The effects of intensive mindfulness interventions on athletes' psychological states and cognitive performance: an fNIRS study.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Psychol . 2026 Mar 9;14:541. doi: 10.1186/s40359-026-04323-w Search in PMC Search in PubMed View in NLM Catalog Add to search The effects of intensive mindfulness interventions on athletes’ psychological states and cognitive performance: an fNIRS study Yunting Song Yunting Song 1 Scientific Research Center, Guangzhou Sport University, Guangzhou, China Find articles by Yunting Song 1, # , Jiaxiu Zhang Jiaxiu Zhang 1 Scientific Research Center, Guangzhou Sport University, Guangzhou, China Find articles by Jiaxiu Zhang 1, # , Shukurjon Gaziev Shukurjon Gaziev 2 Uzbek State University of Physical Education and Sport, Chirchik, Uzbekistan Find articles by Shukurjon Gaziev 2 , Jasur Zarifbayev Jasur Zarifbayev 2 Uzbek State University of Physical Education and Sport, Chirchik, Uzbekistan Find articles by Jasur Zarifbayev 2 , Rashid Matkarimov Rashid Matkarimov 2 Uzbek State University of Physical Education and Sport, Chirchik, Uzbekistan Find articles by Rashid Matkarimov 2 , Pin Zhong Pin Zhong 3 South China Agriculture University, Guangzhou, China Find articles by Pin Zhong 3 , Yuanze Cheng Yuanze Cheng 4 Kuichong Middle School of Dapeng New District, Shenzhen City, China Find articles by Yuanze Cheng 4 , Mingqiang Xiang Mingqiang Xiang 5 Guangdong Provincial Key Laboratory of Human Sports Performance Science, Guangzhou Sport University, Guangzhou, China 6 Graduate School, Guangzhou Sport University, No. 1268, Middle of Guangzhou Avenue, Guangzhou, 510500 China Find articles by Mingqiang Xiang 5, 6, ✉ Author information Article notes Copyright and License information 1 Scientific Research Center, Guangzhou Sport University, Guangzhou, China 2 Uzbek State University of Physical Education and Sport, Chirchik, Uzbekistan 3 South China Agriculture University, Guangzhou, China 4 Kuichong Middle School of Dapeng New District, Shenzhen City, China 5 Guangdong Provincial Key Laboratory of Human Sports Performance Science, Guangzhou Sport University, Guangzhou, China 6 Graduate School, Guangzhou Sport University, No. 1268, Middle of Guangzhou Avenue, Guangzhou, 510500 China ✉ Corresponding author. # Contributed equally. Received 2025 Oct 28; Accepted 2026 Mar 5; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13085727  PMID: 41803998 Abstract This randomized controlled trial examined whether short-term intensive mindfulness training is associated with changes in executive task performance and prefrontal hemodynamic responses in elite athletes. Twenty-eight elite badminton athletes were randomly assigned to a 10-day mindfulness intervention or an active control (regular training plus neutral video viewing). Before and after the intervention, athletes completed the Tower of London (TOL) task while prefrontal responses were recorded using fNIRS; task accuracy (ACC) and reaction time (RT) were assessed. Neural responses were quantified using Hbdiff (HbO–HbR) and a neural efficiency (NE) index, and mindfulness was assessed with the Five-Facet Mindfulness Questionnaire (FFMQ). The intervention group showed increased FFMQ scores, but no significant Group × Time interaction was observed for TOL-related ACC or RT. Under low cognitive load, CH15 (right vlPFC) showed a lower hemodynamic response, and the CH15 NE index showed a higher NE index than the control group; the CH15 (right vlPFC) response was positively associated with RT. These findings add to evidence on mindfulness-related modulation of vlPFC (CH15) responses under low cognitive load during executive task performance, although the absence of robust behavioral changes warrants cautious interpretation. Keywords: Mindfulness training, Neural efficiency, Athletes, Functional near-infrared spectroscopy Introduction Mindfulness training has been consistently shown to enhance cognitive function across diverse populations. For example, systematic reviews report improvements in attention regulation, present-moment awareness, and overall cognitive performance following mindfulness interventions [ 68 ]. In older adults, such training not only bolsters cognitive abilities but also strengthens functional connectivity between the hippocampus and the posteromedial cortex [ 61 ]. Similarly, among military personnel, regular mindfulness practice correlates with superior operational performance, heightened mental resilience, and fewer lapses in attention [ 67 ]. Growing evidence suggests that even short-term mindfulness interventions can produce significant cognitive benefits. For example, a one-week program improved coping flexibility and protected working memory under stress [ 36 ], while a four-day intensive retreat enhanced attentional networks and increased activation in cognitive control regions such as the anterior