Mental fatigue and map memory performance show cognitive resilience through orienteering expertise

Mental fatigue and map memory performance show cognitive resilience through orienteering expertise | 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 Mental fatigue and map memory performance show cognitive resilience through orienteering expertise Jia Liu, Yang Liu, Xiaoping Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8966036/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 Objective : Mental fatigue is known to impair cognitive performance during exercise. This study aims to investigate the impact of mental fatigue on the map-reading memory processing of orienteers and to explore whether the expert advantage developed through long-term specific training can translate into effective cognitive resilience under fatigued conditions. Methods : A 2 (Group: expert group, novice group) × 2 (Time: pre-fatigue, post-fatigue) mixed experimental design was employed. Thirty national level 2 and above elite orienteers (expert group) and 35 novice orienteers completed a 45-minute Stroop task to induce mental fatigue. Before and after the fatigue induction, participants' behavioral performance, eye movement metrics during a map-reading memory task were measured synchronously, along with prefrontal/temporal cortex activation collected via functional near-infrared spectroscopy (fNIRS). Results : Following mental fatigue, the accuracy of both groups decreased significantly, and reaction times were prolonged. Eye movement metrics indicated reduced visual search efficiency, as evidenced by increased pupil diameter and blink count. Concurrently, significant increases in oxygenated hemoglobin concentration were observed in the right dorsolateral prefrontal cortex, left frontal pole area, and right temporal lobe, indicating the emergence of a neural compensatory mechanism. Critically, under the fatigued state, the expert group was able to maintain superior behavioral performance and oculomotor stability compared to the novice group, demonstrating stronger cognitive resilience. Conclusion : Mental fatigue significantly impairs the map-reading memory efficiency of orienteers. However, expert orienteers exhibit a marked cognitive advantage, characterized by behavioral and oculomotor stability, which benefits from more efficient neural resource mobilization. These findings underscore the importance of cognitive resilience developed through long-term training and offer insights into mitigating the negative effects of mental fatigue through specific practice. mental fatigue orienteering map-reading memory expert-novice differences cognitive resilience eye tracking functional near-infrared spectroscopy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction In the ultimate pursuit of excellence in modern competitive sports, the margin between victory and defeat for athletes is often minimal. This depends not only on their peak physiological functions but also on precise and efficient cognitive decision-making abilities under high-pressure and complex environments. Mental fatigue, a psychobiological state induced by prolonged or high-intensity cognitive activity, has been confirmed to significantly impair athletes' concentration, decision-making speed, and technical-tactical execution efficiency, independent of physical fatigue, becoming a key "invisible" factor affecting competition performance (Yang et al., 2024). From tactical choices on the football field to stroke prediction in tennis, the negative impact of mental fatigue has been demonstrated across various sports (Van Cutsem et al., 2017; Kosack et al., 2020). However, existing research predominantly focuses on team ball sports or cyclic sports. For "cognitively intensive" sports like orienteering, which heavily rely on real-time spatial memory and complex decision-making, how mental fatigue erodes its core competitive ability—map-reading memory—remains a critical yet insufficiently revealed question. Orienteering is often called "chess running." Athletes must quickly perform map identification, route planning, and decision-making in unfamiliar environments. Its essence is a continuous, high-load cognitive process involving visual search, working memory updating, and spatial information integration (Eccles et al., 2014). Map-reading memory, as the cornerstone of this process, involves the instantaneous encoding, storage, and retrieval of map symbols, spatial relationships, and route information (Liu Fang et al., 2009). During a competition, athletes repeatedly execute this process, leading to continuous depletion of cognitive resources, which can easily induce deep mental fatigue, resulting in getting lost, decision errors, or even competition failure (Lam et al., 2022). Therefore, investigating the impact of mental fatigue on orienteers' map-reading memory processing holds significant theoretical value and direct practical implications for training. Although the detrimental effects of mental fatigue are universal, athletes' ability to cope varies significantly. The "expert advantage" formed through long-term specialized training often manifests as higher efficiency and stability in cognitive tasks (Zhu Yu et al., 2011). The "neural efficiency hypothesis" in cognitive psychology posits that expert brains can accomplish the same or superior task performance with lower neural resource consumption (Li & Smith, 2021). A core scientific question arises: Can this "neural efficiency" advantage of expert orienteers translate into observable "cognitive resilience" to withstand performance decline under the challenge of mental fatigue? In other words, do experts merely excel in "efficiency," or do they possess stronger "resilience" or "compensatory ability" when facing cognitive resource depletion? Current research has yet to provide a clear, multi-dimensional evidence-based answer to this question. Current research in this area has three main limitations: First, methodological singularity. Most studies rely solely on behavioral indicators or a single physiological modality, such as eye movements, making it difficult to penetrate behavioral representation and reveal the underlying changes in visual processing strategies and central neural mechanisms (Niu Guoqing et al., 2019). Second, weak theoretical framework. Explanations for performance differences under fatigue often remain at the descriptive level of "experience differences," lacking deep integration with theories like neural efficiency and cognitive compensation, failing to clarify whether the expert advantage under adverse conditions manifests as higher efficiency or stronger resilience. Third, superficial mechanism exploration. Although some studies focus on the prefrontal cortex's role in fatigue, map-reading memory in orienteering involves a coordinated network of multiple brain regions, including the dorsolateral prefrontal cortex, frontal pole area, and temporal lobe (Gottlieb et al., 2010; Friedman et al., 2022), responsible for executive control, information integration, and spatial memory, respectively. How fatigue affects this network and how the recruitment patterns differ between experts and novices remains unknown. To overcome these limitations, this study innovatively employs simultaneous measurement using eye tracking and functional near-infrared spectroscopy. Eye-tracking technology can objectively reveal visual search strategies in real-time, serving as an external window into cognitive processing (Tian Yun et al., 2015). fNIRS technology, with its good ecological validity and resistance to motion artifacts, is suitable for investigating cerebral hemodynamic activity during cognitive tasks (Nishiyori et al., 2016). Through this multi-modal analysis paradigm combining behavioral, oculomotor, and neural levels, we aim to achieve the following objectives: First, systematically reveal the multi-faceted impact of mental fatigue on orienteers' map-reading memory in terms of behavioral accuracy, reaction speed, visual search efficiency, and neural activity in specific brain regions; second, deeply test whether and how expert athletes demonstrate stronger cognitive resilience under fatigue; third, explore at the neural mechanism level whether this resilience stems from lower baseline resource consumption (neural efficiency) or a more effective mobilization of relevant brain networks for compensation after fatigue. Based on this, the study proposes the following hypotheses: H1: Mental fatigue will lead to decreased behavioral performance, reduced visual search efficiency in the map-reading memory task of orienteers, and induce compensatory enhanced activation in brain regions responsible for executive control, information integration, and spatial memory, such as the right dorsolateral prefrontal cortex, left frontal pole area, and right temporal lobe cortex. H2: Compared to novices, expert athletes will demonstrate significant cognitive resilience under mental fatigue, i.e., a smaller decline in behavioral performance and oculomotor stability, and their brain neural activity patterns may exhibit higher efficiency or more adaptive compensatory strategies. The significance of this study lies in not only providing a practical warning about the threat of mental fatigue to core orienteering skills but also theoretically introducing the concept of cognitive resilience into the research framework of mental fatigue and expert advantage for the first time, attempting to unravel the underlying behavioral and neural mechanisms using multi-modal technology. The findings are expected to offer new theoretical bases and empirical references for scientific training in orienteering and other cognitively demanding sports, such as anti-fatigue cognitive training and neurofeedback training. 2 Materials and Methods 2.1Participants The sample size for this study was calculated using G*Power 3.1 software (Faul et al., 2009). Parameters were set based on previous studies: effect size f = 0.8, α = 0.05, statistical power (1 - β) = 0.80, resulting in a minimum required sample size of 52. Ultimately, a total of 65 right-handed participants were recruited. All participants had normal (or corrected-to-normal) vision, no history of neurological disorders, color blindness, or color weakness, and no prior experience with similar experiments. Before the experiment, all participants signed an informed consent form. This study was approved by the Ethics Committee of Shaanxi Normal University. Participants were divided into two groups:Expert group: 30 national-level orienteering athletes, with competition levels defined as having achieved top eight positions in national competitions or top sixteen positions in Asian-level events.Novice group: 35 university students majoring in physical education, all of whom had taken orienteering elective courses but had not obtained any sports ranking. Table 1 Participant Basic Information (M ± SD) Group Expert Group (n = 30) Novice Group (n = 35) Rank/Qualification National team athletes (National top 8/Asian top 16) Unranked (Orienteering course class) Age (years) 22.4 ± 0.16 21.3 ± 0.18 Training (years) 5.06 ± 0.13 1.08 ± 0.08 Training (hours/week) 9.89 ± 0.26 1.67 ± 0.06 2.2Experimental Design A 2 (Group: Expert vs. Novice) × 2 (Time: Pre-mental fatigue vs. Post-mental fatigue) mixed experimental design was employed. Independent variables: Group (between-subjects factor), Time (within-subjects factor). Dependent variables: Behavioral indicators: accuracy and reaction time on the map-reading memory task. Eye movement indicators: mean pupil diameter and blink count. Neural activity indicators: β-values representing changes in oxygenated hemoglobin (HbO₂) concentration in regions of interest. 2.3Experimental Materials and Tasks Mental Fatigue Induction Task: A modified Stroop task, as reported by Yang et al. (2024), lasting 45 minutes, was used. The task materials were four Chinese characters for "Red," "Green," "Blue," and "Yellow." The font color was randomly set to one of the four colors incongruent with the character's meaning, presented one by one on a black background computer screen. Participants were required to respond according to specific rules: when the presented color was green, blue, or yellow, they should ignore the character meaning and press the key corresponding to the font color; if the character color was red, they should ignore the color and press the key corresponding to the character meaning (keys: "D," "F," "J," "K"). The task was run using E-Prime 3.0 software. Each trial lasted 2000 ms, including 1000 ms of stimulus presentation and a 1000 ms blank screen interval. The entire task comprised 1350 trials, requiring participants to respond as quickly and accurately as possible while maintaining focus. In case of incorrect responses or timeouts, the system provided immediate auditory feedback via a prompt sound. Map-Reading Memory Task: The materials were standard orienteering competition maps, jointly produced by three national-level orienteering cartographers. Initially, 24 test maps and 2 practice maps were selected from 32 candidate maps. As shown in Fig. 4 (example in original text), the left image is the spatial memory material used in the experiment. The right image shows three selectable pictures A, S, D, where only picture D is identical to the spatial memory material; the other two maps differ in route information or checkpoint locations. In the experimental materials, the triangle represents the start point, the circle represents the checkpoint, blue areas represent impassable water, green areas represent difficult-to-pass forests, gray areas represent buildings, yellow areas represent easy-to-run forest, and "×" represents special man-made features, etc. Subjective Fatigue Assessment: The Visual Analogue Scale (VAS), Rating of Perceived Exertion (RPE) scale, and Brunel Mood Scale (BRUMS) were administered before and after the fatigue induction task to verify the effectiveness of fatigue induction. 