Structured route learning mitigates age-related interference between overlapping spatial representations during virtual wayfinding

Structured route learning mitigates age-related interference between overlapping spatial representations during virtual wayfinding | 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 Research Article Structured route learning mitigates age-related interference between overlapping spatial representations during virtual wayfinding Tim Eberle, Wiebren Zijlstra, Kyungwan Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8778293/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 Age-related declines in spatial navigation are often attributed to impairments in executive control, particularly cognitive flexibility and task switching. However, it remains unclear to what extent these deficits reflect age-related limitations and/or sensitivity to task structure and interference. The present study investigated how switching demands between competing route representations shape spatial knowledge acquisition in older (OA) and younger (YA) adults during complex wayfinding in a virtual environment. Participants learned two partially overlapping routes under either a high-interference alternating condition (A–B–A–B–A–B) or a low-interference sequential condition (A–A–A–B–B–B). Spatial knowledge was assessed using route retracing performance, navigation errors, landmark recognition, and directional estimation between landmarks. OA showed lower overall performance than YA across all measures. Critically, however, OA who learned routes sequentially exhibited substantial improvements in route retracing accuracy and landmark recognition compared to those exposed to alternating learning, thereby markedly reducing age-related performance differences under identical task demands. In contrast, alternating learning disproportionately impaired OA, consistent with elevated switching-related effects between competing route representations. These findings demonstrate that age-related differences in spatial navigation are strongly modulated by learning structure. Reducing switching between competing route representations and stabilizing representational demands mitigates interference-related costs, enabling more efficient encoding and integration of spatial information in older adults. The results suggest that structured, low-interference learning environments may serve as an effective compensatory scaffold for age-related declines in cognitive flexibility, highlighting the importance of task design in supporting navigation and learning across the adult lifespan. Aging wayfinding spatial knowledge interference sequential learning Figures Figure 1 Figure 2 Figure 3 1. Introduction Spatial navigation is a fundamental aspect of human cognition that enables individuals to orient themselves, plan routes, and move effectively through the environment. Successful wayfinding relies on a sequence of cognitive processes, beginning with the perception of task-relevant environmental information, followed by encoding, maintenance, and retrieval of spatial representations. These processes are supported and modulated by attention, working memory, executive control, and spatial representation systems (Lester et al., 2017 ; Moffat, 2009 ). Within this process, individuals rely on different forms of spatial knowledge: landmark knowledge (recognizing distinct features in the environment), route knowledge (understanding sequences of directional decisions), and survey knowledge (forming an allocentric cognitive map) (Kim & Bock, 2020 ). The ability to flexibly integrate these components determines the efficiency and accuracy of navigation. Aging is known to affect these underlying cognitive processes. Older adults (OA) often show reduced accuracy in route retracing, poorer recall of landmark information, and less precise directional estimation compared to young adults (YA) (Head & Isom, 2010 ; Wiener et al., 2012 ). These deficits have been linked to age-related structural and functional changes in the prefrontal cortex and hippocampus, i.e., brain regions that support executive functions and spatial memory (Raz et al., 2007 ). In particular, the decline of executive control, such as working memory updating, inhibition, and cognitive flexibility, may constrain the ability to adapt to new spatial contexts and learn efficiently in complex environments (Kray & Lindenberger, 2000 ; Miyake et al., 2000 ). One important aspect of executive control relevant for wayfinding is the ability to flexibly shift between competing spatial representations. In navigation, this often involves switching between different routes or route-specific memory sets rather than switching between distinct task rules (Monsell, 2003 ). Frequent switching between route representations can induce interference and reconfiguration costs, manifesting as increased response times and error rates (Rogers & Monsell, 1995 ), particularly in OA with reduced cognitive flexibility (Li & Giudice, 2018 ). Consequently, wayfinding tasks that require alternating between overlapping routes or integrating multiple spatial contexts may place disproportionate demands on OA compared with YA. Complex wayfinding environments often involve overlapping routes that share spatial segments or landmarks, thereby increasing interference between route representations. According to theories of memory interference and cognitive control, learning multiple overlapping spatial sequences can lead to competition between existing and newly acquired associations (Anderson, 2003 ; Friedman & Miyake, 2016 ). When learners frequently alternate between two overlapping routes (e.g., A–B–A–B–A–B), they must continuously reconfigure their attentional and mnemonic representations, suppress previously activated information, and update working memory with new spatial details (Harris & Wolbers, 2014 ). This dynamic reconfiguration process imposes a heavy load on executive resources, particularly on inhibitory control and updating processes supported by the prefrontal cortex (Li & Giudice, 2018 ). Because these functions decline with age, OA are especially vulnerable to interference, leading to slower learning, higher error rates, and less integrated spatial knowledge (He et al., 2021 ; Wiener et al., 2012 ). In contrast, sequential learning structures (e.g., A–A–A–B–B–B) may alleviate these demands by reducing the frequency of switching between route representations and by stabilizing the learning context. This structure supports encoding and early consolidation of route-specific information before introducing competing spatial content, thereby minimizing cross-route interference (Colzato et al., 2006 ; Verhaeghen, 2011 ). From a cognitive load perspective, sequential learning reduces the need for continuous attentional reallocation and inhibitory control, allowing resources to be devoted to deeper spatial processing and integration. Moreover, from the standpoint of the multiple memory systems framework (Squire & Dede, 2015 ), sequential exposure may facilitate the transition from procedural route learning to declarative or allocentric representations, as the hippocampal–prefrontal network can stabilize and link route elements without interference from competing task sets. This structured approach may therefore function as a form of environmental scaffolding by stabilizing learning demands and reducing interference between competing route representations, thereby supporting effective encoding and early consolidation of spatial information in OA despite age-related declines in executive control. Taken together, these theoretical perspectives suggest that the structure of learning, whether routes are alternated or learned sequentially, modulates the cognitive demands placed on the learner and interacts with age-related differences in executive functioning. When the frequency of switching between competing route representations and associated interference is high, OA are expected to show pronounced performance deficits; when learning is structured and stable, they may perform comparably to younger adults by leveraging compensatory mechanisms and reduced cognitive load. Importantly, the present design was not intended as a fully crossed age-by-learning-structure factorial experiment. Instead, it specifically targets age-related susceptibility to interference arising from frequent switching between overlapping route representations. YA were tested under high switching demands to establish a reference level of performance under maximal interference conditions, whereas OA were additionally examined under reduced switching demands to assess whether performance limitations primarily reflect structural sensitivity rather than fixed deficits. This design choice allows a focused test of how learning structure modulates age-related differences in spatial navigation, rather than an exhaustive comparison of all possible age-by-structure combinations. Accordingly, the present study aimed to examine how the structure of learning (alternating vs. sequential route exposure) affects spatial knowledge acquisition in OA and YA. We hypothesized that OA would exhibit generally lower performance than YA across measures of wayfinding, including route retracing, landmark recognition, and directional estimation. However, we further expected that a sequential learning order (A–A–B–B) would reduce switching costs and interference, enabling OA to achieve performance levels closer to YA. By systematically manipulating the structure of route learning and the degree of switching between overlapping route representations in a controlled virtual environment, this study seeks to clarify how learning structure can mitigate age-related deficits in spatial cognition and executive control. 2. Methods 2.1 Participants Fifteen healthy YA (YA; 10 females, average age of 26.0 ± 2.8 years), 30 healthy OA (12 females, average age of 69.2 ± 4.1 years) participated in the study. Recruitment strategies were individual contact and handing out information brochures in diverse facilities such as sports clubs, associations, or communities. Exclusion criteria were severe uncorrected visual deficits and moderate to severe cognitive impairments. All participants provided written informed consent before testing began. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the German Sport University Cologne (Nr. 068/2023). Power calculations were conducted using the pwr package in R (Version 2024.09.1 + 394; Posit PBC, Boston, MA, USA; Champely et al., 2020 ) to assess whether the final sample size was appropriate for detecting the expected effects. Assuming a two-tailed t-test with an alpha level of .05 and desired power of .80, a total sample of 45 participants (approximately 15 per group) would be sufficient to detect a medium effect size (d = 0.60). This estimate aligns with prior experimental studies investigating age-related differences in wayfinding and task switching using comparable designs (e.g., Harris & Wolbers, 2014 ; Kim & Bock, 2020 ; Kray & Lindenberger, 2000 ). Accordingly, the achieved sample size of 45 participants was deemed adequate to detect medium effects typically observed in aging-related spatial cognition research. 