cingulate cortex and dorsolateral prefrontal cortex [ 41 ]. These findings support the feasibility of brief mindfulness protocols and justify the 10-day intervention used in this study. Taken together, these findings suggest that short-term mindfulness — and related contemplative practices — can significantly modulate brain function. Nevertheless, the mechanism of mindfulness’s effect on athletes remains an open question. Athletes, in particular, face high cognitive and performance demands that require sustained attention, rapid decision-making, and effective executive control [ 60 ]. These characteristics make athletes an ideal group for investigating the neural mechanisms through which mindfulness enhances cognitive efficiency. Previous research indicates that mindfulness training can strengthen attentional control and increase activation in prefrontal regions such as the dorsolateral prefrontal cortex (dlPFC) [ 23 ]. Moreover, mindfulness has been associated with greater mental resilience [ 50 ] and measurable improvements in athletic performance [ 12 ]. Despite these promising outcomes, the specific prefrontal mechanisms that underlie mindfulness-related enhancements in cognitive control remain to be fully elucidated. Functional neuroimaging studies provide insights into how mindfulness shapes brain activity, revealing heightened activation in attention-related regions (e.g., dlPFC, anterior cingulate) alongside reduced engagement of mind-wandering networks [ 66 ]. Reductions in midline interoceptive areas, such as the anterior insula, ventral anterior cingulate cortex, and precuneus, have also been observed during mindfulness states [ 35 ]. These neural shifts are linked to reliable improvements in attentional performance, executive control, and cognitive processing efficiency. Building upon this neural foundation, mindfulness-based interventions (MBIs) have been applied to athletes, demonstrating improvements in core executive functions—including working memory, inhibitory control, and attentional regulation—through modulation of prefrontal and parietal regions and network efficiency within executive control and default mode networks [ 7 , 70 ]. In athletic populations, mindfulness training has been shown to enhance cognitive and emotional regulation, attentional focus, and overall performance. For instance, Aherne et al., [ 1 ] reported improved attentional focus, while Wong et al., [ 72 ] found Mindfulness-Acceptance-Commitment (MAC) training increased dispositional mindfulness and performance outcomes. A controlled intervention study by Nien et al., [ 55 ] demonstrated that a five-week mindfulness program improved endurance performance and executive function, as evidenced by better Stroop task accuracy and reduced conflict-related neural activation. Other sport-specific studies have documented enhancements in flow, concentration, and consistency across sports such as golf, swimming, and basketball [ 9 , 18 , 38 ]. Collectively, these findings suggest that mindfulness interventions can modulate neural activity and cognitive performance in athletes, highlighting their potential for improving neural efficiency and task execution. A growing body of research has demonstrated that mindfulness interventions exert multifaceted effects on the prefrontal cortex (PFC), a critical hub for executive functions such as attention regulation, working memory, and cognitive flexibility. In elderly populations, mindfulness training has been associated with improvements in working memory and task-switching, both functions closely linked to dorsolateral prefrontal cortex (dlPFC) activity [ 10 ]. Similarly, individuals with attention-deficit disorders—both children and adults—have shown enhanced attentional control and inhibitory processing following mindfulness interventions, reflecting improved executive regulation [ 53 ]. Neuroimaging studies further corroborate these behavioral findings, consistently reporting increased activation in the dlPFC and medial PFC after mindfulness practice, particularly during tasks requiring sustained attention and top-down cognitive control [ 33 , 64 ]. Beyond these enhancements in prefrontal activation, mindfulness also induces reductions in brain regions associated with mind-wandering and self-referential processing, notably within the default mode network (DMN), including the ventral anterior cingulate cortex and precuneus [ 10 ]. Notably, after a two-week mindfulness intervention, individuals with depressive symptoms exhibited reduced dlPFC activation during emotional face recognition tasks, suggesting a shift from effortful cognitive control to more automatic, adaptive emotion regulation [ 6 ]. Similarly, task-based fMRI studies involving focused attention and body-scan practices have reported increased right dlPFC activation alongside decreased rostral PFC activity, reflecting suppression of