Performing Fitts' Tasks in Virtual Reality With and Without Augmented Feedback: a Comparison Between Young and Older Adults
Phase: NA
Conditions: Healthy Older Adults, Healthy Young Adults
Sponsor: Aix Marseille Université

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Investigator: Prof. Jean-Jacques Temprado  
Institute of Movement Sciences – Chaire Active Aging 
 
Date: 10/02/2026 
 
Performing Fitts’ tasks in 
Virtual Reality with and 
without augmented feedback: 
a comparison between young 
and older adults. 
Study Protocol and Statistical Analysis Plan 

Performing Fitts’ tasks in Virtual Reality with and without augmented 
feedback: a comparison between young and older adults. 
 
Abstract 
Maintaining cognitive capacities in older adulthood is a growing challenge, making it increasingly 
important to understand age-related changes in information processing. A well-established motor-
cognitive paradigm to study these changes is Fitts’ task, which consists of rapid, goal-directed aiming 
movements under systematically varied accuracy constraints. According to Fitts’ Law, movement time 
(MT) increases linearly with the index of difficulty (ID), yielding an efficiency function (EF) whose slope 
reflects information processing efficiency (IPE). In real-world laboratory settings, older adults show 
longer MTs and steeper EF slopes than younger adults, indicating reduced IPE. This decline has been 
linked to increased cognitive demands, particularly attentional processes during movement 
deceleration. Fitts’ task therefore provides a robust framework to assess changes in motor-cognitive 
interaction across the adult lifespan. 
At the same time, immersive virtual reality (VR) is increasingly used in rehabilitation and research with 
older adults, making it critical to understand how VR affects motor-cognitive performance. Previous 
studies suggested that aiming movements performed in VR – especially in three-dimensional space – 
place higher demands on information processing capacities, which is reflected by longer MTs, more 
corrective sub-movements, and reduced spatial accuracy compared to real-world performance. These 
effects appear to be most pronounced for movements along the depth axis, likely due to altered depth 
perception. While these studies have provided important insights on changes in motor-cognitive 
demands in VR, they often employed less stringent task implementations, leaving open how well these 
constraints can be met under immersive conditions. Moreover, to our knowledge, older adults have 
scarcely been included in existing VR Fitts’ studies. 
This raises the question of how Fitts’ task can be reliably implemented in VR to assess age-related 
changes in motor-cognitive interactions. We hypothesize that when standard speed-accuracy 
constraints are applied, accurate execution of Fitts’ task in VR becomes challenging and that augmented 
feedback may be necessary to support reliable performance by compensating for VR-specific visual 
distortions and limited depth cues. Augmented visual feedback is of particular interest, as it directly 
targets perceptual limitations inherent to VR. 
The present study addresses these issues by examining: (i) how VR affects aiming performance and 
IPE; (ii) whether Fitts’ Law is followed in VR, (iii) whether augmented visual feedback helps to 
compensate for VR-related performance decline; (iv) whether these effects differ between younger and 
older adults (18-28 vs. 65-75 years). Results are expected to be available at the time of presentation. 
 
 