2.2 Virtual Environment and Apparatus We developed a virtual wayfinding environment based on a commercially available realistic 3D video game (Call of Duty: Black Ops 4, Activision Publishing, Inc) on a video console game platform (Play Station 4, Sony Interactive Entertainment, Ltd.). The selected map contained everyday-like environmental features such as houses, stairs, trees, and walls, providing high degrees of freedom and ecological validity. Two different routes, i.e., route A and route B, were defined within the same environment. These routes overlapped in the middle section, and each contained 15 decision points (e.g., turn left, right, or continue straight) and multiple salient landmarks (e.g., big trees, wall pictures, colorful stairs…). In a preliminary pilot phase, the two routes were tested and adjusted to ensure comparable visual complexity and length. Because prior testing revealed difficulties among OA in handling the game controller, the experimenter-controlled navigation according to participants’ verbal responses following a standardized protocol. Participants were instructed to make directional decisions verbally (e.g., “left,” “right,” “straight ahead”) while the experimenter executed these inputs in real time. This procedure ensured equal task demands and minimized motor confounds between age groups. 2.3 Procedure Every participant learned both routes under one of two learning structures: intra -block (alternating) switching between route representations (A–B–A–B–A–B; hereafter referred to as intra-block switching) or inter -block (sequential) switching between route representations (A–A–A–B–B–B; inter-block switching). The 30 OA were randomly assigned to either learning structure, with 15 participants in each group (i.e., OA_Intra & OA_Inter), whereas all 15 YA were learned using the intra-block (i.e., YA_Intra) switching structure. The assignment of routes and learning order was randomized for each participant using the Research Randomizer software (Urbaniak & Plous, 2013 ). A learning block was defined as the complete learning and testing sequence for one route, consisting of a video-based route learning phase followed by route retracing, landmark recognition, and direction pointing test (see Fig. 1 ). For each learning block, participants first watched a standardized first-person video walkthrough of the respective route and were instructed to memorize it. The measurements used to examine wayfinding ability were based on methods established in the literature and were adapted to the specific conditions of this study. These included the landmark knowledge test for landmark recognition (e.g., He et al., 2021 ; Head & Isom, 2010 ; Kim & Bock, 2020 ), the recording of time and errors per completed route to test route knowledge (e.g., Kim & Bock, 2020 ; Zhong & Moffat, 2016 ), and the direction pointing test to test survey knowledge (e.g., Harris & Wolbers, 2014 ). Route retracing test . Both time and errors were systematically recorded. The time required (in seconds) was measured by a stopwatch, the measurement of which begins at the start of each route and stops when the destination was reached. The best and worst attempt of all results were used as reference values for a time interval ranging from 66 seconds (best possible time) to 320 seconds (worst possible time). 148.5 seconds corresponded to an average time performance. Any deviation from the optimal route was defined as an error. There was a total of 15 decision points per route, at each of which the participants could make one mistake. The best result corresponded to no error, while the worst result corresponded to 15 errors. Four errors were defined as a random result (based on the average errors committed across all test times by all test subjects). Landmark recognition test . Participants were shown eight images per test time on a monitor using the PsychoPy software from Open Science Tools Ltd. Presented (Peirce et al., 2019 ). Half of these images represented landmarks of the route taken, the other half were from a completely unfamiliar route. The images were shown from the first-person perspective experienced during route navigation. Participants were then asked to rate the level of familiarity of each image using a 7-point Likert scale ranging from 1 (completely unknown) to 7 (completely known). Participants had access to a keyboard with the numbers 1 to 7 for the corresponding input. For each previously seen landmark, participants received one point if they answer, “completely unknown” and seven points if they answer, “completely familiar.” For each previously unseen landmark, they received one point if they answer, "completely familiar" and seven points if they answer, "completely unknown." Overall, the landmark recognition score ranged from 48 to 336 points, with a score of 192 points (48 images x 4 point) corresponding to chance-level performance. Direction pointing test . The participants were first presented with a printed out starting position into which they were supposed to imagine themselves. This was the same perspective (egocentric) as the participants experienced when going through the route. Another image then appeared on the screen with a specific area marked. The participants were asked to indicate whether this area of the second image was in one of eight given directions relative to the starting position: left, front left, front, front right, right, back right, back, or back left. To enter the directions, the participants used the keyboard, on which the directions were marked on the number pad. Overall, a starting position was specified for each test time with two images whose directions must be specified. Points were awarded based on the accuracy of the assessment: A correct directional assessment corresponded to a deviation of 0°. The values to be achieved could therefore be between 0° and 180°, with 0° being the best possible result and 180° being the worst possible result. 90° corresponded to a random performance. The actual deviations from the starting position to the marked area in degrees were previously calculated using an allocentric map of the virtual environment used. This task was chosen to assess survey-level spatial knowledge, acknowledging that it imposes higher cognitive demands than sequential route-order tasks. Each participant completed six runs in total (three per route). Short rest breaks were provided between runs to minimize fatigue and maintain attentional stability, particularly for older participants. 2.4 Data Registration and Processing All behavioral data were recorded in PsychoPy, exported, and processed in Microsoft Excel 2021 and RStudio (Posit PBC; Version 2025.09.2–418). To enable comparability across measures, raw scores from landmark recognition, direction pointing, elapsed time and errors were transformed into standardized percentage scores ranging from − 100% (worst) to 100% (best), with 0% indicating chance-level performance (adapted from Kim & Bock, 2020 ). For elapsed time and errors, piecewise linear transformations were applied around the predefined chance anchors: Elapsed time [%] = 100* \(\:\frac{148.5\:-\:\text{E}\text{T}}{148.5\:-\:66}\) *1{ET ≤ 148.5} – 100* \(\:\frac{\text{E}\text{T}\:-\:148.5}{320\:-\:148.5}\) *1{ET > 148.5} Number of errors [%] = 100* \(\:\frac{4\:-\:\text{N}\text{o}\text{E}}{4}\) *1{NoE ≤ 4} – 100* \(\:\frac{\text{N}\text{o}\text{E}\:-\:4}{15\:-\:4}\) *1{NoE > 4} Landmark recognition test [%] = (LR – 192)*100/144; Direction pointing test [%] = (90 – DP)*100/90; Here, LR = total landmark score, DP = mean angular error in degrees, ET = elapsed time in seconds, and NoE = number of errors. Because these transformations may affect distributional properties, all statistical assumptions were empirically tested, and non-parametric analyses were applied whenever normality assumptions were violated. 2.5 Statistical Analysis All statistical analyses were conducted in RStudio (Posit PBC, Boston, MA, US; Version 2025.09.2–418). Descriptive statistics (mean, SD, median, quartiles, minimum, maximum) were computed for each dependent variable. Normality was assessed using the Shapiro–Wilk test, and homogeneity of variances was checked using Levene’s test. Learning trajectories were examined across three repetitions (T1, T2, and T3) of each route (A and B). To characterise learning progress, analyses focused on improvements from the first (T1) to the final trial (T3). In addition, change scores (T3 − T1) were computed to quantify the magnitude of learning. Non-parametric data (elapsed time and number of errors) were analysed using Kruskal–Wallis tests for group comparisons and Friedman tests for changes across trials. Significant main effects were examined using Bonferroni-adjusted Mann–Whitney U tests and Wilcoxon signed-rank tests. Effect sizes were reported as the rank-biserial correlation (r) for pairwise differences and Kendall’s W for the magnitude of concordance across trials. For parametric data (landmark and survey knowledge), a mixed ANOVA was conducted with Group (YA_Intra, OA_Intra, OA_Inter) as the between-subjects factor and Trial (T1–T3) as the within-subjects factor. Significant main effects and interactions were followed up with Bonferroni-adjusted pairwise t-tests. Effect sizes were reported as partial eta-squared (η²) for ANOVA effects and Cohen’s d for pairwise comparisons. All p-values were two-tailed, and statistical significance was set at α = .05. 3. Results 3.1 Sample characteristics Descriptive information is presented in Table 1 . Group differences in SBSOD scores did not differ significantly (YA_Intra vs. OA_Intra: p = .43; OA_Intra vs. OA_Inter: p = .052), indicating comparable self-reported spatial orientation abilities across groups. Thus, groups did not differ in baseline navigation self-assessment, providing comparable starting conditions for subsequent analyses. All 45 participants completed the full testing protocol. Table 1 Descriptive information. Group N Sex Age SBSOD male female Min Max M ± SD M ± SD YA_Intra 15 5 10 23 33 25.9 ± 2.78 4.17 ± 0.99 OA_Intra 15 5 10 62 75 69.2 ± 4.78 4.41 ± 0.61 OA_Inter 15 8 7 64 74 69.1 ± 3.45 4.99 ± 0.84 3.2 Group differences Kruskal–Wallis tests indicated significant group differences in route retracing performance for elapsed time, χ²(2) = 16.6, p < .001, and navigation errors, χ²(2) = 23.6, p < .001. For landmark recognition, the mixed ANOVA showed a significant main effect of Group, F(2, 42) = 11.69, p < .001, η² = .360. Similarly, a significant main effect of Group was found for direction pointing, F(2, 42) = 4.71, p = .014, η²ₚ= .18. Age effects (YA_Intra vs OA_Intra). Mann–Whitney U tests showed that YA_Intra retraced the routes significantly faster than OA_Intra (p = .001, r = .62) and committed significantly fewer navigation errors (p < .001, r = .76). Bonferroni-adjusted pairwise t-tests indicated significantly higher landmark recognition in YA_Intra than OA_Intra (p < .001, d = 1.65) and significantly higher direction pointing performance in YA_Intra (p = .014, d = 1.04). Across all navigation-related measures, YA showed significantly better performance than OA under identical intrablock switching conditions (see Table 2 ). Effects of learning structure in OA (OA_Intra vs. OA_Inter). Mann–Whitney U tests indicated significantly better route retracing in OA_Inter than OA_Intra for elapsed time (p < .001, r = .66) and navigation errors (p = .001, r = .67). Landmark recognition was higher in OA_Inter (48.10% ± 24.60 vs. 38% ± 26.19), although this contrast did not reach statistical significance (p = .053, d = − .91). Direction pointing did not differ between OA_Intra and OA_Inter (p = 1.000, d = − .36). Sequential learning (interblock) yielded clear advantages for route retracing accuracy and speed but did not produce significant differences in landmark or survey knowledge compared to alternating learning within OA. Table 2 Summary of performance across groups. Group M/Mdn. ± SD p (adjusted) Effect size [SI] [%] [r;d] Elapsed time [s] YA_Intra 141.31 ± 31.27 15.19 ± 29.56 .001** < .001*** r = .62 r = .66 OA_Intra 175.30 ± 46.04 -11.98 ± 31.78 OA_Inter 138.90 ± 41.18 20.13 ± 37.19 Number of errors YA_Intra 0.78 ± 1.13 80.6 ± 16.6 < .001*** < .001*** r = .76 r = .67 OA_Intra 3.14 ± 2.06 29.7 ± 29.1 OA_Inter 1.36 ± 1.76 68.2 ± 15.0 Landmark recognition test YA_Intra 5.75 ± 0.68 58.38 ± 22.65 < .001*** .053 d = 1.65 d = − .91 OA_Intra 5.15 ± 0.79 38.24 ± 26.19 OA_Inter 5.44 ± 0.74 48.10 ± 24.60 Direction pointing test [°] YA_Intra 54.00 ± 43.56 40.00 ± 48.40 .014* 1.000 d = 1.04 d = − .36 OA_Intra 79.50 ± 41.88 11.67 ± 46.54 OA_Inter 72.00 ± 37.74 20.00 ± 41.93 SI = raw scores in Système International units (e.g., seconds for elapsed time; degrees for direction pointing); % = standardised performance score (− 100% to 100%) computed from raw values using the transformations described in the text (adapted from Kim & Bock, 2020 ), with higher values indicating better performance (faster time, fewer errors, higher recognition, more accurate pointing); p (adjusted) = Bonferroni-adjusted p-values for pairwise group comparisons; Effect sizes = r (rank-biserial correlation; Mann–Whitney U tests for elapsed time and errors) and d (Cohen’s d; pairwise t-tests for landmark recognition and direction pointing); Significance: * p < .05, ** p < .01, *** p < .001. 