self-referential thought [ 66 ]. This dual modulation—enhancement of task-relevant prefrontal activation and suppression of mind-wandering-related networks—may constitute a core neural mechanism through which mindfulness optimizes cognitive performance. However, how these neurocognitive dynamics unfold in elite athletes experiencing acute pre-competition stress remains largely unexplored. The pre-competition phase represents a stress-sensitive window in which athletes commonly experience acute elevations in state anxiety and perceived pressure. Such acute stress can tax limited cognitive resources and bias attention toward threat-related cues, which may compromise executive control and alter prefrontal regulatory recruitment during complex planning and decision-making. Therefore, focusing on the pre-competition period is not merely logistical but theoretically motivated: mindfulness training may buffer stress-related cognitive disruption by supporting attentional control and emotion regulation, potentially stabilizing executive performance and its prefrontal correlates when athletes are preparing to compete. The neural efficiency hypothesis (NEH) posits that individuals with higher cognitive abilities or extensive training exhibit reduced brain activation during task performance, reflecting more efficient neural processing. This phenomenon has been documented across various domains, including sports and mindfulness. For example, Guo et al., [ 30 ] found that professional table tennis players showed lower cortical activation in task-sensitive regions during visuo-spatial tasks compared to non-athletes, despite comparable accuracy, indicating a more efficient neural organization resulting from long-term training. Similarly, expert athletes demonstrate reduced activation in motor and sensory cortices during sport-specific tasks, consistent with the development of neural efficiency through specialized practice [ 74 ]. In the realm of mindfulness, neuroimaging studies have revealed that experienced meditators often exhibit reduced activation in prefrontal regions during cognitive tasks, which may reflect enhanced neural efficiency. For example, Tomasino and Fabbro , [ 66 ] reported that after mindfulness training, participants showed decreased activation in the rostral prefrontal cortex during attention tasks, suggesting a shift toward more efficient neural processing. Collectively, these findings indicate that both prolonged athletic training and mindfulness practice can foster neural efficiency, characterized by reduced activation without loss of performance accuracy. Building on this framework, the present study aims to evaluate whether findings are consistent with the neural efficiency hypothesis in the context of short-term intensive mindfulness training in elite athletes, examining whether brief mindfulness practice can induce neural efficiency–related changes during cognitive task performance. To empirically examine neural efficiency in the context of mindfulness and athletic performance, high-cognitive-load tasks such as the Tower of London (TOL; [ 56 ]) were employed. The TOL requires individuals to engage in complex reasoning, problem-solving, and executive processing, and has been widely used in cognitive neuroscience to probe the integrity of prefrontal cortex (PFC) function. Task performance has been closely linked to activation in the dorsolateral (dlPFC), ventrolateral (vlPFC), and medial prefrontal regions—areas involved in goal setting, planning, and monitoring of complex sequential behaviors [ 5 , 27 , 54 ]. This task robustly engages high-level executive functions—including planning, abstract reasoning, and working memory—making it an ideal probe of complex cognition [ 22 , 46 ]. Moreover, the TOL has been extensively validated in fNIRS research with mature analysis pipelines [ 59 ], ensuring consistency with prior findings and facilitating theoretical interpretation. Although not sport-specific, its sensitivity to prefrontal activation patterns makes it particularly suitable for assessing the cognitive impacts of mindfulness in elite athletes. Therefore, using the TOL provides a theoretically grounded and neurobiologically validated approach to investigating mindfulness-induced changes in cognitive function and prefrontal activation during the pre-competition period. Although numerous studies have demonstrated that mindfulness can improve attention, emotion regulation, and performance stability, most neuroimaging research has focused on non-athlete populations such as students, older adults, or clinical groups. Even within athlete-focused studies, the neural mechanisms underlying intensive mindfulness interventions remain insufficiently explored. Existing evidence largely relies on self-report measures, which may not accurately reflect underlying brain changes. Moreover, many interventions adopt long-term