2.4Experimental Instruments 2.4.1fNIRS Device A portable functional near-infrared spectroscopy imaging system, NirSmartⅡ (Danyang Huichuang), was used to monitor hemodynamic responses in the cerebral cortex. Light sources and detectors were positioned over the bilateral prefrontal, temporal, frontoparietal sensorimotor, and occipital lobes based on the 10–20 international standard lead system. The channel layout included 24 light sources and 16 detectors, forming 48 measurement channels with a sampling frequency of 11 Hz. Probes were fixed using an elastic headcap. During placement, participants' hair was sufficiently parted to ensure adequate contact between the probes and the scalp. Regions of interest (ROIs) were defined based on the existing anatomical labeling system LBPA40, resulting in 17 ROIs (see Table 2 ). (Image placeholder - note: Blue indicates detector probes; Red indicates emitter probes; 1–48: measurement channels.) Table 2 Correspondence between fNIRS Channel Layout and Brain Regions of Interest Brain Region Corresponding Channels Left Dorsolateral Prefrontal Cortex(L-DLPFC) CH10, CH19, CH20 Right Dorsolateral Prefrontal Cortex(R-DLPFC) CH1, CH13, CH15 Left Frontal Pole Area(L-FPA) CH8, CH9, CH16, CH18 Right Frontal Pole Area(R-FPA) CH4, CH6, CH17 Orbitofrontal Cortex(OFC) CH5, CH7 Left Temporal Cortex(L-TC) CH12, CH23, CH41, CH42 Right Temporal Cortex(R-TC) CH3, CH22, CH34, CH35 Left Premotor & Supplementary Motor Area(L-PMC/SMA) CH37, CH38, CH43 Right Premotor & Supplementary Motor Area(R-PMC/SMA) CH24, CH30, CH31 Left Somatosensory Association Cortex(L-SAC) CH40, CH45, CH46 Right Somatosensory Association Cortex(R-SAC) CH26, CH27, CH33 Left Primary Motor Cortex(L-M1) CH39, CH44 Right Primary Motor Cortex(R-M1) CH25, CH32 Left Broca's Area(L-Broca) CH11, CH21 Right Broca's Area(R-Broca) CH2, CH14 Wernicke's Area(Wernicke) CH28, CH47 Primary Visual Cortex (VC) CH29, CH36, CH48 2.4.2Eye Tracking Device An aSee Pro eye tracker (7Invensun, Beijing) was used to record eye movement data. The sampling rate was 250 Hz, measurement accuracy was 0.5°, field of view (FOV) was 110°, gaze point recovery time was less than 30 ms, and the infrared wavelength was 850 nm. 2.4.3Synchronous Recording Method Two laptop computers were used in this experiment. Computer A (refresh rate 165 Hz, resolution 1920×1080) was connected to a monitor (22 inches, refresh rate 120 Hz, resolution 1980×1080) used to present stimuli. The aSee Pro eye tracker was installed below the monitor to measure eye movement data. Computer B (refresh rate 120 Hz, resolution 1920×1080) was connected to the wireless fNIRS. The eye tracker and fNIRS device were within the same local area network. When presenting stimuli, the eye tracker sent signals to the fNIRS device via a wireless synchronization box to synchronize data. 2.5Experimental Procedure Preparation and Baseline Recording. After familiarizing themselves with the environment, participants completed basic information forms and pre-fatigue scales. Subsequently, they wore the fNIRS cap and eye tracker and underwent calibration. First, 60 seconds of resting-state fNIRS data were collected as a baseline. Pre-test: Map-Reading Memory Task Test. The test consisted of two phases: practice and formal experiment. Participants first practiced with two test maps to become familiar with the procedure. Formal experiment: After collecting 60s resting-state fNIRS baseline data, instructions were presented. Participants pressed the space bar to begin. A red central "+" fixation point appeared for 1s, followed by a 20s map-reading memory phase (participants needed to remember route and checkpoint details within 10s). This was immediately followed by a 10s memory retrieval phase where participants selected the match for the spatial memory material from three pictures (A, S, D; choices corresponded to keys), ending with a 10s rest. The sequence (1s fixation + 20s memory phase + 10s rest) constituted one trial. Eight trials formed one block, with three blocks total and a 60s rest between blocks. Mental Fatigue Induction. Participants completed the 45-minute Stroop task. Post-Induction Assessment. After the task, participants again completed the VAS, RPE, BRUMS scales, and then performed the map-reading memory task test again (post-test). 2.6Data Processing and Statistical Analysis 2.6.1Behavioral Data Preprocessing Behavioral data included accuracy and reaction time. After the experiment, participants' key choices and response times were exported from the computer. Accuracy was calculated by comparing choices with correct answers. Reaction time was calculated as the time from stimulus presentation onset to the participant's key press. 2.6.2 fNIRS data preprocessing The Data Quality Analysis Tool was used to check fNIRS data quality based on the Coefficient of Variation (CV). Channels with CV ≤ 5 indicated good quality; CV between 5–20 indicated fair quality; CV > 20 indicated poor quality. Channels with poor quality were marked, and data from participants with ≥ 30% of channels having CV > 20 were excluded. NirSpark 1.8.8 software (Danyang Huichuang) was used for fNIRS data analysis. Preprocessing in the Preprocess module included: motion artifact correction using spline interpolation per channel, low-pass filtering (0.01–0.20 Hz) to remove physiological noise, and conversion of light intensity data to hemoglobin concentration data using the modified Beer-Lambert law. For brain activation analysis, task conditions were selected and edited in the GLM module to generate HbO₂ β-values. 2.6.3Eye Movement Data Preprocessing The aSee Pro eye tracker's proprietary analysis software, aSee Studio, was used for analysis. For each map, the entire map was defined as the area of interest. Eye movement metrics, including pupil diameter and saccade amplitude, were exported from aSee Studio. 2.6.4Statistical Analysis Processed data (behavioral, eye movement, and fNIRS data) were tested for normality using the Shapiro-Wilk test in SPSS 26.0. If the test result was P > 0.05 (normality assumed), repeated measures ANOVA was conducted. A P-value < 0.05 was considered statistically significant. 3 Results 3.1 Mental Fatigue Manipulation Check To verify the effectiveness of the Stroop task in inducing mental fatigue, paired-sample t-tests were conducted on participants' subjective fatigue ratings and average heart rate before and after the task. As shown in Table 3, all indicators changed significantly after induction, indicating that the 45-minute Stroop task successfully induced the intended state of mental fatigue. Table 3 Subjective and Objective Indicators of Mental Fatigue Induction Effectiveness Indicator Time Pre-MF ( M±SD ) Post-MF ( M±SD ) t p VAS 27.69±10.76 59.06±15.01 -16.494 < 0.001 PRE 8.17±1.47 12.53±2.15 -15.788 < 0.001 Brumsc 5.93±1.04 4.80±0.75 9.834 < 0.001 Average Heart Rate (bpm) 66.18±7.93 69.81±7.83 -5.378 < 0.001 3.2 Behavioral Results A 2 (Group) × 2 (Time) repeated measures ANOVA was performed on the accuracy and reaction time of the map-reading memory task. 3.2.1Accuracy The main effect of Group was significant ( F (1, 63) = 93.841, p < 0.001, η² = 0.598), with the expert group showing significantly higher accuracy than the novice group. The main effect of Time was significant ( F (1, 63) = 40.164, p < 0.001, η² = 0.389), with accuracy significantly decreasing after fatigue. The Group × Time interaction was significant ( F (1, 63) = 5.732, p = 0.020, η² = 0.083). Simple effect analysis indicated that accuracy decreased significantly in both groups after fatigue, but the expert group's accuracy was significantly higher than the novice group at both time points. 3.2.2 Reaction Time The main effect of Group was significant（ F (1, 63) = 14.705, p < 0.001, η² = 0.189）, with the novice group exhibiting longer reaction times. The main effect of Time was significant （ F (1, 63) = 35.173, p < 0.001, η² = 0.358）, with reaction times significantly prolonged after fatigue. The interaction was significant （ F (1, 63) = 7.810, p = 0.007, η² = 0.110）. Simple effect analysis revealed that reaction times were significantly prolonged in both groups after fatigue, and the novice group's reaction times were longer than the expert group's at both pre- and post-fatigue time points. 3.3 Eye Movement Results Repeated measures ANOVA was conducted on blink count and pupil diameter. 3.3.1Blink Count The main effect of Group was significant （ F (1, 63) = 30.335, p < 0.001, η² = 0.325）, with the novice group blinking more frequently. The main effect of Time was significant（ F (1, 63) = 15.194, p < 0.001, η² = 0.194）, with blink count increasing after fatigue. The interaction was significant （ F (1, 63) = 12.163, p < 0.001, η² = 0.162）. Simple effect analysis revealed that fatigue significantly increased blink count only in the novice group, with no significant change in the expert group; furthermore, the novice group's blink count was significantly higher than the expert group's post-fatigue. 3.3.2Pupil Diameter The main effect of Group was significant （ F (1, 63) = 55.045, p < 0.001, η² = 0.466）, with the novice group having larger pupils. The main effect of Time was significant （ F (1, 63) = 30.856, p < 0.001, η² = 0.329）, with pupil diameter increasing after fatigue. The interaction was significant （ F (1, 63) = 4.650, p = 0.035, η² = 0.069）. Simple effect analysis indicated that pupil diameter increased significantly in both groups after fatigue, and the novice group's pupils were larger than the expert group's at both time points. 3.4 fNIRS Results Analysis of oxygenated hemoglobin (HbO₂) concentration β-values in key ROIs revealed significant findings primarily in three brain regions:Right Dorsolateral Prefrontal Cortex (R-DLPFC): The main effect of Time was significant （ F (1, 63) = 49.708, p < 0.001, η² = 0.441）, with increased activation post-fatigue. The main effect of Group was significant（ F (1, 63) = 36.924, p < 0.001, η² = 0.370）, with the novice group showing stronger activation. The interaction was significant （ F (1, 63) = 5.061, p = 0.028, η² = 0.074）. Simple effect analysis showed that activation increased significantly in both groups after fatigue, and the novice group's activation level was higher than the expert group's at both time points. Left Frontal Pole Area (L-FPA):The main effect of Time was significant （ F (1, 63) = 4.451, p = 0.039, η² = 0.066）, with increased activation post-fatigue. The main effect of Group was significant （ F (1, 63) = 30.382, p < 0.001, η² = 0.325）, with the novice group showing stronger activation. The interaction was not significant. Right Temporal Cortex (R-TC):The main effect of Time was significant （ F (1, 63) = 18.287, p < 0.001, η² = 0.225）, with increased activation post-fatigue. The main effect of Group was significant （ F (1, 63) = 4.649, p = 0.035, η² = 0.069）, with the novice group showing stronger activation. The interaction was not significant. 3.5 Correlation Results To explore the relationship between eye movement characteristics and brain activation, Pearson correlation analyses were conducted for the expert and novice groups pre- and post-fatigue. Results showed that only in the expert group pre-fatigue, pupil diameter was significantly positively correlated with HbO₂ β-values in the right dorsolateral prefrontal cortex （ r = 0.441, p = 0.015） and the left frontal pole area （ r = 0.392, p = 0.032）; blink count was also positively correlated with activation in the right dorsolateral prefrontal cortex （ r = 0.379, p = 0.039） and the right temporal cortex （ r = 0.365, p = 0.048）.No significant correlations were found in the novice group at either time point. 4 Discussion By combining behavioral, eye-tracking, and fNIRS techniques, this study systematically investigated the impact of mental fatigue on map-reading memory processing in orienteers and examined whether the "expert advantage" shaped by long-term specialized training translates into "cognitive resilience" under the challenge of fatigue. The results supported our hypotheses: mental fatigue significantly impaired the processing efficiency of map-reading memory, but expert athletes demonstrated stronger behavioral stability and neural adaptability. The following sections discuss the results in detail, integrating the cognitive demands of orienteering and relevant theories. 