Cognitive-Motor Learning in Virtual Reality Enhances Processing Speed and Processing Speed Efficacy in Healthy Adults

Cognitive-Motor Learning in Virtual Reality Enhances Processing Speed and Processing Speed Efficacy in Healthy Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cognitive-Motor Learning in Virtual Reality Enhances Processing Speed and Processing Speed Efficacy in Healthy Adults Carter Witbeck, Brannon Sumner, David Tinjust, Tony Montina, Gerlinde Metz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8429022/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cognitive-motor learning (CML) interventions hold promise for enhancing both cognitive and motor performance, yet a deeper understanding of their underlying mechanisms is essential to optimize their application. This study determined whether a virtual reality (VR)-based CML intervention can improve cognitive-motor speed efficacy (CMSE) in a perceptual-motor task by enhancing processing speed and/or decision making in healthy young adults. Sixty-two participants were assigned to experimental and control conditions before completing a 12-week VR-based CML training protocol composed of a baseline assessment, eight weekly training sessions, a post-test, and a transfer test. Performance was evaluated using response time, CMSE, decision accuracy and proprietary composite performance indexes (Metascore, Zetascore). The experimental group demonstrated clear CML compared to controls, who only showed an effect of practice. Learning gains also transferred to a similar task with new perceptual-motor associations and were mainly driven by faster response times rather than improved decision-making. The saturation trajectories of the novel Metascore and Zetascore indexes appear to reflect the associative and autonomous stages of learning, respectively, with response time serving as a key factor in this progression. These findings underscore the value of composite performance indexes for capturing the dynamics of CML and providing a foundation for future applications in athletic and professional performance optimization and neurorehabilitation. Physical sciences/Mathematics and computing Biological sciences/Neuroscience Neuroplasticity decision-making sensorimotor integration composite indexes cognitive training motor learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Cognitive-motor learning (CML), the integration of cognitive processes with motor skill acquisition, is essential for lifelong neuroplasticity and adaptive behaviour. Neuroplasticity enables the brain to reorganize in response to experience, a capacity that endures across the lifespan and supports targeted interventions to enhance CML 1 . Advances in virtual reality (VR) and gamified digital training platforms have provided immersive and engaging methods for CML 2 . These discoveries have driven the development of non-pharmaceutical strategies aimed at improving cognitive and motor functions in both healthy individuals and those recovering from brain injury 3 . Cognitive and motor functioning can be compromised by neurological disorders such as dementia, stroke, and traumatic brain injury 4 . CML interventions have been increasingly explored in clinical and rehabilitation contexts for their potential to support functional recovery, promote functional independence, and improve quality of life 5 , 6 . Evidence suggests that targeted CML interventions may strengthen executive function, memory, and attentional control, which are essential for restoring independence and reintegration into daily life 7 – 9 . CML approaches are also being applied beyond clinical contexts to enhance performance in domains such as sports, education, and workplace productivity 10 – 12 . CML interventions are particularly valuable to promote neuroplasticity by maintaining and enhancing cognitive and motor functions 13 – 15 . Functional magnetic resonance imaging (fMRI) studies have demonstrated that CML training can modify local functional connectivity in healthy older adults 16 . Moreover, CML interventions can induce both structural and functional plasticity, promoting synaptogenesis 17 and facilitating long-term potentiation (LTP), a process that strengthens synaptic efficacy and supports learning and memory 18 . The cognitive dimension of CML has primarily been studied through cognitive learning, focusing on executive function, attention, speed processing, and decision-making 19 . On the other hand, the motor dimension of CML emphasizes the acquisition and refinement of motor skills 19 . Growing evidence indicates that cognitive and motor learning share common neural mechanisms, suggesting that they are inherently interconnected rather than distinct functions 20 . For example, athletes engaged in cognitive-motor dual-task training demonstrate that motor execution under cognitive load enhances both reaction speed and decision accuracy 10 . This finding supports the view that cognitive and motor learning functions are integrated processes that adapt in parallel during CML. CML methods typically consist of a training protocol and an interface in which the cognitive-motor intervention is administered 21 . Regardless of the delivery format, the training protocol must be validated through rigorous testing to ensure effectiveness. Virtual Reality (VR) has become increasingly prominent in commercial and research applications due to its unique features, such as its immersive nature, flexible design capacity, precise monitoring, and high user motivation 2 , 22 , 23 . This enables researchers to develop safe and controlled virtual environments that closely resemble real-world scenarios while providing real-time performance feedback 7 . VR sensorimotor learning interventions show particular promise as innovative tools for enhancing specific cognitive and motor functions through immersive and interactive experiences. For example, VR training programs have been developed for surgical residents to practice in realistic simulated settings, reducing procedural errors and skill decay 12 , 24 . Despite extensive research on cognitive and motor training, few studies have integrated both domains within an immersive, empirically validated VR environment. The present study aimed to evaluate the efficacy of a novel, VR-based CML-training protocol designed to enhance processing speed and decision-making in healthy young adults. We have also assessed L47's proprietary composite indexes, integrating speed of processing and decision-making and cognitive-motor speed efficacy (CMSE or L47zone). These metrics were assessed across three experimental phases: pre-training (baseline), immediately after training (post-test), and two weeks after training had ended (transfer test). Participants in the experimental group completed one weekly training session with the experimental CML task, while the control group completed matched sessions with a sham task. We hypothesized that (1) no significant performance differences will be observed between groups at baseline; (2) post-training, the experimental group will demonstrate improved performance relative to its own baseline and controls; and (3) the experimental group will maintain these improvements at the transfer test, indicating training transfer, while the control group will show no significant change across phases. The findings clarify the potential of VR-based CML training to induce lasting gains in cognitive-motor performance. 2. Results 2.1 Cognitive-Motor Learning Training Improved Processing Speed (Response Time) Across Learning Phases There was no significant difference in the control group’s response time from BL to PT, PT to TT, and BL to TT. The experimental group’s response time, however, was significantly reduced when comparing PT to BL (p < 0.001, Fig. 1 , Table 1 ). The experimental group’s response time was also significantly slower while performing the TT when compared to their PT (p < 0.001), but their TT response time still remained significantly shorter compared to their BL response time (p < 0.001). Between-group comparisons found no significant difference between the BL response time of the two groups. The experimental group’s response time, however, was significantly faster when compared to the control group at both the PT (p < 0.001) and the TT (p < 0.001, Table 2 ). Table 1 Comparisons within both the experimental and control groups showing the median and significance for each testing measure between the following time points: BL to PT, PT to TT, and BL to TT. Significance was determined using the Wilcoxon Sign Rank test. Measure Experimental Control Median P-value Median P-value Response Time (ms) BL-PT 481.6–181.7 < 0.001 459.8–390.1 Not Sig. PT-TT 181.7–212.4 < 0.001 390.1–389.8 Not Sig. BL-TT 481.6–212.4 < 0.001 459.8–389.8 Not Sig. CTD BL-PT 16.4–22.8 < 0.001 16.0–20.8 < 0.001 PT-TT 22.8–21.8 < 0.05 20.8–21.6 Not Sig. BL-TT 16.4–21.8 < 0.001 16.0–21.6 < 0.001 L47zone BL-PT 0.4–17.8 < 0.001 0.8 − 0.6 Not Sig. PT-TT 17.8–13.2 < 0.001 0.6–0.6 Not Sig. BL-TT 0.4–13.2 < 0.001 0.8 − 0.6 Not Sig. Metascore BL-PT 42.874–85.192 < 0.001 42.830–48.562 0.002 PT-TT 85.192–74.276 < 0.001 48.562–47.926 Not Sig. BL-TT 42.874–74.276 < 0.001 42.830–47.926 < 0.001 Zetascore BL-PT 1.61–71.91 < 0.001 2.44–2.54 Not Sig. PT-TT 71.91–53.02 < 0.001 2.54–1.67 Not Sig. BL-TT 1.61–53.02 < 0.001 2.44–1.67 Not Sig. Table 2 Comparisons between the experimental and control groups for each measure at each testing point. Significance was determined using the Mann-Whitney U test. Measure Experimental Median Control Median P-value Response Time (ms) BL 481.6 459.8 Not Sig. PT 181.7 390.1 < 0.001 TT 212.4 389.8 < 0.001 CTD BL 16.4 16.0 Not Sig. PT 22.8 20.8 < 0.001 TT 21.8 21.6 Not Sig. L47zone BL 0.4 0.8 Not Sig. PT 17.8 0.6 < 0.001 TT 13.2 0.6 < 0.001 Metascore BL 42.874 42.830 Not Sig. PT 85.192 48.562 < 0.001 TT 74.276 47.926 < 0.001 Zetascore BL 1.61 2.44 Not Sig. PT 71.91 2.54 < 0.001 TT 53.02 1.67 < 0.001 2.2 Cognitive-Motor Learning Training Improved Task Accuracy and Decision-Making Performance 2.2.1 Cognitive-Motor Learning Improved Correct Trigger Decisions (CTD) CTD within the control group demonstrated significant improvements from BL to PT (p < 0.001) and from BL to TT (p < 0.001), while there were no significant changes in CTD from PT to the TT (Fig. 2 , Table 1 ). Similarly, the experimental group’s CTD significantly improved from BL to PT (p < 0.001) and from BL to TT (p < 0.001). The CTD for the experimental group, however, shortened significantly from PT to TT (p < 0.05; Fig. 4 , Table 1 ). When comparing CTD between the two groups at each time point, there was no significant difference at either the BL or TT. The experimental group’s CTD at the PT, however, was significantly higher than that of the control group (p < 0.001, Fig. 4 , Table 2 ). 2.2.2 Practice Improved GO Responses (Decisional Execution) For both the control and experimental groups, there was no significant change in the number of correct GO responses from BL to PT and from PT to TT. The number of correct GO responses, however, significantly improved from BL to TT in both groups (control p < 0.05; training p < 0.01; Supplementary Fig. 1A; Supplementary Table 1). There were no significant differences between the two groups when comparing the number of correct GO responses at each of the three experimental phases (Supplementary Table 2). 2.2.3 Cognitive-Motor Learning Improved NO-GO Responses (Decisional Inhibition) The number of correct NO-GO responses for the control group improved significantly from BL to PT (p < 0.001) and from BL to TT (p < 0.001), with no significant change from PT to TT (Supplementary Fig. 1B, Supplementary Table 1). The number of correct NO-GO responses for the experimental group significantly increased from BL to PT (p < 0.001), but it significantly decreased from PT to TT (p < 0.001). There was no significant change between BL and TT for the experimental group (p = 0.053, Supplementary Fig. 1B, Supplementary Table 1). There was also no significant difference in NO-GO performance between the two groups at BL and TT. The number of correct NO-GO responses for the experimental group at the PT, however, increased significantly compared to the control group (p < 0.01, Supplementary Fig. 1B, Supplementary Table 2). 2.3 Cognitive-Motor Learning Improved Cognitive Motor Speed Efficacy (Median Correct L47 zone Responses) There were no significant differences in the number of correct responses in the L47 zone within the control group between all the experimental phases (Fig. 5 A, Table 1 ). For the experimental group, the number of correct responses in the L47 zone significantly increased from BL to PT (p < 0.001) and from BL to TT (p < 0.001). However, there was a significant decline from PT to TT (p 0.05). However, both PT and TT performances in the experimental group were significantly improved over the control group (PT: p < 0.001; TT: p < 0.001). 