protocols lasting 8 to 12 weeks, which are often impractical for athletes due to training demands. Investigating the effects of a short-term but intensive mindfulness program using objective neuroimaging approaches could therefore provide new insights into how mindfulness training may influence cognitive function and prefrontal recruitment patterns (including neural efficiency–related indices) in trained athletes. This study aims to examine the effects of a 10-day intensive mindfulness program on athletes’ cognitive performance. Functional near-infrared spectroscopy (fNIRS) was used to record prefrontal cortex activity during the Tower of London (TOL) task before and after the intervention. We hypothesized that mindfulness training would be associated with changes in executive-function performance and prefrontal hemodynamic responses during task execution, particularly within the stress-relevant pre-competition context. Specifically, we examined whether intervention-related modulation of prefrontal activation co-occurs with comparable or improved behavioral performance. Method Participants All elite athletes were members of badminton teams from the High-Performance Centre in Guangzhou, China, and competed in the same national-level competition. After coordinating with the coaches, athletes were invited to participate through team visits, emails, and posters. Twenty-eight athletes agreed to participate in the experiment and were randomly assigned via computer to either the intervention or the control group. Athletes within the same group were selected from the same training team to ensure consistency. While they might have come from different teams, all participants were at the same competitive level. The intervention group consisted of fourteen athletes (6 male, 8 female) with a mean age of 20.29 years (SD = 1.98, range = 18 to 23) and an average of 13.14 years of training. Similarly, the control group consisted of 14 athletes (8 male, 6 female) with a mean age of 21.14 years (SD = 1.99, range = 18–24) and an average of 12.93 years of training experience. All participants were right-handed with no significant neurological history, reading disorder, or alcohol or drug dependence. To control for prior experience, all participants were screened before the intervention and confirmed they had no history of formal mindfulness practice. A sensitivity analysis using G*Power (Version 3.1.9.3) indicated that with 28 participants (14 per group) and α = 0.05 for a 2 (group) × 2 (time) repeated-measures design, the study was powered at 0.80 to detect a minimum interaction effect size of f = 0.275. According to Cohen [ 14 ] conventions—where f = 0.25 represents a medium effect—this suggests that the study was sufficiently powered to detect moderate effects. The study was approved by the Ethics Committee of Guangzhou Sport University and conducted in accordance with the Declaration of Helsinki. Before participating, participants were fully informed about the purpose of the research and signed an informed consent form. Mindfulness intervention This study used a randomized controlled design to investigate the effects of a 10-day mindfulness intervention on cognitive performance and brain activation in elite badminton athletes. Participants were randomly assigned to either the intervention group, which received mindfulness training, or the control group, which continued their usual training while watching a neutral video. The mindfulness intervention followed established protocols and included guided body-scan meditation sessions to enhance bodily awareness. Participants completed two testing sessions before and after the intervention: (a) the Five-Facet Mindfulness Questionnaire (FFMQ), and (b) the Tower of London task with concurrent brain activation data collection. To ensure consistency and reduce external influences, mindfulness sessions were held in the late afternoon after regular training (around 6:00–7:00 PM), while testing sessions took place in the morning. Fourteen athletes in the intervention group participated in mindfulness training, using a Chinese-language audio version of “Guided Meditation Practices” [ 71 ], translated for clarity. This exercise was selected for two reasons: first, it aligns with mindfulness training protocols successfully used in previous studies [ 1 , 24 ]; second, it emphasizes somatic awareness, a key component of mindfulness interventions [ 2 ]. The training included the “Body Scan” exercise—a 30-minute guided practice where participants directed their attention sequentially to different body parts, from the lower to the upper body. The intervention was conducted once daily for 10 consecutive days after each regular training session, with all sessions led by the same instructor to ensure consistency. The instructor held a master’s degree in psychology and was a certified psychological counselor with