3.3 Learning effects across trials Route knowledge . Friedman tests indicated significant changes across trials in elapsed time for all groups (YA_Intra: χ²(2) = 28.1, p < .001, W = .94; OA_Intra: χ²(2) = 19.6, p < .001, W = .65; OA_Inter: χ²(2) = 30.0, p < .001, W = 1.00). Bonferroni-adjusted Wilcoxon signed-rank tests showed significant differences between T1 and T3 in each group (YA_Intra: p < .001, r = .88; OA_Intra: p < .001, r = .84; OA_Inter: p < .001, r = .88). For navigation errors, Friedman tests were significant in YA_Intra (χ²(2) = 11.3, p = .004, W = .38) and OA_Inter (χ²(2) = 25.1, p < .001, W = .84), but not in OA_Intra (χ²(2) =.667, p = .717, W = .02). Thus, the T1–T3 Wilcoxon comparison was significant in YA_Intra (p = .010, r = .78) and OA_Inter (p = .003, r = .88), but not in OA_Intra (p = 1.000, r = .01). Change scores (T3 − T1) indicated the largest improvement in elapsed time for OA_Inter (M = 71.0%, SE = 2.48), followed by YA_Intra (M = 54.0%, SE = 4.54) and OA_Intra (M = 21.9%, SE = 4.50). Error score improvements showed a similar pattern: OA_Inter (M = 66.1%, SE = 7.59) > YA_Intra (M = 21.7%, SE = 6.16) ≫ OA_Intra (M = 4.85%, SE = 6.92). Overall, the sequential learning structure was associated with the most consistent improvements, whereas intrablock switching was accompanied by limited gains in older adults, particularly for error reduction. Landmark knowledge. A mixed ANOVA on landmark recognition showed a significant main effect of Trial, F(2, 84) = 26.61, p < .001, η²ₚ = .39, with no Group × Trial interaction, F(4, 84) = 0.64, p = .635, η²ₚ = .03. Bonferroni-adjusted paired comparisons indicated significant higher scores at T3 than at T1 (p < .001, d = 1.10). Accordingly, T1–T3 change scores were positive and comparable across groups (YA_Intra: 23.1%; OA_Intra: 19.3%; OA_Inter: 20.6%; Figure X), indicating improved landmark recognition with repetition irrespective of age or learning structure. Survey knowledge. For direction pointing, the mixed ANOVA showed no main effect of Trial, F(2, 84) = 1.28, p = .284, η²ₚ = .03, and no Group × Trial interaction, F(4, 84) = 0.45, p = .773, η²ₚ = .02. T1-T3 change scores were close to zero or negative across groups (YA_Intra: −2.5%; OA_Intra: −19.2%; OA_Inter: −9.2%). Survey knowledge did not improve with repetition, suggesting that allocentric spatial integration remained stable across trials regardless of age or learning condition. 4. Discussion The present study investigated how learning structure interacts with age-related differences in executive control to influence spatial knowledge acquisition in virtual wayfinding. Several important findings emerged. First, OA showed the expected age-related impairments across all measured spatial variables, i.e., longer completion times, more navigation errors, and lower accuracy in landmark and directional judgments, confirming well-documented declines in spatial memory and cognitive flexibility with aging (Lester et al., 2017 ; Wiener et al., 2012 ). Second, and more importantly, OA who learned routes sequentially (A-A-A-B-B-B) performed markedly better than those who alternated between routes (A-B-A-B-A-B). Under the sequential condition, OA reduced navigation time and errors substantially and substantially reduced the performance level of YA in route retracing. This pattern demonstrates that structured learning can compensate for age-related deficits by lowering task switching frequency and memory interference. Mechanistic interpretation Frequent switching between overlapping routes, as required in the A-B-A-B-A-B condition, taxes inhibitory control and working memory updating because learners must repeatedly suppress prior route information while encoding new spatial cues (Anderson, 2003 ; Friedman & Miyake, 2016 ). Such demands overload the prefrontal-hippocampal network and increase competition between route representations, particularly in OA whose executive resources are reduced (Li & Giudice, 2018 ). In contrast, sequential learning provides a stable cognitive context that minimizes these interference effects and supports gradual consolidation of route specific representations. The improvement observed in OA_Inter across repeated trials indicates that once cognitive load is reduced, OA can effectively form and refine route memories through repetition and error-based learning. In contrast to route knowledge, survey (allocentric) knowledge did not show improvement across trials in any group. Although one plausible explanation is that hippocampus-dependent allocentric transformations remain particularly resistant to training in older age (Harris & Wolbers, 2014 ; Iaria et al., 2003 ), the present findings do not permit strong conclusions. Several alternative explanations must also be considered. The direction pointing task may have imposed high cognitive demands, i.e., requiring perspective transformations, spatial updating, and memory of landmark relationships, that could have limited measurable improvements even in young adults. Additionally, survey knowledge is known to benefit more from active exploration and global access to the environment than from repeated route learning alone (Chrastil & Warren, 2012 ; Ishikawa & Montello, 2006 ). Thus, the predominantly procedural, route-based paradigm used here may not have provided the experiential conditions required for constructing more flexible allocentric representations. Furthermore, task sensitivity may have been insufficient to detect subtle improvements. Taken together, the lack of survey knowledge enhancement should be interpreted cautiously, and future studies employing tasks with lower cognitive load, increased ecological validity, or active navigation components are needed to disentangle these competing explanations. Cognitive-theoretical implications Beyond navigation, these findings may generalize to other complex learning situations that involve overlapping information, frequent switching between task representations, or high interference, such as learning procedural sequences, multitasking environments, or technology-based training contexts. The results converge with the cognitive load theory of aging (Verhaeghen, 2011 ), emphasizing that performance differences between age groups often reflect differential sensitivity to task complexity rather than an absolute loss of ability. By reducing switching costs, sequential learning effectively acts as environmental scaffolding (Squire & Dede, 2015 ), allowing OA to allocate limited resources more efficiently. Moreover, the distinct learning trajectories across trials, where OA_Inter showed the steepest improvement, suggest that the benefits of structured learning extend beyond momentary facilitation and may foster genuine adaptive plasticity in later life. These findings also resonate with the compensation-related utilization of neural circuits hypothesis (CRUNCH), which posits that OA can achieve near-young performance by optimizing strategy use and environmental structure when demands remain within their resource limits (Reuter-Lorenz & Cappell, 2008). Sequential learning may thus enable OA to operate below their overload threshold, engaging compensatory neural pathways more efficiently. Practical relevance, limitations, and future directions From an applied standpoint, the study highlights the potential of structured, low-interference VR-based training to support navigation competence in aging. Training programs emphasizing repeated exposure to consistent route structures could enhance everyday spatial orientation and reduce disorientation risks in complex real-world settings such as hospitals, transport hubs, or senior living facilities. Several limitations should be acknowledged. First, YA were not tested under a sequential learning condition, which limits direct inference about age-by-structure interactions. Accordingly, statements about OA “approaching” YA levels should be interpreted cautiously. Second, because sequential and alternating learning differ not only in switching frequency but also in the lag between repetitions, the present design cannot fully disentangle switching-related interference from spacing/recency effects. Third, navigation was experimenter-controlled to reduce motor and interface confounds, but this may have altered the ecological validity of wayfinding and the balance between action-based and decision-based components of navigation. Finally, the survey measure may have been underpowered or insufficiently sensitive to detect gradual allocentric improvements. Future research should therefore (a) implement a fully crossed design including YA sequential learning, (b) orthogonally manipulate switching frequency and repetition lag, and (c) vary route overlap and environmental complexity to directly test interference-based accounts (He et al., 2021 ). In addition, incorporating neuroimaging or physiological monitoring would help verify the hypothesized prefrontal–hippocampal mechanisms underlying these behavioral improvements. Longitudinal intervention studies are needed to determine whether sequential learning protocols yield durable gains in navigation efficiency and executive flexibility. Manipulating environmental complexity or degree of route overlap would also clarify how interference, working memory load, and perceptual cue richness interact to shape spatial learning across the adult lifespan. Future research should integrate neuroimaging or physiological monitoring to verify the hypothesized prefrontal-hippocampal mechanisms underlying these behavioral improvements. Longitudinal intervention studies are needed to determine whether sequential learning protocols yield durable gains in navigation efficiency and executive flexibility. Manipulating environmental complexity or degree of route overlap would also clarify how interference, working memory load, and perceptual cue richness interact to shape spatial learning across the adult lifespan. 