2.4 Cognitive-Motor Learning Improved the Metascore The Metascore algorithm was designed to evaluate the associative stage of cognitive-motor learning. The control group’s Metascore showed significant improvements from both BL to the PT (p < 0.01) and from BL to TT (p 0.05, Fig. 5 B, Table 1 ). Similarly, the experimental group’s Metascore revealed a significant improvement from BL to PT (p < 0.001) and BL to TT (p < 0.001). Their performance, however, significantly declined from PT to TT (p < 0.001, Fig. 5 B, Table 1 ). There was no significant difference in the BL Metascore between the experimental and control groups. However, the experimental group’s Metascore was significantly higher at both the PT (p < 0.001) and TT time points (p < 0.001, Fig. 5 B, Table 1 ). 2.5 Cognitive-Motor Learning Improved the Zetascore The Zetascore algorithm was designed to evaluate the autonomous stage of cognitive-motor learning. There were no significant differences in the control group’s Zetascores between any of the experimental phases (Fig. 5 C, Table 1 ). The experimental group’s Zetascore exhibited a significant increase from BL to PT (p < 0.001) and from BL to TT (p < 0.001). However, there was a significant decline from PT to TT (p 0.05). The experimental group, however, showed a significantly improved PT and TT performance over the control group (PT: p < 0.001; TT: p < 0.001). 2.6 Metascore and Zetascore Plateau Analysis The exponential growth models for both the Metascore and Zetascore demonstrated strong fits, resulting in an RMSE of 0.523 and 1.296, respectively (Fig. 6 ). The Metascore reached its plateau at timepoint T8.34, with a score of 86.47 (performance saturation), indicating a near-complete plateau by the end of the training period. In contrast, the Zetascore exhibited a slower trajectory, reaching a plateau score of 81.52 (performance saturation) at a theoretical timepoint T13.48, well beyond the final training timepoint. 3. Discussion Although relevant for rehabilitation, athletic and professional performance, research combining cognitive and motor domains in validated VR interventions is still scarce. Moreover, there remains a lack of standardized, quantitative indexes that capture the composite interaction of processing speed and decision-making to assess the transfer and durability of CML gains in healthy individuals. The aim of the present study was to evaluate the efficacy of a CML-based VR-training protocol designed to enhance processing speed, decision-making and composite indexes combining speed of processing and efficient decision-making in healthy young adults. Participants engaged in a CML task that required rapid stimulus recognition, decision-making, and rapid motor execution, with the goal of responding quickly and accurately without anticipatory bias. To evaluate the development of CML, we assessed cognitive and motor performance, both separately and through integrated composite metrics, across three experimental phases. The results support the view that cognitive and motor learning are interconnected processes that adapt in parallel during CML. The present results suggest that the experimental group demonstrated a clear establishment of CML, as reflected by improved performance across multiple parameters, including response time, CTD, NO-GO responses, CMSE, Metascore, and Zetascore from baseline to post-test sessions. In contrast, response time and CMSE results for the control group from baseline to post-test do not support the establishment of a CML, even though improvements were observed for some decisional parameters (CTD, GO, and NO-GO responses). However, it is important to note the significantly higher Metascore and CTD in the control group from baseline to post-test. This improvement of the composite indexes and CTD likely reflects an effect of practice, only corresponding to the exposition to the task rather than genuine learning, given the stagnation of response time across these phases in this group. The performance gap between groups appears primarily driven by significant differences in response time and CMSE, which reflect the efficiency of motor execution. This suggests that distinct but interconnected mechanisms underlie stimulus–response association (cognitive control and decision selection) and response execution (motor output and timing precision). Such partial functional segregation between cognitive and motor processes during learning has been reported in neuroimaging and behavioural studies showing parallel adaptations in frontoparietal and motor networks during sensorimotor training 25 . Moreover, the present results demonstrated that the CML observed in the experimental group was partially transferable, as evidenced when participants were exposed to a similar task involving different perceptual elements. This partial transfer was indicated by better response time, CMSE, and composite indexes in TT vs BL, suggesting that specific aspects of the acquired CML generalized beyond the trained task. The observed pattern of results is consistent with evidence that cognitive control/selection (e.g., stimulus-response mapping) and motor execution adapt in parallel yet partly dissociable ways during learning, engaging overlapping fronto-striatal-cerebellar-cortical networks 26 . Improvements in decisional metrics without corresponding gains in speed/CMSE in the control group are characteristic of an effect of practice (greater efficiency in selection) rather than full CML, paralleling work showing reduced reliance on executive/striatal resources as performance becomes more automatic 27 . The partial transfer observed in the experimental group aligns with studies indicating that generalization depends on shared task structure, with the strongest transfer when the novel task preserves core cognitive-motor relationships 28 . More broadly, the integrated VR-based CML protocol presented here accords with frameworks and reviews showing coordinated cognitive and motor plasticity across distributed networks during motor learning 29 . In the present study, the key performance requirement was to take the correct decision (press on the correct trigger associated to the PS when the SS is a GO signal or inhibit the action if the SS is a NO-GO signal) and to press as fast as possible on the correct trigger without anticipation bias. The analysis considered the L47 zone as the time window in which the task is performed at the highest efficacy (101-200ms). We defined a composite metric (CMSE) as achieved only when all key performance indicators were met during task execution. The results showed that the experimental group outperformed the control group on both CMSE and response time, whereas decisional metrics (CTD, GO and NO-GO responses) did not show as clear a separation. This suggests that the performance advantage for the experimental group was driven primarily by a higher capacity to produce faster motor responses rather than mere improvements in decision selection. Accordingly, previous training studies have shown that reaction time and motor-execution speed are crucial determinants of skill acquisition and transfer. For example, instruction emphasizing speed over accuracy yields stronger sequencing improvements 30 and processing-speed interventions lead to decreases in non-decision time and faster drift rates 31 . Together, these findings underscore the importance of enhancing motor-execution efficiency as a key mechanism within CML protocols. The present results demonstrate a different influence in the CML of the decisional dimension and the executional dimension of the task. Even if interconnected, the cognitive and motor processes seem to have differing roles in task learning. For example, earlier research has shown that skill learning involves both selection-related processes and execution-related processes, which appear to engage overlapping yet separable neural representations 32 . In the applied task paradigm, it appears that the percepts associated with decision-making were not the primary factors for achieving the key performance indexes. This may reflect the fact that decision-making processes can consolidate rapidly, leaving execution performance as the main limiting factor in CML 33 . Another possible explanation is that the perceptual stimuli used were not sufficiently complex to differentially engage decisional processes between the experimental and control groups. In motor learning contexts, task difficulty and stimulus complexity have been shown to moderate the rate of change in decision-related versus execution-related components 34 . These findings reinforce the notion that in CML tasks, the core driver of performance improvements may shift toward motor execution efficiency (e.g., response time and CMSE) once initial decision-selection processes stabilize, highlighting the importance of specifically targeting executional mechanisms in training protocols. The different influence of decision and execution in CML was observable through the composite indexes (Metascore and Zetascore), which were significantly higher in the experimental group in both the post-test and transfer test sessions. These composite indexes, integrating both decision-making and response time, have promising potential to discriminate which component of a task (decision vs. execution) is the main factor influencing the learning process or the transferability of a learned task. The present results suggest that a partial transfer of the learned task from post-test to transfer test, and when combined with response time and decision-analysis metrics, they highlight that the execution dimension of the task may have been the element that was learned and partially transferred. The use of these composite indexes could help to ascertain the relative weight of the factor that mainly influences the learning of a task (decision-making, execution, or both) and help to optimize how a CML protocol could be facilitated. In line with the present findings, earlier approaches also integrated both decision accuracy and execution speed into unified performance metrics to effectively capture the cognitive-motor interplay during learning. Traditional analyses that treat accuracy and response time separately often fail to account for the speed-accuracy trade-off inherent in cognitive-motor tasks. This gap has been addressed by other composite measures, such as the Inverse Efficiency Score (IES), calculated as mean reaction time divided by accuracy, which offers a simple integration of decision and execution performance 35 . Moreover, the Linear Integrated Speed-Accuracy Score (LISAS) provided a linear combination of accuracy and response time, improving sensitivity to experimental manipulations while further refinements, such as the Balanced Integration Score (BIS), may control for condition-specific speed-accuracy trade-offs and individual variability 36 – 38 . In parallel, sequential sampling models, such as the drift-diffusion model (DDM), provide a computational framework that explicitly separates decision-making from non-decision processes (encoding and motor execution). These models estimate latent parameters like drift rate (decision efficiency) and non-decision time (execution duration), thereby quantifying how decision and motor processes contribute to overall performance 39 , 40 . Collectively, these methods demonstrate a growing effort to quantify the interaction between decision and execution processes within a single framework. While no studies have employed the exact Metascore and Zetascore indexes described here, such composite approaches are conceptually aligned with the integrated speed-accuracy and diffusion modeling traditions, providing a validated foundation for quantifying and dissociating decision and execution components in CML. The observed evolution and saturation of the Metascore across post-test and transfer test is consistent with classic stage models of skill acquisition in CML, where performance transitions from cognitively demanding operations to more efficient associative processes before approaching an autonomous regime with reduced variability and faster execution. According to the Fitts-Posner framework, the associative stage is marked by steadily improving accuracy and timing as stimulus-response mappings are refined, while the autonomous stage is characterized by minimal attentional demands and near-asymptotic performance 41 . The contemporary account of human motor learning and strategy use reaffirms these stage characteristics and their behavioural signatures in reaction time/accuracy profiles 42 . In parallel, practice functions predict a monotonic approach to an asymptote, which are often well fit by exponential forms 43 , providing a quantitative basis for interpreting a Metascore “saturation” as proximity to an asymptotic learning state. Convergence toward automaticity is further supported by theories and data showing a shift from controlled computation to memory-based retrieval with practice (Instance Theory), typically accompanied by decreases in mean reaction time and its variability and by more efficient neural recruitment patterns that would drive the Zetascore toward a plateau indicating autonomous execution 44 . 