psychology
regarding realistic psycho-behavioural and affective responses (Harris et al.,
2020; Arthur et al., 2023). These examples illustrate how divided the VR
community has been about the term’s understanding and usage.

In a systematic review of the concepts of realism and fidelity for digital
games, Rogers et al. (2022) found a “substantial potential for confusion given
the overlapping and contradictory use of realism types.” The authors report
that the type of realism is often not even further defined but remains vague
in the literature. The rigorous analysis covers VR research as part of gaming
but excludes the realism of other VR interactions and the fidelity compared to
other reference frames. Nevertheless, the survey outlines the vast range of
terms used to describe aspects of realism and fidelity. This emphasizes the
urgent need for a theoretical basis of consistent terminology. Plenty of
research contributes to the understanding of the multidimensional concept of
fidelity. Still, it lacks an umbrella model into which the individual elements
can be integrated to understand the bigger picture. We will consequently use
the IntFi Model’s terminology for the remainder of this paper.

Figure 4. The fidelity spectrum with approximate ranges from low, medium, and
high to maximum fidelity with an example use case: implementations with
different fidelity levels of somebody picking an apple from a tree. The
reference interaction from the real world on the right side is defined as
maximum fidelity. Copyright of the “High Fidelity” images by de Tinguy et al.
(2020). The other images have been photographed or generated with Midjourney
by the authors.

An arrow is labeled with the ranges low, medium, and high fidelity with
maximum fidelity at the end. Aligned with these ranges, example
implementations of a person picking an apple from a tree with the respective
fidelity range are presented. In the low-fidelity example, a user selects the
apple with a computer mouse, indicated with a crosshair in the low-poly
virtual environment. In the medium-fidelity example, the user grasps the apple
with a hand-held Valve Index controller, represented by a low-poly hand avatar
in the virtual environment. The high-fidelity example shows the system
Weatavix by de Tinguy et al. (2020). Here, the user grasps a dynamic, wrist-
mounted sphere proxy providing passive haptic feedback, represented with
detailed visuals in the virtual environment. On the right side, maximum
fidelity is illustrated with the reference interaction from reality: a person
picking an apple from a tree in the real world.

##  3\. Model of Interaction Fidelity

The conceptual model presented here distinguishes various aspects of the
fidelity of interactions in VR. It covers the entire process of a user
interacting with a VR system, from user input over system processing to output
from the system experienced by the user. Instead of assessing the contribution
of each device or system component to the fidelity of the interaction, we
propose distinguishing between the stages of the interaction to systematically
evaluate how true it is to the original. The IntFi Model is based on the HCI
loop (Norman and Draper, 1986), which originates from the model-view-
controller pattern described in Section 2. Following the structure of the
loop, the model assigns one aspect of fidelity (for example, display fidelity)
to one stage of the loop (in this example, output devices), as illustrated in
Figure 1. The loop offers simplicity, yet all fidelity aspects of any
conceivable interaction are integrated. Therefore, it is a sound foundation
for the intuitive differentiation of factors that define the fidelity of any
VR interaction with the user in mind.

Based on the Merriam-Webster Dictionary (Merriam-Webster, 2024), McMahan
(2011), Alexander et al. (2005), and Raser (1969), we consider interaction
fidelity as the degree of exactness with which reference interactions are
reproduced. Thus, it describes how closely a user’s interactions with a VR
system resemble the interactions from a reference system. This reference
system can be the real world, in which case we refer to realism, but we can
also choose any other reference interaction, such as fictional worlds (e.g.,
Star Wars), hyper-realistic interaction techniques (e.g., the Go-Go technique
(Poupyrev et al., 1996)), a previous VR system, a planned system iteration, or
a replicated study. It is important to clearly define the chosen reference
interaction for a meaningful and unambiguous fidelity assessment. If the
reference is changed to make another comparison, the assessed fidelity will
also change.

We can describe the level of fidelity on a spectrum covering low, medium, and
high to maximum fidelity as illustrated in Figure 4. With maximum fidelity,
there is theoretically a perfect correspondence to the original, even if it
might be technologically impossible to achieve. Between perfect and no
correspondence, there is a continuum (Bowman and McMahan, 2007; Bhargava et
al., 2018; Lukosch et al., 2019; Cox et al., 2022) without clear-cut “low”,
“medium”, or “high” states. This wording demonstrates a relative difference or
approximate range on the continuum.

It is crucial to keep in mind that fidelity is an objective concept simply
describing the degree of correspondence without judgment. Higher interaction
fidelity is not necessarily better, more desirable, more effective, or more
immersive but merely implies a closer match to the reference. Although higher
fidelity can have benefits for other metrics or goals, it has also been shown
how lower-fidelity and hyper-natural interactions can be beneficial (McMahan
et al., 2010; Feinstein and Cannon, 2001; Hays et al., 1992; Dewitz et al.,
2023; Nabiyouni et al., 2015; McVeigh-Schultz and Isbister, 2021). On the
other hand, aspects of fidelity often determine the success of a simulation.
For instance, in motor skill learning, the faithfulness of the user’s
movements is crucial. Likewise, authentic scenic details are the key aspect of
a travel simulation, accurate haptic feedback during surgical training, and
plausible situations for phobia therapy. As Section 7 outlines, objective
system fidelity does not necessarily correlate with perceived realism. For
effective and economical planning, interaction designers and VR developers
must reflect on what kind of fidelity is important for the use case.

All aspects of fidelity can be assessed objectively and subjectively depending
on the point of view. When applying standardized metrics for reproducible,
indisputable measures to describe fidelity, we can objectively determine and
verify the exactness of the interaction’s match with the reference
interaction. For example, we can impartially compare two screens regarding
their technical specifications, such as pixel density. When relying on
personal impressions from interviews or questionnaires, we can subjectively
assess fidelity. For example, we can ask a user in a questionnaire how closely
the hand movements in a VR juggling training experience match juggling in
reality. Some fidelity aspects can be assessed objectively in a more
meaningful way, such as system specifications (e.g., screen resolution) or
historical facts (e.g., the 1988 ACM Turing Award recipient) with verifiable
ground truth. For other aspects, on the other hand, it might make sense to
assess them subjectively, such as perception or experience. Currently, we do
not have the means to determine every aspect objectively. This might be
technically possible in the distant future, even for experiential fidelity,
with sufficiently sophisticated brain–computer interfaces.

One could argue that users’ perceptions and experiences are inherently
subjective because they vary between individuals. However, while they are
different between users, we can assess perceptual and experiential fidelity
individually: How would the same person probably perceive and experience the
reference interaction? Because systems are usually not tailored to
individuals, typical or average users from a target group can be considered
for better generalizability. For this pragmatic reason, we advocate for a
population-centric assessment through user-centric research. Some fidelity
aspects can be assessed independently (e.g., rendering fidelity), but
especially for the user-related aspects (i.e., perceptual, experiential, and
action fidelity), the target users’ abilities and characteristics must be
considered to provide accessible systems acknowledging the diversity of users.
For example, people with color vision deficiency perceive the same visual
output differently, which might affect how closely it resembles their real-
life perception. Similarly, target populations can experience a system’s
fidelity differently, for example, depending on their expertise and how
competent they feel in a virtual experience compared to their real-world
competence. For example, experienced soccer players feel more restricted than
novices in virtual kicking with medium simulation fidelity (Bonfert et al.,
2022). In discourse, we must keep in mind that we all perceive the world
subjectively and create a mental model of how it works (Norman, 1989). While
people can have different perspectives on theoretical ground truth and be
challenged in their view, we need to agree in discussions on an explicit
reference interaction that is supposed to be simulated and target user groups
for meaningfully applying the term fidelity.

###  3.1. Development of the Model

We have devised the idea for the IntFi Model when gathering different aspects
of fidelity from the literature and testing possible classifications to bring
structure to the concept. When cross-referencing the first approaches with
related work, we realized that any dimension fits neatly into the HCI loop. In
an iterative process, we refined the labels and definitions of the components
from discussions among the authors, with research peers, in teaching practice,
and at conferences. We conducted semi-structured expert interviews with 14 VR
researchers and practitioners to improve and validate the model. We present
the method and results in detail in Section 5.

###  3.2. Structure of the Model

The model consists of the user and the VR system, which includes input
devices, the computer as the processing unit with data and models, and output
devices. Between these components, Figure 1 uses arrows indicating a
translation from software to hardware (such as the rendering from the
simulation to the output devices) or, vice versa, from physical to intangible
information (such as from the system output to the user’s mind through
perception). Every aspect of interaction fidelity corresponds to one stage of
the HCI loop and is, thus, represented by one component in the model, as
visualized in Figure 1. For example, the fidelity with which the system
detects the user actions corresponds to the input devices in the HCI loop,
which is the controller in the MVC paradigm and is linked to the detection
fidelity in this model. The single components of the model are further
detailed in this section.

Table 1. The definitions of the fidelity components and the categorizing sets of components. |  Aspect |  is defined as the degree of exactness with which… |  Depends on
---|---|---|---
|  Action Fidelity |  user actions resemble those of the reference interaction. |  User
|  Detection Fidelity |  input devices detect the user’s actions. |  System
|  Transfer Fidelity |  virtual actions, derived from the input measurements, resemble the user’s actions of the reference interaction. |  System
|  Simulation Fidelity |  a virtual environment resembles the characteristics of the reference interaction’s world and adequately reacts to the user’s actions. |  System
|  Rendering Fidelity |  the output content generated by the computer resembles what would be presented to the user in the reference interaction. |  System
|  Display Fidelity |  the output devices reproduce the physical stimuli presented to the user in the reference interaction. |  System
|  Perceptual Fidelity |  the user’s perception of the physical stimuli created by the system resembles how the user would perceive the reference interaction. |  User
|  Experiential Fidelity |  the user’s experience of the simulated interaction resembles how the user would experience the reference interaction. |  User
|  Set of aspects |  is defined as the degree of exactness with which… |  Includes
|  Input Fidelity |  the virtual actions generated from the user’s input resemble the user actions of the reference interaction. |  Action, Detection, Transfer
|  Output Fidelity |  the system output is generated and perceived as the user would perceive it in the reference interaction. |  Rendering, Display, Perceptual
|  System Fidelity |  the system reproduces the world of the reference interaction reacting to the user. |  Detection, Transfer, Simulation, Rendering, Display
|
Interaction Fidelity
|  reference interactions are reproduced. |  All aspects

We can consider the model vertically and distinguish aspects of input fidelity
(right side) and output fidelity (left side). We can also consider the model
horizontally and distinguish aspects of fidelity that concern the user with
their perceptions, experiences, and actions (upper part) and those concerning
system fidelity with the detection, transfer, simulation, rendering, and
displays (lower part). Input fidelity in this model is close to what McMahan
(2011) described as interaction fidelity in the Framework for Interaction
Fidelity Analysis (FIFA) and comprises similar components: action, detection,
and transfer fidelity. All further components were not considered in FIFA.

