Exercise Intensity improves performance on a Spatial Memory Task

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Reinders, Gabriel Massarotto, Melissa Lacasse, Tom J. Hazell, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6247198/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Aug, 2025 Read the published version in Experimental Brain Research → Version 1 posted You are reading this latest preprint version Abstract Previous research suggests exercise improves spatial navigation abilities, though the effects of different exercise intensities on this cognitive function have not been explored. The current study assessed the influence of moderate-intensity and high-intensity acute exercise on spatial learning and memory, focusing on the acquisition of survey and route knowledge in young adults. Thirty-two participants (22.6 ± 1.7 y) were randomly assigned to one of three groups: 1) no-exercise control (n = 10); 2) moderate-intensity continuous training (MICT; 30 min at 65% maximal oxygen consumption) (n = 12); 3) sprint interval training (SIT; 4x30 sec all-out interspersed with 4 min recovery) (n = 10). Spatial navigation abilities were assessed using a virtual reality (VR) maze with evaluations at three time points: pre-exercise (TP1), immediately post-exercise (TP2), and 48 h post-exercise (TP3). Angular error (AE) was the primary measure of navigation accuracy. Both MICT and SIT groups exhibited improvements in spatial memory indicated by reductions in AE from TP1 to TP3 (p < .001) though the SIT group showed a greater reduction in AE compared to the MICT group (p = .039), suggesting a more pronounced benefit from higher-intensity exercise. The control group, however, showed no significant change in AE (p = .869), indicating no improvement in spatial memory without exercise intervention. The findings suggest that acute exercise, particularly at higher intensities, enhances spatial memory alongside with learning. It is possible that exercise can be used as a intervention to enhance cognitive functions, particularly spatial navigation. Moderate intensity continuous training Sprint interval training Virtual reality Exercise spatial navigation Spatial memory Learning Maze Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Navigation is a fundamental human ability that involves recalling and integrating spatial information to move effectively through the environment. Whether traversing familiar areas or adapting to unexpected obstacles, this process requires synthesizing various types of spatial knowledge (Siegel & White, 1975 ). This cognitive process was systematically categorized into three types of knowledge: landmark, route, and survey where each type of knowledge plays a role in how individuals perceive and interact with their environment. Landmark knowledge involves recognizing distinctive features within an environment, such as a familiar restaurant serving as a reference point when locating a coffee shop. Route knowledge pertains to understanding directional paths between specific locations, enabling one to navigate based on known sequences of turns from a certain intersection. Survey knowledge encompasses information about metric distances and angles between destinations, akin to having a mental map. More recently, this framework was expanding by introducing graph knowledge (Christal 2013) to aid in understanding networks of interlinked paths allowing individuals to adapt navigation in changing environments. However, these navigation skills are susceptible to decline, particularly with aging or cognitive impairments, posing challenges for independent living and daily activities. Understanding ways to support and improve spatial cognition is essential. Recently, exercise interventions have been recognized for their positive impact on cognitive functions, including memory, attention, and executive functioning (Ludyga et al., 2023 ; Loprinzi et al., 2023 ). Navigation, a key spatial skill, relies on these cognitive processes for interpreting and remembering environmental cues to navigate effectively. Both acute and chronic exercise enhance memory functions in young to older adults and boost hippocampal neurogenesis (Loprinzi et al., 2018 ; Praag, 2018). Notably, high-intensity aerobic exercise leads to significantly greater increases in hippocampal neurogenesis in mice compared to lower intensity exercise, suggesting that exercise intensity may play a critical role in optimizing the cognitive skills involved in navigation (Jiang et al., 2024 ). A component in the relationship between enhanced learning and exercise is brain-derived neurotrophic factor (BDNF), a neurotrophin produced in the hippocampus during exercise that plays a key role in neuroprotection, synaptic plasticity, and learning (Kaplan & Miller, 2000 ). Multiple studies demonstrate that higher-intensity exercise, such as high-intensity interval training (HIIT) and sprint interval training (SIT) generate greater increases in circulating BDNF concentrations compared to moderate-intensity continuous exercise (MICT) or no-exercise conditions (Reycraft et al., 2020 ; Kujach et al., 2020 ; Saucedo Marquez et al., 2015 ; Boyne et al., 2019 ) and these increases are sustained for a longer duration (references). This sustained BDNF elevation suggests that higher-intensity exercise may be more effective in enhancing cognitive functions like spatial navigation, learning, and memory. While both MICT and HIIT improve memory and learning tasks, HIIT consistently leads to greater enhancements (Kovacevic et al., 2019; Dilley et al., 2019 ). HIIT also improves response times and increases brain activity during memory retrieval more effectively than MICT, particularly under high memory demands (reference?). Although both exercise types benefit memory encoding, only HIIT enhances retrieval speed and neural activation, demonstrating its superior impact on cognitive performance (Kao et al., 2021 ). These findings suggest that while moderate-intensity exercise has cognitive benefits, higher-intensity exercise may result in more pronounced and broader improvements, aligning with evidence linking it to increased BDNF levels. While exercise intensity appears more beneficial on some cognitive outcomes, spatial abilities remains a relatively underexplored domain. While existing studies on chronic exercise have shown improvements in spatial memory (Ben-Zeev et al., 2020 ; Sánchez-Horaco et al., 2015), the effects of different exercise intensities on