4. Conclusion The present findings demonstrate that our VR task successfully induced CML. This CML was primarily driven by the execution component of the task, i.e., the speed/kinematics of performance rather than the decision/choice component. The distinction between the effect of practice and genuine learning in the form of durable changes in performance structure and transferability is critical to interpret the results. By dissociating practice vs genuine learning and by using composite indexes that combine decision-making quality with response/execution time, our task was able to probe not just that performance improved, but how it improved (decision vs. execution) and to assess where in the learning cycle the subject is positioned (e.g., associative vs autonomous stage). These findings highlight the potential effectiveness of our VR task for clinical rehabilitation because many therapeutic programs rely on repeated motor tasks, and rehabilitative success hinges on transitioning a patient’s movements from effortfully controlled to more automatic, reliable execution. Moreover, the used composite indexes may offer a practical tool to monitor the learning progress of a patient, potentially signalling when the “execution” dimension is sufficiently mastered and when the “decision” component is the limiting factor or vice-versa. In the domain of athletes and professional performance, the ability to quantify whether performance gains stem more from improved decision-making (tactics, choices, anticipation) or from faster/more precise execution (movement speed, motor control) is highly valuable. Motor-learning literature in sport underscores that skilled performers undergo an associative phase where movements become smoother and less variable, followed by an autonomous phase characterized by minimal cognitive load and near-optimal execution under changing conditions. Composite indexes like the Metascore and Zetascore are able to effectively monitor CML progression. For example, these tools might determine if additional training should emphasize decision-making scenarios (e.g., strategic variation, option selection) or refine execution (e.g., speed drills, technical precision). This tailored insight may optimize training load, reduce redundancy, and facilitate more efficient skill transfer to high-pressure or novel game contexts. The present VR-based CML strategy might therefore provide a platform for important applications to support performance optimization in athletes or professionals, for rehabilitation after traumatic brain injury and for cognitive enhancement in patients with neurodegenerative diseases. 5. Methods 5.1 Participants This study was approved by the ARISE Ethics Board at the University of Alberta (Reference ID: Pro00127901). All research was performed in accordance with the Declaration of Helsinki and all participants provided informed consent. Sixty-two healthy participants were recruited from the University of Lethbridge student population (male n = 26; female n = 36) with the following inclusion criteria: age between 18–29, one year of clean neurological history, and less than an average of 4 hours per day spent playing video games. Half of the participants for each sex were randomly assigned to the training group (male n = 13, avg. age=23土2.4; female n = 18, avg. age=21土2.5) and the remaining half were assigned to the control group (male n = 13, avg. age=21土2.4; female n = 18, avg. age=21土2.1). 5.2 Equipment and VR Software All CML training and testing were performed on a Meta Quest 2 VR system with L47 Nation V1 software (L47 Inc., QC, Canada). The Meta Quest 2 VR system included a head-mounted display and two controllers, each with two triggers (top and bottom), allowing for a double-stimulus visuomotor task. 5.3 Experimental VR Task The L47 Nation V1 software provided a double-stimulus visuomotor task. This task consisted of the presentation of a primary stimulus (PS), indicating which of the four triggers to press on (left or right controller and top or bottom trigger), followed by a secondary stimulus (SS) that acted as a “go” or “no-go” indication of whether the participant should press the trigger or not, respectively. This setup resulted in eight possible responses associated with the possible PS-SS combinations (Figs. 1 A and 1 C). A primer and modulator embedded within the PS image allowed for the differentiation between the four PS and their associated triggers (Fig. 1 B). The PS was displayed for 300 ms in the centre of the field of view. The SS randomly appeared anywhere in the field of view between 200 to 350 ms after the PS disappeared and was displayed for 200 ms. Once the SS disappeared, participants were given 1 second to respond. Both the accuracy and timing of the response were recorded. Following the 1-sec response time, an additional 250 ms delay (inter-repetition interval) occurred before the double-stimulus visuomotor-task was repeated. Each “set” consisted of 48 repetitions of the task, with the choice of PS and SS for each task randomized to ensure that all eight possibilities were utilized equally. The participants were instructed to press the correct trigger as fast as possible, without anticipating. The 1-sec response time window was further split into several zones, each of which was used to assess scoring and error classification. The first hundred milliseconds (100 ms) of this interval represented the anticipation window, and responses within this zone were considered erroneous, as they suggest the participant anticipated the SS rather than reacting to it 45 . The interval from 101–200 ms is called the “L47 zone”, which represented the optimal response window (indicating CMSE), and any correct response between 200–999 ms was recorded for accuracy and timing to allow for assessment of the training efficacy. Failing to respond to a “go” SS within the 1-sec response period was considered a time-out. 5.4 Experimental Timeline, Training, and Assessment Participants completed a 12-week experimental protocol, which included a baseline (BL) assessment during Week 1, eight weekly training sessions (Weeks 2–9), a post-test (PT) (Week 10), a rest week (Week 11), and a transfer test (TT) (Week 12). 5.4.1 Baseline Assessment During their BL session, all study participants were given the same Dry Test, Pre-Test, and BL assessment. The Dry Test was a one-on-one PowerPoint orientation conducted by an experimenter before participants entered the virtual environment. This orientation familiarized participants with the procedures, equipment, and task structure. They were introduced to the PS and their visual semantics, the meaning of secondary stimulus (SS), and forms of knowledge of performance (KP). KP consisted of auditory feedback following each task (repetition) indicating one of the following: (1) the correct response was given; (2) an incorrect response was given; (3) the response fell in the anticipation zone; (4) no response for a “go” action in the 1 sec response window (timed out); or (5) the participant pushed more than one trigger at the same time. In addition, participants were given an example of one double-stimulus visuomotor task and instructions to minimize errors while responding as quickly as possible to the stimuli without anticipation. During the Pre-Test, participants were equipped with the VR headset and allowed to familiarize themselves with the VR environment and the information learned during the Dry Test, ensuring they understood the task before testing. The Pre-Test consisted of three sets of 48 repetitions, and no data were recorded. After completing the Pre-Test session, participants proceeded to the BL assessment, which consisted of five data collection sets, each containing 48 repetitions, with performance being recorded. To prevent performance bias, participants were not provided with any knowledge of their results (KR) during the Pre-Test and baseline assessment. 5.4.2 Training Participants in the experimental and control groups completed eight weekly 30-minute training sessions, each consisting of eight data collection sets of 48 repetitions with no pre-test. The experimental group was trained on the same task used in the BL assessment and received both KP during the test and KR after each set. The KR consisted of a visual presentation (score card) of the Metascore and a percentage of responses in the L47 zone, anticipations, and errors. The control group was trained on a sham task, where the following aspects of the BL assessment were changed: (1) participants were exposed to four PS composed of different colours (Fig. 2 A), and (2) KP and KR were withheld throughout training. In addition, each group received a group-specific Dry Test at the beginning of the first training session. The control participants were instructed to (1) pair the coloured PS to the triggers of their choosing and to maintain them throughout the training phase; and (2) to remember the BL dry-test instructions related to the performance and the goal of the task (this was not explicitly repeated during this dry-test). The experimental group received the same dry test as was presented in the BL assessment, with the addition of an explanation of the KR that they would be presented with. 5.4.3 Post-Test (PT) and Transfer Test (TT) The PT followed the same design and visual semantics as the BL assessment, with a Dry Test, three Pre-Tests, and five data acquisition sets, each consisting of 48 repetitions. Both experimental and control groups were instructed to minimize errors and respond as fast as possible to the task, without anticipating. For both groups, KP was provided during the testing, but KR was withheld. The TT followed the same design as the PT and BL assessment, except that it utilized a novel visual semantic for the PS (Fig. 2 B). The Dry Test and three Pre-Tests ensured that the participants were familiarized with the new PS and had an opportunity to practice with the new information prior to the five data acquisition sets. 5.5 Testing Measures Speed of processing, decision-making and overall performance were recorded using several measures. Speed processing was determined by calculating the average response time for all correct trigger presses. Decision-making was measured independently for both the PS and SS. To determine correct PS decisions, the number of correct trigger presses following a GO-SS were recorded. For correct SS decisions, the number of correct GO-SS and NO-GO-SS responses were recorded separately. A correct GO-SS response was defined by pressing a trigger following the disappearance of the GO-SS, regardless of whether the correct trigger was pressed, while a correct NO-GO-SS response was recorded when no trigger was pressed following a NO-GO-SS. Lastly, participants’ overall performance was determined by CMSE, the Metascore and Zetascore. The CMSE was calculated as the median frequency of correct responses within the L47 zone. The Metascore and Zetascore represent two composite indexes based on L47 Inc.'s proprietary algorithms. These two different indexes differ by their purpose, but both integrate parameters associated with decision-making and speed of processing data. The Metascore algorithm was designed to evaluate the associative stage of cognitive-motor learning, while the Zetascore algorithm was designed to evaluate the autonomous stage of cognitive-motor learning. Together, these proprietary algorithms enable the differentiation of cognitive-motor learning phases by tracking how each index evolves over time, typically showing a faster increase in Metascore during early learning and a slower, later rise in Zetascore as performance becomes more autonomous. 