Aspects of fidelity that determine the characteristics of the VE and react to
the user input are included in the component simulation fidelity. Proceeding
in the loop, the group of output fidelity components comprises rendering
fidelity, display fidelity, and sensory fidelity. Finally, all these aspects
combined determine experiential fidelity, the impression created in the user’s
mind. In the IntFi Model, we focus on the endpoints of the single components
instead of elaborating on the technical processes behind each component. For
example, detection fidelity is determined by the final output of the input
device’s API, not by the sensor’s firmware or signal processing. The single
components can be broken down further as needed (e.g., interaction fidelity →
simulation fidelity → presentational fidelity → 3D model → skin texture →
height map → resolution). In the scope of this paper, we will only detail
conceivable subcomponents through examples, not comprehensively, except for
simulation fidelity. As a whole, all aspects of the model define overall
interaction fidelity. In the following, the components are explained in detail
following this structure: We define the fidelity aspect, distinguish it from
the subsequent aspect, detail its characteristics, state its requirements for
maximum fidelity, illustrate how different levels of fidelity could be
designed, and refer to specialized frameworks or similar definitions.

####  3.2.1. Action Fidelity

Action fidelity is the degree of exactness with which user actions resemble
those of the reference interaction. In the context of this model, we consider
actions as any active behavior or passive state of the user, including sheer
existence. In contrast to the next component, detection fidelity, it is
irrelevant for action fidelity whether the system captures the user actions.

This component comprises all behavior and states of the user, including body
movements, such as grasping or walking, and any other modality and activity,
such as speaking, eye gaze, facial expressions, or brain activities. It is
crucial to consider the full range of user actions. Not only intentional
actions (e.g., gestures) are relevant for action fidelity but also
subconscious (e.g., blinking), uncontrolled (e.g., blushing, swelling),
involuntary (e.g., tremor), passive (e.g., static poses), and unaware actions
(e.g., body temperature).

In the example of grasping an object, the user of a low-fidelity solution
would point at the object with a 3-degrees-of-freedom (DoF) controller and
hold it by pressing a button. With high action fidelity, the user would reach
out the hand to the object’s position, enclose it with the fingers according
to its shape and size, and exert force with the arm in proportion to its
weight. To virtually reproduce interactions with maximum fidelity, the user
must behave exactly as in the reference interaction.

This component is close to the biomechanical symmetry dimension of the revised
FIFA by McMahan et al. (2016). In their definition, only the movements of body
parts are considered user actions. They distinguish between anthropomorphic,
kinematic, and kinetic symmetry of body movements. We include additional forms
of user actions in this model beyond active bodily motion.

####  3.2.2. Detection Fidelity

Detection fidelity is the degree of exactness with which input devices detect
the user’s actions. In contrast to the next component, transfer fidelity, it
is irrelevant for detection fidelity how the system interprets or classifies
the captured sensor data. While detection fidelity is about converting
physical actions into measurements of those actions, transfer fidelity is
about converting those measurements into meaningful virtual actions.

This component involves the capability of sensors to capture relevant signals.
For high detection fidelity, the measurements are accurate, noiseless,
reliable, and immediate. The kind of signal and the appropriate input device
depend on the respective user actions and concerns the same range outlined
under action fidelity, from optical tracking of body movements to microphones
for speech detection to electromyography (EMG) for facial expressions. The
sensor range can cover all possible parameters, such as thermosensors for
measuring body temperature. The system does not need to detect actions or body
signals that would not affect the reference interaction, such as the
temperature of the feet when knitting with one’s hands.

For example, a low-fidelity solution in a horror game would detect the user
jumping in terror and gasping. In contrast, in a high-fidelity solution, the
system would also detect the user’s eyes snapping open, their muscles
contracting, and the intensity of their sweating. To virtually reproduce
interactions with maximum fidelity, the input devices must detect every
relevant user action and state precisely.

This component is close to the input veracity dimension of the revised FIFA
(McMahan et al., 2016). The authors distinguish between the measurement’s
accuracy, precision, and latency as the delay before sensory feedback. We
consider the immediacy of the detection as a decisive factor but independent
of system feedback.

####  3.2.3. Transfer Fidelity

Transfer fidelity is the degree of exactness with which virtual actions,
derived from the input measurements, resemble the user’s actions of the
reference interaction. In contrast to the next component, simulation fidelity,
it is irrelevant for transfer fidelity how the virtual actions affect the
simulation.

This component refers to how the system considers the sensor readings,
processes and transforms them, and interprets them in the context of the
simulation to generate virtual actions mapped from the user’s real actions in
a meaningful way. The virtual actions can deliberately differ from the real
actions, e.g., by modifying the control/display ratio. Therefore, this
component is determined by the correspondence of the virtual actions with the
reference interaction to be simulated, not with the actual user actions, if
these differ. For example, in redirected walking (Razzaque et al., 2001), we
compare the virtual path to the intended straight path, not the circular path
that the user physically takes. In case of incomplete, distorted, or
simplified input data, processing can make up for input deficiencies to allow
finding a probable interpretation.

As an example of low transfer fidelity, when processing noisy capacitive
sensor data of a hand-held controller for approximating the hand pose, a low-
fidelity solution would show jittery and anatomically absurd finger movements.
In contrast, a high-fidelity solution would result in smooth and plausible
finger movements that match the real hand pose of the user, e.g., using
inverse kinematics. To virtually reproduce interactions with maximum fidelity,
the transfer function must allow the system to correctly interpret the
measurements, infer the correct meaning, and enable the virtual actions to
affect the simulation appropriately.

This component is close to the control symmetry dimension of the revised FIFA
(McMahan et al., 2016). It is described to only depend on the transfer
function symmetry where the system’s transfer function is contrasted with a
theoretical transfer function from reality. While we agree that no transfer
function that would correspond to this system component exists in reality, we
consider matching the virtual replication of the user actions with its
counterpart of the reference interaction more practical than constructing a
theoretical dummy.

####  3.2.4. Simulation Fidelity

Simulation fidelity is the degree of exactness with which a virtual
environment resembles the characteristics of the reference interaction’s world
and adequately reacts to the user’s actions. In contrast to the next
component, rendering fidelity, it is irrelevant for simulation fidelity how
the VE is rendered as output. While simulation fidelity considers the
environment with its characteristics, rendering fidelity converts those
characteristics through a dynamic process to the output devices (i.e.,
rendering pipeline).

This component concerns all elements of the simulated environment, including
objects, agents, physics, and any other entity or logic from the reference
interaction’s world. To help categorize these aspects and to avoid simulation
fidelity being a “black box”, we define four subcomponents: presentational
fidelity, behavioral fidelity, physical fidelity, and scenario fidelity.

Presentational fidelity is the degree of exactness with which the presentation
of the simulated world resembles the reference world. Properties that can
affect presentational fidelity include an object’s mesh, colors, materials,
scent, surface, reflectivity, and level of detail. For example, a low-poly
presentation of an apple with a simple color-based material would have lower
presentational fidelity to a real-world apple than a laser-scanned
presentation with a photorealistic texture-based material.

Behavioral fidelity is the degree of exactness with which the behaviors of
agents (e.g., virtual humans) within the simulation resemble the behaviors of
their counterparts in the reference interaction’s world. The fidelity of these
behaviors depends on three aspects of the agent: its perceptual model,
cognitive model, and motor model (McMahan, 2018). The perceptual model defines
what information is made available to the agent, which can vary from all
information about the state of the world (i.e., fully observable) to a subset
of information (i.e., partially observable) (Russell and Norvig, 2020). The
cognitive model defines how the agent will generate actions based on the
information provided by the perceptual model, such as simple reflexive
responses or more complex decisions based on prior events and the agent’s
goals. The motor model defines what observable actions the agent can control,
such as body movements, facial expressions, speech, or manipulating objects
within the environment. Hence, a simple reflex agent that chooses actions from
a small set based on current, partially observable information would have less
behavioral fidelity than a goal-based agent that chooses actions from a large
set of possibilities given fully observable information about the simulation.

Physical fidelity is the degree of exactness with which the physics of the
simulation resembles the physics of the reference interaction’s world.
Physical fidelity can be affected by shapes, mass, distribution of mass, drag,
gravity, or whether collisions are calculated discretely or continuously. For
example, when skipping a stone over water, a high-fidelity system would
simulate how the stone bounces due to the surface tension, travels a realistic
trajectory, and creates ripples on the water’s surface. In contrast, a stone
with low physical fidelity (such as a box collider, a default mass of 1kg, and
simple force estimations) would simply sink to the ground.

Scenario fidelity is the degree of exactness with which the simulated
situation resembles the situation of the reference interaction. While
presentational and behavioral fidelity focus on individual objects or agents,
scenario fidelity focuses on the holistic aspects of the simulation. This
involves the spatial relationships among objects (e.g., a chair is usually
placed under a table and not on top of it), their semantic relationships
(e.g., an indoor room usually has a ceiling), and their logical relationships
(e.g., flipping a light switch turns the light on or off). For example, a VE
with a flat ground plane and no vegetation would provide less scenario
fidelity to hiking through a mountain forest than an environment with uneven
terrain and numerous trees.

As an example of different simulation fidelity, consider the reference
interaction of playing tennis against a friend at your local park. A low-
fidelity implementation may use low-poly representations of the tennis ball,
rackets, and net with a basic virtual agent as the opponent that only reacts
to the user hitting the ball, which follows a simple trajectory. On the other
hand, a high-fidelity implementation would use high-poly representations in an
outdoor park environment surrounding the tennis court, a virtual human driven
by its own goals and capable of social interactions (e.g., congratulatory
comments), and advanced physics that allows the user to apply topspin when
hitting the ball. To virtually reproduce interactions with maximum fidelity,
the simulation must replicate the reference world identically, including its
objects, agents, physics, and situation.