spatial cognition remain less understood and this is fundamental as understanding these effects could guide interventions for populations with spatial deficits. To address this gap, the current study sought to determine whether varying intensities of acute exercise can impact spatial learning, focusing on the acquisition of survey and route knowledge. Therefore, the purpose of the current study was to examine the effect of moderate-intensity and high-intensity exercise on spatial navigation both immediately following exercise and after 48 hours. It was hypothesized that exercise intensity would be a integral factor where higher exercise intensity (i.e., SIT) would demonstrate greater improvements in spatial memory compared to lower exercise intensity (i.e., MICT). METHODS Participants Thirty-two young adults (22.6 ± 1.7 y) were randomized to one of three groups: 1) no-exercise control (CTRL; n = 10; 5 females); 2) moderate-intensity continuous training (MICT) exercise (30 min at 65% maximal oxygen consumption n = 12, 5 females), or 3) sprint interval training (SIT) exercise (4 x 30 sec all-out efforts interspersed by 4 min recovery; n = 10; 5 females). Participants were screened for exercise contraindications using the Get Active Questionnaire and physical activity amounts by the Physical Activity and Sedentary Behaviour Questionnaire (PASBQ) from the Canadian Society of Exercise Physiology (CSEP). Eligibility criteria included being recreationally active, defined as engaging in at least three weekly exercise sessions totaling about 150 minutes, having stereoscopic vision, being non-smokers, not taking dietary supplements, having no post-concussive symptoms, and having limited experience playing 3D open-world video games. Participants were instructed to abstain from recreational drugs and alcohol for 24 hours and to avoid caffeine for at least 10 hours to control for external influences on cognitive and physiological performance (Heatherley et al., 2005 ; Grant et al., 2023 ). Written informed consent was obtained following a briefing on experimental procedures and risks. The research study and its procedures were approved by Research Ethics Board at Wilfrid Laurier University. Experimental session Participants completed two testing sessions 48h apart (Fig. 1 ), both an experimental session and a retention session where sessions occurred at the same time of day. At the start of each session, participants completed sleep logs documenting their sleep quality and quantity for the preceding 24h using a subjective 1–5 scale (1 = very poor, 5 = very good). Upon arrival to the lab, participants spent 5 minutes exploring the VR maze to become acclimatized to the environment (the VR system including the head-mounted display and navigation controls as well as the maze's layout). Following this VR familiarization, participants in the SIT and MICT groups practiced walking/jogging or sprinting on a self-propelled treadmill (4Front, Woodway, Wis., USA) for 5 minutes to ensure comfort with the treadmill mechanics. Participants then completed a spatial navigation task (VR test) in the VR environment before (pre-exercise test, TP1) followed by their assigned protocols (CTRL, MICT, SIT; all described below) immediately followed by another VR test (immediately post-exercise test, TP2). Participants than left the laboratory and returned 48h following for the retention session where participants completed the final VR test (retention test, TP3). The control and SIT protocols were designed to match in duration (26 minutes), as their data were collected before the inclusion of MICT, which required a longer timeframe to complete. During the control protocol, participants were seated and free to engage in conversation with the researcher, use personal devices for work or leisure, or both. The MICT and SIT sessions were performed on the same treadmill, which featured both motorized and dynamic (self-propelled) settings. Each protocol began with a standardized 5-minute warm-up at 3.5 mi·h⁻¹ and ended with a 3-minute cool-down at a self-selected pace. The MICT session consisted of 30 minutes of continuous running at a target of 75–80% of HRmax. Treadmill speed and incline were adjusted individually to maintain the target intensity, with continuous heart rate monitoring to ensure participants remained within the desired range. The SIT session included four 30-second "all-out" efforts performed in the dynamic treadmill mode, interspersed with 4-minute rest intervals. Virtual Reality maze protocol A 12 m × 12 m virtual reality texturized hedge maze was created using Unity and included four start locations and six objects (cabinet, cactus, clock, lamp post, cat statue, and guitar) that were scattered throughout the maze (Fig. 2 ). The objects were situated at the ends of branch hallways to prevent the participants from perceiving the objects from the main corridors. Participants were outfitted with an HTC VIVE Pro2 head-mounted display (HMD) unit to display the VR environment, as well as to record their location in space. The participants remained stationary and navigated through the VR environment using handheld HTC VIVE wireless controllers. Joystick triggers on the controllers were used for forward progression while participants physically rotate their bodies to select heading direction. Each VR test was structured into two distinct phases - the Learning Phase and the Test Phase, with a total of 20 trials. In the Learning Phase, participants engage in 8 find-object trials. During the find-object trials, participants were asked to locate one of the six objects from one of the four starting locations. Participants experienced each start location twice and were asked to navigate to each of the six objects at least once. At the start of each trial, the participants were asked to traverse the maze and locate and 'touch' the designated object using a handheld controller, signaling the trial's end. Once the object was touched, the interior walls of the maze disappeared, and a green square on the ground guided them to the next starting point. The time taken to find each object was recorded. In the Test Phase , participants were required to estimate the locations of specific objects. Participants experienced each starting location three times and interacted with each object at least once, for a total of 12 trials. At the onset of each trial, the participants began in one of the four starting locations and the maze walls appeared, and they were