1. Introduction 
As global life expectancy increases, preserving brain health and cognitive function in older age has 
become a pressing scientific and societal priority, as recognized by the WHO (World Health 
Organization, 2021). 
Physical and cognitive training are two established approaches to cognitive enhancement in older 
adults, both of which have been shown to support brain health and neuroplasticity (e.g., Ball et al., 2002; 
Cassilhas et al., 2007; Colcombe & Kramer, 2003). More recently, the training of complex movements 
(CMT) has emerged as a distinct approach to enhance cognitive function (e.g., Niemann et al., 2014; 
Matthews et al., 2016; Voelcker-Rehage et al., 2010, 2011). “Complex movements” conventionally refer 
to motor tasks requiring the coordination of multiple degrees of freedom (DoF; Bernstein, 1967). These 
tasks place high demands on motor control and thereby engage cognitive processes such as attention 
(Temprado et al., 1999) and inhibition (Temprado et al., 2020). Studies have shown that practicing such 
tasks yields cognitive benefits – particularly in executive functions like inhibitory control (Torre et al., 
2023) and cognitive flexibility (Matthews et al., 2016) – that are distinct from those produced by 
conventional physical training (Budde et al., 2008; Niemann et al., 2014; Matthews et al., 2016; 
Voelcker-Rehage et al., 2011). Examples include bimanual (Temprado et al., 1999, 2020), quadrupedal 
(Matthews et al., 2016), and whole-body coordination tasks (Niemann et al., 2014; Voelcker-Rehage et 
al., 2010, 2011). 
In the present work, we argue that the definition of complexity can be further extended to include tasks 
that involve not only the coordination of multiple DoF but also performance constraints such as high 
speed and accuracy demands. This perspective motivates our focus on aiming movements performed 
in three dimensions within the Fitts’ task paradigm. 
Unimanual aiming and the Fitts’ task paradigm 
Fitts’ task is a well-established experimental paradigm for studying the speed-accuracy tradeoff in 
human motor control (Fitts, 1954). In this task, typically performed in one or two dimensions, participants 
perform rapid, goal-directed aiming movements from a fixed starting point to a target as quickly and 
accurately as possible (Fitts, 1954; MacKenzie & Buxton, 1992). Although unimanual, these movements 
involve controlling multiple DoF under strict accuracy constraints and can therefore be regarded as 
complex movements, engaging cognitive processes such as attention, information-processing speed, 
and possibly inhibition, with the degree of engagement varying as a function of task difficulty. Task 
difficulty itself can be systematically manipulated by varying target distance (D) and/or target width (W): 
increasing D or decreasing W makes the task more difficult. This is commonly quantified by the index of 
difficulty (ID), which combines D and W into a single value and is defined as: 
log2(2 ∗𝐷
𝑊) 
(Fitts, 1954; Fitts and Peterson, 1964) 
 
To better capture actual performance, effective width (We) and effective distance (De) are often used 
instead of nominal W and D (MacKenzie, 2018). While We reflects how spread out the movement 
endpoints are, De reflects the average actual distance moved – both of which can differ from the 
instructed W and D. Using these effective values, throughput (TP) is calculated as ID divided by 
movement time (MT). TP is a global measure of information processing capacity, which reflects the 
balance between speed and accuracy. Higher TP values indicate more effective performance, while 
lower values suggest less efficient processing (MacKenzie, 2018). 
According to Fitts’ Law, MT increases linearly with ID. This relationship is captured by the so-called 
efficiency function (EF), which plots MT against ID (see fig. 1). The slope of the EF reflects an individual’s 
information processing efficiency (IPE): steeper slopes indicate that MT increases more sharply with 
task difficulty, suggesting lower IPE, while shallower slopes indicate more efficient processing. 
 
 
 
 
 

Deceleration phase 
Peak velocity 
Cognitive 
control 
Acceleration phase 
0
150
300
450
600
750
2
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MT [ms]
ID [bits]
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 1: Representative EF in young healthy adults (data from Sleimen-Malkoun et al., 2013). 
Movement kinematics can be divided into acceleration and deceleration phases, commonly defined by 
acceleration time (AT) and deceleration time (DT), separated by peak velocity (see fig. 2; MacKenzie et 
al., 1987). The acceleration phase primarily reflects feed-forward, preprogrammed force generation 
shaped by movement planning and neuromuscular constraints, while the deceleration phase involves 
feedback-based cognitive control to achieve terminal accuracy (Temprado et al., 2013). It has been 
found that acceleration time scales with target distance, as longer reaches require greater initial force 
and planning, whereas deceleration time scales with target width, with smaller targets demanding higher 
precision and more online corrections during movement termination (MacKenzie et al., 1987; Temprado 
et al., 2013). 
 
 
 
 
 
 
 
 
 
 
 
Fig. 2: Typical bell-shaped kinematic profile of an aiming movement accommodating Fitts’ task constraints. The profile is 
separated into acceleration and deceleration phases according to peak velocity. Profile symmetry varies with ID, as well 
as participants’ age or functional status. 
Fitts’ task and aging 
Previous studies on Fitts’ task in real-world settings (R) have shown that older adults generally exhibit 
lower MT and peak velocities compared to younger adults; and, more importantly, they showed reduced 
IPE, reflected in steeper EF slopes (see fig. 3; Temprado et al., 2013). This decline has been linked to 
increased cognitive demands (e.g., in terms of attentional and information processing speed 
requirements), particularly during the deceleration phase (Sleimen-Malkoun et al., 2013; Temprado et 
al., 2013). Interestingly, a similar slowing of information processing speed has been observed in the 
Hick-Hyman task, a cognitive task, reinforcing the assumption that EFs can serve as a measure of age-
related general slowing in the motor-cognitive system (Sleimen-Malkoun et al., 2013). 
 