5. Conclusion Overall, this study demonstrates that age-related declines in wayfinding performance are not fixed but can be significantly alleviated through structured learning design. Sequential route learning reduces the cognitive demands associated with frequent switching between overlapping route representations, allowing OA to construct more accurate and stable spatial representations. These findings contribute to a growing body of evidence that adaptive structuring of learning environments can enhance cognitive performance in aging by mitigating executive control demands and supporting compensatory mechanisms for efficient spatial knowledge acquisition. While sequential learning enabled OA to perform more efficiently, the present findings do not imply a complete normalization of age-related differences. Rather, they demonstrate that a substantial portion of age-related performance variance reflects differential sensitivity to task structure and interference, consistent with accounts emphasizing cognitive load and executive control demands. Declarations Conflict of Interest The authors have no relevant financial or non-financial interests to disclose. The authors report no conflicts of interest. Ethical Approval This study was approved by the Ethics Committee of the German Sport University Cologne (Nr. 068/2023). Consent to Participate All participants provided informed consent. Funding The authors did not receive support from any organization for the submitted work. Author Contribution Tim Eberle contributed to data collection, formal analysis, and the preparation of the Results section. Wiebren Zijlstra contributed to the methodology, provided supervision throughout the research process, and critically reviewed and edited the manuscript. Kyungwan Kim was responsible for the overall conceptualization, experimental design, and coordination of the study, served as the corresponding author, and wrote the Abstract, Introduction, Methods, and Discussion sections. All authors approved the final version of the manuscript. Acknowledgement The authors thank Elisabeth Seil for her support in data collection. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References Anderson, M. C. (2003). Rethinking interference theory: Executive control and the mechanisms of forgetting. Journal of Memory and Language , 49 , 415–445. https://doi.org/10.1016/j.jml.2003.08.006 Champely, S., Ekstrom, C., Dalgaard, P., Gill, J., Weibelzahl, S., Anandkumar, A., Ford, C., Volcic, R., & De Rosario, H. (2020). pwr: Basic functions for power analysis (R package version 1.3-0) . https://cran.r-project.org/package=pwr Chrastil, E. R., & Warren, W. H. (2012). Active and passive contributions to spatial learning. Psychonomic Bulletin and Review , 19 (1), 1–23. https://doi.org/10.3758/s13423-011-0182-x Colzato, L. S., Van Wouwe, N. C., Lavender, T. J., & Hommel, B. (2006). Intelligence and cognitive flexibility: Fluid intelligence correlates with feature unbinding across perception and action. Psychonomic Bulletin and Review , 13 (6), 1043–1048. https://doi.org/10.3758/BF03213923 Friedman, N. P., & Miyake, A. (2016). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. CORTEX , 86 , 186–204. https://doi.org/10.1016/j.cortex.2016.04.023 Harris, M. A., & Wolbers, T. (2014). How age-related strategy switching deficits affect wayfinding in complex environments. Neurobiology of Aging , 35 (5), 1095–1102. https://doi.org/10.1016/j.neurobiolaging.2013.10.086 He, Q., Beveridge, E. H., Starnes, J., Goodroe, S. C., & Brown, T. I. (2021). Environmental overlap and individual encoding strategy modulate memory interference in spatial navigation. Cognition , 207 (104508). https://doi.org/10.1016/j.cognition.2020.104508 Head, D., & Isom, M. (2010). Age effects on wayfinding and route learning skills. Behavioural Brain Research , 209 (1), 49–58. https://doi.org/10.1016/j.bbr.2010.01.012 Iaria, G., Petrides, M., Dagher, A., Pike, B., & Bohbot, V. D. (2003). Cognitive strategies dependent on the hippocampus and caudate nucleus in human navigation: variability and change with practice. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience , 23 (13), 5945–5952. https://doi.org/23/13/5945 [pii]. Ishikawa, T., & Montello, D. R. (2006). Spatial knowledge acquisition from direct experience in the environment: Individual differences in the development of metric knowledge and the integration of separately learned places. Cognitive Psychology , 52 (2), 93–129. https://doi.org/10.1016/j.cogpsych.2005.08.003 Kim, K., & Bock, O. (2020). Acquisition of landmark, route, and survey knowledge in a wayfinding task: in stages or in parallel? Psychological Research Psychologische Forschung . https://doi.org/10.1007/s00426-020-01384-3 Kray, J., & Lindenberger, U. (2000). Adult age differences in task switching. Psychology and Aging , 15 (1), 126–147. https://doi.org/10.1037/0882-7974.15.1.126 Lester, A. W., Moffat, S. D., Wiener, J. M., Barnes, C. A., & Wolbers, T. (2017). The Aging Navigational System. Neuron , 95 (5), 1019–1035. https://doi.org/10.1016/j.neuron.2017.06.037 Li, H., & Giudice, N. A. (2018). Assessment of between-floor structural and topological properties on cognitive map development in multilevel built environments. Spatial Cognition & Computation , 18 (3), 138–172. https://doi.org/10.1080/13875868.2017.1384829 Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex Frontal Lobe tasks: A latent variable analysis. Cognitive Psychology , 41 (1), 49–100. https://doi.org/10.1006/cogp.1999.0734 Moffat, S. D. (2009). Aging and Spatial Navigation: What Do We Know and Where Do We Go? Neuropsychology Review , 19 (4), 478. https://doi.org/10.1007/s11065-009-9120-3 Monsell, S. (2003). Task switching. Trends in Cognitive Sciences (Vol. 7, pp. 134–140). Elsevier Ltd. 3 https://doi.org/10.1016/S1364-6613(03)00028-7 Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., & Lindeløv, J. K. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods , 51 (1), 195–203. https://doi.org/10.3758/s13428-018-01193-y Raz, N., Rodrigue, K. M., Kennedy, K. M., & Acker, J. D. (2007). Vascular health and longitudinal changes in brain and cognition in middle-aged and older adults. Neuropsychology , 21 (2), 149–157. https://doi.org/10.1037/0894-4105.21.2.149 Rogers, R. D., & Monsell, S. (1995). Costs of a predictable switch between simple cognitive tasks. Journal of Experimental Psychology: General , 124 (2), 207–231. https://doi.org/10.1037/0096-3445.124.2.207 Squire, L. R., & Dede, A. J. O. (2015). Conscious and unconscious memory systems. Cold Spring Harbor Laboratory Press , 7 , 1–14. Urbaniak, G. C., & Plous, S. (2013). Research randomizer (version 4.0) [computer software] . Verhaeghen, P. (2011). Aging and executive control: Reports of a demise greatly exaggerated. Current Directions in Psychological Science , 20 (3), 174–180. https://doi.org/10.1177/0963721411408772 Wiener, J. M., Kmecova, H., & de Condappa, O. (2012). Route repetition and route retracing: effects of cognitive aging. Frontiers in Aging Neuroscience , 4 (7), 1–7. https://doi.org/10.3389/fnagi.2012.00007 Zhong, J. Y., & Moffat, S. D. (2016). Age-related differences in associative learning of landmarks and heading directions in a virtual navigation task. Frontiers in Aging Neuroscience , 8 (MAY), 1–11. https://doi.org/10.3389/fnagi.2016.00122 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. 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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-8778293","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588149425,"identity":"5445ef45-8d02-4343-a0f6-b3a9948a5a59","order_by":0,"name":"Tim Eberle","email":"","orcid":"","institution":"German Sport University Cologne","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"","lastName":"Eberle","suffix":""},{"id":588149428,"identity":"96c66a47-7ea9-4b53-b9d3-db2135133747","order_by":1,"name":"Wiebren Zijlstra","email":"","orcid":"","institution":"German Sport University Cologne","correspondingAuthor":false,"prefix":"","firstName":"Wiebren","middleName":"","lastName":"Zijlstra","suffix":""},{"id":588149430,"identity":"9808c035-1ff6-40df-a04b-b6111b356b0f","order_by":2,"name":"Kyungwan Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACCQY2hgMMFcwMBgwMzEC+BRAnEKPlDFyLBHFaGBjbSNEi2cCWeOjmPGt7c/4DzAYfKiTyGNiTD+DVIs3AduBw7rb0xJ0zEpgTZ5yRKGbgeYbfGjkG9gaglsMJBjf4Px/mbZNIbJDIMSBCy5zD9gbnDzAf5v0H0pL/gQiHNRxm3HAggTmZtwFsC14dDJLNbAmHc46lJ264kcBsOOOYRGIbzzP8DpM43mb8OafGGuwwiQ81Non97MkP8FvDjC7Ahl/9KBgFo2AUjAJiAADgP0OIsF1VMQAAAABJRU5ErkJggg==","orcid":"","institution":"German Sport University Cologne","correspondingAuthor":true,"prefix":"","firstName":"Kyungwan","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2026-02-03 16:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8778293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8778293/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102329094,"identity":"b18398f8-d80d-471b-ae04-e19414e3e060","added_by":"auto","created_at":"2026-02-10 14:57:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the test procedure.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8778293/v1/dffdc68a22e5ef0ef7225b08.png"},{"id":102329097,"identity":"cb9a35e4-e64f-471e-a036-45c59b9b913b","added_by":"auto","created_at":"2026-02-10 14:57:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":792036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample landmarks and the keys used for both landmark recognition test (L) and direction pointing test (R).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8778293/v1/8a5c0eb74513cdaa6b3196da.png"},{"id":105032852,"identity":"8a7f3827-89f6-4130-9618-27904a1c3e2f","added_by":"auto","created_at":"2026-03-20 07:05:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChange from T1 to T3 across groups for landmark, route, and survey knowledge measures. \u003c/strong\u003eBars represent mean change scores (T3 – T1, in percentage points) for each group (YA_Intra, OA_Intra, OA_Inter) across four outcome variables: landmark recognition (LM), direction pointing (DP), elapsed time (Time), and number of errors (Error). Positive values indicate performance improvement from T1 to T3, whereas negative values indicate a decline. Error bars represent ±1 standard error of the mean.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8778293/v1/b5181e2139c0f5b1a9aaf58d.jpeg"},{"id":105037086,"identity":"9c6f2dc5-c914-42a3-8722-0f7f8732d0cf","added_by":"auto","created_at":"2026-03-20 07:37:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2018148,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8778293/v1/d267d36f-fb48-422f-a4a2-a4b7ccccd21b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structured route learning mitigates age-related interference between overlapping spatial representations during virtual wayfinding","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSpatial navigation is a fundamental aspect of human cognition that enables individuals to orient themselves, plan routes, and move effectively through the environment. Successful wayfinding relies on a sequence of cognitive processes, beginning with the perception of task-relevant environmental information, followed by encoding, maintenance, and retrieval of spatial representations. These processes are supported and modulated by attention, working memory, executive control, and spatial representation systems (Lester et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Moffat, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Within this process, individuals rely on different forms of spatial knowledge: landmark knowledge (recognizing distinct features in the environment), route knowledge (understanding sequences of directional decisions), and survey knowledge (forming an allocentric cognitive map) (Kim \u0026amp; Bock, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The ability to flexibly integrate these components determines the efficiency and accuracy of navigation.