4.1 Multi-Dimensional Impairment of Map-Reading Memory by Mental Fatigue: From Behavioral Failure to Neural Compensation This study first confirmed that the 45-minute Stroop task successfully induced a significant state of mental fatigue (see Table 3 ). In this state, all athletes showed a systematic decline in map-reading memory performance: decreased accuracy and prolonged reaction times. This finding aligns closely with the consensus on the impact of mental fatigue in orienteering reported by Lam et al. (2022). Orienteering is essentially a cognitively intensive task of "solving complex spatial problems while running at high speed" (Eccles et al., 2014). Map-reading memory, as its core, requires athletes to complete the visual decoding of map symbols, mental representation of spatial relationships, and sequential memory of routes within a very short time (Liu Chuan'an, Mi Jing, 2020). Our results indicate that the cognitive resources depleted by mental fatigue directly erode the efficiency and accuracy of this series of processes, potentially leading to critical errors in competition, such as running the wrong route, missing checkpoints, or decision hesitation. Deeper mechanisms are reflected in the oculomotor and neural levels. After fatigue, athletes showed significantly increased pupil diameter and blink frequency, which are generally considered peripheral physiological markers of increased cognitive load, difficulty maintaining attention, and active regulation of visual information input (Niu Guoqing et al., 2019). In real-world orienteering scenarios, such reduced visual search efficiency means athletes find it harder to quickly filter irrelevant information and lock onto key navigational cues from complex natural environments. Corresponding to this behavioral "failure" was the brain's "compensatory effort." fNIRS data revealed that after fatigue, activation increased significantly in brain regions responsible for executive control and working memory (right dorsolateral prefrontal cortex, R-DLPFC), multi-information integration (left frontal pole area, L-FPA), and spatial scene processing (right temporal cortex, R-TC). This clearly indicates that to maintain basic task performance in a resource-depleted state, the brain is forced to mobilize more neural resources for compensation (Vila-Villar et al., 2022). This "high-energy, low-efficiency" neural pattern physiologically explains why behavior under fatigue becomes slow and error-prone. 4.2 Manifestation of Expert Advantage under Fatigue: Behavioral and Neural Evidence of Cognitive Resilience The core finding of this study lies in revealing the cognitive resilience of expert athletes under mental fatigue. Although both groups faced the negative impact of fatigue, the expert group demonstrated greater stability in behavior (smaller decline in accuracy, maintained reaction time advantage) and oculomotor measures (blink count did not change significantly post-fatigue). This corroborates previous findings that expert athletes possess superior attention allocation and anti-interference abilities (Krzepota et al., 2015) and extends this advantage to the specific stress condition of mental fatigue. Particularly noteworthy is the difference at the neural level. In all brain regions showing enhanced activation, the magnitude of the HbO₂ concentration increase was significantly lower in the expert group compared to the novice group. This finding aligns strongly with the "neural efficiency hypothesis" (Li & Smith, 2021). This hypothesis posits that long-term specialized training optimizes the brain's functional networks, enabling experts to achieve superior or equivalent performance with lower neural resource consumption when performing the same task. In this study, the novice group needed to substantially "increase effort" (significantly higher brain activation) to barely maintain performance after fatigue, whereas the expert group exhibited a more "economical" and "efficient" pattern of neural mobilization. This suggests that their cognitive advantage stems not only from "greater capacity" but also from "superior neural resource allocation strategies." 4.3 Theoretical Integration: From Neural Efficiency to Cognitive Resilience The results of this study organically connect the concepts of "neural efficiency" and "cognitive resilience" within the context of mental fatigue. We propose that the expert advantage shaped by long-term orienteering training may involve a two-level process: In non-fatigued or low-fatigue states, experts exhibit typical "neural efficiency"—completing the same task at a lower neural activation cost (as seen in the group differences in brain activation pre-fatigue in this study). This is attributed to the formation of automated map-reading patterns and spatial memory schemas through extensive practice, reducing reliance on conscious, controlled resources. Under the challenge of high mental fatigue, this efficient neural foundation translates into observable "cognitive resilience." When overall cognitive resources are depleted by fatigue, experts, due to the efficiency and automaticity of their neural circuits, can allocate the limited remaining resources more precisely to the most critical task elements (like key landmark identification and route verification), unlike novices who need to expend substantial resources on basic visual search and information maintenance. This explains why experts show a smaller decline in behavioral performance and oculomotor stability. The correlation analysis provides indirect support for this: only in the expert group pre-fatigue were pupil diameter and activation in multiple brain regions positively correlated. This might reflect their ability to flexibly allocate cognitive resources and physiological arousal levels according to task demands when resources are abundant; the disappearance or absence of this correlation in the expert group post-fatigue and in the novice group suggests a rigidity or failure in their resource regulation systems. 4.4 Research Implications, Limitations, and Future Directions This study has direct implications for orienteering training: beyond traditional physical and technical-tactical training, specialized cognitive training targeting "resistance to mental fatigue" should be incorporated. For example, map-reading decision exercises could be conducted after physical training or in complex scenarios to simulate the fatigue state at the end of a competition, thereby enhancing athletes' cognitive stability and neural efficiency under resource-scarce conditions. This study also has limitations: First, differences exist between the laboratory environment and real wilderness competitions in terms of scene complexity and psychophysiological load. Second, fNIRS technology primarily detects blood oxygen signals from the superficial cerebral cortex and does not capture activity in deeper brain regions. Finally, this study focused on acute mental fatigue, whereas the combined effects of chronic fatigue and psychological stress in actual competitions warrant future investigation. Future research could further explore: 1) Combining high temporal resolution techniques like EEG to reveal the dynamic changes in cognitive processing time courses under fatigue; 2) Designing longitudinal intervention studies to test whether specific cognitive training programs can effectively enhance the cognitive resilience of novice athletes; 3) Validating the ecological validity of these laboratory findings in simulation environments closer to real competition. 5 Conclusion In summary, using a multi-modal approach, this study reveals that: 1) Mental fatigue impairs orienteers' map-reading memory processing at behavioral, oculomotor, and neural levels; 2) The expert advantage formed through long-term specialized training manifests as significant cognitive resilience under the challenge of mental fatigue, potentially underpinned by more economical and efficient patterns of neural resource mobilization. This not only deepens our understanding of the mechanisms of mental fatigue in sports but also provides important theoretical and empirical bases for cultivating athletes' "mental endurance" in cognitively demanding disciplines. Declarations Ethical Approval and accordance This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Shaanxi Normal University. All participants were informed about the purpose and procedures of the study and provided written informed consent prior to participation. The study did not involve any clinical intervention and is classified as an observational study. As such, clinical trial registration was not applicable. Data Availability Statement The raw data supporting the conclusions of this article can be made available by the authors. Clinical trial number : not applicable.（This is an observational study） Declaration of Consent for Publication : We consent to submit this paper to the target journal and agree to cooperate with the editorial office during the peer-review process. Author Contribution Jia Liu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization.Yang Liu: Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition, Writing – Review & Editing.Xiaoping Guo: Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition, Writing – Review & Editing. References Yang, W., Liao, K. F., Gao, C., et al. (2024). The impact of mental fatigue on athletes' competitive performance and its possible mechanisms. Journal of Xi'an Institute of Physical Education, 41(01), 112-129. (In Chinese) Yang, W., Chen, T., Liao, K. F., et al. (2025). Effect of mental fatigue on aerobic endurance performance of male amateur soccer players in the 30-15 Intermittent Fitness Test. Journal of Capital University of Physical Education and Sports, 37(03), 328-338. (In Chinese) KOSACK M H, STAIANO W, FOLINO R, et al. The acute effect of mental fatigue on badminton performance in elite players. International Journal of Sports Physiology and Performance, 2020, 15(5): 632-638. RUSSELL S, JENKINS D, HALSON S, et al. Changes in subjective mental and physical fatigue during netball games in elite development athletes. Journal of Science and Medicine in Sport, 2020, 23(6): 615-620. VAN CUTSEM J, K D E P, BUYSE L, et al. Effects of mental fatigue on endurance performance in the heat. Medicine and Science in Sports and Exercise, 2017, 49(8): 1677-1687. Tian, Y., Yu, S. K., Zhou, Q. X., et al. (2015). Application analysis of eye movement indicators in mental fatigue research. Chinese Journal of Ergonomics, 21(04), 69-73. (In Chinese) Niu, G. Q., & Li, S. (2019). Discrimination of eye movement indicators between mental fatigue and non-fatigue states. Journal of Safety and Environment, 19(01), 88-93. (In Chinese) AYAZ H, SHEWOKIS P A, CURTIN A, et al. Using MazeSuite and functional near-infrared spectroscopy to study learning in spatial navigation. Journal of Visualized Experiments: JoVE, 2011, (56). GAO Y, PAN B, LI K, et al. Shed a light in fatigue detection with near-infrared spectroscopy during long-lasting driving. SPIE BiOS, 2016, doi:10.1117/12.2210846. MUTHALIB M, KAN B, NOSAKA K, et al. Effects of transcranial direct current stimulation of the motor cortex on prefrontal cortex activation during a neuromuscular fatigue task: An fNIRS study. Advances in Experimental Medicine and Biology, 2013, 789: 73-79. Li, Z. Y., Dai, S. X., Zhang, X. Y., et al. (2010). Detection and analysis of cerebral oxygen saturation in driver fatigue state using near-infrared spectroscopy. Spectroscopy and Spectral Analysis, 30(01), 58-61. (In Chinese) MEHTA R K, PARASURAMAN R. Effects of mental fatigue on the development of physical fatigue: a neuroergonomic approach. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2014, 56(4): 645-656. KHAN M J, HONG K S. Passive BCI based on drowsiness detection: An fNIRS study. Biomedical Optics Express, 2015, 6(10): 4063-4078. Liu, Y. (2017). Research on cognitive processing characteristics and skill training of orienteering athletes' map reading [Doctoral dissertation]. Northeast Normal University. (In Chinese) Zhao, M. S., Liu, J. R., Bao, S. B., et al. (2022). The effect of task difficulty on route decision-making of orienteering exercisers: Evidence from fNIRS. Journal of Shandong Sport University, 38(02), 110-118. (In Chinese) Liu, F., Wang, G. X., Qian, H. Z., et al. (2009). The influence of virtual geographic environment on spatial cognitive style. Science of Surveying and Mapping, 34(04), 67-69+33. (In Chinese) Liu, C. A., & Mi, J. (2020). Research on the map-reading process and main techniques in orienteering. Journal of Shandong Sport University, 36(01), 97-104. (In Chinese) Lam H K N, Sproule J, Turner A P, et al. International orienteering experts' consensus on the definition, development, cause, impact and methods to reduce mental fatigue in orienteering: A Delphi study. Journal of Sports Sciences, 2022, 40(23): 2595-2607. Zhu, Y., Xu, C., Wang, Y. Q., et al. (2011). Research on visual attention strategies of orienteers in different cognitive load scenarios. China Sport Science and Technology, 47(06), 82-89. (In Chinese) Tang, S. J., Qin, K. Y., Li, Y., et al. (2023). Characteristics of spatial distance perception in orienteers: Evidence from behavioral and fNIRS studies. China Sport Science and Technology, 59(03), 20-27+36. (In Chinese) Liu, Y., & Tang, S. J. (2022). The influence of map-reading method and map difficulty on orienteers' map-reading decision performance and visual search characteristics. Journal of Psychological Science, 45(06), 1314-1321. (In Chinese) Liu Y, Lu S, Liu J, et al. A characterization of brain area activation in orienteers with different map-recognition memory ability task levels---based on fNIRS evidence. Brain Sciences, 2022, 12(11): 1561. Gottlieb J, Snyder L H. Spatial and non-spatial functions of the parietal cortex. Current Opinion in Neurobiology, 2010, 20(6): 731-740. Murray M M, Thelen A, Thut G, et al. The multisensory function of the human primary visual cortex. Neuropsychologia, 2016, 83: 161-169. Nishiyori R, Bisconti S, Ulrich B. Motor cortex activity during functional motor skills: an fNIRS study. Brain Topography, 2016, 29(1): 42-55. Faul F, Erdfelder E, Buchner A, et al. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 2009, 41(4): 1149-1160. Cohen J. Statistical power analysis for the behavioral sciences. Routledge, 2013. Yang, W., Li, J. D., & Zhao, S. C. (2024). Comparison of the effects of mental fatigue induction protocols. Chinese Journal of Tissue Engineering Research, 28(23), 3722-3728. (In Chinese) Habay J, Van Cutsem J, Verschueren J, et al. Mental fatigue and sport-specific psychomotor performance: a systematic review. Sports Medicine, 2021, 51(7): 1527-1548. Van Cutsem J, De Pauw K, Vandervaeren C, et al. Mental fatigue impairs visuomotor response time in badminton players and controls. Psychology of Sport and Exercise, 2019, 45: 101579. MACMAHON C, SCHUCKER L, HAGEMANN N, et al. Cognitive fatigue effects on physical performance during running. Journal of Sport & Exercise Psychology, 2014, 36(4): 375-381. HEAD J R, TENAN M S, TWEEDELL A J, et al. Cognitive fatigue influences time-on-task during bodyweight resistance training exercise. Frontiers in Physiology, 2016, 7: 1-10. Lam H K N, Sproule J, Turner A P, et al. International orienteering experts' consensus on the definition, development, cause, impact and methods to reduce mental fatigue in orienteering: A Delphi study. Journal of Sports Sciences, 2022, 40(23): 2595-2607. Waddington E. Using Orienteering to Examine the Interactions of Exercise and Cognitive Training on Human Cognition and Brain-Derived Neurotrophic Factor [Doctoral dissertation], 2023. Batista M M, Paludo A C, Gula J N, et al. Physiological and cognitive demands of orienteering: a systematic review. Sport Sciences for Health, 2020, 16(4): 591-600. Eccles D W, Arsal G. How do they make it look so easy? The expert orienteer's cognitive advantage. Journal of Sports Sciences, 2014, 33(6): 609-615. Chai W J, Abd Hamid A I, Abdullah J M. Working memory from the psychological and neurosciences perspectives: a review. Frontiers in Psychology, 2018, 9: 401. Shi, P., Wang, G. D., Wei, Z., et al. (2023). Visual search characteristics of football players' offensive tactical anticipation decision-making: The influence of spatial working memory capacity. China Sport Science and Technology, 59(05), 27-34. (In Chinese) Silva A F, Afonso J, Sampaio A, et al. Differences in visual search behavior between expert and novice team sports athletes: A systematic review with meta-analysis. Frontiers in Psychology, 2022, 13: 1001066. Guo L, Liu Y, Kan C. The effect of sports expertise on the performance of orienteering athletes' real scene image recognition and their visual search characteristics. Scientific Reports, 2024, 14(1): 21498. Krzepota J, Zwierko T, Puchalska-Niedbał L, et al. The efficiency of a visual skills training program on visual search performance. Journal of Human Kinetics, 2015, 46: 231-240. Broadbent D P, Causer J, Williams A M, et al. Perceptual-cognitive skill training and its transfer to expert performance in the field: Future research directions. European Journal of Sport Science, 2015, 15(4): 322-331. Murray N P, Hunfalvay M. A comparison of visual search strategies of elite and non-elite tennis players through cluster analysis. Journal of Sports Sciences, 2017, 35(3): 241-246. Friedman N P, Robbins T W. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology, 2022, 47(1): 72-89. Vila-Villar A, Naya-Fernández M, Madrid A, et al. Exploring the role of the left DLPFC in fatigue during unresisted rhythmic movements. Psychophysiology, 2022, 59(10): e14078. Tomita N, Kumano H. Self-focused attention related to social anxiety during free speaking tasks activates the right frontopolar area. Current Psychology, 2023, 42(12): 10310-10323. Liang, T. F., Wu, H. Y., Zhang, Y., et al. (2018). Attentional selection in perceptual scenes and working memory representations: A unified perspective. Advances in Psychological Science, 26(04), 625-635. (In Chinese) Raslau F D, Mark I T, Klein A P, et al. Memory part 2: the role of the medial temporal lobe. AJNR: American Journal of Neuroradiology, 2015, 36(5): 846-849. Li L, Smith D M. Neural efficiency in athletes: a systematic review. Frontiers in Behavioral Neuroscience, 2021, 15: 698555. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8966036","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616798477,"identity":"87723abf-a130-4cae-9c5a-af7fcd58b6cc","order_by":0,"name":"Jia Liu","email":"","orcid":"","institution":"Shaanxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":616798478,"identity":"1b9213ce-a3fa-424d-8fe5-57a957ac4f90","order_by":1,"name":"Yang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACPmYIzSPP39j4ACpogFcLG1SLnOGMw4dhSglogdLGDAfS0iSI08LOYybxc0dtYmPDGbPKnzvuJDawN2+TYKi5g8dhPGaSvWeOJ7Yz95jd5j3zLLGB51iZBMOxZ3i1SPC2HQPbcpux7XBig0SOmQRjw2H8tvwFamk4kGNW+BOkRf4NYS3SvG01YO8z8IJt4SGkha3YWrbtADiQgXoPG7fxpBVbJBzDrYWf//DGm2/b6sBR+RHoMNl+9sMbb3yowa0FCFiA0YGkABxTCfg0MDAwf2BgqMOvZBSMglEwCkY2AAChFFNzpSQOggAAAABJRU5ErkJggg==","orcid":"","institution":"Shaanxi Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":616798479,"identity":"507df8be-3733-4129-b518-9e5de3112022","order_by":2,"name":"Xiaoping Guo","email":"","orcid":"","institution":"Shaanxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2026-02-25 09:41:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8966036/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8966036/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106189968,"identity":"9d2e9a6e-36ea-4751-9d38-827c23843894","added_by":"auto","created_at":"2026-04-05 17:12:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":646644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample of Map-Reading Memory Task Material\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8966036/v1/a64d5107643e173b92691bc8.png"},{"id":106402235,"identity":"100187f7-ac3d-46e6-96e5-0ba5a5bcefcb","added_by":"auto","created_at":"2026-04-08 09:11:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":306669,"visible":true,"origin":"","legend":"\u003cp\u003eBrain Probe Layout Diagram\u003c/p\u003e\n\u003cp\u003e(Image placeholder - note: Blue indicates detector probes; Red indicates emitter probes; 1-48: measurement channels.)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8966036/v1/b0bdc150fec1f9b848e962ab.png"},{"id":106402981,"identity":"83147236-6898-48bb-aaf6-6d0bf3c6b192","added_by":"auto","created_at":"2026-04-08 09:13:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":271039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental Procedure Flowchart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8966036/v1/59bcd4a45af11f0d8d6b3f67.png"},{"id":106189970,"identity":"3a72b1cb-44e2-4e8c-903c-e745c216851f","added_by":"auto","created_at":"2026-04-05 17:12:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReaction Time and Accuracy of the Two Groups on the Map-Reading Memory Task Pre- and Post-MF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: ***P\u0026lt;0.001, **P\u0026lt;0.01, *0.01\u0026lt;P\u0026lt;0.05\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8966036/v1/402c5d77e7f80e4b378e3245.png"},{"id":106402809,"identity":"bcdb33b9-c367-4d96-99ef-0779c506184c","added_by":"auto","created_at":"2026-04-08 09:12:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBlink Count and Pupil Diameter of the Two Groups on the Map-Reading Memory Task Pre- and Post-MF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote：***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001，**\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01，*0.01\u0026lt;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8966036/v1/ae74eebb9275a5bb410e7d7a.png"},{"id":106189972,"identity":"1d547991-ddf5-4337-9590-1412b10148c2","added_by":"auto","created_at":"2026-04-05 17:12:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":492750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in Brain Activation between the Two Groups Pre- and Post-MF\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8966036/v1/e6f29ff279f27939dfaf9ba2.png"},{"id":106402772,"identity":"b271bcc4-5893-459d-907a-94a389c3d9dd","added_by":"auto","created_at":"2026-04-08 09:12:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":133757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Results between Pupil Diameter, Blink Count, and fNIRS Indicators in Expert Orienteers during Map-Reading Memory\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8966036/v1/af5975cc32d261361103cb55.png"},{"id":108989045,"identity":"f84fa62e-07f8-43cd-a750-638b3123250f","added_by":"auto","created_at":"2026-05-11 13:10:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2235508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8966036/v1/ea3c24e6-4db4-4886-83fa-aa744a6465bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mental fatigue and map memory performance show cognitive resilience through orienteering expertise","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn the ultimate pursuit of excellence in modern competitive sports, the margin between victory and defeat for athletes is often minimal. This depends not only on their peak physiological functions but also on precise and efficient cognitive decision-making abilities under high-pressure and complex environments. Mental fatigue, a psychobiological state induced by prolonged or high-intensity cognitive activity, has been confirmed to significantly impair athletes' concentration, decision-making speed, and technical-tactical execution efficiency, independent of physical fatigue, becoming a key \"invisible\" factor affecting competition performance (Yang et al., 2024). From tactical choices on the football field to stroke prediction in tennis, the negative impact of mental fatigue has been demonstrated across various sports (Van Cutsem et al., 2017; Kosack et al., 2020). However, existing research predominantly focuses on team ball sports or cyclic sports. For \"cognitively intensive\" sports like orienteering, which heavily rely on real-time spatial memory and complex decision-making, how mental fatigue erodes its core competitive ability\u0026mdash;map-reading memory\u0026mdash;remains a critical yet insufficiently revealed question.\u003c/p\u003e \u003cp\u003eOrienteering is often called \"chess running.\" Athletes must quickly perform map identification, route planning, and decision-making in unfamiliar environments. Its essence is a continuous, high-load cognitive process involving visual search, working memory updating, and spatial information integration (Eccles et al., 2014). Map-reading memory, as the cornerstone of this process, involves the instantaneous encoding, storage, and retrieval of map symbols, spatial relationships, and route information (Liu Fang et al., 2009). During a competition, athletes repeatedly execute this process, leading to continuous depletion of cognitive resources, which can easily induce deep mental fatigue, resulting in getting lost, decision errors, or even competition failure (Lam et al., 2022). Therefore, investigating the impact of mental fatigue on orienteers' map-reading memory processing holds significant theoretical value and direct practical implications for training.\u003c/p\u003e \u003cp\u003eAlthough the detrimental effects of mental fatigue are universal, athletes' ability to cope varies significantly. The \"expert advantage\" formed through long-term specialized training often manifests as higher efficiency and stability in cognitive tasks (Zhu Yu et al., 2011). The \"neural efficiency hypothesis\" in cognitive psychology posits that expert brains can accomplish the same or superior task performance with lower neural resource consumption (Li \u0026amp; Smith, 2021). A core scientific question arises: Can this \"neural efficiency\" advantage of expert orienteers translate into observable \"cognitive resilience\" to withstand performance decline under the challenge of mental fatigue? In other words, do experts merely excel in \"efficiency,\" or do they possess stronger \"resilience\" or \"compensatory ability\" when facing cognitive resource depletion? Current research has yet to provide a clear, multi-dimensional evidence-based answer to this question.