given 8s to orientated themselves. After the 8s, the walls vanished and only a ground plane view was visible. Participants were asked to move towards the estimated location of the desired object. Once the participants believed that they were in the location of the desired object, they pressed the trigger on the remote and their estimated location was recorded. Following the identification of the estimated object location, participants proceeded to the next starting point, marked by a green square on the floor. The Fast Motion Sickness Scale (FMS) was administered verbally every ~ 6 trials (3 times per test). The FMS is a verbal rating scale, which ranges from 0 (no sickness at all) to 20 (severe sickness). The scale is formulated to quantify symptoms of nausea, induced by visual content, particularly VR. Data Analysis Angular Error (AE): the angular difference between the estimated location of an object and the actual location of the object in degrees. Statistical Analysis A mixed repeated measures analysis of variance (rmANOVA) was used to account for both within-subject factors (time-points, 3 levels) and between-subject factors (group, 3 levels). Additionally, a correlation analysis was performed to determine if motion sickness (maximum FMS score), induced by VR exposure, and sleep quality was related to average AE. RESULTS There was a significant time-by-group interaction, F(4, 141.104) = 4.432, p = .003, np2 = .234) where changes in AE over time differed among the exercise groups (Fig. 3 ). Post hoc pairwise comparisons revealed significant improvements in AE (i.e., decrease in AE) from the pre-exercise test to the retention-test for both MICT (p < .001) and SIT (p < .001) with no change in the control group (p = .869). The improvement in the SIT group was also greater than the improvement in the MICT group (p = .039) (Fig. 4 ). Furthermore, analyses revealed no significant correlation between VR-induced motion sickness or sleep quality and average AE, indicating that these variables did not confound the observed improvements in spatial navigation performance. DISCUSSION The purpose of the current study was to determine the acute effects of exercise on spatial learning and identify whether exercise alone or exercise intensity increased one’s ability to learn and effectively navigate a virtual reality maze. It was expected that SIT would be more effective than MICT in the spatial memory task, and that both exercise groups would outperform a no exercise control condition. The findings revealed that participants in both the MICT and SIT groups showed significant improvements in maze retention, 48 hours after the exercise session (Fig. 3 ), suggesting that acute exercise can enhance both spatial memory and one’s learning capabilities. Additionally, the SIT group demonstrated enhanced performance in comparison to the MICT group, indicating that higher exercise intensities may be more effective in fostering cognitive benefits The results from the current study align with Kovacevic and colleagues (2019), who demonstrated that HIIT yielded the most significant memory performance improvements. Therefore, the intensity of exercise plays a role in cognitive enhancements, potentially by inducing more pronounced changes that benefit cognitive processing and memory retention. The influence of exercise on enhancing learning, observed following SIT and MICT, could be explained by neurobiological changes triggered by acute physical exercise, particularly the modulation of BDNF. Previous research has demonstrated increased BDNF concentrations in an exercise intensity-dependent manner following exercise (Reycraft et al. 2020 ) where the elevation in BDNF was more greater following SIT and remained elevated for a longer duration compared to a no-exercise and moderate-intensity condition. It is possible that the greater elevations in BDNF following the SIT exercise led to better learning outcomes. BDNF facilitates the growth and differentiation of new neurons and synapses in the hippocampus, which are essential processes in learning and memory. The sustained elevations in BDNF following higher-intensity exercise could enhance cognitive functions by improving and strengthening neural connections while also enhancing the efficiency of neurotransmission (Kaplan & Miller, 2000 ). The enhanced efficiency of neurotransmission benefits the processing and retention of new spatial information, as required in tasks like navigating a virtual reality maze. Additionally, acute spikes in BDNF levels may interact with other neurochemical systems, such as the endorphins and neurotransmitters like dopamine, which are also elevated during and after intense physical activity (Heyes et al., 1988 ). Dopamine is known for its roles in reward and motivation circuits but is equally crucial for memory and attention—both vital for effective learning and navigation (Aalto et al., 2005 ). Moreover, the increased metabolic activity associated with higher-intensity exercise leads to increased cerebral blood flow and increased delivery of oxygen and nutrients to the brain further supporting the enhanced cognitive function (Northey et al., 2019 ). This combination of neurotrophic, neurochemical, and metabolic effects provides a potential explanation for the observed improvements in spatial learning and navigation post-exercise in our current results. The current study provides valuable insights into the cognitive benefits of exercise and exercise intensity, yet it is constrained by several factors. The use of a small sample of young adults limits the generalizability of the findings across different age groups and fitness levels. Another potential limitation is the virtual reality maze setting. While participants actively controlled their movement within the virtual environment and could turn around in place, they were not walking through the space, which might have limited the sensory information typically received during actual navigation. Such sensory inputs are crucial for memory and learning in real-world settings. Although the setup in the study allowed for some level of active learning, it may not fully engage all cognitive and physical processes involved in spatial navigation. Furthermore, the intricate relationship between neuroprotective hormones like BDNF and cognitive performance following exercise are only speculative as the current study did not directly measure BDNF at each time point. Given the established role of BDNF in supporting