The first part of the definition is based on McMahan (2011). This component
closely links to the concept of simulation fidelity by Nilsson et al. (2017).
They accept the definition by McMahan (2011) and further attribute simulation
fidelity to “the realism of the models forming the basis for the generation of
the VE (e.g., geometric, lighting, or physical models).” While we largely
agree, we extend the scope of simulation fidelity by the system’s response to
the user’s input. This is also reflected in the concept of functional fidelity
by Alexander et al. (2005), which requires in a training context that “the
simulation acts like the operational equipment in reacting to the tasks
executed by the trainee.” However, in this definition, the characteristics of
the VE independent of the task execution are disregarded. Further, we adopt
and expand the subcategories attribute, behavioral, and physical coherence by
McMahan (2018) as well as physical and functional characteristics by Hays and
Singer (1989).

####  3.2.5. Rendering Fidelity

Rendering fidelity is the degree of exactness with which the output content
generated by the computer resembles what would be presented to the user in the
reference interaction. In contrast to the next component, display fidelity, it
is irrelevant for rendering fidelity whether the output devices can display
the rendered output (accurately) and make it perceptible to the user.

This component involves any modality, including visual, auditory, haptic,
olfactory, gustatory, and vestibular stimuli. The sensory stimuli must be
rendered for any output device of the system. Systems with a visual display
must render graphical images that can differ in their resolution, aspect
ratio, framerate, visual style, antialiasing, texture resolution, detail of
height maps, shadow, specular effects, and much more. Similarly, the sensory
stimuli for any other kind of display must be rendered considering the
respective parameters affecting the level of fidelity, such as audio, haptics,
etc. (see display fidelity). The rendering parameters do not necessarily
correspond to the parameters of the simulation or the display; e.g., the
rendered audio might compress the audio sources from the simulation and still
have a higher resolution than what the earphones can display.

As an example, for the sound of a virtual human walking, a low-fidelity
solution would play a loop of the same footstep recording with strong
compression. In contrast, a high-fidelity solution would synchronize the
timing with the foot movement, adjust the volume, pitch, reverberation, and
direction to the user’s position, and dynamically blend high-resolution sound
samples matching the floor material and the impact of the foot. To virtually
reproduce interactions with maximum fidelity, the rendered output for all
modalities must be indistinguishable from what the user would perceive in the
reference interaction.

This component is related to the definition of display fidelity by McMahan et
al. (2012): “the objective degree of exactness with which real-world sensory
stimuli are reproduced.” In our model, we divide this aspect into rendering
and display fidelity to reflect the independence of calculating output from
displaying it.

####  3.2.6. Display Fidelity

Display fidelity is the degree of exactness with which the output devices
reproduce the physical stimuli presented to the user in the reference
interaction. In contrast to the next component, perceptual fidelity, it is
irrelevant for display fidelity whether and how the user perceives the
stimuli.

This component covers displays that concern any modality, including visual,
auditory, haptic, olfactory, gustatory, and vestibular displays. Since the
alignment of output devices and human perception is highly complex, countless
factors must be considered for display fidelity. For instance, concerning the
graphical output of a head-mounted display, visual fidelity can be affected by
the screen resolution, pixel density, field of view, refresh rate, contrast,
and color depth, but also optical properties of the lenses and optical
interferences such as god rays. The situation is again entirely different in a
CAVE system with wall projections. With any modality, the properties of the
displays might not match those of the renderings or what the user would be
capable of perceiving, e.g., a screen might have a different resolution than
the rendered image and the user’s retina.

As an example of haptic fidelity, considering the task of sawing through a
plank, a low-fidelity solution would create static vibration feedback in a
hand-held controller when moving the saw. In contrast, a high-fidelity
solution would display force feedback that resembles the resistance of the
wood and restricts the hand’s lateral movement, as well as generate contact
forces from the saw handle on the hand, render the gravitational pull of the
heavy saw, and produce dynamic vibrations matching the jerky movement through
the wood. To virtually reproduce interactions with maximum fidelity, the
sensory stimuli displayed to the user address all senses and perfectly
resemble the stimuli in the reference interaction.

This component is related to various frameworks that detail display fidelity
addressing a single sense, such as the Haptic Fidelity Framework by Muender et
al. (2022), components of visual display fidelity according to Bowman and
McMahan (2007), or dimensions of the Spatial Audio Questionnaire by Lindau et
al. (2014).

####  3.2.7. Perceptual Fidelity

Perceptual fidelity is the degree of exactness with which the user’s
perception of the physical stimuli created by the system resembles how the
user would perceive the reference interaction. In contrast to the next
component, experiential fidelity, it is irrelevant for perceptual fidelity how
users interpret the perceived stimuli, what meaning they assign to them, and
what consequences they draw.

This component concerns all sensory cues that the user registers with any
sense: vision, audition, touch, smell, taste, proprioception,
equilibrioception, nociception, etc. The physical stimuli produced by the
output devices are registered through sensory receptors. The information is
transduced to and processed in the user’s brain. The impressions from the
different senses are integrated into one unified perception, called multimodal
integration. We consider the interpretation of the stimuli already part of the
user’s experience and, therefore, included in experiential fidelity. We assume
that maximum display fidelity inevitably leads to maximum perceptual fidelity
because the human sensory system cannot identify the origin of a physical
stimulus, whether the real world or a simulation generates it. With an
imperfect display, however, perceptual fidelity may deviate. Mechanisms in
human perception can compensate for display deficiencies, enabling phenomena
such as illusions, biases, or sensory substitution. Thus, high perceptual
fidelity may be achieved even without high display fidelity in some cases.
Further, the qualia of perception can vary immensely between individuals,
which is why perceptual fidelity must be assessed on a user-by-user basis.
Consider a color-blind user who experiences the real world in shades of grey.
A black-and-white scene would look more realistic to that user than to users
with full-color vision, thus yielding higher perceptual fidelity. Considering
future possibilities of perception through direct neural manipulation without
the respective receptors being stimulated, our understanding of this component
remains the same: How closely does the user’s perception correspond to the
reference? However, the restriction to simulation through physical cues must
then be omitted.

In the example of someone touching different locations on the user’s back, a
solution with low display fidelity, such as a haptic vest with low-resolution
actuators, would still result in high perceptual fidelity, as the two-point
discrimination of skin receptors on the back is relatively poor (Lederman and
Klatzky, 2009). A low-fidelity solution would have the same display resolution
on the user’s hand as the receptors are more sensitive to local variations
here. Perceptual fidelity is also low in this example if the user only
perceives visual cues and feels no tactile sensation. To virtually reproduce
interactions with maximum fidelity, the stimuli by the output devices must
evoke the same sensation for all senses and create the same perception in the
user’s brain as in the reference interaction.

####  3.2.8. Experiential Fidelity

Experiential fidelity is the degree of exactness with which the user’s
experience of the simulated interaction resembles how the user would
experience the reference interaction. When comparing to the real world, this
is often referred to as perceived realism. In contrast to the next component,
action fidelity, it is irrelevant for experiential fidelity how the user
reacts to the simulation.

This component is based on how faithful to the reference interaction the user
considers their own actions, how the observed world behaves, and how it can be
perceived. Experiential fidelity is often the ultimate objective when
optimizing any other component, as the perceived authenticity can determine
the subjective quality of a simulation. In other use cases, however, it can be
subordinate, as in a technical proof of concept or a training situation that
must prioritize action or simulation fidelity, whether experienced as faithful
or not. Curiously, an objectively highly realistic system is not necessarily
experienced as such, as discussed in Section 7.6. Various factors affect
experiential fidelity, such as individual differences in perception and
judgment, since systems are often not optimized for one specific user and use
case but are designed to be versatile and adaptable. Also, suspension of
disbelief—or lack thereof—can substantially impact perceived fidelity, e.g.,
due to different assumptions and expectations, distractions from other
realities, and the credibility or plausibility of the simulation. Also,
unconscious effects need to be considered. For example, users can experience a
higher cognitive load even for unnoticeable manipulations with redirected
walking techniques (Bruder et al., 2015). Furthermore, multisensory
integration can influence how users interpret conflicting stimuli, which can
be demonstrated with phenomena such as the McGurk effect (Tiippana, 2014).
Another critical factor is the awareness of the experience being simulated or
the memory of having entered a simulation.

Table 2. Selected frameworks, models, and instruments that include details
specialized on a certain aspect of fidelity, listed with its original
designation. The references are grouped by their correspondence to the IntFi
ModelṪhis does not represent an exhaustive list of all relevant, prior
literature, nor does it comprehensively address all subcomponents.