formal training in mindfulness-based interventions. Meanwhile, the control group watched neutral, non-arousing nature documentaries in Mandarin (e.g., Animal World, China Central Television [CCTV]) for an equivalent duration (30 min), to match the time and attentional engagement of the intervention group. All control sessions were conducted in a quiet room under the researcher’s supervision, with no mobile phone use or conversation permitted during viewing. Given the behavioral nature of the intervention, participant blinding was not feasible (mindfulness training vs. video viewing); all testing procedures and instructions were standardized across groups. The post-intervention assessment took place on the morning of Day 11, and the official competition was held on Day 12. After the intervention, the FFMQ was re-administered to assess changes in athletes’ mindfulness levels. A detailed timeline of the intervention and testing procedures is shown in Fig. 1 . Fig. 1. Open in a new tab Experimental procedure of the mindfulness intervention in badminton athletes Assessment of questionnaire The Five-Facet Mindfulness Questionnaire (FFMQ) [ 4 ] was used to assess the nature of mindfulness. The Chinese version of the FFMQ possesses strong psychometric properties [ 19 ]. This questionnaire assesses five facets of mindfulness, each targeting a different aspect: observing (noticing internal and external stimuli, including sensations, emotions, cognitions, and visual perceptions; eight items, e.g., “When I take a shower or bath, I stay alert to the sensations of water on my body”); describing (mentally identifying and noting internal experiences with words; eight items, e.g., “I can easily put my beliefs, opinions, and expectations into words”); acting with awareness (focusing on one’s current activities rather than behaving automatically or absentmindedly; eight items, e.g., “When I do things, my mind wanders off and I’m easily distracted”); non-judging of inner experience (refraining from evaluating one’s sensations, cognitions, and emotions; eight items, e.g., “I tell myself I shouldn’t be feeling the way I’m feeling”); and non-reactivity to inner experience (allowing thoughts and feelings to come and go without being absorbed in them; seven items, e.g., “When I have distressing thoughts or images, I just notice them and let them go”). Items are rated on a five-point Likert scale, ranging from 1 (never or very rarely true) to 5 (very often or always true). A higher score indicates a better mindfulness state. Behavior measurement and analysis The Tower of London (TOL; [ 39 ]) is a well-established paradigm for assessing executive functions such as planning and problem-solving. The TOL test consists of three pegs of different lengths on a strip and three colored balls (red, green, and blue). Participants were required to manipulate the balls to replicate a predefined goal configuration using the fewest possible moves, adhering to specific rules (e.g., only one ball could be moved at a time, and no peg could hold more balls than its length allowed). In this study, participants first received on-screen instructions, followed by a brief practice session to ensure task comprehension. After reading the instructions, they pressed the SPACE key to begin the practice phase. Each trial—both practice and experimental—was preceded by a centrally presented fixation cross (“+”) displayed for 1000 ms. A total of three practice trials were completed before the experimental trials. The formal TOL task consisted of 20 trials, divided into two levels of cognitive load. Low-load trials required 3 to 4 moves, while high-load trials required 5 to 6 moves to complete, as shown in Fig. 2 . The paradigm followed an event-related design: trials within the same load level were presented consecutively without inter-trial intervals, while a 30,000-ms rest interval was inserted between the low and high cognitive load blocks to allow for cognitive recovery and baseline stabilization. Participants used the computer mouse to perform all manipulations. Fig. 2. Open in a new tab Cognitive tasks In the Tower of London (TOL) task, behavioral performance was evaluated using accuracy rate (ACC), correct reaction time (correct RT), and error reaction time (error RT). Correct RT was defined as the response time for trials in which participants completed the problem according to task rules. Error RT was defined as the response time for trials in which participants produced an incorrect solution (i.e., violations of task rules or failure to reach the target configuration). fNIRS measurement A multichannel wearable fNIRS system NIRSport (NIRx Medical Technologies LLC, Glen Head, NY, USA) simultaneously captured dual-wavelength signals (760 and 850 nm) at a sampling rate of 7.81 Hz. The fNIRS optodes, comprising 8 LED sources and eight detectors, were affixed to the