neurogenesis and synaptic plasticity, subsequent studies could incorporate blood draws before and after exercise sessions to measure changes in BDNF and other relevant biomarkers, correlating them to a similar protocol measuring spatial navigation. In doing so, researchers could more directly link physiological responses to the cognitive benefits observed where this approach would allow a deeper understanding of the mechanisms through which exercise intensity influences cognitive enhancements, particularly in tasks involving spatial learning and memory. The cognitive benefits identified in this study can be particularly valuable for individuals with spatial memory deficits, such as those suffering from neurodegenerative diseases. Implementing exercise regimes that improve spatial memory could help mitigate the progression of such deficits, offering a non-pharmacological approach to enhance quality of life. Furthermore, given the rigorous demands of SIT, it may not a feasible option for everyone and the intensity and physical requirements of SIT might exclude individuals with certain health conditions, older adults, or those just beginning an exercise regimen. However, the findings that acute MICT also leads to significant cognitive improvements where MICT presents an effective modality to enhance spatial memory. CONCLUSION The current study investigated the effects of moderate- and high-exercise intensity on cognition through learning a spatial map in virtual reality. The results suggest that acute exercise improves one’s ability to learn a spatial map, with notably greater learning improvements observed in the high intensity exercise group. Therefore, acute exercise can enhance spatial memory, but exercise intensity does play a crucial role. Declarations Author Contribution NR, GM, MC, and TH wrote and edited the manuscriptML, MC, and TH conceived the study design and research questionsML, GM, NR, and MC collected and analyzed dataNR, GM, ML, MC, and TH interpreted findings Acknowledgements This work was supported by the Natural Sciences and Engineering Council of Canada under Grant# 2019–05894 to MEC. References Aalto, S., Brück, A., Laine, M., Någren, K., & Rinne, J. (2005). Frontal and temporal dopamine release during working memory and attention tasks in healthy humans: A positron emission tomography study using the high-affinity dopamine D2 receptor ligand [11C]FLB 457. The Journal of Neuroscience, 25 (10), 2471-2477. https://doi.org/10.1523/JNEUROSCI.2097-04.2005 Ben-Zeev, T., Hirsh, T., Weiss, I., Gornstein, M., & Okun, E. (2020). The effects of high-intensity functional training (HIFT) on spatial learning, visual pattern separation and attention span in adolescents. 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NeuroMolecular Medicine, 10 (2), 128-140. https://doi.org/10.1007/s12017-008-8028-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Aug, 2025 Read the published version in Experimental Brain Research → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6247198","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434431966,"identity":"f403dfc9-89b9-4412-a046-0aba89f54f16","order_by":0,"name":"Nicholas P. 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Hazell","email":"","orcid":"","institution":"Wilfrid Laurier University","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"J.","lastName":"Hazell","suffix":""},{"id":434431970,"identity":"b185f49f-110b-4081-b565-0567aac073a4","order_by":4,"name":"Michael. E. Cinelli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYFACxgYGhgILHn4G5gZStBhI8Eg2MBKtBQQMJBgMDhCrRbf9cNuDDwYSMsY3EtskGGrsCGsxO5PYbjgD6DAzsJZjyURoucHYJs0D0nIbqIWxgZlILX+AWoxng7XUE6kFFGIG0mAth4nyS5tkD1CLxP2HzRYJx44ToeX48WcSPyps7Pl7Dh+88aGmmrAWVJBAqoZRMApGwSgYBdgBANjdMvLTxdM3AAAAAElFTkSuQmCC","orcid":"","institution":"Wilfrid Laurier University","correspondingAuthor":true,"prefix":"","firstName":"Michael.","middleName":"E.","lastName":"Cinelli","suffix":""}],"badges":[],"createdAt":"2025-03-17 19:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6247198/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6247198/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00221-025-07142-4","type":"published","date":"2025-08-18T16:29:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80049817,"identity":"d402f74d-0f3a-4928-bd6c-61a5e2bdf089","added_by":"auto","created_at":"2025-04-07 10:14:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114870,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline of protocols.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6247198/v1/aa81a82afeb767fe5f64f501.png"},{"id":80051749,"identity":"3cf8c476-1f39-413b-9deb-fbcc11c277ec","added_by":"auto","created_at":"2025-04-07 10:30:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64763,"visible":true,"origin":"","legend":"\u003cp\u003eAerial view of VR Hedge Maze. Green squares indicate the four starting locations and the circles identify the location of the target objects. Each object was located at the end of a branch hallway.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6247198/v1/4c278bdbbe40b53f1ceb986e.png"},{"id":80049812,"identity":"abcc5a94-3d4b-442a-b833-ebc85259fe16","added_by":"auto","created_at":"2025-04-07 10:14:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67069,"visible":true,"origin":"","legend":"\u003cp\u003eThe average AE in degrees for the Control, MICT, and SIT groups across three time points (TP): TP1=pre-exercise, TP2=immediately post-exercise, and TP3= retention period (48 hours later). The data revealed that both MICT and SIT groups experienced a significant decrease in AE from pre-exercise to retention, highlighting improved spatial navigation performance. Conversely, the control group did not exhibit improvements, maintaining a consistent AE across all time points.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6247198/v1/8bf90cb9c7de711df6bde19c.png"},{"id":80051748,"identity":"fa50599b-dc61-4e6a-8fd3-f544bc38e8b4","added_by":"auto","created_at":"2025-04-07 10:30:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34728,"visible":true,"origin":"","legend":"\u003cp\u003eAverage change in AE for the MICT and SIT groups. The bars represent the mean reduction in AE from the pre-exercise to the retention test. The SIT group demonstrated a larger decrease in AE compared to the MICT group, suggesting a greater improvement in spatial memory and navigation accuracy.