 
 

0
200
400
600
800
1000
1200
2
4
6
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MT [ms]
ID [bits]
Young
Older
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 3: Representative EFs for younger and older adults. A significant difference was observed between the slopes of the 
two groups (data from Sleimen-Malkoun et al., 2013). 
Fitts’ task in 3D 
While Fitts’ task has traditionally been studied in one- (1D) and two-dimensional (2D) settings (Fitts, 
1954; MacKenzie & Buxton, 1992), recent research has extended its application to three-dimensional 
(3D) environments, which requires the control of additional muscular and articular DoF (Cha & Myung, 
2013; Murata & Iwase, 2001; Zeng et al., 2012). Thus, compared to 2D movements, several additional 
factors have been found to influence MT in 3D, which may, consequently, also affect the linear 
relationship with ID and the slope of the EFs. Relevant factors include movement direction along the 
three spatial axes – up/down, left/right, and forward/backward (Cha & Myung, 2013; Murata & Iwase, 
2001); the number of DoF involved, such as shoulder rotation, elbow elevation, and wrist compensation 
(e.g., Morasso, 1983); and the influence of gravity (Jang et al., 2017; Triantafyllidis & Li, 2021). Despite 
the increased complexity described above, it has generally been shown that Fitts’ Law – reflected in the 
linearity of EFs – also holds for aiming movements in 3D (Cha & Myung, 2013; Murata & Iwase, 2001), 
though it appears to manifest in longer MTs and potentially steeper EF slopes. 
Fitts’ task in 3D in virtual reality (VR) 
Immersive VR systems are becoming increasingly available for entertainment (e.g., exergames) and are 
also being integrated into healthcare and rehabilitation (Kirthika et al., 2025), with solutions such as 
H’ability (H’ability SAS) and VR Essential (ezyGain SAS). As a result, both gamers and patients 
commonly perform 3D movements to interact with virtual environments, such as reaching, aiming, and 
selecting virtual objects. Many of these movements correspond to Fitts-like tasks performed in 3D. In 
this context, VR presents both opportunities and challenges for studying human motor control, and 
establishing the validity and reliability of Fitts’ task in VR environments is crucial. On the one hand, VR 
facilitates flexible task design, including: (i) manipulation of spatial parameters and informational context 
(e.g., augmented feedback [FB], structured environments), (ii) gamification of exercises, and (iii) 
simulation of realistic and ecologically valid motor tasks (Raichlen & Alexander, 2017; Temprado, 2021). 
On the other hand, VR also introduces unique factors affecting movement control that are absent in R 
conditions (Barrera Machuca & Stuerzlinger, 2019; Batmaz et al., 2019; Lane et al., 2025; Xiao et al., 
2024), namely (i) the presence of visual distortion (Melo et al., 2022) and (ii) the lack of reliable depth 
cues (Gerig et al., 2018). These changes can affect the multisensory integration process, particularly of 
visual and proprioceptive signals (Pratviel et al., 2021), which is critical for accurate movement control. 
Moreover, several studies have shown that when performed in 3D and in VR, movements exhibit more 
sub-movements than in real-world environments, along with longer MTs (Liu et al., 2009), reduced 
spatial accuracy and lower overall task performance (Barrera Machuca & Stuerzlinger, 2019; Batmaz & 
Stuerzlinger, 2022). Although these studies did not systematically analyze EFs, the observed 
performance patterns suggest that they may be steeper in VR environments (see fig. 4). Notably, these 
performance differences were found to be particularly pronounced in the anterior-posterior 