\u003c/p\u003e \u003cp\u003eAging is known to affect these underlying cognitive processes. Older adults (OA) often show reduced accuracy in route retracing, poorer recall of landmark information, and less precise directional estimation compared to young adults (YA) (Head \u0026amp; Isom, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wiener et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These deficits have been linked to age-related structural and functional changes in the prefrontal cortex and hippocampus, i.e., brain regions that support executive functions and spatial memory (Raz et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In particular, the decline of executive control, such as working memory updating, inhibition, and cognitive flexibility, may constrain the ability to adapt to new spatial contexts and learn efficiently in complex environments (Kray \u0026amp; Lindenberger, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Miyake et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). One important aspect of executive control relevant for wayfinding is the ability to flexibly shift between competing spatial representations. In navigation, this often involves switching between different routes or route-specific memory sets rather than switching between distinct task rules (Monsell, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Frequent switching between route representations can induce interference and reconfiguration costs, manifesting as increased response times and error rates (Rogers \u0026amp; Monsell, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), particularly in OA with reduced cognitive flexibility (Li \u0026amp; Giudice, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, wayfinding tasks that require alternating between overlapping routes or integrating multiple spatial contexts may place disproportionate demands on OA compared with YA.\u003c/p\u003e \u003cp\u003eComplex wayfinding environments often involve overlapping routes that share spatial segments or landmarks, thereby increasing interference between route representations. According to theories of memory interference and cognitive control, learning multiple overlapping spatial sequences can lead to competition between existing and newly acquired associations (Anderson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Friedman \u0026amp; Miyake, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). When learners frequently alternate between two overlapping routes (e.g., A\u0026ndash;B\u0026ndash;A\u0026ndash;B\u0026ndash;A\u0026ndash;B), they must continuously reconfigure their attentional and mnemonic representations, suppress previously activated information, and update working memory with new spatial details (Harris \u0026amp; Wolbers, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This dynamic reconfiguration process imposes a heavy load on executive resources, particularly on inhibitory control and updating processes supported by the prefrontal cortex (Li \u0026amp; Giudice, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Because these functions decline with age, OA are especially vulnerable to interference, leading to slower learning, higher error rates, and less integrated spatial knowledge (He et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wiener et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, sequential learning structures (e.g., A\u0026ndash;A\u0026ndash;A\u0026ndash;B\u0026ndash;B\u0026ndash;B) may alleviate these demands by reducing the frequency of switching between route representations and by stabilizing the learning context. This structure supports encoding and early consolidation of route-specific information before introducing competing spatial content, thereby minimizing cross-route interference (Colzato et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Verhaeghen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). From a cognitive load perspective, sequential learning reduces the need for continuous attentional reallocation and inhibitory control, allowing resources to be devoted to deeper spatial processing and integration. Moreover, from the standpoint of the multiple memory systems framework (Squire \u0026amp; Dede, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), sequential exposure may facilitate the transition from procedural route learning to declarative or allocentric representations, as the hippocampal\u0026ndash;prefrontal network can stabilize and link route elements without interference from competing task sets. This structured approach may therefore function as a form of environmental scaffolding by stabilizing learning demands and reducing interference between competing route representations, thereby supporting effective encoding and early consolidation of spatial information in OA despite age-related declines in executive control.\u003c/p\u003e \u003cp\u003eTaken together, these theoretical perspectives suggest that the structure of learning, whether routes are alternated or learned sequentially, modulates the cognitive demands placed on the learner and interacts with age-related differences in executive functioning. When the frequency of switching between competing route representations and associated interference is high, OA are expected to show pronounced performance deficits; when learning is structured and stable, they may perform comparably to younger adults by leveraging compensatory mechanisms and reduced cognitive load. Importantly, the present design was not intended as a fully crossed age-by-learning-structure factorial experiment. Instead, it specifically targets age-related susceptibility to interference arising from frequent switching between overlapping route representations. YA were tested under high switching demands to establish a reference level of performance under maximal interference conditions, whereas OA were additionally examined under reduced switching demands to assess whether performance limitations primarily reflect structural sensitivity rather than fixed deficits. This design choice allows a focused test of how learning structure modulates age-related differences in spatial navigation, rather than an exhaustive comparison of all possible age-by-structure combinations.\u003c/p\u003e \u003cp\u003eAccordingly, the present study aimed to examine how the structure of learning (alternating vs. sequential route exposure) affects spatial knowledge acquisition in OA and YA. We hypothesized that OA would exhibit generally lower performance than YA across measures of wayfinding, including route retracing, landmark recognition, and directional estimation. However, we further expected that a sequential learning order (A\u0026ndash;A\u0026ndash;B\u0026ndash;B) would reduce switching costs and interference, enabling OA to achieve performance levels closer to YA. By systematically manipulating the structure of route learning and the degree of switching between overlapping route representations in a controlled virtual environment, this study seeks to clarify how learning structure can mitigate age-related deficits in spatial cognition and executive control.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eFifteen healthy YA (YA; 10 females, average age of 26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 years), 30 healthy OA (12 females, average age of 69.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 years) participated in the study. Recruitment strategies were individual contact and handing out information brochures in diverse facilities such as sports clubs, associations, or communities. Exclusion criteria were severe uncorrected visual deficits and moderate to severe cognitive impairments. All participants provided written informed consent before testing began. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the German Sport University Cologne (Nr. 068/2023).\u003c/p\u003e \u003cp\u003ePower calculations were conducted using the pwr package in R (Version 2024.09.1\u0026thinsp;+\u0026thinsp;394; Posit PBC, Boston, MA, USA; Champely et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to assess whether the final sample size was appropriate for detecting the expected effects. Assuming a two-tailed t-test with an alpha level of .05 and desired power of .80, a total sample of 45 participants (approximately 15 per group) would be sufficient to detect a medium effect size (d\u0026thinsp;=\u0026thinsp;0.60). This estimate aligns with prior experimental studies investigating age-related differences in wayfinding and task switching using comparable designs (e.g., Harris \u0026amp; Wolbers, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kim \u0026amp; Bock, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kray \u0026amp; Lindenberger, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Accordingly, the achieved sample size of 45 participants was deemed adequate to detect medium effects typically observed in aging-related spatial cognition research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Virtual Environment and Apparatus\u003c/h2\u003e \u003cp\u003eWe developed a virtual wayfinding environment based on a commercially available realistic 3D video game (Call of Duty: Black Ops 4, Activision Publishing, Inc) on a video console game platform (Play Station 4, Sony Interactive Entertainment, Ltd.). The selected map contained everyday-like environmental features such as houses, stairs, trees, and walls, providing high degrees of freedom and ecological validity. Two different routes, i.e., route A and route B, were defined within the same environment. These routes overlapped in the middle section, and each contained 15 decision points (e.g., turn left, right, or continue straight) and multiple salient landmarks (e.g., big trees, wall pictures, colorful stairs\u0026hellip;).\u003c/p\u003e \u003cp\u003eIn a preliminary pilot phase, the two routes were tested and adjusted to ensure comparable visual complexity and length. Because prior testing revealed difficulties among OA in handling the game controller, the experimenter-controlled navigation according to participants\u0026rsquo; verbal responses following a standardized protocol. Participants were instructed to make directional decisions verbally (e.g., \u0026ldquo;left,\u0026rdquo; \u0026ldquo;right,\u0026rdquo; \u0026ldquo;straight ahead\u0026rdquo;) while the experimenter executed these inputs in real time. This procedure ensured equal task demands and minimized motor confounds between age groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Procedure\u003c/h2\u003e \u003cp\u003eEvery participant learned both routes under one of two learning structures: \u003cem\u003eintra\u003c/em\u003e-block (alternating) switching between route representations (A\u0026ndash;B\u0026ndash;A\u0026ndash;B\u0026ndash;A\u0026ndash;B; hereafter referred to as intra-block switching) or \u003cem\u003einter\u003c/em\u003e-block (sequential) switching between route representations (A\u0026ndash;A\u0026ndash;A\u0026ndash;B\u0026ndash;B\u0026ndash;B; inter-block switching). The 30 OA were randomly assigned to either learning structure, with 15 participants in each group (i.e., OA_Intra \u0026amp; OA_Inter), whereas all 15 YA were learned using the intra-block (i.e., YA_Intra) switching structure. The assignment of routes and learning order was randomized for each participant using the \u003cem\u003eResearch Randomizer\u003c/em\u003e software (Urbaniak \u0026amp; Plous, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A learning block was defined as the complete learning and testing sequence for one route, consisting of a video-based route learning phase followed by route retracing, landmark recognition, and direction pointing test (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For each learning block, participants first watched a standardized first-person video walkthrough of the respective route and were instructed to memorize it.