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6247198/v1/630e73e05aad839e2d2f324a.png"},{"id":89847217,"identity":"1477a77a-062f-48ef-b04a-44ea5e5d94b7","added_by":"auto","created_at":"2025-08-25 16:42:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":652958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6247198/v1/52402847-9a49-47a6-9a9e-8b509f0e74f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exercise Intensity improves performance on a Spatial Memory Task","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNavigation is a fundamental human ability that involves recalling and integrating spatial information to move effectively through the environment. Whether traversing familiar areas or adapting to unexpected obstacles, this process requires synthesizing various types of spatial knowledge (Siegel \u0026amp; White, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). This cognitive process was systematically categorized into three types of knowledge: landmark, route, and survey where each type of knowledge plays a role in how individuals perceive and interact with their environment. Landmark knowledge involves recognizing distinctive features within an environment, such as a familiar restaurant serving as a reference point when locating a coffee shop. Route knowledge pertains to understanding directional paths between specific locations, enabling one to navigate based on known sequences of turns from a certain intersection. Survey knowledge encompasses information about metric distances and angles between destinations, akin to having a mental map. More recently, this framework was expanding by introducing graph knowledge (Christal 2013) to aid in understanding networks of interlinked paths allowing individuals to adapt navigation in changing environments. However, these navigation skills are susceptible to decline, particularly with aging or cognitive impairments, posing challenges for independent living and daily activities. Understanding ways to support and improve spatial cognition is essential.\u003c/p\u003e \u003cp\u003eRecently, exercise interventions have been recognized for their positive impact on cognitive functions, including memory, attention, and executive functioning (Ludyga et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Loprinzi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Navigation, a key spatial skill, relies on these cognitive processes for interpreting and remembering environmental cues to navigate effectively. Both acute and chronic exercise enhance memory functions in young to older adults and boost hippocampal neurogenesis (Loprinzi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Praag, 2018). Notably, high-intensity aerobic exercise leads to significantly greater increases in hippocampal neurogenesis in mice compared to lower intensity exercise, suggesting that exercise intensity may play a critical role in optimizing the cognitive skills involved in navigation (Jiang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA component in the relationship between enhanced learning and exercise is brain-derived neurotrophic factor (BDNF), a neurotrophin produced in the hippocampus during exercise that plays a key role in neuroprotection, synaptic plasticity, and learning (Kaplan \u0026amp; Miller, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Multiple studies demonstrate that higher-intensity exercise, such as high-intensity interval training (HIIT) and sprint interval training (SIT) generate greater increases in circulating BDNF concentrations compared to moderate-intensity continuous exercise (MICT) or no-exercise conditions (Reycraft et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kujach et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Saucedo Marquez et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Boyne et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and these increases are sustained for a longer duration (references). This sustained BDNF elevation suggests that higher-intensity exercise may be more effective in enhancing cognitive functions like spatial navigation, learning, and memory.\u003c/p\u003e \u003cp\u003eWhile both MICT and HIIT improve memory and learning tasks, HIIT consistently leads to greater enhancements (Kovacevic et al., 2019; Dilley et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). HIIT also improves response times and increases brain activity during memory retrieval more effectively than MICT, particularly under high memory demands (reference?). Although both exercise types benefit memory encoding, only HIIT enhances retrieval speed and neural activation, demonstrating its superior impact on cognitive performance (Kao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings suggest that while moderate-intensity exercise has cognitive benefits, higher-intensity exercise may result in more pronounced and broader improvements, aligning with evidence linking it to increased BDNF levels.\u003c/p\u003e \u003cp\u003eWhile exercise intensity appears more beneficial on some cognitive outcomes, spatial abilities remains a relatively underexplored domain. While existing studies on chronic exercise have shown improvements in spatial memory (Ben-Zeev et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S\u0026aacute;nchez-Horaco et al., 2015), the effects of different exercise intensities on spatial cognition remain less understood and this is fundamental as understanding these effects could guide interventions for populations with spatial deficits. To address this gap, the current study sought to determine whether varying intensities of acute exercise can impact spatial learning, focusing on the acquisition of survey and route knowledge. Therefore, the purpose of the current study was to examine the effect of moderate-intensity and high-intensity exercise on spatial navigation both immediately following exercise and after 48 hours. It was hypothesized that exercise intensity would be a integral factor where higher exercise intensity (i.e., SIT) would demonstrate greater improvements in spatial memory compared to lower exercise intensity (i.e., MICT).