0
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2000
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3
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MT [ms]
ID [bits]
Young (R)
Older (R)
Young (VR)
Older (VR)
(forward/backward) direction, where movement control depends heavily on depth perception. By 
contrast, lateral movements are less affected in VR and are typically performed faster and more 
accurately (Barrera Machuca & Stuerzlinger, 2019; Batmaz et al., 2019). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 4: Representative EFs for younger and older adults in R condition (data from Sleimen-Malkoun et al., 2013), alongside 
expected EFs for both groups in VR condition. A significant slope difference was observed between young (R) and older 
(R) participants. EF slopes are expected to be higher and steeper in VR than R conditions. Potentially, an aging effect will 
be present also in VR conditions, with older adults exhibiting higher EFs than young adults. 
This raises the question of whether augmented feedback (FB) can help compensate for visual distortions 
and limited depth cues in VR, potentially improving performance via enhanced multisensory integration. 
Augmented FB can be provided through different channels, including visual, auditory, or haptic. Prior 
work, however, has rarely compared augmented FB to true no-FB conditions. Instead, augmented visual 
FB was typically provided in all conditions (thus not controlled), with other modalities such as auditory 
or haptic FB added on top (e.g., Batmaz et al., 2019, 2020; Batmaz & Stuerzlinger, 2022; Kourtesis et 
al., 2022; Tanacar et al., 2023). Notably, this additional auditory or haptic FB rarely yielded clear 
performance benefits over augmented visual FB only. The most compelling evidence for the benefit of 
augmented FB comes from McAnally and Wallis (2024) who explicitly compared augmented auditory 
FB to a true control condition without any augmented FB (neither visual nor auditory). They showed that 
auditory error FB significantly reduced performance errors compared to the no-FB condition. Error FB 
through different channels, such as auditory and visual, conveys similar information. As VR alters visual 
perception, augmented visual FB may, however, offer specific advantages and therefore it is the focus 
of the present study. However, its effectiveness remains to be established. Moreover, because errors 
can take different forms – i.e., overshoot (moving beyond the target), undershoot (falling short), lateral 
deviation (deviating sideways from the target) or vertical deviation (deviating upward or downward) – it 
is important to compare the effects of binary FB (indicating success or failure) and more specific FB that 
indicates the exact nature of each error. 
Study rationale and research gaps 
Most research on Fitts’ task has focused on performance in real-world (R) and 2D settings, primarily 
measuring information processing speed using MTs and EFs (i.e., Fitts’ Law). In this context, cognitive 
control is required to manage the speed-accuracy trade-off and coordinate multiple DoF, particularly 
during the deceleration phase. This requires cognitive processes to integrate visual, proprioceptive, and, 
in sliding movements, haptic information (e.g., friction between the effector and its support; Temprado 
et al., 2013). However, many questions remain which this study aims to answer: 
Fitts’ task has increasingly been examined in 3D, but the specific influence of spatial dimensionality on 
task performance, information processing, and the slopes of EFs remains unclear. Presumably, 
performing the task in 3D imposes greater cognitive demands, engaging additional attentional and 
inhibitory resources. In VR, these demands might be further amplified, as multisensory integration is 

often disrupted. However, the specific effects of such sensory disruptions on task performance and 
information processing have not been systematically investigated. They may result in lowered task 
performance (longer MT, higher error rate, and lower TP), less efficient information processing (possibly 
reflected in steeper EF slopes), and altered movement kinematics (such as disproportionately prolonged 
DT). 
In this context, although augmented error FB might help compensate for VR-induced visual distortions, 
its effectiveness remains to be determined, notably by comparing the information conveyed across 
different FB types (i.e., no FB, FB indicating whether the target was hit or missed, and FB indicating the 
nature of the error; see Methods for details). 
Finally, the effects of ageing on unimanual aiming in VR remain unexplored. Indeed, to our knowledge, 
healthy older adults (HOA) have scarcely been included in existing 3D VR Fitts’ studies, despite being 
a population that could benefit most from VR-based CMT aimed at counteracting motor-cognitive 
decline. 
Building on this rationale, the present study aims to fill these gaps. 
 
1.1 Objectives 
The objectives of the present studies are the following: 
- 
To assess whether Fitts’ Law holds in 3D immersive VR compared to R conditions when only 
intrinsic visual FB is available, in both YA and HOA. 
➔ RQ1: Does Fitts’ Law hold in 3D immersive VR compared to R conditions? 
- 
To determine whether Fitts’ Law is maintained in VR under two types of augmented visual FB 
(global FB and specific FB; see Methods for details), and to compare their relative effectiveness 
in supporting task performance and the speed-accuracy trade‑off. 
➔ RQ2: Can augmented visual error FB induce Fitts’ Law-like behavior in VR and improve 
performance, and does feedback specificity matter? 
- 
To compare the performance patterns of YA and HOA across these conditions. 
➔ RQ3: How does age affect 3D Fitts’ task performance and kinematics? 
 