\u003c/p\u003e \u003cp\u003eCurrent research in this area has three main limitations: First, methodological singularity. Most studies rely solely on behavioral indicators or a single physiological modality, such as eye movements, making it difficult to penetrate behavioral representation and reveal the underlying changes in visual processing strategies and central neural mechanisms (Niu Guoqing et al., 2019). Second, weak theoretical framework. Explanations for performance differences under fatigue often remain at the descriptive level of \"experience differences,\" lacking deep integration with theories like neural efficiency and cognitive compensation, failing to clarify whether the expert advantage under adverse conditions manifests as higher efficiency or stronger resilience. Third, superficial mechanism exploration. Although some studies focus on the prefrontal cortex's role in fatigue, map-reading memory in orienteering involves a coordinated network of multiple brain regions, including the dorsolateral prefrontal cortex, frontal pole area, and temporal lobe (Gottlieb et al., 2010; Friedman et al., 2022), responsible for executive control, information integration, and spatial memory, respectively. How fatigue affects this network and how the recruitment patterns differ between experts and novices remains unknown.\u003c/p\u003e \u003cp\u003eTo overcome these limitations, this study innovatively employs simultaneous measurement using eye tracking and functional near-infrared spectroscopy. Eye-tracking technology can objectively reveal visual search strategies in real-time, serving as an external window into cognitive processing (Tian Yun et al., 2015). fNIRS technology, with its good ecological validity and resistance to motion artifacts, is suitable for investigating cerebral hemodynamic activity during cognitive tasks (Nishiyori et al., 2016). Through this multi-modal analysis paradigm combining behavioral, oculomotor, and neural levels, we aim to achieve the following objectives: First, systematically reveal the multi-faceted impact of mental fatigue on orienteers' map-reading memory in terms of behavioral accuracy, reaction speed, visual search efficiency, and neural activity in specific brain regions; second, deeply test whether and how expert athletes demonstrate stronger cognitive resilience under fatigue; third, explore at the neural mechanism level whether this resilience stems from lower baseline resource consumption (neural efficiency) or a more effective mobilization of relevant brain networks for compensation after fatigue.\u003c/p\u003e \u003cp\u003eBased on this, the study proposes the following hypotheses: H1: Mental fatigue will lead to decreased behavioral performance, reduced visual search efficiency in the map-reading memory task of orienteers, and induce compensatory enhanced activation in brain regions responsible for executive control, information integration, and spatial memory, such as the right dorsolateral prefrontal cortex, left frontal pole area, and right temporal lobe cortex. H2: Compared to novices, expert athletes will demonstrate significant cognitive resilience under mental fatigue, i.e., a smaller decline in behavioral performance and oculomotor stability, and their brain neural activity patterns may exhibit higher efficiency or more adaptive compensatory strategies.\u003c/p\u003e \u003cp\u003eThe significance of this study lies in not only providing a practical warning about the threat of mental fatigue to core orienteering skills but also theoretically introducing the concept of cognitive resilience into the research framework of mental fatigue and expert advantage for the first time, attempting to unravel the underlying behavioral and neural mechanisms using multi-modal technology. The findings are expected to offer new theoretical bases and empirical references for scientific training in orienteering and other cognitively demanding sports, such as anti-fatigue cognitive training and neurofeedback training.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Participants\u003c/h2\u003e \u003cp\u003eThe sample size for this study was calculated using G*Power 3.1 software (Faul et al., 2009). Parameters were set based on previous studies: effect size f\u0026thinsp;=\u0026thinsp;0.8, α\u0026thinsp;=\u0026thinsp;0.05, statistical power (1 - β)\u0026thinsp;=\u0026thinsp;0.80, resulting in a minimum required sample size of 52. Ultimately, a total of 65 right-handed participants were recruited. All participants had normal (or corrected-to-normal) vision, no history of neurological disorders, color blindness, or color weakness, and no prior experience with similar experiments. Before the experiment, all participants signed an informed consent form. This study was approved by the Ethics Committee of Shaanxi Normal University.\u003c/p\u003e \u003cp\u003eParticipants were divided into two groups:Expert group: 30 national-level orienteering athletes, with competition levels defined as having achieved top eight positions in national competitions or top sixteen positions in Asian-level events.Novice group: 35 university students majoring in physical education, all of whom had taken orienteering elective courses but had not obtained any sports ranking.\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\u003eParticipant Basic Information (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eExpert Group (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovice Group (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank/Qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational team athletes (National top 8/Asian top 16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnranked (Orienteering course class)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining (hours/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2Experimental Design\u003c/h2\u003e \u003cp\u003eA 2 (Group: Expert vs. Novice) \u0026times; 2 (Time: Pre-mental fatigue vs. Post-mental fatigue) mixed experimental design was employed. Independent variables: Group (between-subjects factor), Time (within-subjects factor). Dependent variables: Behavioral indicators: accuracy and reaction time on the map-reading memory task. Eye movement indicators: mean pupil diameter and blink count. Neural activity indicators: β-values representing changes in oxygenated hemoglobin (HbO₂) concentration in regions of interest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3Experimental Materials and Tasks\u003c/h2\u003e \u003cp\u003eMental Fatigue Induction Task: A modified Stroop task, as reported by Yang et al. (2024), lasting 45 minutes, was used. The task materials were four Chinese characters for \"Red,\" \"Green,\" \"Blue,\" and \"Yellow.\" The font color was randomly set to one of the four colors incongruent with the character's meaning, presented one by one on a black background computer screen. Participants were required to respond according to specific rules: when the presented color was green, blue, or yellow, they should ignore the character meaning and press the key corresponding to the font color; if the character color was red, they should ignore the color and press the key corresponding to the character meaning (keys: \"D,\" \"F,\" \"J,\" \"K\"). The task was run using E-Prime 3.0 software. Each trial lasted 2000 ms, including 1000 ms of stimulus presentation and a 1000 ms blank screen interval. The entire task comprised 1350 trials, requiring participants to respond as quickly and accurately as possible while maintaining focus. In case of incorrect responses or timeouts, the system provided immediate auditory feedback via a prompt sound.\u003c/p\u003e \u003cp\u003eMap-Reading Memory Task: The materials were standard orienteering competition maps, jointly produced by three national-level orienteering cartographers. Initially, 24 test maps and 2 practice maps were selected from 32 candidate maps. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (example in original text), the left image is the spatial memory material used in the experiment. The right image shows three selectable pictures A, S, D, where only picture D is identical to the spatial memory material; the other two maps differ in route information or checkpoint locations. In the experimental materials, the triangle represents the start point, the circle represents the checkpoint, blue areas represent impassable water, green areas represent difficult-to-pass forests, gray areas represent buildings, yellow areas represent easy-to-run forest, and \"\u0026times;\" represents special man-made features, etc.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubjective Fatigue Assessment: The Visual Analogue Scale (VAS), Rating of Perceived Exertion (RPE) scale, and Brunel Mood Scale (BRUMS) were administered before and after the fatigue induction task to verify the effectiveness of fatigue induction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4Experimental Instruments\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1fNIRS Device\u003c/h2\u003e \u003cp\u003eA portable functional near-infrared spectroscopy imaging system, NirSmartⅡ (Danyang Huichuang), was used to monitor hemodynamic responses in the cerebral cortex. Light sources and detectors were positioned over the bilateral prefrontal, temporal, frontoparietal sensorimotor, and occipital lobes based on the 10\u0026ndash;20 international standard lead system. The channel layout included 24 light sources and 16 detectors, forming 48 measurement channels with a sampling frequency of 11 Hz. Probes were fixed using an elastic headcap. During placement, participants' hair was sufficiently parted to ensure adequate contact between the probes and the scalp. Regions of interest (ROIs) were defined based on the existing anatomical labeling system LBPA40, resulting in 17 ROIs (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Image placeholder - note: Blue indicates detector probes; Red indicates emitter probes; 1\u0026ndash;48: measurement channels.)\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\u003eCorrespondence between fNIRS Channel Layout and Brain Regions of Interest\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorresponding Channels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Dorsolateral Prefrontal Cortex(L-DLPFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH10, CH19, CH20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Dorsolateral Prefrontal Cortex(R-DLPFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH1, CH13, CH15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Frontal Pole Area(L-FPA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH8, CH9, CH16, CH18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Frontal Pole Area(R-FPA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH4, CH6, CH17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrbitofrontal Cortex(OFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH5, CH7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Temporal Cortex(L-TC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH12, CH23, CH41, CH42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Temporal Cortex(R-TC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH3, CH22, CH34, CH35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Premotor \u0026amp; Supplementary Motor Area(L-PMC/SMA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH37, CH38, CH43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Premotor \u0026amp; Supplementary Motor Area(R-PMC/SMA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH24, CH30, CH31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Somatosensory Association Cortex(L-SAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH40, CH45, CH46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Somatosensory Association Cortex(R-SAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH26, CH27, CH33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Primary Motor Cortex(L-M1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH39, CH44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Primary Motor Cortex(R-M1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH25, CH32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Broca's Area(L-Broca)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH11, CH21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Broca's Area(R-Broca)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH2, CH14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWernicke's Area(Wernicke)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH28, CH47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Visual Cortex\u0026nbsp;(VC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH29, CH36, CH48\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=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2Eye Tracking Device\u003c/h2\u003e \u003cp\u003eAn aSee Pro eye tracker (7Invensun, Beijing) was used to record eye movement data. The sampling rate was 250 Hz, measurement accuracy was 0.5\u0026deg;, field of view (FOV) was 110\u0026deg;, gaze point recovery time was less than 30 ms, and the infrared wavelength was 850 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3Synchronous Recording Method\u003c/h2\u003e \u003cp\u003eTwo laptop computers were used in this experiment. Computer A (refresh rate 165 Hz, resolution 1920\u0026times;1080) was connected to a monitor (22 inches, refresh rate 120 Hz, resolution 1980\u0026times;1080) used to present stimuli. The aSee Pro eye tracker was installed below the monitor to measure eye movement data. Computer B (refresh rate 120 Hz, resolution 1920\u0026times;1080) was connected to the wireless fNIRS. The eye tracker and fNIRS device were within the same local area network. When presenting stimuli, the eye tracker sent signals to the fNIRS device via a wireless synchronization box to synchronize data.