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThirty-two young adults (22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 y) were randomized to one of three groups: 1) no-exercise control (CTRL; n\u0026thinsp;=\u0026thinsp;10; 5 females); 2) moderate-intensity continuous training (MICT) exercise (30 min at 65% maximal oxygen consumption n\u0026thinsp;=\u0026thinsp;12, 5 females), or 3) sprint interval training (SIT) exercise (4 x 30 sec all-out efforts interspersed by 4 min recovery; n\u0026thinsp;=\u0026thinsp;10; 5 females). Participants were screened for exercise contraindications using the Get Active Questionnaire and physical activity amounts by the Physical Activity and Sedentary Behaviour Questionnaire (PASBQ) from the Canadian Society of Exercise Physiology (CSEP). Eligibility criteria included being recreationally active, defined as engaging in at least three weekly exercise sessions totaling about 150 minutes, having stereoscopic vision, being non-smokers, not taking dietary supplements, having no post-concussive symptoms, and having limited experience playing 3D open-world video games. Participants were instructed to abstain from recreational drugs and alcohol for 24 hours and to avoid caffeine for at least 10 hours to control for external influences on cognitive and physiological performance (Heatherley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Grant et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Written informed consent was obtained following a briefing on experimental procedures and risks. The research study and its procedures were approved by Research Ethics Board at Wilfrid Laurier University.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental session\u003c/h3\u003e\n\u003cp\u003eParticipants completed two testing sessions 48h apart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), both an experimental session and a retention session where sessions occurred at the same time of day. At the start of each session, participants completed sleep logs documenting their sleep quality and quantity for the preceding 24h using a subjective 1\u0026ndash;5 scale (1\u0026thinsp;=\u0026thinsp;very poor, 5\u0026thinsp;=\u0026thinsp;very good). Upon arrival to the lab, participants spent 5 minutes exploring the VR maze to become acclimatized to the environment (the VR system including the head-mounted display and navigation controls as well as the maze's layout). Following this VR familiarization, participants in the SIT and MICT groups practiced walking/jogging or sprinting on a self-propelled treadmill (4Front, Woodway, Wis., USA) for 5 minutes to ensure comfort with the treadmill mechanics.\u003c/p\u003e \u003cp\u003eParticipants then completed a spatial navigation task (VR test) in the VR environment before (pre-exercise test, TP1) followed by their assigned protocols (CTRL, MICT, SIT; all described below) immediately followed by another VR test (immediately post-exercise test, TP2). Participants than left the laboratory and returned 48h following for the retention session where participants completed the final VR test (retention test, TP3).\u003c/p\u003e \u003cp\u003eThe control and SIT protocols were designed to match in duration (26 minutes), as their data were collected before the inclusion of MICT, which required a longer timeframe to complete. During the control protocol, participants were seated and free to engage in conversation with the researcher, use personal devices for work or leisure, or both. The MICT and SIT sessions were performed on the same treadmill, which featured both motorized and dynamic (self-propelled) settings. Each protocol began with a standardized 5-minute warm-up at 3.5 mi\u0026middot;h⁻\u0026sup1; and ended with a 3-minute cool-down at a self-selected pace. The MICT session consisted of 30 minutes of continuous running at a target of 75\u0026ndash;80% of HRmax. Treadmill speed and incline were adjusted individually to maintain the target intensity, with continuous heart rate monitoring to ensure participants remained within the desired range. The SIT session included four 30-second \"all-out\" efforts performed in the dynamic treadmill mode, interspersed with 4-minute rest intervals.\u003c/p\u003e\n\u003ch3\u003eVirtual Reality maze protocol\u003c/h3\u003e\n\u003cp\u003eA 12 m \u0026times; 12 m virtual reality texturized hedge maze was created using Unity and included four start locations and six objects (cabinet, cactus, clock, lamp post, cat statue, and guitar) that were scattered throughout the maze (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The objects were situated at the ends of branch hallways to prevent the participants from perceiving the objects from the main corridors. Participants were outfitted with an HTC VIVE Pro2 head-mounted display (HMD) unit to display the VR environment, as well as to record their location in space. The participants remained stationary and navigated through the VR environment using handheld HTC VIVE wireless controllers. Joystick triggers on the controllers were used for forward progression while participants physically rotate their bodies to select heading direction. Each VR test was structured into two distinct phases - the Learning Phase and the Test Phase, with a total of 20 trials. In the Learning Phase, participants engage in 8 \u003cem\u003efind-object\u003c/em\u003e trials. During the \u003cem\u003efind-object\u003c/em\u003e trials, participants were asked to locate one of the six objects from one of the four starting locations. Participants experienced each start location twice and were asked to navigate to each of the six objects at least once. At the start of each trial, the participants were asked to traverse the maze and locate and 'touch' the designated object using a handheld controller, signaling the trial's end. Once the object was touched, the interior walls of the maze disappeared, and a green square on the ground guided them to the next starting point. The time taken to find each object was recorded. In the \u003cem\u003eTest Phase\u003c/em\u003e, participants were required to estimate the locations of specific objects. Participants experienced each starting location three times and interacted with each object at least once, for a total of 12 trials. At the onset of each trial, the participants began in one of the four starting locations and the maze walls appeared, and they were given 8s to orientated themselves. After the 8s, the walls vanished and only a ground plane view was visible. Participants were asked to move towards the estimated location of the desired object. Once the participants believed that they were in the location of the desired object, they pressed the trigger on the remote and their estimated location was recorded. Following the identification of the estimated object location, participants proceeded to the next starting point, marked by a green square on the floor.