1.2 Hypotheses 
- 
The R condition is considered as the reference condition in which participants are expected to 
follow Fitts’ Law, with performance producing linear EFs. 
- 
In general, task performance is expected to be higher in R than VR with shorter MT, lower ER, 
and higher TP. Further, DT is expected to be disproportionately long relative to AT in VR. 
- 
In VR without augmented FB, Fitts’ Law may not hold (or at least, it should be nearly flat) since 
participants might fail to reach targets reliably and be unable to appropriately modulate the 
speed-accuracy trade-off as a function of ID. 
- 
When providing augmented visual FB in VR, it is expected to improve task performance and to 
restore Fitts’ Law-like behavior, potentially producing higher EF slopes than in R. In this, specific 
FB is hypothesized to be more effective than global FB, as the greater amount of information 
should better support an optimal speed-accuracy trade-off. 
- 
Regarding age effects, HOA are predicted to have generally lower performance (higher error 
rate, longer MTs, lower TP), steeper EF slopes, and disproportionately longer DT. Differences 
are expected to be amplified in VR due to greater cognitive and sensorimotor demands from 
distorted sensory input. Furthermore, HOA may benefit more from augmented FB, particularly 
specific FB, than YA as a greater amount of information may better compensate for age‑related 
declines in cognitive control essential for precise movement termination. 
 
2. Methods 
2.1 Participants 
A total of 30 participants (women and men) will be recruited, specifically 15 YA (aged 18 to 28) and 15 
HOA (aged 65-75). This sample size was determined through an a priori power analysis using G*Power 
3.1.9.7 software (for details, see Faul et al., 2007) for a mixed-design repeated-measures ANOVA with 
two groups and four within-subject measurements (reflecting the between- and within-subject factors 

described later, i.e., age group and FB conditions). The calculation was based on the primary outcome 
measure, namely the slopes of the EFs (reflecting IPE). The analysis assumed an alpha level of 0.05, 
statistical power of 0.80, a medium effect size (f = 0.26), a correlation among repeated measures of 0.5, 
and a non-sphericity correction factor of 1.0. As no VR Fitts’ task studies have analyzed EF slopes, to 
our knowledge, we based our effect size estimate on TP analyses, which is derived from the same 
underlying variables as EFs (MT and ID) and additionally accounts for endpoint accuracy. Specifically, 
Batmaz and Stuerzlinger (2001) reported a within-subject interaction of selection technique × auditory 
FB on TP (partial η² = 0.22, corresponding to f = 0.53; converted following Kim, 2016). Because this was 
a pure within-subject interaction on TP, whereas our primary hypothesis concerns a mixed interaction 
on EF slopes – including additional between-subject variability – we conservatively halved the effect 
size for the power calculation. Since this yielded an estimated required sample size of 22 participants, 
recruiting a total of 30 provides a margin for potential attrition or unusable data and aligns with sample 
sizes in similar VR studies. 
Recruitment for both age groups will take place on a rolling basis over a period of approximately two 
months (between February and April 2026). 
HOA (65 to 75): individuals living autonomously at home through multiple ways: 
- 
In the existing cohort of ‘Chair Active Aging’ which is a well-established database of 
approximately 100 HOA (men and women) who have previously expressed interest in 
participating in research on motor control and physical activity training. 
- 
Through calls for participation in local newspapers and outreach to leisure clubs for seniors in 
the city of Marseille will be used to broaden the recruitment base. 
- 
Through calls for voluntary participation in the SMUC-Maison Sport Santé of Marseille. 
- 
These strategies are intended to increase inclusivity by reaching individuals outside the existing 
cohort and to minimize the risk of insufficient enrollment or difficulty assembling a homogeneous 
sample of HOA. 
YA (18 to 28): Recruitment will be made within the student population of the University campus (Luminy 
Campus in Marseille) through on-campus posters, university mailing lists, and word-of-mouth referrals. 
General inclusion criteria 
- 
Aged 18 to 28 (YA group) or 65 to 75 (HOA group) 
- 
Right-handed 
- 
Normal or corrected-to-normal vision (glasses or contact lenses permitted) 
- 
Clear and comfortable vision through the head-mounted display (HMD) during a brief fitting (very 
bulky glasses may be incompatible) 
- 
No self-reported history of neurological or psychiatric disorders, as confirmed by participant 
report and cross-checked against a standardized list of relevant medications 
- 
Able to provide informed consent and follow experimental instructions in French or English 
Additional criteria for HOA 
- 
Normal cognitive functioning (Montreal cognitive assessment [MoCA] score ≥ 26; Nasreddine 
et al., 2005) 
- 
No self-reported acute or chronic pain in the dominant arm, shoulder, or elbow that would 
preclude performing repetitive arm movements in space. 
- 
Self-reported full functional range of motion in the dominant arm (able to extend the arm fully 
without discomfort or restriction) 
Exclusion criteria 
- 
Individuals currently playing video games more than 5 hours/week. 
- 
Uncorrected visual, auditory, or motor impairments that would interfere with task performance. 
- 
Participant height outside the range of 1.50 - 1.80 m. 
- 
Self-reported diagnosis of a neurodegenerative disease (e.g., Parkinson’s disease, Alzheimer’s 
disease) 
- 
Self-reported use of medications known to significantly affect cognitive or motor function (a list 
of relevant medications will be presented during screening). 
- 
Cervical pain that could preclude wearing the VR headset during the full duration of the 
experimental session. 
- 
Self-reported history of severe motion sickness or vestibular issues that could be exacerbated 
by VR exposure 