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe measurements used to examine wayfinding ability were based on methods established in the literature and were adapted to the specific conditions of this study. These included the landmark knowledge test for landmark recognition (e.g., He et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Head \u0026amp; Isom, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kim \u0026amp; Bock, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the recording of time and errors per completed route to test route knowledge (e.g., Kim \u0026amp; Bock, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhong \u0026amp; Moffat, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and the direction pointing test to test survey knowledge (e.g., Harris \u0026amp; Wolbers, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRoute retracing test\u003c/b\u003e. Both time and errors were systematically recorded. The time required (in seconds) was measured by a stopwatch, the measurement of which begins at the start of each route and stops when the destination was reached. The best and worst attempt of all results were used as reference values for a time interval ranging from 66 seconds (best possible time) to 320 seconds (worst possible time). 148.5 seconds corresponded to an average time performance. Any deviation from the optimal route was defined as an error. There was a total of 15 decision points per route, at each of which the participants could make one mistake. The best result corresponded to no error, while the worst result corresponded to 15 errors. Four errors were defined as a random result (based on the average errors committed across all test times by all test subjects).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLandmark recognition test\u003c/b\u003e. Participants were shown eight images per test time on a monitor using the \u003cem\u003ePsychoPy\u003c/em\u003e software from Open Science Tools Ltd. Presented (Peirce et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Half of these images represented landmarks of the route taken, the other half were from a completely unfamiliar route. The images were shown from the first-person perspective experienced during route navigation. Participants were then asked to rate the level of familiarity of each image using a 7-point Likert scale ranging from 1 (completely unknown) to 7 (completely known). Participants had access to a keyboard with the numbers 1 to 7 for the corresponding input. For each previously seen landmark, participants received one point if they answer, \u0026ldquo;completely unknown\u0026rdquo; and seven points if they answer, \u0026ldquo;completely familiar.\u0026rdquo; For each previously unseen landmark, they received one point if they answer, \"completely familiar\" and seven points if they answer, \"completely unknown.\" Overall, the landmark recognition score ranged from 48 to 336 points, with a score of 192 points (48 images x 4 point) corresponding to chance-level performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDirection pointing test\u003c/b\u003e. The participants were first presented with a printed out starting position into which they were supposed to imagine themselves. This was the same perspective (egocentric) as the participants experienced when going through the route. Another image then appeared on the screen with a specific area marked. The participants were asked to indicate whether this area of the second image was in one of eight given directions relative to the starting position: left, front left, front, front right, right, back right, back, or back left. To enter the directions, the participants used the keyboard, on which the directions were marked on the number pad. Overall, a starting position was specified for each test time with two images whose directions must be specified. Points were awarded based on the accuracy of the assessment: A correct directional assessment corresponded to a deviation of 0\u0026deg;. The values to be achieved could therefore be between 0\u0026deg; and 180\u0026deg;, with 0\u0026deg; being the best possible result and 180\u0026deg; being the worst possible result. 90\u0026deg; corresponded to a random performance. The actual deviations from the starting position to the marked area in degrees were previously calculated using an allocentric map of the virtual environment used. This task was chosen to assess survey-level spatial knowledge, acknowledging that it imposes higher cognitive demands than sequential route-order tasks.\u003c/p\u003e \u003cp\u003eEach participant completed six runs in total (three per route). Short rest breaks were provided between runs to minimize fatigue and maintain attentional stability, particularly for older participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Registration and Processing\u003c/h2\u003e \u003cp\u003eAll behavioral data were recorded in PsychoPy, exported, and processed in Microsoft Excel 2021 and RStudio (Posit PBC; Version 2025.09.2\u0026ndash;418). To enable comparability across measures, raw scores from landmark recognition, direction pointing, elapsed time and errors were transformed into standardized percentage scores ranging from \u0026minus;\u0026thinsp;100% (worst) to 100% (best), with 0% indicating chance-level performance (adapted from Kim \u0026amp; Bock, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For elapsed time and errors, piecewise linear transformations were applied around the predefined chance anchors:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eElapsed time [%]\u0026thinsp;=\u0026thinsp;100*\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{148.5\\:-\\:\\text{E}\\text{T}}{148.5\\:-\\:66}\\)\u003c/span\u003e\u003c/span\u003e*1{ET\u0026thinsp;\u0026le;\u0026thinsp;148.5} \u0026ndash; 100*\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{E}\\text{T}\\:-\\:148.5}{320\\:-\\:148.5}\\)\u003c/span\u003e\u003c/span\u003e*1{ET\u0026thinsp;\u0026gt;\u0026thinsp;148.5}\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNumber of errors [%]\u0026thinsp;=\u0026thinsp;100*\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{4\\:-\\:\\text{N}\\text{o}\\text{E}}{4}\\)\u003c/span\u003e\u003c/span\u003e*1{NoE\u0026thinsp;\u0026le;\u0026thinsp;4} \u0026ndash; 100*\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{N}\\text{o}\\text{E}\\:-\\:4}{15\\:-\\:4}\\)\u003c/span\u003e\u003c/span\u003e*1{NoE\u0026thinsp;\u0026gt;\u0026thinsp;4}\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLandmark recognition test [%] = (LR \u0026ndash; 192)*100/144;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDirection pointing test [%] = (90 \u0026ndash; DP)*100/90;\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eHere, LR\u0026thinsp;=\u0026thinsp;total landmark score, DP\u0026thinsp;=\u0026thinsp;mean angular error in degrees, ET\u0026thinsp;=\u0026thinsp;elapsed time in seconds, and NoE\u0026thinsp;=\u0026thinsp;number of errors. Because these transformations may affect distributional properties, all statistical assumptions were empirically tested, and non-parametric analyses were applied whenever normality assumptions were violated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted in RStudio (Posit PBC, Boston, MA, US; Version 2025.09.2\u0026ndash;418). Descriptive statistics (mean, SD, median, quartiles, minimum, maximum) were computed for each dependent variable. Normality was assessed using the Shapiro\u0026ndash;Wilk test, and homogeneity of variances was checked using Levene\u0026rsquo;s test. Learning trajectories were examined across three repetitions (T1, T2, and T3) of each route (A and B). To characterise learning progress, analyses focused on improvements from the first (T1) to the final trial (T3). In addition, change scores (T3\u0026thinsp;\u0026minus;\u0026thinsp;T1) were computed to quantify the magnitude of learning.\u003c/p\u003e \u003cp\u003eNon-parametric data (elapsed time and number of errors) were analysed using Kruskal\u0026ndash;Wallis tests for group comparisons and Friedman tests for changes across trials. Significant main effects were examined using Bonferroni-adjusted Mann\u0026ndash;Whitney U tests and Wilcoxon signed-rank tests. Effect sizes were reported as the rank-biserial correlation (r) for pairwise differences and Kendall\u0026rsquo;s W for the magnitude of concordance across trials. For parametric data (landmark and survey knowledge), a mixed ANOVA was conducted with Group (YA_Intra, OA_Intra, OA_Inter) as the between-subjects factor and Trial (T1\u0026ndash;T3) as the within-subjects factor. Significant main effects and interactions were followed up with Bonferroni-adjusted pairwise t-tests. Effect sizes were reported as partial eta-squared (η\u0026sup2;) for ANOVA effects and Cohen\u0026rsquo;s d for pairwise comparisons. All p-values were two-tailed, and statistical significance was set at α\u0026thinsp;=\u0026thinsp;.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample characteristics\u003c/h2\u003e \u003cp\u003eDescriptive information is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Group differences in SBSOD scores did not differ significantly (YA_Intra vs. OA_Intra: \u003cem\u003ep\u003c/em\u003e = .43; OA_Intra vs. OA_Inter: \u003cem\u003ep\u003c/em\u003e = .052), indicating comparable self-reported spatial orientation abilities across groups. Thus, groups did not differ in baseline navigation self-assessment, providing comparable starting conditions for subsequent analyses. All 45 participants completed the full testing protocol.\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\u003eDescriptive information.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSBSOD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA_Inter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Group differences\u003c/h2\u003e \u003cp\u003eKruskal\u0026ndash;Wallis tests indicated significant group differences in route retracing performance for elapsed time, χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;16.6, p \u0026lt; .001, and navigation errors, χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;23.6, p \u0026lt; .001. For landmark recognition, the mixed ANOVA showed a significant main effect of Group, F(2, 42)\u0026thinsp;=\u0026thinsp;11.69, p \u0026lt; .001, η\u0026sup2; = .360. Similarly, a significant main effect of Group was found for direction pointing, F(2, 42)\u0026thinsp;=\u0026thinsp;4.71, p = .014, η\u0026sup2;ₚ= .18.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAge effects (YA_Intra vs OA_Intra).