participant’s frontal lobe to monitor hemodynamics across 8 Regions of Interest (ROI): (1) Right dorsolateral prefrontal cortex (right dlPFC), including CH10, CH11, CH14, CH20; (2) Left dorsolateral prefrontal cortex (left dlPFC), including CH6, CH7, CH12, CH19; (3) Median dorsolateral prefrontal cortex (middle dlPFC), including CH18; (4) Right ventral prefrontal cortex (right vlPFC), including CH15, CH17; (5) Left ventral prefrontal cortex (left vlPFC), including CH13, CH16; (6) Right frontal pole area (right FPA), including CH1, CH8, CH9; (7) Left frontal pole area (left FPA), including CH2, CH4, CH5; (8) Median frontal pole area (middle FPA), including CH3. The fNIRS head cap was positioned according to the international 10–10 system [ 75 ]. The montage used in the fNIRS experiment is shown in Fig. 3 . Fig. 3. Open in a new tab Montage used in the fNIRS experiment. a Selected 10–20 positions for optodes (created with NIRsite, NIRx.); b Channels location in terms of closest Montreal Neurological Institute (MNI) fNIRS data analysis The fNIRS data were analyzed using MATLAB (MathWorks, Natick, MA, USA) based on the HOMER3 package [ 34 ], and custom scripts were used to extract the brain activation index. First, the raw data were converted to optical density. Subsequently, spike artifacts were identified and eliminated using the hmrMotionArtifactByChannel [ 34 ] and hmrMotionCorrectionWavelet [ 52 ] algorithms in the HOMER3 fNIRS processing package. The fNIRS data underwent bandpass filtering (with a low cut-off frequency of 0.01 Hz and a high cut-off frequency of 0.08 Hz) to remove the effects of heartbeat, breathing, and low-frequency signal drifts for each wavelength. The pre-processed signals for each source-detector channel were then converted to concentration changes in oxygenated hemoglobin (HbO) using the modified Beer-Lambert law [ 38 ]. Subsequently, HbO and HbR signals were further scrutinized to identify residual noise through a correlation-based technique. This process excluded channels displaying excessive noise from subsequent analysis [ 16 ]. As Hb concentration changes did not directly represent the signal in ROI due to an unknown optical path length [ 37 ], channel-wise statistical analyses were performed. Brain activation was evaluated using an index of hemoglobin differential (Hbdiff = HbO–HbR) [ 28 , 29 , 42 ]. Neuronal activation typically triggers a swift rise in HbO levels and a smaller decrease in HbR levels, as per neurovascular coupling, leading to enhanced local oxygenation [ 43 , 73 ]. Neural efficiency To evaluate changes in neural efficiency (NE) following the intervention, we computed NE scores by integrating behavioral performance with cortical activation. NE was calculated using the z-scores of task performance (i.e., correct reaction time) and task-related Hbdiff changes in the significant channel(s), following prior work [ 17 , 40 ]. z (cortical activation) represents the z-scores of task-related Hbdiff changes in the specific channel. The equations below depict the NE calculation (see Eq. [ 1 ]). 1 z (task performance) represents the z-scores of correct RT in the TOL task, and z (cortical activation) represents the z-scores of task-related Hbdiff changes in the specific channel. Statistical analyses Statistical analysis was performed using SPSS version 26.0 (IBM, USA). Data preprocessing included screening for missing values and outliers, and normality was assessed using the Shapiro–Wilk test. For each participant and condition, behavioral data were averaged across valid trials before analysis. A three-factor repeated-measures ANOVA was conducted, with group (control vs. intervention) as the between-subject factor and cognitive load (low vs. high) and time (pre-test vs. post-test) as within-subject factors. Interaction effects among these factors were also evaluated. These procedures aimed to analyze behavioral performance metrics and neuroimaging activation data. To account for multiple comparisons across channels, p-values were adjusted using the Benjamini–Hochberg [ 8 ] false discovery rate (FDR) correction (q < 0.05). Effect sizes were measured using partial eta-squared (partial η²), with thresholds of 0.01–0.06 indicating small effects, 0.06–0.14 medium effects, and greater than 0.14 large effects, according to Pierce et al., [ 58 ]. To further validate the neural efficiency hypothesis, channels that showed significant differences in the above analyses were subsequently examined using the NE index. Results The five-facet mindfulness questionnaire A mixed-design analysis of variance (ANOVA) with Group (mindfulness intervention vs. control) and Time (pre-test vs. post-test) was conducted for the FFMQ total score and each facet (Table 1 ). For the total FFMQ score, a significant Group × Time interaction was observed (F = 44.524, p = 0.00015, partial η² = 0.774). Simple effects analyses