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Fast Motion Sickness Scale\u003c/em\u003e (FMS) was administered verbally every\u0026thinsp;~\u0026thinsp;6 trials (3 times per test). The FMS is a verbal rating scale, which ranges from 0 (no sickness at all) to 20 (severe sickness). The scale is formulated to quantify symptoms of nausea, induced by visual content, particularly VR.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eAngular Error (AE): the angular difference between the estimated location of an object and the actual location of the object in degrees.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eA mixed repeated measures analysis of variance (rmANOVA) was used to account for both within-subject factors (time-points, 3 levels) and between-subject factors (group, 3 levels). Additionally, a correlation analysis was performed to determine if motion sickness (maximum FMS score), induced by VR exposure, and sleep quality was related to average AE.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThere was a significant time-by-group interaction, F(4, 141.104)\u0026thinsp;=\u0026thinsp;4.432, p\u0026thinsp;=\u0026thinsp;.003, np2\u0026thinsp;=\u0026thinsp;.234) where changes in AE over time differed among the exercise groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Post hoc pairwise comparisons revealed significant improvements in AE (i.e., decrease in AE) from the pre-exercise test to the retention-test for both MICT (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and SIT (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) with no change in the control group (p\u0026thinsp;=\u0026thinsp;.869). The improvement in the SIT group was also greater than the improvement in the MICT group (p\u0026thinsp;=\u0026thinsp;.039) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, analyses revealed no significant correlation between VR-induced motion sickness or sleep quality and average AE, indicating that these variables did not confound the observed improvements in spatial navigation performance.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe purpose of the current study was to determine the acute effects of exercise on spatial learning and identify whether exercise alone or exercise intensity increased one\u0026rsquo;s ability to learn and effectively navigate a virtual reality maze. It was expected that SIT would be more effective than MICT in the spatial memory task, and that both exercise groups would outperform a no exercise control condition. The findings revealed that participants in both the MICT and SIT groups showed significant improvements in maze retention, 48 hours after the exercise session (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that acute exercise can enhance both spatial memory and one\u0026rsquo;s learning capabilities. Additionally, the SIT group demonstrated enhanced performance in comparison to the MICT group, indicating that higher exercise intensities may be more effective in fostering cognitive benefits\u003c/p\u003e \u003cp\u003eThe results from the current study align with Kovacevic and colleagues (2019), who demonstrated that HIIT yielded the most significant memory performance improvements. Therefore, the intensity of exercise plays a role in cognitive enhancements, potentially by inducing more pronounced changes that benefit cognitive processing and memory retention. The influence of exercise on enhancing learning, observed following SIT and MICT, could be explained by neurobiological changes triggered by acute physical exercise, particularly the modulation of BDNF. Previous research has demonstrated increased BDNF concentrations in an exercise intensity-dependent manner following exercise (Reycraft et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) where the elevation in BDNF was more greater following SIT and remained elevated for a longer duration compared to a no-exercise and moderate-intensity condition.\u003c/p\u003e \u003cp\u003eIt is possible that the greater elevations in BDNF following the SIT exercise led to better learning outcomes. BDNF facilitates the growth and differentiation of new neurons and synapses in the hippocampus, which are essential processes in learning and memory. The sustained elevations in BDNF following higher-intensity exercise could enhance cognitive functions by improving and strengthening neural connections while also enhancing the efficiency of neurotransmission (Kaplan \u0026amp; Miller, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The enhanced efficiency of neurotransmission benefits the processing and retention of new spatial information, as required in tasks like navigating a virtual reality maze. Additionally, acute spikes in BDNF levels may interact with other neurochemical systems, such as the endorphins and neurotransmitters like dopamine, which are also elevated during and after intense physical activity (Heyes et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Dopamine is known for its roles in reward and motivation circuits but is equally crucial for memory and attention\u0026mdash;both vital for effective learning and navigation (Aalto et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Moreover, the increased metabolic activity associated with higher-intensity exercise leads to increased cerebral blood flow and increased delivery of oxygen and nutrients to the brain further supporting the enhanced cognitive function (Northey et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This combination of neurotrophic, neurochemical, and metabolic effects provides a potential explanation for the observed improvements in spatial learning and navigation post-exercise in our current results.