5.6 Statistical Analysis Statistical analyses were conducted using IBM SPSS Statistics (Windows, version 27). Data were first tested for normality using the Shapiro-Wilk test and found to violate normality assumptions (p < 0.05); therefore, non-parametric tests were used for all comparisons. Within-group and between-group comparisons across and at each of the three testing points, respectively, were carried out using the Wilcoxon Signed Rank and Mann-Whitney U tests, respectively. All statistical tests were conducted using an alpha level of 0.05, and P-values were Bonferroni corrected for multiple comparisons. Plateaus for cognitive performance were calculated using a non-linear regression model that was fit separately for Metascores and Zetascores across the nine timepoints (BL-T8) using the following exponential growth function: $$\:{T}_{inf}-({T}_{inf}-{T}_{0})\cdot\:{e}^{(-K\cdot\:\text{t})}$$ The T inf (performance plateau) and k (rate constant) parameters were optimized by minimizing the root mean square error (RMSE) using non-linear least squares fitting, and the performance was considered to have reached a plateau at 97.5% of the calculated T inf value. Declarations Data Availability Statement The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding: This work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC), Discovery Grant #05628 to GM. CW was also supported by Mathematics of Information Technology and Complex Systems (MITACS) Accelerate Program. Acknowledgements: The authors would like to thank and recognize: Michaela Zipursky and Emma Doxtater for their help in managing participants, administering questionnaires, and preparing samples. Author Contributions: Carter Witbeck received ethical approval, recruited participants, collected data, prepared samples, analyzed cognitive-motor performance and metabolomic data, created tables and figures, and gave research presentations. Brannon Sumner aided in Cohort recruitment, sample preparation, analyzed cognitive-motor performance and metabolomic data, and manuscript edits. Dr. David Tinjust aided in study design and manuscript edits, while Tony Montina aided in study design, metabolomic analysis, and thesis/manuscript edits. Lastly, Dr. Gerlinde Metz contributed to the study design, collaboration, ethics application edits, thesis edits, and manuscript edits. Competing Interests Statement The authors would like to disclose one potential competing interest. One co-author is the CEO of L47 Inc. (QC, Canada), the company that developed the VR training software and designed the composite performance indexes evaluated in this study. This collaboration was established through the MITACS Accelerate program, which supports partnerships in which an external research team may independently validate an industry partner’s product. The author’s contributions included conceptualization, methodology, software development, supervision, and review and editing of the manuscript. No other competing interests are declared. References Markham, J. A. & Greenough, W. T. Experience-driven brain plasticity: beyond the synapse. Neuron Glia Biol 1 , 351–363 (2004). https://doi.org/10.1017/s1740925x05000219 Tao, G., Garrett, B., Taverner, T., Cordingley, E. & Sun, C. Immersive virtual reality health games: a narrative review of game design. 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Training Motor Sequences: Effects of Speed and Accuracy Instructions. Journal of Motor Behavior 51 , 540–550 (2019). https://doi.org/10.1080/00222895.2018.1528202 Reinhartz, A., Strobach, T., Jacobsen, T. & von Bastian, C. C. Mechanisms of Training-Related Change in Processing Speed: A Drift-Diffusion Model Approach. Journal of Cognition (2023). https://doi.org/10.5334/joc.310 Diedrichsen, J. & Kornysheva, K. Motor skill learning between selection and execution. Trends Cogn Sci 19 , 227–233 (2015). https://doi.org/10.1016/j.tics.2015.02.003 Spampinato, D. & Celnik, P. Multiple Motor Learning Processes in Humans: Defining Their Neurophysiological Bases. Neuroscientist 27 , 246–267 (2021). https://doi.org/10.1177/1073858420939552 Bootsma, J. M. et al. Neural Correlates of Motor Skill Learning Are Dependent on Both Age and Task Difficulty. Front Aging Neurosci 13 , 643132 (2021). https://doi.org/10.3389/fnagi.2021.643132 Bruyer, R. & Brysbaert, M. Combining speed and accuracy in cognitive psychology: Is the inverse efficiency score (IES) a better dependent variable than the mean reaction time (RT) and the percentage of errors (PE)? Psychologica Belgica 51 , 5–13 (2011). https://doi.org/10.5334/pb-51-1-5 Vandierendonck, A. Further Tests of the Utility of Integrated Speed-Accuracy Measures in Task Switching. J Cogn 1 , 8 (2018). https://doi.org/10.5334/joc.6 Liesefeld, H. R. & Janczyk, M. Combining speed and accuracy to control for speed-accuracy trade-offs(?). Behavior Research Methods 51 , 40–60 (2019). https://doi.org/10.3758/s13428-018-1076-x Liesefeld, H. R. & Janczyk, M. Same same but different: Subtle but consequential differences between two measures to linearly integrate speed and accuracy (LISAS vs. BIS). Behav Res Methods 55 , 1175–1192 (2023). https://doi.org/10.3758/s13428-022-01843-2 Ratcliff, R., Smith, P. L., Brown, S. D. & McKoon, G. Diffusion Decision Model: Current Issues and History. Trends Cogn Sci 20 , 260–281 (2016). https://doi.org/10.1016/j.tics.2016.01.007 Myers, C. E., Interian, A. & Moustafa, A. A. A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences. Front Psychol 13 , 1039172 (2022). https://doi.org/10.3389/fpsyg.2022.1039172 Furley, P. A. & Memmert, D. The role of working memory in sport. International Review of Sport and Exercise Psychology 3 , 171–194 (2010). https://doi.org/10.1080/1750984X.2010.526238 Tenison, C., Fincham, J. M. & Anderson, J. R. Phases of learning: How skill acquisition impacts cognitive processing. Cognitive Psychology 87 , 1–28 (2016). https://doi.org/10.1016/j.cogpsych.2016.03.001 Heathcote, A., Brown, S. & Mewhort, D. J. The power law repealed: the case for an exponential law of practice. Psychon Bull Rev 7 , 185–207 (2000). https://doi.org/10.3758/bf03212979 Logan, G. D. Toward an instance theory of automatization. Psychological Review 95 , 492–527 (1988). https://doi.org/10.1037/0033-295X.95.4.492 Gu, C., Pruszynski, J. A., Gribble, P. L. & Corneil, B. D. Done in 100 ms: path-dependent visuomotor transformation in the human upper limb. J Neurophysiol 119 , 1319–1328 (2018). https://doi.org/10.1152/jn.00839.2017 Additional Declarations Competing interest reported. The authors would like to disclose one potential competing interest. One co-author is the CEO of L47 Inc. (QC, Canada), the company that developed the VR training software and designed the composite performance indexes evaluated in this study. This collaboration was established through the MITACS Accelerate program, which supports partnerships in which an external research team may independently validate an industry partner’s product. The author’s contributions included conceptualization, methodology, software development, supervision, and review and editing of the manuscript. No other competing interests are declared. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8429022","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":584685929,"identity":"eb0de7b1-30fb-4937-9206-0e12ca4f0e48","order_by":0,"name":"Carter Witbeck","email":"","orcid":"","institution":"University of Lethbridge","correspondingAuthor":false,"prefix":"","firstName":"Carter","middleName":"","lastName":"Witbeck","suffix":""},{"id":584685930,"identity":"c855e52a-5bf2-431e-9a20-f3cbc0cdf261","order_by":1,"name":"Brannon Sumner","email":"","orcid":"","institution":"University of Lethbridge","correspondingAuthor":false,"prefix":"","firstName":"Brannon","middleName":"","lastName":"Sumner","suffix":""},{"id":584685931,"identity":"94572041-d147-4fc8-b288-a7df9d89d5a3","order_by":2,"name":"David Tinjust","email":"","orcid":"","institution":"L47 Technologies Inc.","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Tinjust","suffix":""},{"id":584685937,"identity":"0fbfb8db-f617-4728-9f7c-0f433009daec","order_by":3,"name":"Tony Montina","email":"","orcid":"","institution":"University of Lethbridge","correspondingAuthor":false,"prefix":"","firstName":"Tony","middleName":"","lastName":"Montina","suffix":""},{"id":584685938,"identity":"7218ea1f-3e26-4f4b-b782-c145be591c1f","order_by":4,"name":"Gerlinde Metz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYHACxgMJDMxQdgWDDAMDD2E9MC2MDQxngOrZiNHCANPC2EaEFnn33gcHHu6wlmMQO3z84c95h3n45XsPMPyowa3F8MxxgwOJZ9KNGaTTEhsktx3mkWzjS2DsOYZHy4w0hgOJbYcTG6RzDBsMgVoMjvEYMDOw4dEy/xlYS32DdP7HhsQ5h3nswVr+4fGLBBtYSwKDdA5jw8EGoC1sQC3AcMAJDHjADks3bJNOM5zZcCydR+JYjsHB3j48trQfY3z4s81anl86+cHHHzXWcvzNZwwf/PiGx5YDUAaKfw9gqEO2pQGf7CgYBaNgFIwCEAAA/ltQHVI1rv0AAAAASUVORK5CYII=","orcid":"","institution":"University of Lethbridge","correspondingAuthor":true,"prefix":"","firstName":"Gerlinde","middleName":"","lastName":"Metz","suffix":""}],"badges":[],"createdAt":"2025-12-23 01:23:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8429022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8429022/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101847289,"identity":"209833a6-4194-4529-818b-27d423e84f8e","added_by":"auto","created_at":"2026-02-04 09:28:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238466,"visible":true,"origin":"","legend":"\u003cp\u003eSpeed processing measurement. Median response times (ms) for control and experimental groups across (BL), post-test (PT), and transfer test (TT). The bars represent group medians with error bars indicating 95% Cl. Letter labels denote significance: bars sharing the same letters are not significantly different, whereas bars with differing letters indicate a significant difference (p\u0026lt;0.001 unless otherwise indicated). The experimental group demonstrated significantly faster response times at the post-test and the transfer test compared to their baseline assessment and the control group.\u003c/p\u003e","description":"","filename":"VRFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/061dadcea073588a4be04705.jpg"},{"id":101847280,"identity":"b3965404-7948-4516-ac3c-77d78f35463d","added_by":"auto","created_at":"2026-02-04 09:28:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203885,"visible":true,"origin":"","legend":"\u003cp\u003eDecision-making measurement. Median number of correct trigger decisions (CTD) for control and experimental groups across (BL), post-test (PT), and transfer test (TT). The bars represent group medians with error bars indicating 95% Cl. Letter labels denote significance: bars sharing the same letters are not significantly different, whereas bars with differing letters indicate a significant difference (p\u0026lt;0.001 unless otherwise indicated). Both groups improved from baseline to the post-test, with the experimental group showing greater CTD at the post-test. The experimental group’s transfer test CTD were comparable to the control group. Asterisks indicate significance: *p\u0026lt;0.05, Wilcoxon Signed Rank test.\u003c/p\u003e","description":"","filename":"VRFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/a6c28294db1de4979acedf7a.jpg"},{"id":101847288,"identity":"819fa928-e4ad-473a-9a12-151b73292aa9","added_by":"auto","created_at":"2026-02-04 09:28:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":558646,"visible":true,"origin":"","legend":"\u003cp\u003eOverall performance measurement. (a) Median number of correct L47 responses (Decisional Speed Efficacy) for control and experimental groups across (BL), post-test (PT), and transfer test (TT). The bars represent group medians with error bars indicating 95% Cl. Letter labels denote significance: bars sharing the same letters are not significantly different, whereas bars with differing letters indicate a significant difference (p\u0026lt;0.001 unless otherwise indicated). (A) The experimental group demonstrated significant improvements in their median number of correct L47 responses at the post-test and the transfer test compared to their baseline assessment and the control group. (B) Median Metascore for control and experimental groups across baseline, post-test, and transfer test. The experimental group demonstrated significant improvements in Metascores at the post-test and the transfer test compared to their baseline assessment and the control group. In contrast, the control group showed a slight significant increase in the post-test and transfer test Metascore. (C) Median Zetascore for control and experimental groups across baseline, post-test, and transfer test. The experimental group demonstrated significant improvements in Zetascores at the post-test and the transfer test compared to their baseline assessment and the control group. Asterisks indicate significance: **p\u0026lt;0.01, Wilcoxon Signed Rank test.\u003c/p\u003e","description":"","filename":"VRFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/8b81a3090db3832525a9a89d.jpg"},{"id":101847296,"identity":"0d3a4580-bd56-4c05-87c4-144ffa9284cd","added_by":"auto","created_at":"2026-02-04 09:28:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":225304,"visible":true,"origin":"","legend":"\u003cp\u003eMetascore and Zetascore plateaus. Exponential growth curves showing Metascore and Zetascore from baseline to a theoretical training session (T14). The curves were fit using a non-linear regression model with parameters optimized by minimizing the root mean square error (RMSE). The Metascore model demonstrated a strong fit (RMSE = 0.523) and plateaued at 86.47 by timepoint T8.34, indicating near-complete stabilization by the end of training. The Zetascore model also demonstrated a strong fit (RMSE = 1.296) but followed a slower trajectory, reaching a lower plateau of 81.52 at a theoretical timepoint T13.48, extending beyond the measured sessions.\u003c/p\u003e","description":"","filename":"VRFigure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/be92f9fe436cf6a8e0eec026.jpg"},{"id":101847304,"identity":"8efac68a-679a-48f4-b15f-233933a11f8f","added_by":"auto","created_at":"2026-02-04 09:28:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":794858,"visible":true,"origin":"","legend":"\u003cp\u003ePrimary and secondary stimuli in VR-based cognitive-motor learning. (A) Primary stimuli used for the baseline assessment, the experimental group’s training, and the post-test. (B) An example of a primary stimulus highlighting the primer and modulator symbols that combine to indicate a trigger. (C) Secondary stimuli used throughout the training protocol.