- 
High susceptibility to cybersickness, as assessed via the Visually Induced Motion Sickness 
Susceptibility Questionnaire (VIMSSQ) which was developed specifically for pre-exposure 
screening; cut-off: ≥ 12 (Golding et al., 2021; Golding & Keshavarz, 2025). 
- 
Individuals for whom the headset cannot be properly adjusted, e.g., due to an interpupillary 
distance (IPD) outside the adjustment range of the HMD (i.e., <53 mm or >75 mm). 
Note: All health-related criteria above are based on self-report only. No medical examination or physician 
confirmation will be conducted, as the study involves minimal risk and a non-invasive motor task. 
 
2.2 Experimental protocol 
Each participant will complete the study in four successive phases: an initial information process, 
followed by consent collection seven days later, the data acquisition phase (conducted between days 8 
and 10), and a final voluntary information session (see fig. 5). 
 
 
 
 
 
 
 
 
Fig. 5: Overview of the participant procedure and timeline, illustrating the four consecutive study phases. 
Information process 
During the information process, participants will be provided with comprehensive details regarding the 
study’s purpose, procedures, potential risks and benefits, data handling, and their rights, including the 
right to withdraw at any time without consequences. If they wish, participants will also have the 
opportunity to view the experimental setup, including the VR equipment, and briefly try it out. They will 
then have seven days to consider their participation before providing formal written consent. 
Description of the task 
Participants will perform a 3D Fitts’ task with their dominant right hand, reaching with a handheld 
controller from a fixed start sphere to a target positioned along the body’s sagittal midline. The target 
will be placed 32 cm forward (anterior-posterior) and 24 cm upward (vertical) from the start position, 
yielding a resultant movement distance of 40 cm according to Pythagoras’ theorem 𝑎2 = 𝑏2 + 𝑐2. This 
distance will at most correspond to about 80% of maximal forward reach. 
Apparatus and set-up 
Participants will be seated comfortably against a backrest in front of a table throughout the whole 
experiment, with chair height and apparatus position adjusted individually so that the start sphere is 
aligned to an ergonomically optimal desk height, comparable to that used for writing. Feet will be 
positioned flat on the floor; with a footrest used when necessary to maintain this posture. Movements 
will be performed using a handheld controller tracked by the HMD’s built-in system. The HMD will be 
worn in all conditions: in the R condition, participants will view the physical apparatus through 
passthrough mode, while in VR, they will see a visually identical virtual replication. Participants will only 
use their dominant right hand. Before each aiming movement, the right hand will be rested on the table 
while pointing at the start sphere. 
In the R condition, the start sphere will be represented by an open frame mounted on a thin rod on the 
table in front of the participant. The frame outlines a partial spherical contour that indicates the full size 
of a sphere (following Barrera Machuca & Stuerzlinger, 2019) and is oriented partly toward the target 
and partly upward, allowing the intended anterior-posterior and upward toward the target without 
contacting a solid surface. The target will use the same open-frame spherical design, but with its opening 
Participant 
receives 
study info
Information 
process
Signs 
consent form
Consent 
collection
Participates 
in experiment
Data 
acquisition
Researcher 
presents 
outcomes
Voluntary 
information 
session
Day 
Approx. within 3 months 
Day 8-10 
Day 7 