TBD  IntFi Model components Literature covering this component Action Fidelity
Biomechanical Symmetry (McMahan, 2011) (only active motions), Motion Realism
(Rogers et al., 2022) (only in games) Detection Fidelity Input Veracity /
System Appropriateness (McMahan, 2011), Tracking System Fidelity (Al-Jundi and
Tanbour, 2022) Transfer Fidelity Control Symmetry (McMahan, 2011) Simulation
Fidelity Simulation System Fidelity (Al-Jundi and Tanbour, 2022), Physics
Realism / Avatar Realism (Rogers et al., 2022) (only in games), Functional
Fidelity (Alexander et al., 2005), Naive Physics (Jacob et al., 2008)
Rendering Fidelity Software (Muender et al., 2022) (only haptics),
Visual/Graphic & Auditory Realism (Rogers et al., 2022) (only in games)
Display Fidelity Hardware (Muender et al., 2022) (only haptics), Spatial Audio
Quality (Lindau et al., 2014) (only audio), Visual & Auditory & Haptic System
Fidelity (Al-Jundi and Tanbour, 2022), Device Realism (Rogers et al., 2022)
(only in games) Perceptual Fidelity Sensing (Muender et al., 2022) (only
haptics), Spatial Audio Quality (Lindau et al., 2014) (only audio), Sensory
Realism (Rogers et al., 2022) (only in games), Body Awareness and Skills
(Jacob et al., 2008) Experiential Fidelity Experiential Fidelity (Lindeman and
Beckhaus, 2009a), Player Response Realism (Rogers et al., 2022) (only in
games), Presence (Slater and Wilbur, 1997; Skarbez et al., 2017a),
Psychological Fidelity (Alexander et al., 2005), Haptic Experience (HX) (Kim
and Schneider, 2020), Environment & Social Awareness and Skills (Jacob et al.,
2008) Input Fidelity Action Fidelity (Stoffregen et al., 2003) Output Fidelity
Physical Fidelity (Alexander et al., 2005), Digital Sensory System Fidelity
(Al-Jundi and Tanbour, 2022) System Fidelity System Fidelity (McMahan, 2018)

For example, when interacting with highly but not perfectly realistic virtual
humans, the experiential fidelity has often been found to be low due to the
uncanny valley (Masahiro, 1970), despite high rendering and display fidelity.
On the other hand, even with low display fidelity, it is possible to achieve
high experiential fidelity, e.g., Valve’s experience of drawing a longbow with
only vibration and sound feedback using a hand-held controller.111The Lab
(https://steamcommunity.com/app/450390?, last access: 2023-08-15). Valve
Corporation, 2016. Another example is the rubber hand illusion and its virtual
replication (Ma and Hommel, 2015), which demonstrates how users can have
strong body ownership and perceive haptic sensations with high perceived
realism despite only sensing visual cues. To virtually reproduce interactions
with maximum fidelity, the interaction with the system must convince the user
to experience the reference interaction, not a simulation. This would be the
equivalent of a successful Turing Test for VR interactions, as proposed by
Stoffregen et al. (2003).

This component is related to concepts such as presence, immersion, coherence,
or body ownership and is assessed in several corresponding questionnaires
(Skarbez et al., 2017a). We discuss this in more detail in Section 7.3.

###  3.3. Dedicated Literature on Subcomponents

With these eight distinct components, the IntFi Model illustrates what design
decisions influence the fidelity and characteristics of VR systems enabling
immersive interactions. When zooming out, the model provides an umbrella
framework. When zooming in, further specialized models are needed to
investigate the underlying complexity of the components. In multi-modal
simulations, fidelity has countless detailed determinants that can be finely
dissected as required. For example, when looking at the wrinkles of a virtual
human, we can go further down in the component hierarchy: interaction fidelity
→ simulation fidelity → presentational fidelity → 3D model → skin texture →
height map → resolution. But while skin characteristics can be rendered
visually, they can also be rendered haptically: interaction fidelity → display
fidelity → haptics. The Haptic Fidelity Framework (Muender et al., 2022) is a
good example that illustrates the complexity of one of the modalities of
display fidelity. The framework comprises 14 distinct criteria defining just
this one output modality.

In the interest of this model’s simplicity, we refrained from further
detailing the included components. Instead, the work builds on various rich
and informative works we refer to in Table 2 as a signpost. It lists related
frameworks and models from the literature that tie into the components of the
IntFi Model. They specialize in one or a few aspects and provide in-depth
information as needed. While the referred works provide further details
concerning a component, they are not necessarily in exact correspondence with
the component. Beyond the works listed, we encourage the HCI and VR community
to devise further dedicated frameworks and measurement instruments to fill the
current gaps. For instance, regarding simulation fidelity, the broad range of
influences is not yet covered adequately by any specialized framework.
Further, while the FIFA framework (McMahan et al., 2016) allows a detailed
analysis of body movements as a part of input fidelity, other elements of user
actions and states are disregarded, such as speech, gaze, or body temperature.

Figure 5. (left) The reference interactions in the real world to be simulated:
A surgeon and his team performing a laparoscopy. (middle) The user operating
the VR training system. (right) The user’s view inside the VE: the operating
room and a virtual screen with the laparoscopic camera view. Images by
Surgical Science, modified by the authors.

Reference interaction on the left: A photograph depicts a team of surgeons
operating with surgical instruments and equipment visible. In the center,
representing the input/output devices, a man is wearing a VR headset and
manipulating controls on a simulation device. The screen in front of him
displays the procedure, mirroring his actions. The rightmost image shows the
virtual surgery environment. A virtual endoscopic view of a surgical procedure
is depicted on a screen, with medical instruments interacting with virtual
tissues. A virtual surgeon is standing next to it.

##  4\. Examples of Applying the Model

Let us look at three diverse examples to bring the theory to life. In this
section, we walk you through the analysis process of three use cases with
different types of reference interactions. Example 1 demonstrates how we can
use the IntFi Model to evaluate how realistic a training system for surgeons
is. The goal is to come as close as possible to real surgery to practice under
safe conditions. Therefore, high interaction fidelity is essential for
acquiring skills in VR so they can be transferred to real-life surgery. In
contrast, Example 2 outlines skill training in which low-fidelity elements are
helpful. Here, we assess the realism of a game for learning to juggle. The
goal is to provide deliberately low interaction fidelity for effective
training, accompanied by empirical research (Adolf et al., 2019). Example 3
shows how the IntFi Model can be applied to fictional reference interactions.
We discuss the fidelity of a VR game from the Star Wars universe.

The authors conducted these exemplary assessments. They are subjective in
nature, thus contestable. The examples illustrate how a complete evaluation
based on the model could be performed. However, the outcome can differ
depending on the analysis goals, context, and individual perspective. In our
experience, disagreeing with an assessment and justifying the opposing opinion
already provides a deeper understanding. Therefore, we encourage reasoned
disagreement with our evaluation.

###  4.1. Example 1: Surgical Training

The LAPSIM® by Surgical
Science222https://surgicalscience.com/simulators/lapsim/ is a commercial
surgical training simulator for laparoscopic interventions. The system offers
medical simulation training for effective and patient‐safe training of
surgical competence that can be transferred to the real operating
room.333Overview of the system https://www.youtube.com/watch?v=7wlIBBm1RXU
Laparoscopic surgery is a technique in which short, narrow tubes are inserted
into the abdomen through small incisions. Long, narrow instruments are
inserted and used to manipulate, cut, and sew tissue, as shown in Figure 5
(left). The LAPSIM is an advanced simulator with detailed graphics and haptic
feedback to train these procedures. The system consists of a custom input
device with two laparoscopic grips with precise tracking through a wire system
and a third grip to control the camera position inside the virtual patient’s
body. The grips offer accurate haptic feedback for soft tissue and hard
surfaces, such as bones, with force feedback delivered through the wire
system. For the experience of being in a virtual operating room, the system is
equipped with an Oculus Rift headset with outside-in head tracking. The Oculus
controllers are not used. The virtual scene consists of a patient, a monitor
with a view of the laparoscopic camera, and assisting surgery staff, e.g.,
nurses.

We now go through the loop clockwise, starting with the user’s actions. The
user interacts with the LAPSIM through custom laparoscopic grips. As a result,
the user performs actions with their body that match well with real
laparoscopic surgery, particularly the movements of the arms, fingers, and one
hand. However, the other hand and the body posture differ as users stand
comfortably in front of the LAPSIM while in the real surgery, they must lean
in, as can be compared in Figure 5. Still, action fidelity can be considered
high. The actions’ detection is realized with a wire system precisely
measuring the motions in four DoF (three rotational and insertion depth) of
the laparoscopic grips—the same as in the reference. The system provides very
high detection fidelity of the surgical tools. However, finger, arm, other
body motions and the voice are not captured, decreasing detection fidelity.
The system transfers the measurements into appropriate positions and
orientations of the virtual grips. In addition, the system uses inverse
kinematics to estimate hand and arm motions based on the end position of the
grips. This gives the system high transfer fidelity.

Figure 6. (left) The reference interaction of a person juggling with balls.
(middle) A user juggling in the VR simulation using a VR headset and
controllers. (right) The user’s view within the VE: three balls are being
thrown in a cascade pattern in a Mars-inspired environment. The two rings
represent the user’s hands. Left image by Loris Bottello (CC BY-NC-SA 2.0,
modified). Middle and right images (modified) from (Adolf et al., 2019).

On the left, a person is juggling four balls outdoors, looking upwards as they
catch and throw them in the air. In the middle, a child uses a VR headset and
hand controllers indoors to juggle virtually. On the right, the virtual
environment shows a juggling simulation, with a first-person perspective of
hands and balls in motion, alongside interface elements that provide feedback
or instructions.

Based on the input, the LAPSIM simulates tissue properties very accurately,
such as its softness or response when cutting it. When blood vessels get
damaged, the system simulates bleeding abstractly in the form of blood
spilling out but not flowing anywhere. Outside the surgical site, the behavior
of the surgical staff is simulated quite well as they perform relevant tasks
in the operating room and react to the progress of the surgery. However, their
animations seem sluggish and unrealistic. Therefore, simulation fidelity is in
some aspects high, in others low. The VE in the LAPSIM can be considered as
two separate parts: (1) the operating room the user is standing in and (2) the
surgical site inside the virtual patient’s body, which is displayed on a
virtual screen within the operating room. The in-body view is rendered with
highly detailed textures, reflections, and lighting for the internal tissue.
On the other hand, the operating room and staff are rendered in a cartoon
style that does not reflect the real environment well. Therefore, rendering
fidelity is in parts high and medium.

The laparoscopic grips display the haptic feedback precisely by considering
tissue softness and resistance. This aspect of display fidelity is high. In
contrast, the visual and auditory output is displayed on the Oculus Rift
headset, which differs from what is perceivable in the real world due to
constraints such as a limited field of view or screen resolution. Olfactory
cues are missing. Concerning modalities other than haptics, display fidelity
is medium-low. Limited by each modality’s display fidelity, the user’s
perception is equally restricted compared to reality. Hence, perceptual
fidelity is in parts medium-low or high.

As a user’s experience is highly subjective, we conducted an informal
interview with an experienced surgeon to assess experiential fidelity. He has
practiced with the LAPSIM and frequently conducts this type of surgery. The
surgeon described the haptic feedback as extremely close to the real world,
with a high contribution to the experience, as this is the focus of the
intervention. On the other hand, the animations were described as not
convincing. Some surgery procedures are missing in the LAPSIM, such as
changing the physical instruments by completely pulling them out and inserting
a new instrument. Overall, the surgeon assessed the system to have high
realism as it comes close to the experience of a real laparoscopic surgery
with a match of 70% to 80%.