\u003c/b\u003e Mann\u0026ndash;Whitney U tests showed that YA_Intra retraced the routes significantly faster than OA_Intra (p = .001, r = .62) and committed significantly fewer navigation errors (p \u0026lt; .001, r = .76). Bonferroni-adjusted pairwise t-tests indicated significantly higher landmark recognition in YA_Intra than OA_Intra (p \u0026lt; .001, d\u0026thinsp;=\u0026thinsp;1.65) and significantly higher direction pointing performance in YA_Intra (p = .014, d\u0026thinsp;=\u0026thinsp;1.04). Across all navigation-related measures, YA showed significantly better performance than OA under identical intrablock switching conditions (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffects of learning structure in OA (OA_Intra vs. OA_Inter).\u003c/b\u003e Mann\u0026ndash;Whitney U tests indicated significantly better route retracing in OA_Inter than OA_Intra for elapsed time (p \u0026lt; .001, r = .66) and navigation errors (p = .001, r = .67). Landmark recognition was higher in OA_Inter (48.10% \u0026plusmn; 24.60 vs. 38% \u0026plusmn; 26.19), although this contrast did not reach statistical significance (p = .053, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.91). Direction pointing did not differ between OA_Intra and OA_Inter (p\u0026thinsp;=\u0026thinsp;1.000, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.36). Sequential learning (interblock) yielded clear advantages for route retracing accuracy and speed but did not produce significant differences in landmark or survey knowledge compared to alternating learning within OA.\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\u003eSummary of performance across groups.\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\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eM/Mdn. \u0026plusmn; SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep (adjusted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect size\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[SI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[r;d]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eElapsed time\u0026nbsp;[s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.31\u0026thinsp;\u0026plusmn;\u0026thinsp;31.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.19\u0026thinsp;\u0026plusmn;\u0026thinsp;29.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.001**\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003er = .62\u003c/p\u003e \u003cp\u003er = .66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175.30\u0026thinsp;\u0026plusmn;\u0026thinsp;46.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.98\u0026thinsp;\u0026plusmn;\u0026thinsp;31.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA_Inter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.90\u0026thinsp;\u0026plusmn;\u0026thinsp;41.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.13\u0026thinsp;\u0026plusmn;\u0026thinsp;37.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of errors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003er = .76\u003c/p\u003e \u003cp\u003er = .67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA_Inter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLandmark recognition test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.38\u0026thinsp;\u0026plusmn;\u0026thinsp;22.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003cp\u003e.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ed\u0026thinsp;=\u0026thinsp;1.65\u003c/p\u003e \u003cp\u003ed\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.24\u0026thinsp;\u0026plusmn;\u0026thinsp;26.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA_Inter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.10\u0026thinsp;\u0026plusmn;\u0026thinsp;24.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDirection pointing test\u0026nbsp;[\u0026deg;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.00\u0026thinsp;\u0026plusmn;\u0026thinsp;43.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.00\u0026thinsp;\u0026plusmn;\u0026thinsp;48.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.014*\u003c/p\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ed\u0026thinsp;=\u0026thinsp;1.04\u003c/p\u003e \u003cp\u003ed\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA_Intra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.50\u0026thinsp;\u0026plusmn;\u0026thinsp;41.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.67\u0026thinsp;\u0026plusmn;\u0026thinsp;46.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOA_Inter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.00\u0026thinsp;\u0026plusmn;\u0026thinsp;37.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.00\u0026thinsp;\u0026plusmn;\u0026thinsp;41.93\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\u003eSI\u0026thinsp;=\u0026thinsp;raw scores in Syst\u0026egrave;me International units (e.g., seconds for elapsed time; degrees for direction pointing); % = standardised performance score (\u0026minus;\u0026thinsp;100% to 100%) computed from raw values using the transformations described in the text (adapted from Kim \u0026amp; Bock, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with higher values indicating better performance (faster time, fewer errors, higher recognition, more accurate pointing); p (adjusted) = Bonferroni-adjusted p-values for pairwise group comparisons; Effect sizes\u0026thinsp;=\u0026thinsp;r (rank-biserial correlation; Mann\u0026ndash;Whitney U tests for elapsed time and errors) and d (Cohen\u0026rsquo;s d; pairwise t-tests for landmark recognition and direction pointing); Significance: * p \u0026lt; .05, ** p \u0026lt; .01, *** p \u0026lt; .001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Learning effects across trials\u003c/h2\u003e \u003cp\u003e \u003cb\u003eRoute knowledge\u003c/b\u003e. Friedman tests indicated significant changes across trials in elapsed time for all groups (YA_Intra: χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;28.1, p \u0026lt; .001, W = .94; OA_Intra: χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;19.6, p \u0026lt; .001, W = .65; OA_Inter: χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;30.0, p \u0026lt; .001, W\u0026thinsp;=\u0026thinsp;1.00). Bonferroni-adjusted Wilcoxon signed-rank tests showed significant differences between T1 and T3 in each group (YA_Intra: p \u0026lt; .001, r = .88; OA_Intra: p \u0026lt; .001, r = .84; OA_Inter: p \u0026lt; .001, r = .88). For navigation errors, Friedman tests were significant in YA_Intra (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;11.3, p = .004, W = .38) and OA_Inter (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;25.1, p \u0026lt; .001, W = .84), but not in OA_Intra (χ\u0026sup2;(2) =.667, p = .717, W = .02). Thus, the T1\u0026ndash;T3 Wilcoxon comparison was significant in YA_Intra (p = .010, r = .78) and OA_Inter (p = .003, r = .88), but not in OA_Intra (p\u0026thinsp;=\u0026thinsp;1.000, r = .01). Change scores (T3\u0026thinsp;\u0026minus;\u0026thinsp;T1) indicated the largest improvement in elapsed time for OA_Inter (M\u0026thinsp;=\u0026thinsp;71.0%, SE\u0026thinsp;=\u0026thinsp;2.48), followed by YA_Intra (M\u0026thinsp;=\u0026thinsp;54.0%, SE\u0026thinsp;=\u0026thinsp;4.54) and OA_Intra (M\u0026thinsp;=\u0026thinsp;21.9%, SE\u0026thinsp;=\u0026thinsp;4.50). Error score improvements showed a similar pattern: OA_Inter (M\u0026thinsp;=\u0026thinsp;66.1%, SE\u0026thinsp;=\u0026thinsp;7.59) \u0026gt; YA_Intra (M\u0026thinsp;=\u0026thinsp;21.7%, SE\u0026thinsp;=\u0026thinsp;6.16) ≫ OA_Intra (M\u0026thinsp;=\u0026thinsp;4.85%, SE\u0026thinsp;=\u0026thinsp;6.92). Overall, the sequential learning structure was associated with the most consistent improvements, whereas intrablock switching was accompanied by limited gains in older adults, particularly for error reduction.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLandmark knowledge.\u003c/b\u003e A mixed ANOVA on landmark recognition showed a significant main effect of Trial, F(2, 84)\u0026thinsp;=\u0026thinsp;26.61, p \u0026lt; .001, η\u0026sup2;ₚ = .39, with no Group \u0026times; Trial interaction, F(4, 84)\u0026thinsp;=\u0026thinsp;0.64, p = .635, η\u0026sup2;ₚ = .03. Bonferroni-adjusted paired comparisons indicated significant higher scores at T3 than at T1 (p \u0026lt; .001, d\u0026thinsp;=\u0026thinsp;1.10). Accordingly, T1\u0026ndash;T3 change scores were positive and comparable across groups (YA_Intra: 23.1%; OA_Intra: 19.3%; OA_Inter: 20.6%; Figure X), indicating improved landmark recognition with repetition irrespective of age or learning structure.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSurvey knowledge.\u003c/b\u003e For direction pointing, the mixed ANOVA showed no main effect of Trial, F(2, 84)\u0026thinsp;=\u0026thinsp;1.28, p = .284, η\u0026sup2;ₚ = .03, and no Group \u0026times; Trial interaction, F(4, 84)\u0026thinsp;=\u0026thinsp;0.45, p = .773, η\u0026sup2;ₚ = .02. T1-T3 change scores were close to zero or negative across groups (YA_Intra: \u0026minus;2.5%; OA_Intra: \u0026minus;19.2%; OA_Inter: \u0026minus;9.2%). Survey knowledge did not improve with repetition, suggesting that allocentric spatial integration remained stable across trials regardless of age or learning condition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study investigated how learning structure interacts with age-related differences in executive control to influence spatial knowledge acquisition in virtual wayfinding. Several important findings emerged. First, OA showed the expected age-related impairments across all measured spatial variables, i.e., longer completion times, more navigation errors, and lower accuracy in landmark and directional judgments, confirming well-documented declines in spatial memory and cognitive flexibility with aging (Lester et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wiener et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, and more importantly, OA who learned routes sequentially (A-A-A-B-B-B) performed markedly better than those who alternated between routes (A-B-A-B-A-B). Under the sequential condition, OA reduced navigation time and errors substantially and substantially reduced the performance level of YA in route retracing. This pattern demonstrates that structured learning can compensate for age-related deficits by lowering task switching frequency and memory interference.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMechanistic interpretation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFrequent switching between overlapping routes, as required in the A-B-A-B-A-B condition, taxes inhibitory control and working memory updating because learners must repeatedly suppress prior route information while encoding new spatial cues (Anderson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Friedman \u0026amp; Miyake, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such demands overload the prefrontal-hippocampal network and increase competition between route representations, particularly in OA whose executive resources are reduced (Li \u0026amp; Giudice, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, sequential learning provides a stable cognitive context that minimizes these interference effects and supports gradual consolidation of route specific representations. The improvement observed in OA_Inter across repeated trials indicates that once cognitive load is reduced, OA can effectively form and refine route memories through repetition and error-based learning.