\u003c/p\u003e","description":"","filename":"VRFigure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/d51f601a485d804be7fedfbe.jpg"},{"id":101847309,"identity":"ab37e892-aae8-46ee-874d-c101f82a9cca","added_by":"auto","created_at":"2026-02-04 09:28:57","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4690218,"visible":true,"origin":"","legend":"\u003cp\u003eSham training and transfer test primary stimuli. (A) Primary stimuli presented to the control group during the sham task training. (B) The four primary stimuli used during the transfer test. Red boxes surround the symbols that indicate the triggers to the participant. (C) An example of a primary stimulus highlighting the primer and modulator symbols that combine to indicate a trigger.\u003c/p\u003e","description":"","filename":"VRFigure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/97468bd54fb6e132615e745f.jpg"},{"id":104218882,"identity":"931b7868-6bfe-4c48-a53d-0cce1ea13f18","added_by":"auto","created_at":"2026-03-09 09:43:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7840354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/bf29ab1a-869a-4824-90db-0915b75e7cfc.pdf"},{"id":101847299,"identity":"bb767279-c17f-4f14-805b-0cc20e8f6384","added_by":"auto","created_at":"2026-02-04 09:28:53","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":464270,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/5ef85ba968c1f8e9d52160f6.jpg"},{"id":101847300,"identity":"1e6526de-16e0-4c55-b22d-b778069b7aa8","added_by":"auto","created_at":"2026-02-04 09:28:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17680,"visible":true,"origin":"","legend":"","description":"","filename":"VRCMLManuscriptSupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8429022/v1/0bced37928040586b04851f3.docx"}],"financialInterests":"Competing interest reported. The authors would like to disclose one potential competing interest. One co-author is the CEO of L47 Inc. (QC, Canada), the company that developed the VR training software and designed the composite performance indexes evaluated in this study. This collaboration was established through the MITACS Accelerate program, which supports partnerships in which an external research team may independently validate an industry partner’s product. The author’s contributions included conceptualization, methodology, software development, supervision, and review and editing of the manuscript. No other competing interests are declared.","formattedTitle":"Cognitive-Motor Learning in Virtual Reality Enhances Processing Speed and Processing Speed Efficacy in Healthy Adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCognitive-motor learning (CML), the integration of cognitive processes with motor skill acquisition, is essential for lifelong neuroplasticity and adaptive behaviour. Neuroplasticity enables the brain to reorganize in response to experience, a capacity that endures across the lifespan and supports targeted interventions to enhance CML\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Advances in virtual reality (VR) and gamified digital training platforms have provided immersive and engaging methods for CML\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These discoveries have driven the development of non-pharmaceutical strategies aimed at improving cognitive and motor functions in both healthy individuals and those recovering from brain injury\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Cognitive and motor functioning can be compromised by neurological disorders such as dementia, stroke, and traumatic brain injury\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. CML interventions have been increasingly explored in clinical and rehabilitation contexts for their potential to support functional recovery, promote functional independence, and improve quality of life\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Evidence suggests that targeted CML interventions may strengthen executive function, memory, and attentional control, which are essential for restoring independence and reintegration into daily life\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. CML approaches are also being applied beyond clinical contexts to enhance performance in domains such as sports, education, and workplace productivity\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCML interventions are particularly valuable to promote neuroplasticity by maintaining and enhancing cognitive and motor functions\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Functional magnetic resonance imaging (fMRI) studies have demonstrated that CML training can modify local functional connectivity in healthy older adults\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Moreover, CML interventions can induce both structural and functional plasticity, promoting synaptogenesis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and facilitating long-term potentiation (LTP), a process that strengthens synaptic efficacy and supports learning and memory\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The cognitive dimension of CML has primarily been studied through cognitive learning, focusing on executive function, attention, speed processing, and decision-making\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. On the other hand, the motor dimension of CML emphasizes the acquisition and refinement of motor skills\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Growing evidence indicates that cognitive and motor learning share common neural mechanisms, suggesting that they are inherently interconnected rather than distinct functions\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For example, athletes engaged in cognitive-motor dual-task training demonstrate that motor execution under cognitive load enhances both reaction speed and decision accuracy\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This finding supports the view that cognitive and motor learning functions are integrated processes that adapt in parallel during CML.\u003c/p\u003e \u003cp\u003eCML methods typically consist of a training protocol and an interface in which the cognitive-motor intervention is administered\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Regardless of the delivery format, the training protocol must be validated through rigorous testing to ensure effectiveness. Virtual Reality (VR) has become increasingly prominent in commercial and research applications due to its unique features, such as its immersive nature, flexible design capacity, precise monitoring, and high user motivation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This enables researchers to develop safe and controlled virtual environments that closely resemble real-world scenarios while providing real-time performance feedback\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. VR sensorimotor learning interventions show particular promise as innovative tools for enhancing specific cognitive and motor functions through immersive and interactive experiences. For example, VR training programs have been developed for surgical residents to practice in realistic simulated settings, reducing procedural errors and skill decay\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite extensive research on cognitive and motor training, few studies have integrated both domains within an immersive, empirically validated VR environment. The present study aimed to evaluate the efficacy of a novel, VR-based CML-training protocol designed to enhance processing speed and decision-making in healthy young adults. We have also assessed L47's proprietary composite indexes, integrating speed of processing and decision-making and cognitive-motor speed efficacy (CMSE or L47zone). These metrics were assessed across three experimental phases: pre-training (baseline), immediately after training (post-test), and two weeks after training had ended (transfer test). Participants in the experimental group completed one weekly training session with the experimental CML task, while the control group completed matched sessions with a sham task. We hypothesized that (1) no significant performance differences will be observed between groups at baseline; (2) post-training, the experimental group will demonstrate improved performance relative to its own baseline and controls; and (3) the experimental group will maintain these improvements at the transfer test, indicating training transfer, while the control group will show no significant change across phases. The findings clarify the potential of VR-based CML training to induce lasting gains in cognitive-motor performance.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cognitive-Motor Learning Training Improved Processing Speed (Response Time) Across Learning Phases\u003c/h2\u003e \u003cp\u003eThere was no significant difference in the control group\u0026rsquo;s response time from BL to PT, PT to TT, and BL to TT. The experimental group\u0026rsquo;s response time, however, was significantly reduced when comparing PT to BL (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The experimental group\u0026rsquo;s response time was also significantly slower while performing the TT when compared to their PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but their TT response time still remained significantly shorter compared to their BL response time (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Between-group comparisons found no significant difference between the BL response time of the two groups. The experimental group\u0026rsquo;s response time, however, was significantly faster when compared to the control group at both the PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons within both the experimental and control groups showing the median and significance for each testing measure between the following time points: BL to PT, PT to TT, and BL to TT. Significance was determined using the Wilcoxon Sign Rank test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eResponse Time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-PT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e481.6\u0026ndash;181.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e459.8\u0026ndash;390.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181.7\u0026ndash;212.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e390.1\u0026ndash;389.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e481.6\u0026ndash;212.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e459.8\u0026ndash;389.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-PT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.4\u0026ndash;22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.0\u0026ndash;20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8\u0026ndash;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.8\u0026ndash;21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.4\u0026ndash;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.0\u0026ndash;21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eL47zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-PT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026ndash;17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026minus;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u0026ndash;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u0026ndash;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026ndash;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026minus;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMetascore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-PT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.874\u0026ndash;85.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.830\u0026ndash;48.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.192\u0026ndash;74.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.562\u0026ndash;47.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.874\u0026ndash;74.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.830\u0026ndash;47.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZetascore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-PT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u0026ndash;71.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.44\u0026ndash;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.91\u0026ndash;53.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.54\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL-TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u0026ndash;53.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.44\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons between the experimental and control groups for each measure at each testing point. Significance was determined using the Mann-Whitney U test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperimental Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eResponse Time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e481.