In conclusion, the LAPSIM provides high fidelity for aspects that are most
important for the training of hand-eye coordination, surgical procedures, and
the development of manual dexterity. The system provides medium to low
fidelity for less relevant aspects, such as staff animations and environmental
graphics. However, this suffices for the training purpose, as scientific
validation confirmed. Studies have shown that skills trained in LAPSIM can be
successfully transferred to real surgery (Calatayud et al., 2010; Cosman et
al., 2007). Our detailed assessment of LAPSIM’s interaction fidelity
identifies the strengths and weaknesses of the system, confirms adequate
prioritization in its development, and shows opportunities to improve realism
further. The example demonstrates that thoughtful interaction and system
design can help achieve the purpose of a VR system without improving fidelity
in every aspect.

###  4.2. Example 2: Learning to Juggle

Juggling with three or more balls is a complex activity that can be
challenging to learn. There is a steep learning curve as you either throw and
catch the balls with the correct timing, or they will fall to the ground
repeatedly. To make learning to juggle easier, a virtual simulation can
deliberately deviate from the reference interaction in the real world, thus
lowering interaction fidelity. In this example, we analyze the VR software
Planet Juggle by Benjamin Outram,444Description, video trailer, and free
download of Planet Juggle for the Oculus Quest and Rift at
https://www.benjaminoutram.com/planet-juggle, last accessed 2023-08-15 which
provides various features to facilitate a gentler learning process of juggling
movements, such as the cascade pattern. For instance, the user can activate
the following assistive features. Slow motion allows the user to practice and
internalize movement sequences without getting hectic. When touching, the
balls can snap to the hand to make catching easier. Visual indicators can show
the trail of the balls and preview where they will go to achieve the ideal
cascade trajectory. The balls can always reach the ideal height independent of
the throwing impulse. And there is background music that helps get the ideal
rhythm. When getting more confident, the user can turn off the features for
closer-to-reality training.

Again, we now systematically look at the interactions’ fidelity along the loop
when all assistive features are activated. The 1-to-1 mapping of the motions
within the 3D space would allow high correspondence of the body movements to
the movements of real juggling. However, the grasping and releasing actions
differ since the user controls the virtual balls with the trigger buttons of
the hand-held Oculus controllers instead of physical balls. For holding the
controllers comfortably, the hands are rotated inwards for virtual juggling,
other than in real juggling, where the palms must face up to catch a ball.
Further, supportive features such as snapping, slow motion, or automatic
height make the system tolerant of discrepancies in hand position or rotation,
throwing power, and bad timing. While this allows successful first steps in
virtual juggling, the same movements would lead to failure in the real world,
resulting in low action fidelity. The detection of hand movements works
precisely using the Oculus tracking systems. Other body movements are not
registered, although less relevant to the activity. Therefore, we can assess
high detection fidelity. The system tolerates discrepancies in hand movements
by making up for them with transfer functions. Although the user actions may
differ from realistic juggling movements, the transformed virtual juggling
actions closely correspond to the reference interaction, which leads to high
transfer fidelity.

Figure 7. (left) A person holding a lightsaber as the reference to be
simulated. (middle) A user with a headset and a controller using the virtual
lightsaber. (right) The player’s view in the VE. Left image generated with
OpenAI DALL·E 3, right image captured from “Vader Immortal: A Star Wars Series
– Episode III”.

On the left is a photorealistic render of a woman holding a blue lightsaber,
standing in a desert-like environment. In the middle is a photo of a woman
wearing a Meta Quest Pro. She is extending her arm forward, holding a VR
controller. On the right is a digital render with the first-person perspective
of the virtual environment, with the user’s hand holding a lightsaber
identical to the one in the left image, in the same pose as the person in the
middle image. A dark desert landscape is in the background.

The lower gravity leading to the slow motion effect fits the narrative of
Planet Juggle as the user is supposed to juggle on the Moon or Neptune.
Disregarding side effects such as the dreadful death from cold, the
simulation’s physics is generally sophisticated regarding ball behavior.
However, our reference interaction for this comparison is real juggling on
Earth. Features such as slow motion or visual trajectory indicators limit
realism. Particularly, the strong predetermination of where the balls can
travel and land, such as the spatial confinement to a plane disregarding the
third dimension away from the user, leads to low simulation fidelity. Moving
on to output fidelity, the system renders an abstract environment with grids
on the ground, geometrical landscapes, and glossy, highly reflective surfaces.
Rhythmic music is generated to influence the juggling tempo, and the balls
create abstract sounds when touching the hands. All this can be considered low
rendering fidelity. The visual quality depends on the Oculus hardware used,
but even with the superior Rift headset, the visual impression can be clearly
distinguished from what the user could see in reality. Similarly, haptic
fidelity is limited because the system only provides continuous contact forces
from holding the controller and abstract vibrations when touching a ball.
Overall, display fidelity is medium to low. The controller’s vibration
feedback acts as a sensory substitution. The user cannot feel the momentum and
weight of the falling ball, but the vibration intensity is calculated from
these parameters. The haptic cues can inform the user about the ball’s
properties and the impact of catching. Similarly, audible cues represent the
ball’s force on the user’s hand. Consequently, perceptual fidelity can be
considered higher than display fidelity, with medium fidelity.

Finally, to assess experiential fidelity, we tested the simulator ourselves
with a think-aloud approach. The app gave a juggling novice the impression of
quickly acquiring juggling skills as the movements for the cascade pattern
were quickly performed. He felt competent to virtually juggle after a few
minutes, thanks to the assistive features. On the other hand, transferring
these skills to the real world was a completely different story, as too many
aspects differ from juggling with real balls. Another of the authors,
proficient in juggling, struggled with the skill transfer in the other
direction. At first, he could not accomplish a stable juggling pattern because
it felt so different from what he was used to. Mainly the haptics were found
to be too different as the soft vibration gave no impression of an impact.
Also, the lack of catching and releasing the balls with the hand felt
unfamiliar. The author further missed the third dimension because the ball
trajectory is restricted to a 2D plane. He disliked the assistive features in
VR: “I find reality much more ‘assistive.’ I missed that in VR.” Overall, the
authors enjoyed the playful VR activity but did not get the feeling of real
juggling. We assess experiential fidelity subjectively as low.

In summary, the simulator provides virtual aids to learn the principles of
juggling by deliberately deviating from a highly realistic replication. The
supportive features allow quick progress for virtual juggling and can help
build muscle memory through authentic arm movements. A scientific evaluation
of the app has explored how it can be integrated into learning to juggle with
real balls (Adolf et al., 2019). The study showed how users had more fun
training with the VR simulation but struggled with transferring the acquired
skills. Therefore, developers should carefully consider which fidelity aspects
should maintain high realism to ensure skill transferability.

###  4.3. Example 3: Lightsabers from Star Wars

In this third example, we turn to a reference interaction from a fictional
narrative: using a lightsaber, the energy sword from the Star Wars franchise.
Specifically, we examine the interaction fidelity of using lightsabers in the
VR game Vader Immortal: A Star Wars Series – Episode III by ILMxLAB.555Trailer
and download at https://www.oculus.com/experiences/quest/2426206484098337/ The
player can use the weapon in lightsaber duels, to block blaster bolts, hit
enemies, and throw it at targets. It is operated with hand-held Oculus Touch
controllers. But what do we compare the VR interactions to if nobody has ever
held a real lightsaber? The depiction in Star Wars media that also informed
the game’s development seems a reasonable match. Even in the best case, a
fidelity assessment is debatable as we currently lack objective measures. Our
evaluation is even more contestable when we compare to a fictional reference
interaction since that already leaves room for speculation and disagreement.
Interpretations can vary depending on the canonical choice of Star Wars media,
such as comics, TV shows, books, video games, or merchandise artifacts. For
this reason, we focus on the original movies as a reference. We invite all
readers to question our stance and justify their proposal.

Instead of a circular procedure, we look at the most striking characteristics
of the interaction. The weapon functions in the virtual world much as you
would expect it to. The player holds the lightsaber’s hilt, wields it, and
throws it as the plasma blade blocks attacks and cuts through any objects.
However, most objects are not cut into pieces when the blade goes through
them, and it hardly leaves a trace. Due to the simplistic game mechanics of
the choreographed lightsaber duels, also the opponents do not always respond
to being touched by the lightsaber; similarly, the player does not die
instantly from being hit by an enemy’s lightsaber. In this game, using a
lightsaber has medium simulation fidelity. The controllers as input devices
are beneficial for action fidelity because the player has a similar hand pose
as when gripping the lightweight, slim saber hilt. Even the activation by
pressing a button corresponds well. How the player holds the weapon and
performs combat movements with six DoF contributes to overall high action
fidelity. It is sometimes lower, though. If the player holds the hilt with
both hands, having two separate controllers limits action fidelity. It further
suddenly decreases when there should be resistance from hitting another
lightsaber, but the player’s arm continues to move unhindered.

This equally affects display fidelity due to the lack of force feedback.
Because the controllers only provide passive haptic feedback and vibrations,
we assess this aspect as low-fidelity, even more so when virtually throwing
the lightsaber but still gripping the controller. The headset’s constraints on
the visual impression reinforce this impression. At the same time, transfer
fidelity can be considered high as the wrist’s flicking motion and the grip
button’s release are interpreted as a throw, sending the weapon on a boomerang
trajectory in an adequate direction. Generally, movements are recognized
accurately, resulting in high detection fidelity. When activating the weapon,
the player hears the iconic hissing sound. The pitch of the electric swoosh
sound is higher when wielding the sword. Together with the convincing glowing
effect of the blade, we thus assess rendering fidelity as high. Given the
limited skills of the testing author, we are comforted by the low nociceptual
fidelity as there is no pain from getting hit. Instead, the view is overlaid
with a red vignette, and we hear the avatar moan, which could be considered a
decreasing factor for rendering fidelity. Apart from that, perceptual fidelity
is restricted by low display fidelity.