indicated that, at post-test, the intervention group scored significantly higher than the control group on total FFMQ (t = 5.764, p = 5.0 × 10⁻⁶, Cohen’s d = 2.179). In addition, within the intervention group, total FFMQ scores increased significantly from pre-test to post-test (t = -7.638, p = 4.0 × 10⁻⁶, Cohen’s d = 2.041). Table 1. Descriptive statistics and Group × Time ANOVA results for FFMQ scores FFMQ scores Pre-test Post-test F p partial η² intervention control intervention control observe 13.29 ± 3.1 12.79 ± 2.61 15.00 ± 3.04 12.57 ± 3.67 1.469 0.247 0.102 describe 12.07 ± 2.84 12.36 ± 2.41 14.07 ± 3.15 12.43 ± 2.56 3.121 0.101 0.194 actaware 11.29 ± 3.87 11.79 ± 2.86 14.79 ± 3.12 11.79 ± 2.86 3.36 0.090 0.205 nonjudge 12.29 ± 3.00 12.50 ± 3.30 14.93 ± 3.02 12.43 ± 3.18 3.635 0.079 0.219 nonreact 11.50 ± 3.03 11.29 ± 3.87 14.50 ± 3.06 11.50 ± 2.98 1.872 0.194 0.126 total 60.43 ± 4.11 60.71 ± 4.23 73.29 ± 6.56 60.71 ± 4.86 44.524 0.00015 0.774 Open in a new tab Behavioral outcome A three-way repeated-measures ANOVA was conducted on correct reaction time, error reaction time, and accuracy rate, with cognitive load (high vs. low), time (pre vs. post), and group (intervention vs. control) as within- and between-subject factors. The results indicated that there were no significant interaction effects for correct reaction time (F = 0.912, p = 0.348, partial η 2 = 0.034), error reaction time (F = 2.025, p = 0.167, partial η 2 = 0.072), or accuracy rate (F = 1.644, p = 0.211, partial η 2 = 0.059), as illustrated in Table 2 . Changes in mindfulness (ΔFFMQ total, post–pre) were not significantly associated with changes in CH15 hemodynamic response under low cognitive load (ΔCH15, post–pre; r = 0.347, p = 0.224, 95% CI [-0.225, 0.741]). Table 2. The behavioral performance of the tower of London Task Intervention group Control group Pre-test Post-test Pre-test Post-test Low cognitive load ACC 0.98 (0.04) 1.00 (0.00) 0.98 (0.05) 0.99 (0.01) High cognitive load ACC 0.98 (0.05) 0.98 (0.06) 0.94 (0.09) 0.98 (0.05) Low cognitive load correct RT 10535.35 (2702.72) 11623.15 (3429.59) 21067.19 (7787.59) 11521.87 (1943.08) Low cognitive load error RT 15875.03 (7253.81) 20201.29 (3389.43) 20601.69 (11115.87) 18675.18 (6295.65) High cognitive load correct RT 21064.37 (7544.72) 18897.21 (3631.15) 35660.37 (25352.86) 16102.98 (4549.85) High cognitive load error RT 35489.32 (13966.75) 21152.36 (5387.24) 47285.13 (20150.46) 18603.04 (4614.56) Open in a new tab N = 28. Data are presented as mean and standard deviation. Reaction time is reported in milliseconds. Higher ACC indicates better accuracy. Error RT refers to the reaction time measured in trials with incorrect responses Neural activation in the prefrontal channels To evaluate whether intervention-related changes depended on cognitive load, we treated the Group × Time × Load interaction as the primary test of load-dependent effects. At the channel level, only CH15 showed a significant three-way interaction after FDR correction, and post-hoc/simple-effects analyses were therefore conducted for CH15 to characterize the load-specific pattern. Other channels showed some significant lower-order effects (main effects and/or two-way interactions), which are fully reported in Table 3 ; however, because their three-way interactions were not significant, these effects were not interpreted as evidence for load-dependent intervention modulation. Table 3. Channel-wise Δ(Post–Pre) descriptive statistics (mean ± SD) by cognitive load and Group × Time × Load interaction results during the Tower of London task CHs Intervention group Control group Group* Time* Load time * group load * group time * load Main effects Δ(Post–Pre) Δ(Post–Pre) Time Load Group Low load High load Low load High load F p_FDR * partial η² F p_FDR * partial η² F p_FDR * partial η² F p_FDR * partial η² F p_FDR * partial η² F p_FDR * partial η² F p_FDR * partial η² CH1 -60.22 ± 57.33 -36.06 ± 78.08 21.75 ± 54.33 -23.72 ± 87.9 2.367 0.194 0.083 10.853 0.020 0.294 0.489 0.655 0.018 0.489 0.695 0.018 10.776 0.009 0.293 6.539 0.042 0.201 5.176 0.078 0.166 CH2 -103.31 ± 75.95 -34.17 ± 196.15 -11.77 ± 48.68 -46.34 ± 94.99 1.633 0.251 0.059 4.009 0.187 0.134 0.527 0.655 0.02 0.000155 0.990 0.00006 16.459 0.002 0.388 10.798 0.010 0.293 5.872 0.066 0.184 CH3 -81.09 ± 51.61 -8.84 ± 65.34 -27.32 ± 65.68 -35.34 ± 106.5 3.838 0.178 0.129 1.619 0.391 0.059 1.891 0.655 0.068 3.666 0.223 0.124 26.723 0.00022 0.507 10.979 0.010 0.297 15.808 0.010 0.378 CH4 -117.5 ± 73.13 23.94 ± 88.51 1.79 ± 55.69 -42.28 ± 50.47 3.14 0.178 0.108 0.007 0.935 0.00026 1.814 0.655 0.065 8.007 0.090 0.235 7.873 0.020 0.232 11.43 0.010 0.305 4.195 0.102 0.139 CH5 -45.36 ± 69.97 22.62 ± 65.96 -7.29 ± 66.82 -10.39 ± 49.53 3.114 0.178 0.107 2.378 0.318 0.084 0.731 0.655 0.027 2.595 0.340 0.091 2.378 0.169 0.084 16.474 0.004 0.388 7.398 0.044 0.222 CH6 -33.19 ± 44.79 -0.51 ± 