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e459.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e389.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eL47zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMetascore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZetascore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot Sig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cognitive-Motor Learning Training Improved Task Accuracy and Decision-Making Performance\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Cognitive-Motor Learning Improved Correct Trigger Decisions (CTD)\u003c/h2\u003e \u003cp\u003eCTD within the control group demonstrated significant improvements from BL to PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and from BL to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while there were no significant changes in CTD from PT to the TT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, the experimental group\u0026rsquo;s CTD significantly improved from BL to PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and from BL to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The CTD for the experimental group, however, shortened significantly from PT to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When comparing CTD between the two groups at each time point, there was no significant difference at either the BL or TT. The experimental group\u0026rsquo;s CTD at the PT, however, was significantly higher than that of the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Practice Improved GO Responses (Decisional Execution)\u003c/h2\u003e \u003cp\u003eFor both the control and experimental groups, there was no significant change in the number of correct GO responses from BL to PT and from PT to TT. The number of correct GO responses, however, significantly improved from BL to TT in both groups (control p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; training p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Supplementary Fig.\u0026nbsp;1A; Supplementary Table\u0026nbsp;1). There were no significant differences between the two groups when comparing the number of correct GO responses at each of the three experimental phases (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Cognitive-Motor Learning Improved NO-GO Responses (Decisional Inhibition)\u003c/h2\u003e \u003cp\u003eThe number of correct NO-GO responses for the control group improved significantly from BL to PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and from BL to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with no significant change from PT to TT (Supplementary Fig.\u0026nbsp;1B, Supplementary Table\u0026nbsp;1). The number of correct NO-GO responses for the experimental group significantly increased from BL to PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but it significantly decreased from PT to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was no significant change between BL and TT for the experimental group (p\u0026thinsp;=\u0026thinsp;0.053, Supplementary Fig.\u0026nbsp;1B, Supplementary Table\u0026nbsp;1). There was also no significant difference in NO-GO performance between the two groups at BL and TT. The number of correct NO-GO responses for the experimental group at the PT, however, increased significantly compared to the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Supplementary Fig.\u0026nbsp;1B, Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cognitive-Motor Learning Improved Cognitive Motor Speed Efficacy (Median Correct L47 zone Responses)\u003c/h2\u003e \u003cp\u003eThere were no significant differences in the number of correct responses in the L47 zone within the control group between all the experimental phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the experimental group, the number of correct responses in the L47 zone significantly increased from BL to PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and from BL to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was a significant decline from PT to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Between-group comparisons found that there was no significant difference in decisional speed efficacy performance at BL (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, both PT and TT performances in the experimental group were significantly improved over the control group (PT: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; TT: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Cognitive-Motor Learning Improved the Metascore\u003c/h2\u003e \u003cp\u003eThe Metascore algorithm was designed to evaluate the associative stage of cognitive-motor learning. The control group\u0026rsquo;s Metascore showed significant improvements from both BL to the PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and from BL to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant change, however, was observed between PT and TT (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, the experimental group\u0026rsquo;s Metascore revealed a significant improvement from BL to PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BL to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Their performance, however, significantly declined from PT to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was no significant difference in the BL Metascore between the experimental and control groups. However, the experimental group\u0026rsquo;s Metascore was significantly higher at both the PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TT time points (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cognitive-Motor Learning Improved the Zetascore\u003c/h2\u003e \u003cp\u003eThe Zetascore algorithm was designed to evaluate the autonomous stage of cognitive-motor learning. There were no significant differences in the control group\u0026rsquo;s Zetascores between any of the experimental phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The experimental group\u0026rsquo;s Zetascore exhibited a significant increase from BL to PT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and from BL to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was a significant decline from PT to TT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Between-group comparisons revealed that there were no significant differences in Zetascore performance at BL (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The experimental group, however, showed a significantly improved PT and TT performance over the control group (PT: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; TT: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Metascore and Zetascore Plateau Analysis\u003c/h2\u003e \u003cp\u003eThe exponential growth models for both the Metascore and Zetascore demonstrated strong fits, resulting in an RMSE of 0.523 and 1.296, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The Metascore reached its plateau at timepoint T8.34, with a score of 86.47 (performance saturation), indicating a near-complete plateau by the end of the training period. In contrast, the Zetascore exhibited a slower trajectory, reaching a plateau score of 81.52 (performance saturation) at a theoretical timepoint T13.48, well beyond the final training timepoint.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eAlthough relevant for rehabilitation, athletic and professional performance, research combining cognitive and motor domains in validated VR interventions is still scarce. Moreover, there remains a lack of standardized, quantitative indexes that capture the composite interaction of processing speed and decision-making to assess the transfer and durability of CML gains in healthy individuals. The aim of the present study was to evaluate the efficacy of a CML-based VR-training protocol designed to enhance processing speed, decision-making and composite indexes combining speed of processing and efficient decision-making in healthy young adults. Participants engaged in a CML task that required rapid stimulus recognition, decision-making, and rapid motor execution, with the goal of responding quickly and accurately without anticipatory bias. To evaluate the development of CML, we assessed cognitive and motor performance, both separately and through integrated composite metrics, across three experimental phases. The results support the view that cognitive and motor learning are interconnected processes that adapt in parallel during CML.\u003c/p\u003e \u003cp\u003eThe present results suggest that the experimental group demonstrated a clear establishment of CML, as reflected by improved performance across multiple parameters, including response time, CTD, NO-GO responses, CMSE, Metascore, and Zetascore from baseline to post-test sessions. In contrast, response time and CMSE results for the control group from baseline to post-test do not support the establishment of a CML, even though improvements were observed for some decisional parameters (CTD, GO, and NO-GO responses). However, it is important to note the significantly higher Metascore and CTD in the control group from baseline to post-test. This improvement of the composite indexes and CTD likely reflects an effect of practice, only corresponding to the exposition to the task rather than genuine learning, given the stagnation of response time across these phases in this group.\u003c/p\u003e \u003cp\u003eThe performance gap between groups appears primarily driven by significant differences in response time and CMSE, which reflect the efficiency of motor execution. This suggests that distinct but interconnected mechanisms underlie stimulus\u0026ndash;response association (cognitive control and decision selection) and response execution (motor output and timing precision). Such partial functional segregation between cognitive and motor processes during learning has been reported in neuroimaging and behavioural studies showing parallel adaptations in frontoparietal and motor networks during sensorimotor training\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Moreover, the present results demonstrated that the CML observed in the experimental group was partially transferable, as evidenced when participants were exposed to a similar task involving different perceptual elements. This partial transfer was indicated by better response time, CMSE, and composite indexes in TT vs BL, suggesting that specific aspects of the acquired CML generalized beyond the trained task.\u003c/p\u003e \u003cp\u003eThe observed pattern of results is consistent with evidence that cognitive control/selection (e.g., stimulus-response mapping) and motor execution adapt in parallel yet partly dissociable ways during learning, engaging overlapping fronto-striatal-cerebellar-cortical networks\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Improvements in decisional metrics without corresponding gains in speed/CMSE in the control group are characteristic of an effect of practice (greater efficiency in selection) rather than full CML, paralleling work showing reduced reliance on executive/striatal resources as performance becomes more automatic\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The partial transfer observed in the experimental group aligns with studies indicating that generalization depends on shared task structure, with the strongest transfer when the novel task preserves core cognitive-motor relationships\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. More broadly, the integrated VR-based CML protocol presented here accords with frameworks and reviews showing coordinated cognitive and motor plasticity across distributed networks during motor learning\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the present study, the key performance requirement was to take the correct decision (press on the correct trigger associated to the PS when the SS is a GO signal or inhibit the action if the SS is a NO-GO signal) and to press as fast as possible on the correct trigger without anticipation bias. The analysis considered the L47 zone as the time window in which the task is performed at the highest efficacy (101-200ms). We defined a composite metric (CMSE) as achieved only when all key performance indicators were met during task execution. The results showed that the experimental group outperformed the control group on both CMSE and response time, whereas decisional metrics (CTD, GO and NO-GO responses) did not show as clear a separation. This suggests that the performance advantage for the experimental group was driven primarily by a higher capacity to produce faster motor responses rather than mere improvements in decision selection. Accordingly, previous training studies have shown that reaction time and motor-execution speed are crucial determinants of skill acquisition and transfer. For example, instruction emphasizing speed over accuracy yields stronger sequencing improvements\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and processing-speed interventions lead to decreases in non-decision time and faster drift rates\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Together, these findings underscore the importance of enhancing motor-execution efficiency as a key mechanism within CML protocols.