Lastly, to inform our assessment of experiential fidelity, we informally
tested the game with a Star Wars enthusiast and someone who has only seen a
few movies. The enthusiast was amazed at how much the lightsaber made him feel
like a Jedi. It felt convincing, wielding a glowing, humming lightsaber in 3D
space with high levels of embodiment, especially compared to playing the game
series Star Wars: Jedi Knight on the computer with a mouse and keyboard or
using sticks as a child. Only the crude combat game mechanics detracted from
the experience. The other tester, who did not care about Star Wars, also
enjoyed handling the lightsaber. When asked if this is how he would expect a
lightsaber to feel and behave, he was unsure as he never contemplated it. He
was surprised that there was no air resistance or weight like from a metal
longsword but then assumed: “Probably, this is just what a lightsaber feels
like.” Furthermore, he was confused by the inconsistent controls: a blaster
requires continued pressing of the grip buttons while the lightsaber only
needs one short press. Also, the vibrations and the headset’s visual
limitations were described as irritating. Overall, both testers quickly forgot
about the system’s shortcomings and were immersed in the experience. We
suggest a medium-high fidelity rating.

This example demonstrates that the reference interaction does not need to be
based on the real world. As long as the original to be simulated is clearly
defined, the IntFi Model can be applied meaningfully. Regarding experiential
fidelity, users with no knowledge of the reference cannot make a competent
comparison, but they still have their assumptions. Similarly, non-swimmers
cannot compare virtual swimming to real experiences but can make an informed
guess, assess the simulation’s coherence and credibility, and form their
impression intuitively. Moreover, the lightsaber interaction exemplifies how
other factors (such as fun in gaming) should be prioritized over maximum
fidelity. The game would hardly be playable if the weapon sliced every object
and the self-avatar, when touching it with the deadly blade.

##  5\. Validation

To polish and validate the IntFi Model, we conducted expert interviews with 14
established VR specialists from academia and the industry. The goal of this
evaluation was to collect feedback and criticism, discuss the proposed
terminology, and put the model to the test in terms of its applicability.

###  5.1. Method

We conducted semi-structured, interactive interviews via Zoom or in person. We
prepared an interview guideline to structure the conversations and make them
comparable. The only variation between the interviews concerned the experts’
example projects to which they applied the model during the interactive
sessions. At least two of the authors were present for each session. We
conducted the interviews between March and May 2023. The conversations took
between 45 and 79 minutes, averaging 63 minutes. We recorded the sessions with
the participants’ consent and collected 14.75 hours of material. We performed
a thematic analysis of the data to process and structure the findings. Two of
the interviews could not be recorded due to a technical error, so we resorted
to notes on these sessions. After finalizing the manuscript, we asked all
interviewees whether the interviews had been adequately summarized in the
reported findings. One expert did not find the time, but everybody else
confirmed that the text accurately reflects the conversations without any
requests for changes or additions.

####  5.1.1. Sample

We selected researchers, interaction designers, developers, and managers for
their contributions to the VR field and their professional experience. We
invited 19 selected candidates, of whom two could not find the time, and three
did not respond. The diverse sample covered a broad range of VR-related
research fields, backgrounds, and perspectives. Because we assured all
participants that they would remain unidentifiable, we can only report broadly
on the sample’s composition.

The sample included five full professors, three assistant or associate
professors, and a lecturer from academia, as well as two senior professionals,
a research scientist manager, a product owner, and a consultant from the
industry. Among the experts were distinguished scientists who were awarded
three IEEE VGTC VR Technical Achievement Awards, the IEEE VGTC VR Significant
New Researcher Award, an IEEE VGTC VR Best Dissertation Honorable Mention, and
various career awards. The sample also included three IEEE VGTC VR Academy
members and an ACM Distinguished Scientist. The ten researchers in our sample
had an average citation count of 7,043 and an average h-index of 35 as of 14
February 2024 according to Google Scholar. The h-index ranged from 11 to 69
and reflects the spectrum of our selection that includes young scientists
publishing with a focus close to the IntFi Model and experts with decades of
experience in VR research. All participants primarily work in VR or HCI. The
experts’ focus includes haptics, locomotion, interaction techniques,
embodiment, computer graphics, visualization, presence, training and
education, avatars, perception, medicine, collaboration, artificial
intelligence, and multimodal interfaces. The variety of topics demonstrates
how broadly the model can be applied in practice. After analyzing the last
interview, we invited three interviewees to join the author team and further
contribute to this work, which they accepted.

####  5.1.2. Interview Structure

The interviews followed a semi-structured guideline based on a slide deck.

  1. (1)

Working definition. We defined the terms fidelity and realism in the context
of our model.

  2. (2)

Task 1. Without any previous biasing, we asked the experts to describe the
fidelity of the interactions in one of their works and with their own words.
Find more details below on how we selected the work.

  3. (3)

Introduction to the IntFi Model. We explained the purpose of the model, the
objective approach, the fidelity spectrum, the HCI loop as the foundation, and
the components of the model integrated into the loop. We then elaborated on
the single aspects with definitions and characteristics.

  4. (4)

Initial feedback. We asked for first thoughts, any confusion or questions,
criticism, and concerns regarding the terminology. In some conversations, this
feedback was already raised during the presentation of the model.

  5. (5)

Task 2. We suggested different ways to apply the model. Once more, we asked
the expert to elaborate on the interaction fidelity of the same work as in
task 1, only this time using the IntFi Model as a basis. We discussed it in
more detail and asked follow-up questions as needed, such as identifying key
aspects with prioritized fidelity, low-hanging fruit for improving fidelity,
connections between aspects, etc.

  6. (6)

Conclusion. Finally, we asked for the expert’s overall evaluation of the
model. Depending on the time left, we went into more detail regarding possible
benefits, improvements, or research opportunities that the experts wanted to
add.

For the two tasks of assessing interaction fidelity in a specific example, we
selected one project, system, or user study of the expert. The interview
partners were familiar with the work, so we could directly dive into the
discussion. This approach also allowed us to authentically test the practical
applicability of the IntFi Model in genuine use cases. By using the example in
both tasks, we could compare how the experts approached the analysis either
with or without the model providing structure. While we observed the differing
levels of detail and evaluation strategies between the first and second task
execution, the experts could experience how the model can facilitate the
assessment of interaction fidelity.

The works covered a wide variety of topics, including avatars, locomotion,
haptics, perceptual illusions, presence, object manipulation, social
interactions, embodiment, emotions, virtual environments, and training. To
preserve the anonymity of our interview partners, we do not specify the chosen
publications or projects. The works were required to link to the model, i.e.,
depend on at least two aspects of fidelity, aim for any kind of fidelity, or
be inspired by an explicit reference interaction that has been reproduced.

###  5.2. Findings

In this section, we present the insights from the expert interviews. We refer
to the experts from our sample with E01 to E14. Overall, the interviewed
experts found the IntFi Model “useful” (E04), “comprehensible” (E07), “sound”
(E02), and “a meaningful contribution” (E03). However, there was also reasoned
criticism, opposing views, and a need for clarifying discussions. For example,
some terms were described as “not perfect” (E11). Expert E10 was not convinced
of our validation process’ rigor, while E04 particularly liked it and found it
“very systematic”. E08 struggled with the HCI loop as a not entirely suitable
basis but also found that “some more modern reflection on the old interaction
loop is definitely interesting.” Each of our interview partners appreciated
the IntFi Model as interesting and helpful.

The findings are structured by the identified themes: concept criticism,
terminology criticism, applicability criticism, application strategies, and
contributions. The themes application traps and patterns were moved to the
separate Sections 6.3 and 7.1. Some critiques are addressed in the discussion
section and, therefore, only mentioned here as a topic but not directly
elaborated upon to reduce redundancy. Comments, misunderstandings, and
concerns that were resolved in the interviews are not outlined. Similarly, any
additional literature suggested by the experts was integrated into the Related
Work Section but is not mentioned here separately. We directly adjusted the
model and this paper according to the experts’ suggestions without reporting
before-and-after comparisons to avoid confusing readers with changes from an
unknown earlier version. These unreported improvements primarily concern
details of descriptions, definitions, framing, or illustrations.

####  5.2.1. Theme: Concept Criticism

The fundamental conceptualization of the IntFi Model, linking the fidelity
aspects to the stages of the HCI loop, convinced most of our interview
participants. E03 found that “on a conceptual level, this is a very nice piece
of work. I think it would be a meaningful contribution to how we think about
and talk about virtual reality systems. […] It’s obvious a lot of thought went
into this.” One participant called the model “a very high-dimensional matrix”
(E08).

However, there was also criticism on a conceptual level. The two most
prominent critiques addressed the claim of objectifiability and the lack of
quantification. Concerning the former, some participants doubted that all
aspects of the model can be described objectively and struggled with the
impracticality of ever determining an underlying ground truth. E14
hypothesized that system-related aspects (i.e., the bottom five) can only be
assessed objectively, while the user-related aspects (i.e., the top three)
only subjectively. We argue in the discussion (Section 7) that all components
can be assessed objectively and subjectively depending on the approach.
Concerning the latter, most experts indulged at some point in the interview in
the idea of how helpful it would be to quantify interaction fidelity and its
components: “This scale from low to maximum fidelity: this is a scale from 0
to 100, and I want to have that value. It would be nice if I could assess
that, of course.” (E14). Enjoying the prospect of universal fidelity metrics
and measurements ourselves, we explain in the discussion why this is not in
the scope of this paper and might need many more years of systematic research.

For some interviewees, the concept of fidelity was less intuitive than
realism. They initially proposed reframing the model for comparisons to real-
life interactions as the most common replication reference of VR simulations.
After looking at practical examples with reference frames other than reality,
most experts supported using the fidelity concept and preferred the more
universal applicability. One of the FIFA authors criticized this limitation in
their own work in hindsight: “That was a weakness we had with [FIFA] that we
were gauging everything with regards to the real world.”

One expert from our sample, E08, raised doubts that it makes sense to consider
perceptual fidelity “because we can’t know—it’s unknowable!” Although it is
currently challenging or impossible in many cases to determine what a user
objectively perceives, there is a ground truth to it that has a higher or
lower match to what the user would perceive in the reference interaction. With
this, independent of its ascertainability, perceptual fidelity is a valid
construct. Some interviewees pointed out that perception might even be
measurable in the future if we develop more sophisticated neural interfaces.
E08 continued, that “obviously, nobody knows if perceptions are the same.
That’s the whole philosophical debate about qualia.” For this reason, we
recommend considering perceptual and experiential fidelity on an individual
level. Only a user-by-user assessment can do justice to different personal
abilities and characteristics. From a practical stance, E05 argued that while
perceptual fidelity “is user-dependent, it is not unknowable or unpredictable
if you have enough information about the user.”