\u003c/p\u003e \u003cp\u003eThe current study provides valuable insights into the cognitive benefits of exercise and exercise intensity, yet it is constrained by several factors. The use of a small sample of young adults limits the generalizability of the findings across different age groups and fitness levels. Another potential limitation is the virtual reality maze setting. While participants actively controlled their movement within the virtual environment and could turn around in place, they were not walking through the space, which might have limited the sensory information typically received during actual navigation. Such sensory inputs are crucial for memory and learning in real-world settings. Although the setup in the study allowed for some level of active learning, it may not fully engage all cognitive and physical processes involved in spatial navigation. Furthermore, the intricate relationship between neuroprotective hormones like BDNF and cognitive performance following exercise are only speculative as the current study did not directly measure BDNF at each time point. Given the established role of BDNF in supporting neurogenesis and synaptic plasticity, subsequent studies could incorporate blood draws before and after exercise sessions to measure changes in BDNF and other relevant biomarkers, correlating them to a similar protocol measuring spatial navigation. In doing so, researchers could more directly link physiological responses to the cognitive benefits observed where this approach would allow a deeper understanding of the mechanisms through which exercise intensity influences cognitive enhancements, particularly in tasks involving spatial learning and memory.\u003c/p\u003e \u003cp\u003eThe cognitive benefits identified in this study can be particularly valuable for individuals with spatial memory deficits, such as those suffering from neurodegenerative diseases. Implementing exercise regimes that improve spatial memory could help mitigate the progression of such deficits, offering a non-pharmacological approach to enhance quality of life. Furthermore, given the rigorous demands of SIT, it may not a feasible option for everyone and the intensity and physical requirements of SIT might exclude individuals with certain health conditions, older adults, or those just beginning an exercise regimen. However, the findings that acute MICT also leads to significant cognitive improvements where MICT presents an effective modality to enhance spatial memory.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe current study investigated the effects of moderate- and high-exercise intensity on cognition through learning a spatial map in virtual reality. The results suggest that acute exercise improves one\u0026rsquo;s ability to learn a spatial map, with notably greater learning improvements observed in the high intensity exercise group. Therefore, acute exercise can enhance spatial memory, but exercise intensity does play a crucial role.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNR, GM, MC, and TH wrote and edited the manuscriptML, MC, and TH conceived the study design and research questionsML, GM, NR, and MC collected and analyzed dataNR, GM, ML, MC, and TH interpreted findings\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the Natural Sciences and Engineering Council of Canada under Grant# 2019\u0026ndash;05894 to MEC.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAalto, S., Br\u0026uuml;ck, A., Laine, M., N\u0026aring;gren, K., \u0026amp; Rinne, J. (2005). 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W., \u0026amp; White, S. H. (1975). The development of spatial representations of large-scale environments. In H. W. Reese (Ed.), \u003cem\u003eAdvances in child development\u003c/em\u003e (Vol. 10, pp. 9-55). Academic Press.\u003c/li\u003e\n\u003cli\u003eMassarotto, D. (2023).\u003c/li\u003e\n\u003cli\u003ePraag, H. (2008). Neurogenesis and exercise: Past and future directions. \u003cem\u003eNeuroMolecular Medicine, 10\u003c/em\u003e(2), 128-140. https://doi.org/10.1007/s12017-008-8028-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Moderate intensity continuous training, Sprint interval training, Virtual reality, Exercise spatial navigation, Spatial memory, Learning, Maze","lastPublishedDoi":"10.21203/rs.3.rs-6247198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6247198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious research suggests exercise improves spatial navigation abilities, though the effects of different exercise intensities on this cognitive function have not been explored. The current study assessed the influence of moderate-intensity and high-intensity acute exercise on spatial learning and memory, focusing on the acquisition of survey and route knowledge in young adults. Thirty-two participants (22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 y) were randomly assigned to one of three groups: 1) no-exercise control (n\u0026thinsp;=\u0026thinsp;10); 2) moderate-intensity continuous training (MICT; 30 min at 65% maximal oxygen consumption) (n\u0026thinsp;=\u0026thinsp;12); 3) sprint interval training (SIT; 4x30 sec all-out interspersed with 4 min recovery) (n\u0026thinsp;=\u0026thinsp;10). Spatial navigation abilities were assessed using a virtual reality (VR) maze with evaluations at three time points: pre-exercise (TP1), immediately post-exercise (TP2), and 48 h post-exercise (TP3). Angular error (AE) was the primary measure of navigation accuracy. Both MICT and SIT groups exhibited improvements in spatial memory indicated by reductions in AE from TP1 to TP3 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) though the SIT group showed a greater reduction in AE compared to the MICT group (p\u0026thinsp;=\u0026thinsp;.039), suggesting a more pronounced benefit from higher-intensity exercise. The control group, however, showed no significant change in AE (p\u0026thinsp;=\u0026thinsp;.869), indicating no improvement in spatial memory without exercise intervention. The findings suggest that acute exercise, particularly at higher intensities, enhances spatial memory alongside with learning. It is possible that exercise can be used as a intervention to enhance cognitive functions, particularly spatial navigation.\u003c/p\u003e","manuscriptTitle":"Exercise Intensity improves performance on a Spatial Memory Task","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 10:14:06","doi":"10.21203/rs.3.rs-6247198/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":"b5948b9b-c423-4c69-abf7-18379f32c14f","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:36:04+00:00","versionOfRecord":{"articleIdentity":"rs-6247198","link":"https://doi.org/10.1007/s00221-025-07142-4","journal":{"identity":"experimental-brain-research","isVorOnly":false,"title":"Experimental Brain Research"},"publishedOn":"2025-08-18 16:29:31","publishedOnDateReadable":"August 18th, 2025"},"versionCreatedAt":"2025-04-07 10:14:06","video":"","vorDoi":"10.1007/s00221-025-07142-4","vorDoiUrl":"https://doi.org/10.1007/s00221-025-07142-4","workflowStages":[]},"version":"v1","identity":"rs-6247198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6247198","identity":"rs-6247198","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}