\u003c/p\u003e \u003cp\u003eThe present results demonstrate a different influence in the CML of the decisional dimension and the executional dimension of the task. Even if interconnected, the cognitive and motor processes seem to have differing roles in task learning. For example, earlier research has shown that skill learning involves both selection-related processes and execution-related processes, which appear to engage overlapping yet separable neural representations\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In the applied task paradigm, it appears that the percepts associated with decision-making were not the primary factors for achieving the key performance indexes. This may reflect the fact that decision-making processes can consolidate rapidly, leaving execution performance as the main limiting factor in CML\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Another possible explanation is that the perceptual stimuli used were not sufficiently complex to differentially engage decisional processes between the experimental and control groups. In motor learning contexts, task difficulty and stimulus complexity have been shown to moderate the rate of change in decision-related versus execution-related components\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. These findings reinforce the notion that in CML tasks, the core driver of performance improvements may shift toward motor execution efficiency (e.g., response time and CMSE) once initial decision-selection processes stabilize, highlighting the importance of specifically targeting executional mechanisms in training protocols.\u003c/p\u003e \u003cp\u003eThe different influence of decision and execution in CML was observable through the composite indexes (Metascore and Zetascore), which were significantly higher in the experimental group in both the post-test and transfer test sessions. These composite indexes, integrating both decision-making and response time, have promising potential to discriminate which component of a task (decision vs. execution) is the main factor influencing the learning process or the transferability of a learned task. The present results suggest that a partial transfer of the learned task from post-test to transfer test, and when combined with response time and decision-analysis metrics, they highlight that the execution dimension of the task may have been the element that was learned and partially transferred. The use of these composite indexes could help to ascertain the relative weight of the factor that mainly influences the learning of a task (decision-making, execution, or both) and help to optimize how a CML protocol could be facilitated.\u003c/p\u003e \u003cp\u003eIn line with the present findings, earlier approaches also integrated both decision accuracy and execution speed into unified performance metrics to effectively capture the cognitive-motor interplay during learning. Traditional analyses that treat accuracy and response time separately often fail to account for the speed-accuracy trade-off inherent in cognitive-motor tasks. This gap has been addressed by other composite measures, such as the Inverse Efficiency Score (IES), calculated as mean reaction time divided by accuracy, which offers a simple integration of decision and execution performance\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, the Linear Integrated Speed-Accuracy Score (LISAS) provided a linear combination of accuracy and response time, improving sensitivity to experimental manipulations while further refinements, such as the Balanced Integration Score (BIS), may control for condition-specific speed-accuracy trade-offs and individual variability\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In parallel, sequential sampling models, such as the drift-diffusion model (DDM), provide a computational framework that explicitly separates decision-making from non-decision processes (encoding and motor execution). These models estimate latent parameters like drift rate (decision efficiency) and non-decision time (execution duration), thereby quantifying how decision and motor processes contribute to overall performance\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Collectively, these methods demonstrate a growing effort to quantify the interaction between decision and execution processes within a single framework. While no studies have employed the exact Metascore and Zetascore indexes described here, such composite approaches are conceptually aligned with the integrated speed-accuracy and diffusion modeling traditions, providing a validated foundation for quantifying and dissociating decision and execution components in CML.\u003c/p\u003e \u003cp\u003eThe observed evolution and saturation of the Metascore across post-test and transfer test is consistent with classic stage models of skill acquisition in CML, where performance transitions from cognitively demanding operations to more efficient associative processes before approaching an autonomous regime with reduced variability and faster execution. According to the Fitts-Posner framework, the associative stage is marked by steadily improving accuracy and timing as stimulus-response mappings are refined, while the autonomous stage is characterized by minimal attentional demands and near-asymptotic performance\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The contemporary account of human motor learning and strategy use reaffirms these stage characteristics and their behavioural signatures in reaction time/accuracy profiles\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In parallel, practice functions predict a monotonic approach to an asymptote, which are often well fit by exponential forms\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, providing a quantitative basis for interpreting a Metascore \u0026ldquo;saturation\u0026rdquo; as proximity to an asymptotic learning state. Convergence toward automaticity is further supported by theories and data showing a shift from controlled computation to memory-based retrieval with practice (Instance Theory), typically accompanied by decreases in mean reaction time and its variability and by more efficient neural recruitment patterns that would drive the Zetascore toward a plateau indicating autonomous execution\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe present findings demonstrate that our VR task successfully induced CML. This CML was primarily driven by the execution component of the task, i.e., the speed/kinematics of performance rather than the decision/choice component. The distinction between the effect of practice and genuine learning in the form of durable changes in performance structure and transferability is critical to interpret the results. By dissociating practice vs genuine learning and by using composite indexes that combine decision-making quality with response/execution time, our task was able to probe not just that performance improved, but how it improved (decision vs. execution) and to assess where in the learning cycle the subject is positioned (e.g., associative vs autonomous stage). These findings highlight the potential effectiveness of our VR task for clinical rehabilitation because many therapeutic programs rely on repeated motor tasks, and rehabilitative success hinges on transitioning a patient\u0026rsquo;s movements from effortfully controlled to more automatic, reliable execution. Moreover, the used composite indexes may offer a practical tool to monitor the learning progress of a patient, potentially signalling when the \u0026ldquo;execution\u0026rdquo; dimension is sufficiently mastered and when the \u0026ldquo;decision\u0026rdquo; component is the limiting factor or vice-versa.\u003c/p\u003e \u003cp\u003eIn the domain of athletes and professional performance, the ability to quantify whether performance gains stem more from improved decision-making (tactics, choices, anticipation) or from faster/more precise execution (movement speed, motor control) is highly valuable. Motor-learning literature in sport underscores that skilled performers undergo an associative phase where movements become smoother and less variable, followed by an autonomous phase characterized by minimal cognitive load and near-optimal execution under changing conditions. Composite indexes like the Metascore and Zetascore are able to effectively monitor CML progression. For example, these tools might determine if additional training should emphasize decision-making scenarios (e.g., strategic variation, option selection) or refine execution (e.g., speed drills, technical precision). This tailored insight may optimize training load, reduce redundancy, and facilitate more efficient skill transfer to high-pressure or novel game contexts. The present VR-based CML strategy might therefore provide a platform for important applications to support performance optimization in athletes or professionals, for rehabilitation after traumatic brain injury and for cognitive enhancement in patients with neurodegenerative diseases.\u003c/p\u003e"},{"header":"5. Methods","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Participants\u003c/h2\u003e \u003cp\u003e This study was approved by the ARISE Ethics Board at the University of Alberta (Reference ID: Pro00127901). All research was performed in accordance with the Declaration of Helsinki and all participants provided informed consent. Sixty-two healthy participants were recruited from the University of Lethbridge student population (male n\u0026thinsp;=\u0026thinsp;26; female n\u0026thinsp;=\u0026thinsp;36) with the following inclusion criteria: age between 18\u0026ndash;29, one year of clean neurological history, and less than an average of 4 hours per day spent playing video games. Half of the participants for each sex were randomly assigned to the training group (male n\u0026thinsp;=\u0026thinsp;13, avg. age=23土2.4; female n\u0026thinsp;=\u0026thinsp;18, avg. age=21土2.5) and the remaining half were assigned to the control group (male n\u0026thinsp;=\u0026thinsp;13, avg. age=21土2.4; female n\u0026thinsp;=\u0026thinsp;18, avg. age=21土2.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Equipment and VR Software\u003c/h2\u003e \u003cp\u003eAll CML training and testing were performed on a Meta Quest 2 VR system with L47 Nation V1 software (L47 Inc., QC, Canada). The Meta Quest 2 VR system included a head-mounted display and two controllers, each with two triggers (top and bottom), allowing for a double-stimulus visuomotor task.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Experimental VR Task\u003c/h2\u003e \u003cp\u003eThe L47 Nation V1 software provided a double-stimulus visuomotor task. This task consisted of the presentation of a primary stimulus (PS), indicating which of the four triggers to press on (left or right controller and top or bottom trigger), followed by a secondary stimulus (SS) that acted as a \u0026ldquo;go\u0026rdquo; or \u0026ldquo;no-go\u0026rdquo; indication of whether the participant should press the trigger or not, respectively. This setup resulted in eight possible responses associated with the possible PS-SS combinations (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). A primer and modulator embedded within the PS image allowed for the differentiation between the four PS and their associated triggers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The PS was displayed for 300 ms in the centre of the field of view. The SS randomly appeared anywhere in the field of view between 200 to 350 ms after the PS disappeared and was displayed for 200 ms. Once the SS disappeared, participants were given 1 second to respond. Both the accuracy and timing of the response were recorded. Following the 1-sec response time, an additional 250 ms delay (inter-repetition interval) occurred before the double-stimulus visuomotor-task was repeated. Each \u0026ldquo;set\u0026rdquo; consisted of 48 repetitions of the task, with the choice of PS and SS for each task randomized to ensure that all eight possibilities were utilized equally.\u003c/p\u003e \u003cp\u003eThe participants were instructed to press the correct trigger as fast as possible, without anticipating. The 1-sec response time window was further split into several zones, each of which was used to assess scoring and error classification. The first hundred milliseconds (100 ms) of this interval represented the anticipation window, and responses within this zone were considered erroneous, as they suggest the participant anticipated the SS rather than reacting to it\u003csup\u003e45\u003c/sup\u003e. The interval from 101\u0026ndash;200 ms is called the \u0026ldquo;L47 zone\u0026rdquo;, which represented the optimal response window (indicating CMSE), and any correct response between 200\u0026ndash;999 ms was recorded for accuracy and timing to allow for assessment of the training efficacy. Failing to respond to a \u0026ldquo;go\u0026rdquo; SS within the 1-sec response period was considered a time-out.