oriented toward the participant and slightly downward, enabling participants to move into the sphere 
without touching its surface. Both start and target structures will be fabricated from lightweight 3D-printed 
plastic (2 to 3 mm thickness) with rounded edges. Target diameters will correspond to the W values 
while the start sphere will have a medium diameter of 7 cm. 
In the VR condition, the set-up is visually and spatially replicated (see fig. 6). The start and targets are 
represented as full grey spheres with a grid-like surface, making their 3D structure visible. Because 
there are no physical surfaces in the virtual environment, this set-up again allows participants to perform 
the intended anterior–posterior and upward movements freely.  
Material 
- 
HMD: Meta Quest 3 (weight: 515 grams) 
- 
Handheld controllers: Meta Quest Touch Plus 
- 
Sampling frequency: 120 Hz 
- 
Tracking system: In-built 
- 
Software: Unity 6000.0.37f 
- 
Computer system: Windows 11 (64-bit) PC with Intel® Core™ i5-13500H processor, 16 GB 
RAM, and NVIDIA RTX 4060 GPU 
The equipment is commercially available and non-invasive. 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 6: Experimental set-up and participant view. Left: Schematic side view of a participant in the VR condition. W 
represents the 3D extent of the target (x-, y-, z-axes), and movement distance is defined as 𝐷= √𝐷ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙² + 𝐷𝑣𝑒𝑟𝑡𝑖𝑐𝑎𝑙². 
In R condition, the start sphere is an open frame partially outlining the full target size, allowing unobstructed movement 
toward the target. Right: Example of the virtual environment as seen by participants, showing the controller, the start, and 
the target itself. 
Manipulation of task parameters 
• 
Task difficulty 
ID will be manipulated via target width (W = 14.1 cm, 7.1 cm, 3.5 cm, 1.8 cm), yielding IDs of 2.5, 3.5, 
4.5, and 5.5 bits. Each trial will begin with the controller tip resting on the start sphere. Participants will 
move as quickly and accurately as possible toward the target, remaining inside the target sphere 
continuously for 0.6 seconds. 
• 
FB conditions: 
- 
VR-intrinsic (VR-I). The immersive virtual setup will be presented without augmented FB about 
whether targets were hit correctly (or not); participants will rely on intrinsic FB only. 
- 
VR-augmented global (VR-AG). Augmented visual error FB will indicate target entry and trial 
outcome. Generally, the target turns blue when entered. A successful trial is confirmed when 
participants remain inside for 0.6s, after which it turns green. For an error, the target turns red; 
either directly from grey if never entered, or after briefly turning blue if entered and then exited. 
Alternative colors will be used as needed to accommodate participants with color deficiencies.  
D 
W 
Dvertical 
Dhorizontal 

- 
VR-augmented specific (VR-AS). In this condition, augmented visual error FB will further 
indicate the type of error. Overshoots trigger a written message reading ‘too long’, undershoots 
display ‘too short’, lateral deviations show a message of either ‘error to the right’ or ‘error to the 
left’, and vertical deviations show a message of either ‘too high’ or ‘too low’. 
 
• 
Age groups: 
- 
YA (18 to 28 years) 
- 
HOA (65 to 75 years) 
 
Thus, the study design results in a 3-way mixed factorial design with two within-subject factors (FB 
condition and ID) and one between-subject factor (age group). 
Prior to data recording, participants will complete a familiarization phase in the VR-AS condition, which 
offers the highest informational value by encompassing all functionalities included in the study. 
Participants will perform practice trials at IDs 3 and 5 (D = 40 cm; W = 10.0 cm and W = 2.5 cm, 
respectively), with the order of IDs counterbalanced. Participants will complete as many trials per ID as 
needed until they feel they have a sufficient understanding of the task, the experimental set-up, and the 
materials. After each condition, participants will provide a brief motion sickness rating using the 
Simulator Sickness Questionnaire (SSQ; Kennedy et al., 1993) and have a short break (about 60 s, 
longer if needed). The main experiment will consist of 120 trials per participant (3 FB conditions × 4 IDs 
× 10 trials). Trials will be organized into blocks of 40, each comprising four consecutive runs of 10 trials 
at a single ID (e.g., 10 trials at ID 3, then ID 4, etc.). The order of IDs within each block will be 
randomized, and the condition order will be counterbalanced across participants using a Latin square 
design. 
Measurement 
The motion tracking system of the Meta Quest 3 will be used to capture 3D positional data of the 
handheld controller throughout each trial (nominal sampling frequency = about 120 Hz). To ensure that 
only intentional movements will be captured, thresholds were defined for movement onset and offset: 
Movement onset was defined as when the controller speed exceeded 12.5 cm/sec in a single frame, 
and movement offset occurred after the controller remained below this threshold for at least 0.6 sec. 
These thresholds were determined through pilot testing.   MT was defined as the time elapsed between 
movement onset and offset. 
Further performance metrics will be computed based on the recorded positional data and MT: 
- 
Error rate, calculated as the proportion of missed targets relative to total number of trials 
- 
We, calculated from the dispersion of movement endpoints relative to the target center 
- 
De, calculated from the actual movement distance relative to the starting point 
- 
Peak velocity, identified as the maximum velocity reached during the movement for each trial 
- 
AT and DT, extracted from the velocity profile of the movement 
- 
TP, computed based on the effective ID (IDe) and MT 
- 
EFs, computed by performing linear regressions of MT, AT, and DT against ID 
After completing each experimental condition, participants will rate their perceived cognitive effort using 
the 9-point Likert-type Paas scale developed by Paas et al. (1994, 2008) as well as their perceived 
exertion using Borg-CR10 scale (Borg, 1982, 2008). 
Outcomes 
The primary outcome is the comparison of EF slopes across age groups and FB conditions. Secondary 
outcomes include the quality of EF fits (R² values) and the effects of FB condition, ID, and age group 
(and their interactions) on performance metrics: MT, AT, DT, error rate, and TP. 
Duration 
Each participant will take part in one experimental session lasting approximately 75 minutes, with short 
breaks between blocks as needed. 
 