Another critique by E08 concerned the HCI loop as it is segmented into stages,
while interactions in VR often occur with simultaneous input and output in
parallel. Appreciating the debate about the loop’s limited applicability for
direct manipulation techniques due to the concurrence of input and output, we
argue that it is nonetheless insightful to use the loop as a theoretical
construct for abstracting and distinguishing the involved elements of the
reciprocal VR interactions. Breaking down the permanent exchange of user and
system can help understand the ongoing parallel processes while identifying
isolated aspects relevant for analysis.

Fundamentally, E08 was skeptical of operationalizing fidelity at all “as it’s
obviously impossible to reproduce things.” In our conversation, we agreed that
perfectly replicating an original is extremely difficult and potentially
impossible, especially regarding something as complex as bodily interaction
with the world. However, using the IntFi Model to determine interaction
fidelity is most helpful on the vast spectrum before reaching “maximum”, i.e.,
to describe how imperfectly something is reproduced. Realistically, we are
nowhere close to the upper extreme of the spectrum with current technology.
Until we achieve perfection, the model can help assess the degree of fidelity.

Several experts wondered how some aspects can be evaluated independently of
others as they seem inseparably linked in practice. For example, rendering
fidelity seems to be coupled to simulation fidelity on a computational level
and to display fidelity on a hardware level. We agree that from the
optimization viewpoint, the components should strongly depend on each other.
Consequently, they are usually configured in combination by game engines and
developers. Still, it can be beneficial to consider the aspects individually
from an interaction design perspective. Consider a use case where a user
cannot see the hair on his arm. It makes a difference if it is incorrectly
represented in his avatar model, if it is not rendered due to missing height
maps, or if the display resolution is too low for the hair to be recognizable.
Further, distinguishing the aspects can even be required from a technical
point of view. For example, in cloud computing or when watching 360° videos,
the rendered output can be displayed on HMDs with different screen
resolutions, affecting rendering and display fidelity separately.

As an addition to the model, E03 proposed integrating a ninth component along
the middle axis of the loop: an element that moderates between the user’s
mental model (as part of experiential fidelity) and the system’s data model
(as part of simulation fidelity). We have not implemented the proposed
extension of the IntFi Model for two reasons. First, there is no
correspondence of the abstract concept of a mental model to any reference
interaction, which is why the term “fidelity” does not apply. Second, the HCI
loop has no equivalent component, and we prefer sticking to the original
conceptual foundation.

####  5.2.2. Theme: Terminology Criticism

We asked the experts about the clarity and suitability of the chosen terms.
Most interviewees had no concerns regarding any of the labels and appreciated
how consistent they are. E02 said: “I think the terms are perfectly clear as
they are. And it’s good that they’re short, both in writing and when talking
about it.” Although many experts pointed out how introducing a defined
vocabulary can be helpful for communication, others cautioned against
conflicts with existing terms or definitions. In the literature and oral
vocabulary, some terms are used differently from the IntFi Model
understanding, and sometimes different terms are used for the same thing. For
example, interaction fidelity is coined more narrowly in the Framework of
Interaction Fidelity Analysis (McMahan et al., 2016). One of its co-authors,
who is part of our sample, commented on the redefinition: “In our previous
work, we used [interaction fidelity] almost exclusively for the input side.
But it was always a problem. […] That will be a battle you’ll have to fight to
get the term to mean more. But it makes sense overall.”

Furthermore, there was criticism on a few specific terms, mainly by E11. We
thoroughly discussed the critique with the experts and among the authors. In
the end, we chose the term that seemed most suitable. For example, we changed
sensory to perception fidelity to avoid confusing it with system sensors, and
later from perception to perceptual fidelity for linguistic reasons and
consistency with experiential fidelity. We also changed the previous term
feedback fidelity to output fidelity for higher symmetry with input fidelity
and a better match with the component output devices of the HCI loop.
Moreover, the term feedback implies a reaction of the system to user input.
Hence, in a non-interactive $360^{\circ}$ movie without user input, there can
be no feedback, but the system can still give output.

Other alternative labels were suggested in the interviews but not adopted
after careful consideration. For example, tracking fidelity was suggested
instead of detection fidelity, though this term implies including the transfer
component. Alternatively, it was proposed to be called human sensory fidelity
while technical sensory fidelity would replace perceptual fidelity. However,
this might facilitate misunderstandings and would be less concise. Further,
the label action fidelity was criticized for not encompassing passive states
and uncontrolled behavior by the user. Instead, behavior fidelity was
suggested, although this has the same limitations. Alternatively, body
fidelity was suggested. However, this has an overly passive framing and could
be misleading by implying only to represent the user’s body as an avatar and
neglecting voice or neural input. None of the terms are perfect, so we chose
action fidelity as the most intuitive and flexible compromise.

The experts also criticized terms for which we could not find any better
alternative. For instance, input fidelity was challenged because undetected
actions never enter the system and, thus, are not indeed system input. We
argue that the user’s intent and the potential of capturing all actions are
decisive for being included in input fidelity as the user would expect a high-
fidelity system to detect everything. Lastly, we observed that display and
rendering fidelity have strong connotations with visual screens for some
people. In our sample, this was especially pronounced in the industry. In the
instructional material, we emphasized that any modality can be rendered and
displayed.

####  5.2.3. Theme: Application Criticism

With an example of their own previous work, all experts utilized the IntFi
Model to analyze and reflect on a user study or system design. This enabled
them to assess its applicability in a realistic scenario. Some of the experts
experienced the onboarding as demanding: “It’s a lot to take in. […] If I
would have to use it as a tool, I would require a bit of time to get familiar
with all the intricacies” (E02). It was a challenge for the participants to
understand the model’s concept and remember the components’ definitions. Some
of them grasped the ideas quickly as they studied the subject intensely. To
others, the proposed way to contemplate fidelity was unfamiliar, so they
needed more time, detailed explanations, and examples to wrap their head
around the model. Most participants asked several comprehension questions
during or after the presentation of the model.

Even interviewees experienced in fidelity research recommended creating
accessible instruction material: “ideally, a graphical version that would make
it very easy to use” (E02). To enable teams to use IntFi Model as a practical
tool, there must be a quick and easy way to learn how to work with it.
Therefore, we developed an informative and intuitive poster that gives a
visually appealing introduction to the model. E02 suggested “creating a very
nice graphical representation of it that you can print out in A0 and have in
your lab. […] Having all of it up on a wall would be very good for me.” This
agrees with our experience of hanging it on the wall as a poster and referring
to it during our daily work. We also prepared a slide deck that can be
conveniently adapted for teaching material. Both can be accessed in the
appendix and used for free under a Creative Commons license.

All experts but two stated that they would like to use the model in their work
in the future. One of the two, E08, wanted to read the paper first. The other
expert, E10, was concerned about adequate validation before employing it: “I
think validation is important when it comes to something like this.” E04
appreciated the expert interviews as a validation method: “I like the
approach. Very systematic.” For E10, however, to sufficiently verify the
soundness and acceptance of the proposed model, it must prove itself in
practice on a large scale. Only if the community can work with it and embraces
it the IntFi Model will be valid to E10. At the same time, this seemed to be
the most promising prospect: “At the moment, it’s a theoretical model, a
conceptual model. Making it more than that, that’s a research opportunity.
Bring it to practice!”

When applying the model, some experts fell into application traps, which led
to criticism of the model’s applicability. We collected these traps in Section
6.3 as guidance for readers without elaborating on them further here. We also
collected special use cases and integrated them at appropriate parts of this
publication, such as the applicability for multi-user systems (E06), mixed
reality (E13, E06), or troubleshooting (E12, E14).

####  5.2.4. Theme: Application Strategies

From the observations in our interview workshops, we can conclude that the
IntFi Model successfully guided the participants through the analysis process
and was a helpful basis for discussion. This is evident in the different
approaches and outcomes from the first and second tasks, i.e., without or with
the help of the model. In the first task, without the model, the participants’
explanations and considerations were unstructured, and occasionally seemed
lost in the complexity of the subject. Most were uncertain about where to
start and stopped after mentioning only a few aspects. In contrast, equipped
with the model in the second task, all participants identified many more
relevant aspects and kept elaborating on the interactions’ fidelity.
Typically, it took them a few moments to think about it and match the model
with the use case. Then, they went through the single aspects and their
connections, talking like a waterfall and coming up with additional insights
they had not considered in the first task. Further, they now involved every
part of the interaction in their analysis. E12 described this experience with:
“I thought it was really cool because [with the model] you could just work
through several points step by step, whereas before I just groped in the
dark.” With this, the IntFi Model seemed to have an empowering effect on our
participants.

The experts used different strategies for applying the model to their
individual use cases. We did not instruct them how to proceed but just set the
task to describe interaction fidelity using the model. Some explicitly asked
for the correct way to use it: “Where does it start? On the top with the user?
Or with action fidelity?” (E12). Indeed, half of the sample adapted the
approach we chose for presenting the model, starting with action fidelity and
continuing in the loop aspect by aspect. As a variation, E02 started with
action fidelity but then followed the logic of the interaction technique.
Alternatively, three experts started with their study’s independent variable
and went in the loop from there. Similarly, E01 started with the aspect most
important for the application but then went through aspects chaotically and
went back and forth, addressing different modalities. E04 and E09 had a purely
chaotic approach, following their narrative organically as it evolved. E14
deliberately made two rounds in the loop: first for a “clean” version of the
interaction technique, then for the modified version with sensory
manipulation. This helped him contrast the differences.

It was our impression that the experts from the industry generally needed more
information and assistance for applying the IntFi Model. For example, E12 and
E07 asked to return to the introductory slides with the descriptions for
performing the second task. As a result, their analysis was conspicuously
close to the notes and talking points on the slides. Although it seemed more
demanding, they appreciated how beneficial it was. E09 concluded that “a more
analytical approach for designing [immersive] interfaces and experiences would
be reasonable because I think a lot of designers shoot in the dark.”