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Experimental Timeline, Training, and Assessment\u003c/h2\u003e \u003cp\u003eParticipants completed a 12-week experimental protocol, which included a baseline (BL) assessment during Week 1, eight weekly training sessions (Weeks 2\u0026ndash;9), a post-test (PT) (Week 10), a rest week (Week 11), and a transfer test (TT) (Week 12).\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Baseline Assessment\u003c/h2\u003e \u003cp\u003eDuring their BL session, all study participants were given the same Dry Test, Pre-Test, and BL assessment. The Dry Test was a one-on-one PowerPoint orientation conducted by an experimenter before participants entered the virtual environment. This orientation familiarized participants with the procedures, equipment, and task structure. They were introduced to the PS and their visual semantics, the meaning of secondary stimulus (SS), and forms of knowledge of performance (KP). KP consisted of auditory feedback following each task (repetition) indicating one of the following: (1) the correct response was given; (2) an incorrect response was given; (3) the response fell in the anticipation zone; (4) no response for a \u0026ldquo;go\u0026rdquo; action in the 1 sec response window (timed out); or (5) the participant pushed more than one trigger at the same time. In addition, participants were given an example of one double-stimulus visuomotor task and instructions to minimize errors while responding as quickly as possible to the stimuli without anticipation.\u003c/p\u003e \u003cp\u003eDuring the Pre-Test, participants were equipped with the VR headset and allowed to familiarize themselves with the VR environment and the information learned during the Dry Test, ensuring they understood the task before testing. The Pre-Test consisted of three sets of 48 repetitions, and no data were recorded. After completing the Pre-Test session, participants proceeded to the BL assessment, which consisted of five data collection sets, each containing 48 repetitions, with performance being recorded. To prevent performance bias, participants were not provided with any knowledge of their results (KR) during the Pre-Test and baseline assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Training\u003c/h2\u003e \u003cp\u003eParticipants in the experimental and control groups completed eight weekly 30-minute training sessions, each consisting of eight data collection sets of 48 repetitions with no pre-test. The experimental group was trained on the same task used in the BL assessment and received both KP during the test and KR after each set. The KR consisted of a visual presentation (score card) of the Metascore and a percentage of responses in the L47 zone, anticipations, and errors. The control group was trained on a sham task, where the following aspects of the BL assessment were changed: (1) participants were exposed to four PS composed of different colours (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and (2) KP and KR were withheld throughout training. In addition, each group received a group-specific Dry Test at the beginning of the first training session. The control participants were instructed to (1) pair the coloured PS to the triggers of their choosing and to maintain them throughout the training phase; and (2) to remember the BL dry-test instructions related to the performance and the goal of the task (this was not explicitly repeated during this dry-test). The experimental group received the same dry test as was presented in the BL assessment, with the addition of an explanation of the KR that they would be presented with.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e5.4.3 Post-Test (PT) and Transfer Test (TT)\u003c/h2\u003e \u003cp\u003eThe PT followed the same design and visual semantics as the BL assessment, with a Dry Test, three Pre-Tests, and five data acquisition sets, each consisting of 48 repetitions. Both experimental and control groups were instructed to minimize errors and respond as fast as possible to the task, without anticipating. For both groups, KP was provided during the testing, but KR was withheld. The TT followed the same design as the PT and BL assessment, except that it utilized a novel visual semantic for the PS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The Dry Test and three Pre-Tests ensured that the participants were familiarized with the new PS and had an opportunity to practice with the new information prior to the five data acquisition sets.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Testing Measures\u003c/h2\u003e \u003cp\u003eSpeed of processing, decision-making and overall performance were recorded using several measures. Speed processing was determined by calculating the average response time for all correct trigger presses. Decision-making was measured independently for both the PS and SS. To determine correct PS decisions, the number of correct trigger presses following a GO-SS were recorded. For correct SS decisions, the number of correct GO-SS and NO-GO-SS responses were recorded separately. A correct GO-SS response was defined by pressing a trigger following the disappearance of the GO-SS, regardless of whether the correct trigger was pressed, while a correct NO-GO-SS response was recorded when no trigger was pressed following a NO-GO-SS. Lastly, participants\u0026rsquo; overall performance was determined by CMSE, the Metascore and Zetascore. The CMSE was calculated as the median frequency of correct responses within the L47 zone. The Metascore and Zetascore represent two composite indexes based on L47 Inc.'s proprietary algorithms. These two different indexes differ by their purpose, but both integrate parameters associated with decision-making and speed of processing data. The Metascore algorithm was designed to evaluate the associative stage of cognitive-motor learning, while the Zetascore algorithm was designed to evaluate the autonomous stage of cognitive-motor learning. Together, these proprietary algorithms enable the differentiation of cognitive-motor learning phases by tracking how each index evolves over time, typically showing a faster increase in Metascore during early learning and a slower, later rise in Zetascore as performance becomes more autonomous.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using IBM SPSS Statistics (Windows, version 27). Data were first tested for normality using the Shapiro-Wilk test and found to violate normality assumptions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); therefore, non-parametric tests were used for all comparisons. Within-group and between-group comparisons across and at each of the three testing points, respectively, were carried out using the Wilcoxon Signed Rank and Mann-Whitney U tests, respectively. All statistical tests were conducted using an alpha level of 0.05, and P-values were Bonferroni corrected for multiple comparisons. Plateaus for cognitive performance were calculated using a non-linear regression model that was fit separately for Metascores and Zetascores across the nine timepoints (BL-T8) using the following exponential growth function:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{T}_{inf}-({T}_{inf}-{T}_{0})\\cdot\\:{e}^{(-K\\cdot\\:\\text{t})}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003einf\u003c/em\u003e\u003c/sub\u003e (performance plateau) and \u003cem\u003ek\u003c/em\u003e (rate constant) parameters were optimized by minimizing the root mean square error (RMSE) using non-linear least squares fitting, and the performance was considered to have reached a plateau at 97.5% of the calculated \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003einf\u003c/em\u003e\u003c/sub\u003e value.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThis work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC), Discovery Grant #05628 to GM. CW was also supported by Mathematics of Information Technology and Complex Systems (MITACS) Accelerate Program.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank and recognize: Michaela Zipursky and Emma Doxtater for their help in managing participants, administering questionnaires, and preparing samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarter Witbeck received ethical approval, recruited participants, collected data, prepared samples, analyzed cognitive-motor performance and metabolomic data, created tables and figures, and gave research presentations. Brannon Sumner aided in Cohort recruitment, sample preparation, analyzed cognitive-motor performance and metabolomic data, and manuscript edits. Dr. David Tinjust aided in study design and manuscript edits, while Tony Montina aided in study design, metabolomic analysis, and thesis/manuscript edits. Lastly, Dr. Gerlinde Metz contributed to the study design, collaboration, ethics application edits, thesis edits, and manuscript edits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to disclose one potential competing interest. One co-author is the CEO of L47 Inc. (QC, Canada), the company that developed the VR training software and designed the composite performance indexes evaluated in this study. This collaboration was established through the MITACS Accelerate program, which supports partnerships in which an external research team may independently validate an industry partner\u0026rsquo;s product. The author\u0026rsquo;s contributions included conceptualization, methodology, software development, supervision, and review and editing of the manuscript. No other competing interests are declared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMarkham, J. A. \u0026amp; Greenough, W. T. Experience-driven brain plasticity: beyond the synapse. \u003cem\u003eNeuron Glia Biol\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 351–363 (2004). https://doi.org/10.1017/s1740925x05000219\u003c/li\u003e\n \u003cli\u003eTao, G., Garrett, B., Taverner, T., Cordingley, E. \u0026amp; Sun, C. 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Done in 100 ms: path-dependent visuomotor transformation in the human upper limb. \u003cem\u003eJ Neurophysiol\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, 1319–1328 (2018). https://doi.org/10.1152/jn.00839.2017\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neuroplasticity, decision-making, sensorimotor integration, composite indexes, cognitive training, motor learning","lastPublishedDoi":"10.21203/rs.3.rs-8429022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8429022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCognitive-motor learning (CML) interventions hold promise for enhancing both cognitive and motor performance, yet a deeper understanding of their underlying mechanisms is essential to optimize their application. This study determined whether a virtual reality (VR)-based CML intervention can improve cognitive-motor speed efficacy (CMSE) in a perceptual-motor task by enhancing processing speed and/or decision making in healthy young adults. Sixty-two participants were assigned to experimental and control conditions before completing a 12-week VR-based CML training protocol composed of a baseline assessment, eight weekly training sessions, a post-test, and a transfer test. Performance was evaluated using response time, CMSE, decision accuracy and proprietary composite performance indexes (Metascore, Zetascore). The experimental group demonstrated clear CML compared to controls, who only showed an effect of practice. Learning gains also transferred to a similar task with new perceptual-motor associations and were mainly driven by faster response times rather than improved decision-making. The saturation trajectories of the novel Metascore and Zetascore indexes appear to reflect the associative and autonomous stages of learning, respectively, with response time serving as a key factor in this progression. These findings underscore the value of composite performance indexes for capturing the dynamics of CML and providing a foundation for future applications in athletic and professional performance optimization and neurorehabilitation.\u003c/p\u003e","manuscriptTitle":"Cognitive-Motor Learning in Virtual Reality Enhances Processing Speed and Processing Speed Efficacy in Healthy Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 09:27:42","doi":"10.21203/rs.3.rs-8429022/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"358c8d69-59d5-413d-a363-51d1adb81b39","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62198375,"name":"Physical sciences/Mathematics and computing"},{"id":62198376,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-03-09T09:43:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 09:27:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8429022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8429022","identity":"rs-8429022","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}