Tab. 1: Overview of experimental procedure per participant 
Measure 
Timeline 

Recruitment
Months 1 & 2
Testing
Months 1 - 3
Data analysis
Months 4 & 5
Publication & 
dissemination
Months 6+
Instructions 
5 minutes 
Familiarization 
15 minutes 
Experimental trials 
50 minutes 
Post-experiment debriefing 
5 minutes 
 
Data collection is scheduled to take place over a period of approximately three months, beginning upon 
approval from the CERSTAPS ethics committee. The study is set to start in January 2026 and conclude 
by April 2026. Recruitment and testing will be carried out on a rolling basis throughout this period, 
depending on participant availability. After data collection is completed, the data will be analyzed, and 
participants will be invited to an information meeting where the study's aims and findings will be 
presented and discussed. This meeting will also offer an opportunity for participants to ask any 
remaining questions. The study results will subsequently be prepared for publication in a peer-reviewed 
scientific journal. See fig. 7 for further illustration. 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 7: Study timeline and dissemination flow. * Refers to the voluntary information session with participants. 
2.3 Data analysis 
All statistical analyses will be conducted using Python, with an alpha level of 0.05 and reporting of effect 
sizes where appropriate. 
Fitts’ Law verification 
To verify whether Fitts’ Law holds across all conditions, linear regressions will be performed for each 
participant and FB condition, relating MT, AT, and DT (dependent variables) to ID (independent 
variable). For each regression, the slope, coefficient of determination (R²), and p-value will be extracted 
to evaluate model fit and statistical significance. 
Analysis of EF slopes (primary outcome) 
To evaluate the influence of FB condition and age group on the slope of the EFs, i.e., on IPE, a 2-way 
mixed-design repeated-measures ANOVA will be conducted separately for the EF slopes of MT, AT, 
and DT. The dependent variables are the EF slope values derived for each participant and condition by 
regressing MT, AT, and DT against ID. The independent variables include FB condition (4 levels) as 
within-subject factor and age group (younger vs. older adults) as a between-subject factor. Main effects, 
interaction, and post hoc comparisons will be reported. 
Analysis of performance metrics (secondary outcome) 
To explore how age group, task difficulty, and FB condition influence performance, a 3-way repeated-
measures ANOVA will be conducted on raw performance metrics. The dependent variables are MT, AT, 
DT, error rate, and TP. The independent variables include ID (4 levels) and FB condition (4 levels) as 
within-subject factors and age group (young vs. older) as a between-subject factor. The analysis will 
test for main effects and all possible two-way and three-way interactions. Significant effects will be 
further explored using post hoc comparisons. 
Analysis of subjective cognitive load and physical exertion 
To compare the subjective cognitive and physical exertion across FB conditions, a 2-way repeated-
measures ANOVA will be conducted on Paas and Borg scores as dependent variables. The independent 
variables include FB condition (4 levels) as within-subject factor and age group (younger vs. older adults) 
as a between-subject factor. Main effects, interaction, and post hoc comparisons will be reported. 

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