\u003c/p\u003e \u003cp\u003eIn contrast to route knowledge, survey (allocentric) knowledge did not show improvement across trials in any group. Although one plausible explanation is that hippocampus-dependent allocentric transformations remain particularly resistant to training in older age (Harris \u0026amp; Wolbers, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Iaria et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), the present findings do not permit strong conclusions. Several alternative explanations must also be considered. The direction pointing task may have imposed high cognitive demands, i.e., requiring perspective transformations, spatial updating, and memory of landmark relationships, that could have limited measurable improvements even in young adults. Additionally, survey knowledge is known to benefit more from active exploration and global access to the environment than from repeated route learning alone (Chrastil \u0026amp; Warren, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ishikawa \u0026amp; Montello, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Thus, the predominantly procedural, route-based paradigm used here may not have provided the experiential conditions required for constructing more flexible allocentric representations. Furthermore, task sensitivity may have been insufficient to detect subtle improvements.\u003c/p\u003e \u003cp\u003eTaken together, the lack of survey knowledge enhancement should be interpreted cautiously, and future studies employing tasks with lower cognitive load, increased ecological validity, or active navigation components are needed to disentangle these competing explanations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCognitive-theoretical implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBeyond navigation, these findings may generalize to other complex learning situations that involve overlapping information, frequent switching between task representations, or high interference, such as learning procedural sequences, multitasking environments, or technology-based training contexts. The results converge with the cognitive load theory of aging (Verhaeghen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), emphasizing that performance differences between age groups often reflect differential sensitivity to task complexity rather than an absolute loss of ability. By reducing switching costs, sequential learning effectively acts as environmental scaffolding (Squire \u0026amp; Dede, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), allowing OA to allocate limited resources more efficiently. Moreover, the distinct learning trajectories across trials, where OA_Inter showed the steepest improvement, suggest that the benefits of structured learning extend beyond momentary facilitation and may foster genuine adaptive plasticity in later life.\u003c/p\u003e \u003cp\u003eThese findings also resonate with the compensation-related utilization of neural circuits hypothesis (CRUNCH), which posits that OA can achieve near-young performance by optimizing strategy use and environmental structure when demands remain within their resource limits (Reuter-Lorenz \u0026amp; Cappell, 2008). Sequential learning may thus enable OA to operate below their overload threshold, engaging compensatory neural pathways more efficiently.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePractical relevance, limitations, and future directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFrom an applied standpoint, the study highlights the potential of structured, low-interference VR-based training to support navigation competence in aging. Training programs emphasizing repeated exposure to consistent route structures could enhance everyday spatial orientation and reduce disorientation risks in complex real-world settings such as hospitals, transport hubs, or senior living facilities.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, YA were not tested under a sequential learning condition, which limits direct inference about age-by-structure interactions. Accordingly, statements about OA \u0026ldquo;approaching\u0026rdquo; YA levels should be interpreted cautiously. Second, because sequential and alternating learning differ not only in switching frequency but also in the lag between repetitions, the present design cannot fully disentangle switching-related interference from spacing/recency effects. Third, navigation was experimenter-controlled to reduce motor and interface confounds, but this may have altered the ecological validity of wayfinding and the balance between action-based and decision-based components of navigation. Finally, the survey measure may have been underpowered or insufficiently sensitive to detect gradual allocentric improvements.\u003c/p\u003e \u003cp\u003eFuture research should therefore (a) implement a fully crossed design including YA sequential learning, (b) orthogonally manipulate switching frequency and repetition lag, and (c) vary route overlap and environmental complexity to directly test interference-based accounts (He et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, incorporating neuroimaging or physiological monitoring would help verify the hypothesized prefrontal\u0026ndash;hippocampal mechanisms underlying these behavioral improvements. Longitudinal intervention studies are needed to determine whether sequential learning protocols yield durable gains in navigation efficiency and executive flexibility. Manipulating environmental complexity or degree of route overlap would also clarify how interference, working memory load, and perceptual cue richness interact to shape spatial learning across the adult lifespan.\u003c/p\u003e \u003cp\u003eFuture research should integrate neuroimaging or physiological monitoring to verify the hypothesized prefrontal-hippocampal mechanisms underlying these behavioral improvements. Longitudinal intervention studies are needed to determine whether sequential learning protocols yield durable gains in navigation efficiency and executive flexibility. Manipulating environmental complexity or degree of route overlap would also clarify how interference, working memory load, and perceptual cue richness interact to shape spatial learning across the adult lifespan.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOverall, this study demonstrates that age-related declines in wayfinding performance are not fixed but can be significantly alleviated through structured learning design. Sequential route learning reduces the cognitive demands associated with frequent switching between overlapping route representations, allowing OA to construct more accurate and stable spatial representations. These findings contribute to a growing body of evidence that adaptive structuring of learning environments can enhance cognitive performance in aging by mitigating executive control demands and supporting compensatory mechanisms for efficient spatial knowledge acquisition. While sequential learning enabled OA to perform more efficiently, the present findings do not imply a complete normalization of age-related differences. Rather, they demonstrate that a substantial portion of age-related performance variance reflects differential sensitivity to task structure and interference, consistent with accounts emphasizing cognitive load and executive control demands.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. The authors report no conflicts of interest.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003e This study was approved by the Ethics Committee of the German Sport University Cologne (Nr. 068/2023).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003e All participants provided informed consent.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTim Eberle contributed to data collection, formal analysis, and the preparation of the Results section. Wiebren Zijlstra contributed to the methodology, provided supervision throughout the research process, and critically reviewed and edited the manuscript. Kyungwan Kim was responsible for the overall conceptualization, experimental design, and coordination of the study, served as the corresponding author, and wrote the Abstract, Introduction, Methods, and Discussion sections. All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Elisabeth Seil for her support in data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson, M. C. (2003). 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Age-related differences in associative learning of landmarks and heading directions in a virtual navigation task. \u003cem\u003eFrontiers in Aging Neuroscience\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(MAY), 1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnagi.2016.00122\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2016.00122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Aging, wayfinding, spatial knowledge, interference, sequential learning","lastPublishedDoi":"10.21203/rs.3.rs-8778293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8778293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAge-related declines in spatial navigation are often attributed to impairments in executive control, particularly cognitive flexibility and task switching. However, it remains unclear to what extent these deficits reflect age-related limitations and/or sensitivity to task structure and interference. The present study investigated how switching demands between competing route representations shape spatial knowledge acquisition in older (OA) and younger (YA) adults during complex wayfinding in a virtual environment.\u003c/p\u003e \u003cp\u003eParticipants learned two partially overlapping routes under either a high-interference alternating condition (A\u0026ndash;B\u0026ndash;A\u0026ndash;B\u0026ndash;A\u0026ndash;B) or a low-interference sequential condition (A\u0026ndash;A\u0026ndash;A\u0026ndash;B\u0026ndash;B\u0026ndash;B). Spatial knowledge was assessed using route retracing performance, navigation errors, landmark recognition, and directional estimation between landmarks.\u003c/p\u003e \u003cp\u003eOA showed lower overall performance than YA across all measures. Critically, however, OA who learned routes sequentially exhibited substantial improvements in route retracing accuracy and landmark recognition compared to those exposed to alternating learning, thereby markedly reducing age-related performance differences under identical task demands. In contrast, alternating learning disproportionately impaired OA, consistent with elevated switching-related effects between competing route representations.\u003c/p\u003e \u003cp\u003eThese findings demonstrate that age-related differences in spatial navigation are strongly modulated by learning structure. Reducing switching between competing route representations and stabilizing representational demands mitigates interference-related costs, enabling more efficient encoding and integration of spatial information in older adults. The results suggest that structured, low-interference learning environments may serve as an effective compensatory scaffold for age-related declines in cognitive flexibility, highlighting the importance of task design in supporting navigation and learning across the adult lifespan.\u003c/p\u003e","manuscriptTitle":"Structured route learning mitigates age-related interference between overlapping spatial representations during virtual wayfinding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 14:57:35","doi":"10.21203/rs.3.rs-8778293/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":"67f7f958-acf1-4a8d-b0e4-53eac9a64085","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T08:56:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 14:57:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8778293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8778293","identity":"rs-8778293","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}