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5Experimental Procedure\u003c/h2\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePreparation and Baseline Recording. After familiarizing themselves with the environment, participants completed basic information forms and pre-fatigue scales. Subsequently, they wore the fNIRS cap and eye tracker and underwent calibration. First, 60 seconds of resting-state fNIRS data were collected as a baseline.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePre-test: Map-Reading Memory Task Test. The test consisted of two phases: practice and formal experiment. Participants first practiced with two test maps to become familiar with the procedure. Formal experiment: After collecting 60s resting-state fNIRS baseline data, instructions were presented. Participants pressed the space bar to begin. A red central \"+\" fixation point appeared for 1s, followed by a 20s map-reading memory phase (participants needed to remember route and checkpoint details within 10s). This was immediately followed by a 10s memory retrieval phase where participants selected the match for the spatial memory material from three pictures (A, S, D; choices corresponded to keys), ending with a 10s rest. The sequence (1s fixation\u0026thinsp;+\u0026thinsp;20s memory phase\u0026thinsp;+\u0026thinsp;10s rest) constituted one trial. Eight trials formed one block, with three blocks total and a 60s rest between blocks.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMental Fatigue Induction. Participants completed the 45-minute Stroop task.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePost-Induction Assessment. After the task, participants again completed the VAS, RPE, BRUMS scales, and then performed the map-reading memory task test again (post-test).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6Data Processing and Statistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1Behavioral Data Preprocessing\u003c/h2\u003e \u003cp\u003eBehavioral data included accuracy and reaction time. After the experiment, participants' key choices and response times were exported from the computer. Accuracy was calculated by comparing choices with correct answers. Reaction time was calculated as the time from stimulus presentation onset to the participant's key press.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 fNIRS data preprocessing\u003c/h2\u003e \u003cp\u003eThe Data Quality Analysis Tool was used to check fNIRS data quality based on the Coefficient of Variation (CV). Channels with CV\u0026thinsp;\u0026le;\u0026thinsp;5 indicated good quality; CV between 5\u0026ndash;20 indicated fair quality; CV\u0026thinsp;\u0026gt;\u0026thinsp;20 indicated poor quality. Channels with poor quality were marked, and data from participants with \u0026ge;\u0026thinsp;30% of channels having CV\u0026thinsp;\u0026gt;\u0026thinsp;20 were excluded. NirSpark 1.8.8 software (Danyang Huichuang) was used for fNIRS data analysis. Preprocessing in the Preprocess module included: motion artifact correction using spline interpolation per channel, low-pass filtering (0.01\u0026ndash;0.20 Hz) to remove physiological noise, and conversion of light intensity data to hemoglobin concentration data using the modified Beer-Lambert law. For brain activation analysis, task conditions were selected and edited in the GLM module to generate HbO₂ β-values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.6.3Eye Movement Data Preprocessing\u003c/h2\u003e \u003cp\u003eThe aSee Pro eye tracker's proprietary analysis software, aSee Studio, was used for analysis. For each map, the entire map was defined as the area of interest. Eye movement metrics, including pupil diameter and saccade amplitude, were exported from aSee Studio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.6.4Statistical Analysis\u003c/h2\u003e \u003cp\u003eProcessed data (behavioral, eye movement, and fNIRS data) were tested for normality using the Shapiro-Wilk test in SPSS 26.0. If the test result was P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (normality assumed), repeated measures ANOVA was conducted. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Mental Fatigue Manipulation Check\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo verify the effectiveness of the Stroop task in inducing mental fatigue, paired-sample t-tests were conducted on participants\u0026apos; subjective fatigue ratings and average heart rate before and after the task. As shown in Table 3, all indicators changed significantly after induction, indicating that the 45-minute Stroop task successfully induced the intended state of mental fatigue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Subjective and Objective Indicators of Mental Fatigue Induction Effectiveness\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 78px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ePre-MF (\u003cem\u003eM\u0026plusmn;SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003ePost-MF (\u003cem\u003eM\u0026plusmn;SD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eVAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e27.69\u0026plusmn;10.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e59.06\u0026plusmn;15.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-16.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003ePRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e8.17\u0026plusmn;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e12.53\u0026plusmn;2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-15.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eBrumsc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e5.93\u0026plusmn;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e4.80\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e9.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eAverage Heart Rate (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e66.18\u0026plusmn;7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e69.81\u0026plusmn;7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-5.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Behavioral Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 2 (Group)\u0026nbsp;\u0026times;\u0026nbsp;2 (Time) repeated measures ANOVA was performed on the accuracy and reaction time of the map-reading memory task.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1Accuracy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main effect of Group was significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 93.841,\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.598), with the expert group showing significantly higher accuracy than the novice group. The main effect of Time was significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 40.164,\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.389), with accuracy significantly decreasing after fatigue. The Group \u0026times; Time interaction was significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u0026nbsp;\u003c/sub\u003e= 5.732,\u003cem\u003ep\u003c/em\u003e= 0.020, \u0026eta;\u0026sup2; = 0.083). Simple effect analysis indicated that accuracy decreased significantly in both groups after fatigue, but the expert group\u0026apos;s accuracy was significantly higher than the novice group at both time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 Reaction Time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main effect of Group was significant（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 14.705, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.189）, with the novice group exhibiting longer reaction times. The main effect of Time was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 35.173, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.358）, with reaction times significantly prolonged after fatigue. The interaction was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 7.810, \u003cem\u003ep\u003c/em\u003e= 0.007, \u0026eta;\u0026sup2; = 0.110）. Simple effect analysis revealed that reaction times were significantly prolonged in both groups after fatigue, and the novice group\u0026apos;s reaction times were longer than the expert group\u0026apos;s at both pre- and post-fatigue time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Eye Movement Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepeated measures ANOVA was conducted on blink count and pupil diameter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1Blink Count\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main effect of Group was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u0026nbsp;\u003c/sub\u003e= 30.335, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.325）, with the novice group blinking more frequently. The main effect of Time was significant（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u0026nbsp;\u003c/sub\u003e= 15.194, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.194）, with blink count increasing after fatigue. The interaction was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u0026nbsp;\u003c/sub\u003e= 12.163, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.162）. Simple effect analysis revealed that fatigue significantly increased blink count only in the novice group, with no significant change in the expert group; furthermore, the novice group\u0026apos;s blink count was significantly higher than the expert group\u0026apos;s post-fatigue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2Pupil Diameter\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main effect of Group was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u0026nbsp;\u003c/sub\u003e= 55.045, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.466）, with the novice group having larger pupils. The main effect of Time was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u0026nbsp;\u003c/sub\u003e= 30.856, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.329）, with pupil diameter increasing after fatigue. The interaction was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 4.650, \u003cem\u003ep\u003c/em\u003e= 0.035, \u0026eta;\u0026sup2; = 0.069）. Simple effect analysis indicated that pupil diameter increased significantly in both groups after fatigue, and the novice group\u0026apos;s pupils were larger than the expert group\u0026apos;s at both time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 fNIRS Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of oxygenated hemoglobin (HbO₂) concentration \u0026beta;-values in key ROIs revealed significant findings primarily in three brain regions:Right Dorsolateral Prefrontal Cortex (R-DLPFC): The main effect of Time was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 49.708, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.441）, with increased activation post-fatigue. The main effect of Group was significant（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 36.924, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.370）, with the novice group showing stronger activation. The interaction was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u0026nbsp;\u003c/sub\u003e= 5.061, \u003cem\u003ep\u003c/em\u003e= 0.028, \u0026eta;\u0026sup2; = 0.074）. Simple effect analysis showed that activation increased significantly in both groups after fatigue, and the novice group\u0026apos;s activation level was higher than the expert group\u0026apos;s at both time points.\u003c/p\u003e\n\u003cp\u003eLeft Frontal Pole Area (L-FPA):The main effect of Time was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 4.451, \u003cem\u003ep\u003c/em\u003e= 0.039, \u0026eta;\u0026sup2; = 0.066）, with increased activation post-fatigue. The main effect of Group was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 30.382, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.325）, with the novice group showing stronger activation. The interaction was not significant.