The experts in our sample used the model for different goals. In most cases,
they roughly located all aspects within the fidelity spectrum as a starting
point. After this analysis phase, they interpreted their findings depending on
their goal. For example, some attempted to reason or predict experiential
fidelity through the other aspects (E01, E05, E06, E09, E12). Others wanted to
set informed design priorities by weighing the aspects regarding their
relevance for a use case (E01, E06). For this, they also linked their
interpretations to established design principles and effects, such as visual
dominance. For some researchers, assessing the degree of fidelity was less
decisive. Instead, they wanted to identify which components the independent
variables of a user study were part of (E03, E14): “This is helpful! Because
it makes more precise what I did and did not manipulate in my research” (E03).
This researcher realized that all of their manipulated study variables affect
simulation fidelity, which they first criticized as not that interesting.
Still, while contemplating using the IntFi Model, they realized they had
achieved what they set out to do in the study: alter aspects of the
interaction influencing plausibility illusion. Since E03 interpreted this as
the relationship between simulation and experiential fidelity, they concluded
to have chosen a suitable focus. In a further step, some researchers reflected
on aspects that would be interesting to manipulate in follow-up studies (E02,
E06): “It has been very useful trying to understand what it is I’m
manipulating, and when designing an experiment, figuring out what are the
factors that are relevant to manipulate” (E02).

A further reoccurring strategy of applying the model was identifying aspects
of utmost importance for specific use cases or purposes (E06, E09, E13). For
example, in applications for motor skill training, it is critical to optimize
for high action fidelity, while simulation fidelity must be as high as
possible for educational purposes. Therefore, some participants tried to
define target variables, such as learning success, confidence in use, fun, or
control precision, to deduce the determining components. Overall, the
strategies and paths of applying the IntFi Model were as varied as the
participants’ objectives.

####  5.2.5. Theme: Contributions

Although the purpose of the interviews was to obtain criticism from experts in
the field, we also want to outline the commendatory remarks of our
participants. This theme includes positive remarks, suggested use cases, and
contributions of the IntFi Model brought up by the experts.

Overall, the interviewees appreciated the model: “It’s a nice theoretical
framework. It’s well polished” (E04). Many of them enjoyed contemplating and
discussing VR interactions with the model as the basis of the conversation:
“Interesting! That gives me some thoughts” (E08). The model was described as
“super cool! super exciting! super useful!” (E06), and E07 concluded that it
“makes sense! It’s all comprehensible”. An interviewee from the industry liked
the IntFi Model as it helped reflect on fidelity in VR: “It’s interesting
because I understand it intuitively, but I don’t know how to formalize it. And
this is spelling it out for me. […] I love it!” (E09).

E02 acknowledged that the model builds on existing frameworks and concepts: “I
like the model. I used similar models myself, but this one takes it a step
further and adds detail. I think it makes really good sense. […] It adds
structure to a large body of literature. It distills a lot of different
concepts and presents them in a single model. The fact that we can go one
level deeper and get more sub-components, I think, has a lot of utility—both
for designing studies and as a pedagogical tool.” Contrasting it to similar
frameworks, E11 liked the “agnostic approach”. Extending previous work, E05
found that “comprehensiveness and consistency around the entire loop of VR
interactions is the main benefit.” In particular, the advancements compared to
the Framework for Interaction Fidelity Analysis (FIFA) (McMahan et al., 2016)
were addressed in the interviews. While the FIFA only considered input, the
IntFi Model also considers the system output. Also, FIFA’s focus on the system
was extended to the user and their mutual influence. And while FIFA was
limited to realism, the IntFi Model extends to any reference interaction, from
reality or not.

Further, it seemed important to many of the experts “to have a precise and
integrated set of terminology. I think that’s good and the biggest benefit”
(E03). For unambiguous communication within the community, participants found
it “very useful to get a shared vocabulary” (E02). Beyond using the same
labels, E13 emphasized: “The benefit is not only to talk about the same thing
but also to trust the other person refer to the same thing.” While we also
received critical feedback and alternative suggestions, most participants
liked the chosen wording. The terminology was described as “accurate” (E03)
and “consistent across the model” (E02).

Most of the experts were eager to use the IntFi Model in their work, described
it as “helpful” (E01), and found various use cases: “I like this a lot in many
ways!” (E05). Many of the proposed applications concerned system design and
development. One utility often mentioned was the help in understanding the
single components of interaction fidelity to predict experiential fidelity or
the user experience. Distinguishing the fidelity aspects was also described as
helpful in setting priorities depending on the goals or purpose of a system.
As a next step, the model was considered helpful for iterating system designs
to increase interaction fidelity. Also, participants liked comparing two
similar systems based on the model.

To analyze existing systems, the IntFi Model was used by some participants
like a checklist: “It’s cool to think it through in small steps: like check
boxes that you can tick off” (E12). Also, E01 emphasized: “The structure is
helpful when going through the individual fidelity categories.” Similarly,
troubleshooting was mentioned as a use case. If there is an issue with the
system or the users are unsatisfied, E12 and E14 suggested using the model to
search for the problem systematically: “I like how you can examine a user’s
experience in smaller steps: Where exactly now does the error get into the
system?” (E12). E06 and E14 further suggested use cases outside VR, such as
for mixed reality interactions, video instructions, or other applications with
a simulation approach.

Furthermore, the experts suggested numerous ways the model can be used for
research. In the interviews, many researchers used it to understand a study
better in hindsight: “What is it that we manipulated in our study?” (E02). We
observed many instances where the experts tried to identify the modified
independent variable of a study and how they searched for dependencies within
the loop. This was also considered helpful for planning upcoming studies.
Several researchers suggested a systematic literature review to identify which
fidelity aspects were investigated in VR user studies. The model could provide
a structure for a large body of literature (E02), and it could be expanded to
be a signpost to all related work with details on specialized subcomponents
(E13). Nine interviewees mentioned the significant research opportunity of
systematically studying the fidelity components across the loop, both
individually and regarding their influence on each other. Due to the model’s
structure, high comparability might help identify patterns (E05, E06, E13,
E14), such as uncanny valleys in different sensory modalities (E04, E12).
These endeavors were often considered a long-term community effort.

Lastly, teaching was mentioned repeatedly as an ideal use case for the IntFi
Model: “Excellent presentation! I would include this in my curriculum right
away. These are good learning materials for the students” (E13). The
participants liked how they could convey the processes and connections of
different factors in VR interactions with one central figure as an overview.

##  6\. Application of the Model

In this section, we illustrate ways to use the IntFi Model in the work of
researchers, designers, developers, practitioners, teachers, and students who
work with VR. We collected best practices from our own experience and the
expert interviews (see Section 5), possibilities for applying the model as a
tool, and common traps to be careful of.

###  6.1. Best Practices

We recommend the following guidelines for applying the IntFi Model
purposefully and effectively. They build on experiences from using the model
for over two years and observing the participants in our expert interviews use
it.

  * •

Mind your reference. It is crucial to keep the reference interaction in mind
when assessing fidelity. Define the reference as specific and detailed as
possible before comparing it to the VR interaction. Keep recalling the
reference interaction throughout the analysis, not only at the start. When
using several references simultaneously, be aware of which one you currently
compare to.

  * •

Choose a focus. Decide how holistic or focused your analysis should be. Are
you interested in just one isolated aspect of the experience (e.g., a
handshake) or all elements involved (e.g., the full complexity of greeting
conventions, environmental conditions, multiple users, etc.)? It can be easier
to break down the interactions for analysis.

  * •

Set a goal. Reflect on the purpose of applying the model. The IntFi Model can
be used as various lenses through which interactions can be examined, allowing
different perspectives. We propose a number of ways to apply the model in the
next section.

  * •

Skip irrelevant aspects. You can use the IntFi Model modularly. In most cases,
only parts of the loop are needed for analyzing an interaction. Feel free to
ignore irrelevant components and instead focus on the key aspects.

  * •

Be objective. The connotation of “high-fidelity” as “better” is common in
practice as the Better–Worse Trap in Section 6.3 illustrates. However, the
IntFi Model works best as an objective tool. The fidelity concept is free of
judgment. Therefore, clearly differentiate between “high experiential
fidelity” and “great user experience.” While higher fidelity can be desirable
for interactions, deliberately decreasing fidelity can also help reach a goal.

  * •

Justify your assessment. Be more specific than only assigning low- or high-
fidelity labels. The insights from analyses or discussions can be richer if
you argue how you came to that conclusion. This can help identify dependencies
or patterns.

  * •

Adhere to the terminology. Please stick to the official terms used in the
IntFi Model when referring to it in scientific communication. If you need to
specify a fidelity subcomponent (e.g., haptic fidelity), additionally mention
the higher-level component it belongs to in the model (in this example,
display fidelity).

###  6.2. Lenses of the Model

The model can serve as different lenses through which interactions can be
viewed. Depending on your goals, the IntFi Model can be applied with various
strategies yielding different insights.

Describe & Report: The IntFi Model provides well-founded and consistent
terminology for the different aspects of fidelity in VEs that all contribute
to the overall fidelity of a system. This structure helps to identify and name
the fidelity aspect of interest and provides a foundation for an informed and
differentiated discussion about fidelity in VR. In communicating study
results, the investigated realism dimension can be unambiguously specified,
supporting comprehension and retrieval by other researchers. Furthermore, the
model offers a structure for reporting VR system specifications rigorously
within the VR research community.

Understand & Distinguish: The IntFi Model can be used to understand the
influencing factors on the overall fidelity in VEs at different stages of the
HCI loop. It facilitates a comprehensive understanding of distinct factors and
typical relations between the single components that contribute to the overall
fidelity in VR. The model can also help to untangle interwoven components such
as input and output through the same device. For example, user actions and
haptic feedback when using force-feedback gloves directly depend on each
other. Designers and developers can apply the framework to understand why
their product performs the way it does and make informed decisions that
explicitly address the influences of the single fidelity components on the
overall fidelity. This could, for example, include a coherent level of
fidelity across all stages of the loop or strategies to compensate for
limitations in one component through improvements in others, e.g., when bodily
trembling in an interaction can be compensated through denoising the signal
with the transfer function. Overall, educators can use the model to explain
the complexity of fidelity in VR and give students an overview of approaches
for the different components of fidelity and examples of how these typically
unfold in combination.

Compare & Analyze: Researchers, designers, and developers can apply the IntFi
Model to compare variants in VR setups, e.g., different input devices, and