\u003c/p\u003e\n\u003cp\u003eRight Temporal Cortex (R-TC):The main effect of Time was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 18.287, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.225）, with increased activation post-fatigue. The main effect of Group was significant\u0026nbsp;（\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 4.649, \u003cem\u003ep\u003c/em\u003e= 0.035, \u0026eta;\u0026sup2; = 0.069）, with the novice group showing stronger activation. The interaction was not significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Correlation Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between eye movement characteristics and brain activation, Pearson correlation analyses were conducted for the expert and novice groups pre- and post-fatigue. Results showed that only in the expert group pre-fatigue, pupil diameter was significantly positively correlated with HbO₂ \u0026beta;-values in the right dorsolateral prefrontal cortex\u0026nbsp;（\u003cem\u003er\u003c/em\u003e= 0.441, \u003cem\u003ep\u003c/em\u003e= 0.015）\u0026nbsp;and the left frontal pole area\u0026nbsp;（\u003cem\u003er\u003c/em\u003e= 0.392, \u003cem\u003ep\u003c/em\u003e= 0.032）; blink count was also positively correlated with activation in the right dorsolateral prefrontal cortex\u0026nbsp;（\u003cem\u003er\u003c/em\u003e= 0.379, \u003cem\u003ep\u003c/em\u003e= 0.039）\u0026nbsp;and the right temporal cortex\u0026nbsp;（\u003cem\u003er\u003c/em\u003e= 0.365, \u003cem\u003ep\u003c/em\u003e= 0.048）.No significant correlations were found in the novice group at either time point.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBy combining behavioral, eye-tracking, and fNIRS techniques, this study systematically investigated the impact of mental fatigue on map-reading memory processing in orienteers and examined whether the \"expert advantage\" shaped by long-term specialized training translates into \"cognitive resilience\" under the challenge of fatigue. The results supported our hypotheses: mental fatigue significantly impaired the processing efficiency of map-reading memory, but expert athletes demonstrated stronger behavioral stability and neural adaptability. The following sections discuss the results in detail, integrating the cognitive demands of orienteering and relevant theories.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Multi-Dimensional Impairment of Map-Reading Memory by Mental Fatigue: From Behavioral Failure to Neural Compensation\u003c/h2\u003e \u003cp\u003eThis study first confirmed that the 45-minute Stroop task successfully induced a significant state of mental fatigue (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In this state, all athletes showed a systematic decline in map-reading memory performance: decreased accuracy and prolonged reaction times. This finding aligns closely with the consensus on the impact of mental fatigue in orienteering reported by Lam et al. (2022). Orienteering is essentially a cognitively intensive task of \"solving complex spatial problems while running at high speed\" (Eccles et al., 2014). Map-reading memory, as its core, requires athletes to complete the visual decoding of map symbols, mental representation of spatial relationships, and sequential memory of routes within a very short time (Liu Chuan'an, Mi Jing, 2020). Our results indicate that the cognitive resources depleted by mental fatigue directly erode the efficiency and accuracy of this series of processes, potentially leading to critical errors in competition, such as running the wrong route, missing checkpoints, or decision hesitation.\u003c/p\u003e \u003cp\u003eDeeper mechanisms are reflected in the oculomotor and neural levels. After fatigue, athletes showed significantly increased pupil diameter and blink frequency, which are generally considered peripheral physiological markers of increased cognitive load, difficulty maintaining attention, and active regulation of visual information input (Niu Guoqing et al., 2019). In real-world orienteering scenarios, such reduced visual search efficiency means athletes find it harder to quickly filter irrelevant information and lock onto key navigational cues from complex natural environments.\u003c/p\u003e \u003cp\u003eCorresponding to this behavioral \"failure\" was the brain's \"compensatory effort.\" fNIRS data revealed that after fatigue, activation increased significantly in brain regions responsible for executive control and working memory (right dorsolateral prefrontal cortex, R-DLPFC), multi-information integration (left frontal pole area, L-FPA), and spatial scene processing (right temporal cortex, R-TC). This clearly indicates that to maintain basic task performance in a resource-depleted state, the brain is forced to mobilize more neural resources for compensation (Vila-Villar et al., 2022). This \"high-energy, low-efficiency\" neural pattern physiologically explains why behavior under fatigue becomes slow and error-prone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Manifestation of Expert Advantage under Fatigue: Behavioral and Neural Evidence of Cognitive Resilience\u003c/h2\u003e \u003cp\u003eThe core finding of this study lies in revealing the cognitive resilience of expert athletes under mental fatigue. Although both groups faced the negative impact of fatigue, the expert group demonstrated greater stability in behavior (smaller decline in accuracy, maintained reaction time advantage) and oculomotor measures (blink count did not change significantly post-fatigue). This corroborates previous findings that expert athletes possess superior attention allocation and anti-interference abilities (Krzepota et al., 2015) and extends this advantage to the specific stress condition of mental fatigue.\u003c/p\u003e \u003cp\u003eParticularly noteworthy is the difference at the neural level. In all brain regions showing enhanced activation, the magnitude of the HbO₂ concentration increase was significantly lower in the expert group compared to the novice group. This finding aligns strongly with the \"neural efficiency hypothesis\" (Li \u0026amp; Smith, 2021). This hypothesis posits that long-term specialized training optimizes the brain's functional networks, enabling experts to achieve superior or equivalent performance with lower neural resource consumption when performing the same task. In this study, the novice group needed to substantially \"increase effort\" (significantly higher brain activation) to barely maintain performance after fatigue, whereas the expert group exhibited a more \"economical\" and \"efficient\" pattern of neural mobilization. This suggests that their cognitive advantage stems not only from \"greater capacity\" but also from \"superior neural resource allocation strategies.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Theoretical Integration: From Neural Efficiency to Cognitive Resilience\u003c/h2\u003e \u003cp\u003eThe results of this study organically connect the concepts of \"neural efficiency\" and \"cognitive resilience\" within the context of mental fatigue. We propose that the expert advantage shaped by long-term orienteering training may involve a two-level process:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn non-fatigued or low-fatigue states, experts exhibit typical \"neural efficiency\"\u0026mdash;completing the same task at a lower neural activation cost (as seen in the group differences in brain activation pre-fatigue in this study). This is attributed to the formation of automated map-reading patterns and spatial memory schemas through extensive practice, reducing reliance on conscious, controlled resources.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUnder the challenge of high mental fatigue, this efficient neural foundation translates into observable \"cognitive resilience.\" When overall cognitive resources are depleted by fatigue, experts, due to the efficiency and automaticity of their neural circuits, can allocate the limited remaining resources more precisely to the most critical task elements (like key landmark identification and route verification), unlike novices who need to expend substantial resources on basic visual search and information maintenance. This explains why experts show a smaller decline in behavioral performance and oculomotor stability. The correlation analysis provides indirect support for this: only in the expert group pre-fatigue were pupil diameter and activation in multiple brain regions positively correlated. This might reflect their ability to flexibly allocate cognitive resources and physiological arousal levels according to task demands when resources are abundant; the disappearance or absence of this correlation in the expert group post-fatigue and in the novice group suggests a rigidity or failure in their resource regulation systems.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Research Implications, Limitations, and Future Directions\u003c/h2\u003e \u003cp\u003eThis study has direct implications for orienteering training: beyond traditional physical and technical-tactical training, specialized cognitive training targeting \"resistance to mental fatigue\" should be incorporated. For example, map-reading decision exercises could be conducted after physical training or in complex scenarios to simulate the fatigue state at the end of a competition, thereby enhancing athletes' cognitive stability and neural efficiency under resource-scarce conditions.\u003c/p\u003e \u003cp\u003eThis study also has limitations: First, differences exist between the laboratory environment and real wilderness competitions in terms of scene complexity and psychophysiological load. Second, fNIRS technology primarily detects blood oxygen signals from the superficial cerebral cortex and does not capture activity in deeper brain regions. Finally, this study focused on acute mental fatigue, whereas the combined effects of chronic fatigue and psychological stress in actual competitions warrant future investigation.\u003c/p\u003e \u003cp\u003eFuture research could further explore: 1) Combining high temporal resolution techniques like EEG to reveal the dynamic changes in cognitive processing time courses under fatigue; 2) Designing longitudinal intervention studies to test whether specific cognitive training programs can effectively enhance the cognitive resilience of novice athletes; 3) Validating the ecological validity of these laboratory findings in simulation environments closer to real competition.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, using a multi-modal approach, this study reveals that: 1) Mental fatigue impairs orienteers' map-reading memory processing at behavioral, oculomotor, and neural levels; 2) The expert advantage formed through long-term specialized training manifests as significant cognitive resilience under the challenge of mental fatigue, potentially underpinned by more economical and efficient patterns of neural resource mobilization. This not only deepens our understanding of the mechanisms of mental fatigue in sports but also provides important theoretical and empirical bases for cultivating athletes' \"mental endurance\" in cognitively demanding disciplines.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Shaanxi Normal University. All participants were informed about the purpose and procedures of the study and provided written informed consent prior to participation. The study did not involve any clinical intervention and is classified as an observational study. As such, clinical trial registration was not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article can be made available by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.（This is an observational study）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Consent for Publication\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe consent to submit this paper to the target journal and agree to cooperate with the editorial office during the peer-review process.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJia Liu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization.Yang Liu: Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition, Writing \u0026ndash; Review \u0026amp; Editing.Xiaoping Guo: Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eYang, W., Liao, K. F., Gao, C., et al. (2024). The impact of mental fatigue on athletes\u0026apos; competitive performance and its possible mechanisms. Journal of Xi\u0026apos;an Institute of Physical Education, 41(01), 112-129. (In Chinese)\u003c/li\u003e\n \u003cli\u003eYang, W., Chen, T., Liao, K. F., et al. (2025). Effect of mental fatigue on aerobic endurance performance of male amateur soccer players in the 30-15 Intermittent Fitness Test. Journal of Capital University of Physical Education and Sports, 37(03), 328-338. (In Chinese)\u003c/li\u003e\n \u003cli\u003eKOSACK M H, STAIANO W, FOLINO R, et al. The acute effect of mental fatigue on badminton performance in elite players. International Journal of Sports Physiology and Performance, 2020, 15(5): 632-638.\u003c/li\u003e\n \u003cli\u003eRUSSELL S, JENKINS D, HALSON S, et al. Changes in subjective mental and physical fatigue during netball games in elite development athletes. Journal of Science and Medicine in Sport, 2020, 23(6): 615-620.\u003c/li\u003e\n \u003cli\u003eVAN CUTSEM J, K D E P, BUYSE L, et al. Effects of mental fatigue on endurance performance in the heat. Medicine and Science in Sports and Exercise, 2017, 49(8): 1677-1687.\u003c/li\u003e\n \u003cli\u003eTian, Y., Yu, S. K., Zhou, Q. X., et al. (2015). Application analysis of eye movement indicators in mental fatigue research. Chinese Journal of Ergonomics, 21(04), 69-73. (In Chinese)\u003c/li\u003e\n \u003cli\u003eNiu, G. Q., \u0026amp; Li, S. (2019). Discrimination of eye movement indicators between mental fatigue and non-fatigue states. Journal of Safety and Environment, 19(01), 88-93. (In Chinese)\u003c/li\u003e\n \u003cli\u003eAYAZ H, SHEWOKIS P A, CURTIN A, et al. Using MazeSuite and functional near-infrared spectroscopy to study learning in spatial navigation. Journal of Visualized Experiments: JoVE, 2011, (56).\u003c/li\u003e\n \u003cli\u003eGAO Y, PAN B, LI K, et al. Shed a light in fatigue detection with near-infrared spectroscopy during long-lasting driving. SPIE BiOS, 2016, doi:10.1117/12.2210846.\u003c/li\u003e\n \u003cli\u003eMUTHALIB M, KAN B, NOSAKA K, et al. Effects of transcranial direct current stimulation of the motor cortex on prefrontal cortex activation during a neuromuscular fatigue task: An fNIRS study. Advances in Experimental Medicine and Biology, 2013, 789: 73-79.\u003c/li\u003e\n \u003cli\u003eLi, Z. Y., Dai, S. X., Zhang, X. Y., et al. (2010). Detection and analysis of cerebral oxygen saturation in driver fatigue state using near-infrared spectroscopy. Spectroscopy and Spectral Analysis, 30(01), 58-61. (In Chinese)\u003c/li\u003e\n \u003cli\u003eMEHTA R K, PARASURAMAN R. Effects of mental fatigue on the development of physical fatigue: a neuroergonomic approach. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2014, 56(4): 645-656.\u003c/li\u003e\n \u003cli\u003eKHAN M J, HONG K S. Passive BCI based on drowsiness detection: An fNIRS study. Biomedical Optics Express, 2015, 6(10): 4063-4078.\u003c/li\u003e\n \u003cli\u003eLiu, Y. (2017). Research on cognitive processing characteristics and skill training of orienteering athletes\u0026apos; map reading [Doctoral dissertation]. Northeast Normal University. (In Chinese)\u003c/li\u003e\n \u003cli\u003eZhao, M. S., Liu, J. R., Bao, S. B., et al. (2022). The effect of task difficulty on route decision-making of orienteering exercisers: Evidence from fNIRS. Journal of Shandong Sport University, 38(02), 110-118. (In Chinese)\u003c/li\u003e\n \u003cli\u003eLiu, F., Wang, G. X., Qian, H. Z., et al. (2009). The influence of virtual geographic environment on spatial cognitive style. Science of Surveying and Mapping, 34(04), 67-69+33. (In Chinese)\u003c/li\u003e\n \u003cli\u003eLiu, C. A., \u0026amp; Mi, J. (2020). Research on the map-reading process and main techniques in orienteering. Journal of Shandong Sport University, 36(01), 97-104. (In Chinese)\u003c/li\u003e\n \u003cli\u003eLam H K N, Sproule J, Turner A P, et al. International orienteering experts\u0026apos; consensus on the definition, development, cause, impact and methods to reduce mental fatigue in orienteering: A Delphi study. Journal of Sports Sciences, 2022, 40(23): 2595-2607.\u003c/li\u003e\n \u003cli\u003eZhu, Y., Xu, C., Wang, Y. Q., et al. (2011). Research on visual attention strategies of orienteers in different cognitive load scenarios. China Sport Science and Technology, 47(06), 82-89. (In Chinese)\u003c/li\u003e\n \u003cli\u003eTang, S. J., Qin, K. Y., Li, Y., et al. (2023). Characteristics of spatial distance perception in orienteers: Evidence from behavioral and fNIRS studies. China Sport Science and Technology, 59(03), 20-27+36. (In Chinese)\u003c/li\u003e\n \u003cli\u003eLiu, Y., \u0026amp; Tang, S. J. (2022). The influence of map-reading method and map difficulty on orienteers\u0026apos; map-reading decision performance and visual search characteristics. Journal of Psychological Science, 45(06), 1314-1321. (In Chinese)\u003c/li\u003e\n \u003cli\u003eLiu Y, Lu S, Liu J, et al. A characterization of brain area activation in orienteers with different map-recognition memory ability task levels---based on fNIRS evidence. Brain Sciences, 2022, 12(11): 1561.\u003c/li\u003e\n \u003cli\u003eGottlieb J, Snyder L H. Spatial and non-spatial functions of the parietal cortex. Current Opinion in Neurobiology, 2010, 20(6): 731-740.\u003c/li\u003e\n \u003cli\u003eMurray M M, Thelen A, Thut G, et al. The multisensory function of the human primary visual cortex. Neuropsychologia, 2016, 83: 161-169.\u003c/li\u003e\n \u003cli\u003eNishiyori R, Bisconti S, Ulrich B. Motor cortex activity during functional motor skills: an fNIRS study. Brain Topography, 2016, 29(1): 42-55.\u003c/li\u003e\n \u003cli\u003eFaul F, Erdfelder E, Buchner A, et al. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 2009, 41(4): 1149-1160.\u003c/li\u003e\n \u003cli\u003eCohen J. Statistical power analysis for the behavioral sciences. Routledge, 2013.\u003c/li\u003e\n \u003cli\u003eYang, W., Li, J. D., \u0026amp; Zhao, S. C. (2024). Comparison of the effects of mental fatigue induction protocols. Chinese Journal of Tissue Engineering Research, 28(23), 3722-3728. (In Chinese)\u003c/li\u003e\n \u003cli\u003eHabay J, Van Cutsem J, Verschueren J, et al. Mental fatigue and sport-specific psychomotor performance: a systematic review. Sports Medicine, 2021, 51(7): 1527-1548.\u003c/li\u003e\n \u003cli\u003eVan Cutsem J, De Pauw K, Vandervaeren C, et al. Mental fatigue impairs visuomotor response time in badminton players and controls. Psychology of Sport and Exercise, 2019, 45: 101579.\u003c/li\u003e\n \u003cli\u003eMACMAHON C, SCHUCKER L, HAGEMANN N, et al. Cognitive fatigue effects on physical performance during running. Journal of Sport \u0026amp; Exercise Psychology, 2014, 36(4): 375-381.\u003c/li\u003e\n \u003cli\u003eHEAD J R, TENAN M S, TWEEDELL A J, et al. Cognitive fatigue influences time-on-task during bodyweight resistance training exercise. Frontiers in Physiology, 2016, 7: 1-10.\u003c/li\u003e\n \u003cli\u003eLam H K N, Sproule J, Turner A P, et al. International orienteering experts\u0026apos; consensus on the definition, development, cause, impact and methods to reduce mental fatigue in orienteering: A Delphi study. Journal of Sports Sciences, 2022, 40(23): 2595-2607.\u003c/li\u003e\n \u003cli\u003eWaddington E. Using Orienteering to Examine the Interactions of Exercise and Cognitive Training on Human Cognition and Brain-Derived Neurotrophic Factor [Doctoral dissertation], 2023.\u003c/li\u003e\n \u003cli\u003eBatista M M, Paludo A C, Gula J N, et al. Physiological and cognitive demands of orienteering: a systematic review. Sport Sciences for Health, 2020, 16(4): 591-600.\u003c/li\u003e\n \u003cli\u003eEccles D W, Arsal G. How do they make it look so easy? The expert orienteer\u0026apos;s cognitive advantage. Journal of Sports Sciences, 2014, 33(6): 609-615.\u003c/li\u003e\n \u003cli\u003eChai W J, Abd Hamid A I, Abdullah J M. Working memory from the psychological and neurosciences perspectives: a review. Frontiers in Psychology, 2018, 9: 401.\u003c/li\u003e\n \u003cli\u003eShi, P., Wang, G. D., Wei, Z., et al. (2023). Visual search characteristics of football players\u0026apos; offensive tactical anticipation decision-making: The influence of spatial working memory capacity. China Sport Science and Technology, 59(05), 27-34. (In Chinese)\u003c/li\u003e\n \u003cli\u003eSilva A F, Afonso J, Sampaio A, et al. Differences in visual search behavior between expert and novice team sports athletes: A systematic review with meta-analysis. Frontiers in Psychology, 2022, 13: 1001066.\u003c/li\u003e\n \u003cli\u003eGuo L, Liu Y, Kan C. The effect of sports expertise on the performance of orienteering athletes\u0026apos; real scene image recognition and their visual search characteristics. Scientific Reports, 2024, 14(1): 21498.\u003c/li\u003e\n \u003cli\u003eKrzepota J, Zwierko T, Puchalska-Niedbał L, et al. The efficiency of a visual skills training program on visual search performance. Journal of Human Kinetics, 2015, 46: 231-240.\u003c/li\u003e\n \u003cli\u003eBroadbent D P, Causer J, Williams A M, et al. Perceptual-cognitive skill training and its transfer to expert performance in the field: Future research directions. European Journal of Sport Science, 2015, 15(4): 322-331.\u003c/li\u003e\n \u003cli\u003eMurray N P, Hunfalvay M. A comparison of visual search strategies of elite and non-elite tennis players through cluster analysis. Journal of Sports Sciences, 2017, 35(3): 241-246.\u003c/li\u003e\n \u003cli\u003eFriedman N P, Robbins T W. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology, 2022, 47(1): 72-89.\u003c/li\u003e\n \u003cli\u003eVila-Villar A, Naya-Fern\u0026aacute;ndez M, Madrid A, et al. Exploring the role of the left DLPFC in fatigue during unresisted rhythmic movements. Psychophysiology, 2022, 59(10): e14078.\u003c/li\u003e\n \u003cli\u003eTomita N, Kumano H. Self-focused attention related to social anxiety during free speaking tasks activates the right frontopolar area. Current Psychology, 2023, 42(12): 10310-10323.\u003c/li\u003e\n \u003cli\u003eLiang, T. F., Wu, H. Y., Zhang, Y., et al. (2018). Attentional selection in perceptual scenes and working memory representations: A unified perspective. Advances in Psychological Science, 26(04), 625-635. (In Chinese)\u003c/li\u003e\n \u003cli\u003eRaslau F D, Mark I T, Klein A P, et al. Memory part 2: the role of the medial temporal lobe. AJNR: American Journal of Neuroradiology, 2015, 36(5): 846-849.\u003c/li\u003e\n \u003cli\u003eLi L, Smith D M. Neural efficiency in athletes: a systematic review. Frontiers in Behavioral Neuroscience, 2021, 15: 698555.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mental fatigue, orienteering, map-reading memory, expert-novice differences, cognitive resilience, eye tracking, functional near-infrared spectroscopy","lastPublishedDoi":"10.21203/rs.3.rs-8966036/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8966036/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: Mental fatigue is known to impair cognitive performance during exercise. This study aims to investigate the impact of mental fatigue on the map-reading memory processing of orienteers and to explore whether the expert advantage developed through long-term specific training can translate into effective cognitive resilience under fatigued conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A 2 (Group: expert group, novice group) × 2 (Time: pre-fatigue, post-fatigue) mixed experimental design was employed. Thirty national level 2 and above elite orienteers (expert group) and 35 novice orienteers completed a 45-minute Stroop task to induce mental fatigue. Before and after the fatigue induction, participants' behavioral performance, eye movement metrics during a map-reading memory task were measured synchronously, along with prefrontal/temporal cortex activation collected via functional near-infrared spectroscopy (fNIRS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Following mental fatigue, the accuracy of both groups decreased significantly, and reaction times were prolonged. Eye movement metrics indicated reduced visual search efficiency, as evidenced by increased pupil diameter and blink count. Concurrently, significant increases in oxygenated hemoglobin concentration were observed in the right dorsolateral prefrontal cortex, left frontal pole area, and right temporal lobe, indicating the emergence of a neural compensatory mechanism. Critically, under the fatigued state, the expert group was able to maintain superior behavioral performance and oculomotor stability compared to the novice group, demonstrating stronger cognitive resilience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Mental fatigue significantly impairs the map-reading memory efficiency of orienteers. However, expert orienteers exhibit a marked cognitive advantage, characterized by behavioral and oculomotor stability, which benefits from more efficient neural resource mobilization. These findings underscore the importance of cognitive resilience developed through long-term training and offer insights into mitigating the negative effects of mental fatigue through specific practice.\u003c/p\u003e","manuscriptTitle":"Mental fatigue and map memory performance show cognitive resilience through orienteering expertise","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-05 17:12:40","doi":"10.21203/rs.3.rs-8966036/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":"03f9fa2d-f874-40b6-979c-ba6f8d7a18bb","owner":[],"postedDate":"April 5th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-11T12:20:20+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T12:59:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-05 17:12:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8966036","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8966036","identity":"rs-8966036","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}