56.31 -27.71 ± 54.59 -39.53 ± 40.48 2.566 0.192 0.09 0.873 0.513 0.032 0.338 0.666 0.013 1.44 0.438 0.052 11.017 0.009 0.298 15.164 0.007 0.368 2.477 0.197 0.087 CH7 -79.69 ± 80.57 0.78 ± 67.34 3.08 ± 55.93 -54.35 ± 42.30 3.368 0.178 0.115 2.551 0.318 0.089 1.811 0.655 0.065 1.73 0.428 0.062 1.036 0.353 0.038 2.16 0.192 0.077 0.112 0.741 0.004 CH8 -69.46 ± 76.94 3.87 ± 78.03 -4.43 ± 66.67 -68.18 ± 42.65 1.883 0.236 0.068 10.248 0.020 0.283 1.436 0.655 0.052 2.288 0.355 0.081 3.042 0.124 0.105 2.962 0.149 0.102 10.271 0.040 0.283 CH9 -66.32 ± 68.95 26.53 ± 60.83 -0.68 ± 67.84 -24.36 ± 50.01 4.199 0.178 0.139 0.055 0.907 0.002 0.353 0.666 0.013 0.135 0.842 0.005 3.071 0.124 0.106 3.205 0.149 0.11 1.275 0.336 0.047 CH10 -88.55 ± 107.15 23.54 ± 59.74 -23.84 ± 102.79 -62.50 ± 64.40 8.186 0.080 0.239 0.221 0.755 0.008 0.689 0.655 0.026 3.863 0.223 0.129 4.857 0.057 0.157 2.527 0.165 0.089 2.036 0.237 0.073 CH11 -82.8 ± 63.09 36.36 ± 85.93 -6.29 ± 60.33 -46.59 ± 56.24 2.511 0.192 0.088 0.339 0.706 0.013 0.563 0.655 0.021 0.364 0.695 0.014 7.94 0.020 0.234 7.807 0.029 0.231 1.125 0.352 0.041 CH12 -47.3 ± 51.34 -3.10 ± 18.77 6.97 ± 29.34 18.95 ± 76.75 1.503 0.257 0.055 7.648 0.040 0.227 1.464 0.655 0.053 0.053 0.911 0.002 0.858 0.382 0.032 0.018 0.894 0.001 0.418 0.581 0.016 CH13 -51.49 ± 33.29 -9.29 ± 46.38 -10.25 ± 60.78 -40.97 ± 47.68 1.251 0.288 0.046 0.439 0.684 0.017 0.49 0.655 0.019 1.153 0.488 0.042 12.673 0.004 0.328 6.305 0.042 0.195 4.564 0.093 0.149 CH14 -80.36 ± 88.09 -11.66 ± 45.06 -40.25 ± 97.6 -24.20 ± 29.34 4.013 0.178 0.134 1.267 0.452 0.046 0.033 0.903 0.001 1.623 0.428 0.059 7.772 0.020 0.23 0.514 0.511 0.019 8.205 0.040 0.24 CH15 -71.15 ± 19.23 -3.36 ± 46.46 -12.14 ± 17.83 -34.05 ± 35.63 34.151 8.0 × 10⁻⁵ 0.586 41.335 0.00016 0.614 28.927 0.00024 0.527 8.937 0.090 0.256 41.335 1.642 × 10⁻⁵ 0.614 23.873 0.001 0.479 8.388 0.040 0.244 CH16 -58.49 ± 43.1 -51.31 ± 115.16 12.4 ± 55.11 -37.09 ± 43.35 3.374 0.178 0.115 10.507 0.020 0.288 0.006 0.938 0.00237 0.01 0.971 0.00373 18.515 0.001 0.416 3.352 0.149 0.114 3.564 0.127 0.121 CH17 -83.46 ± 71.52 23.3 ± 98.53 29.97 ± 63.77 -24.61 ± 36.33 1.816 0.236 0.065 1.646 0.391 0.06 1.084 0.655 0.04 0.523 0.695 0.02 1.946 0.206 0.07 0.501 0.511 0.019 6.957 0.047 0.211 CH18 -35.23 ± 69.42 18.17 ± 113.51 -14.63 ± 59.79 -26.47 ± 90.76 4.078 0.178 0.136 0.019 0.935 0.001 1.058 0.655 0.039 0.356 0.695 0.013 0.695 0.412 0.026 2.651 0.165 0.093 1.329 0.336 0.049 CH19 -31.55 ± 35.88 0.96 ± 71.12 -12.1 ± 58.73 -1.72 ± 31.65 0.529 0.474 0.020 0.936 0.513 0.035 12.3 0.020 0.321 5.301 0.150 0.169 6.969 0.025 0.211 2.065 0.192 0.074 2.567 0.197 0.09 CH20 -49.68 ± 67.01 -1.22 ± 23.64 -6.91 ± 52.32 -16.51 ± 51.40 2.861 0.187 0.099 2.282 0.318 0.081 0.048 0.903 0.002 5.757 0.150 0.181 4.829 0.057 0.157 3.094 0.149 0.106 0.271 0.639 0.01 Open in a new tab Δ(Post–Pre) values are reported in µM × 10⁻⁶ (post − pre). The table reports repeated-measures ANOVA results for each channel, including the Group × Time × Load interaction, lower-order interactions, and main effects (F, p_FDR, and partial η²). The FDR correction was applied to p-values across channels. Because Δ scores reflect change (post − pre), relatively large SDs can arise from substantial inter-individual variability and occasional opposite-direction changes between pre- and post-test The findings revealed a significant three-way interaction effect on channel-level hemodynamic response at channel 15 (F = 34.151, p_FDR = 8.0 × 10⁻⁵, partial η 2 = 0.586). Post hoc analysis showed a significant reduction in brain activation for the intervention group compared with the control group at CH15 (F = 206.06, p = 7.179 × 10⁻ 14 , partial η 2 = 0.888). Moreover, a significant between-group difference was observed at CH15 during the low cognitive load condition after the intervention (F = 79.12, p = 2.289 × 10⁻ 9 , partial η 2 = 0.753), as illustrated in Fig. 4 (a). Notably, CH17 (the other channel covering the right vlPFC in the present montage) showed a directionally similar decrease under low cognitive load, although it did not reach statistical significance. No significant interaction effect was detected under the high cognitive load condition. A comprehensive summary of the ANOVA effects across all channels is presented in Table 3 . Fig. 4. Open in a new tab Results of brain activation. a Post hoc brain activation in low-load TOL. b Changes in the NE score of low-load TOL in CH15 after intervention Neural efficiency index (NE) results in CH15 To further examine the effect of the intervention on neural efficiency, neural efficiency (NE) scores after the intervention were calculated for the channels that exhibited significant activation differences in the main analysis. The t-test on NE scores associated with correct reaction time revealed a significant between-group difference under the low cognitive load condition after the intervention (t = 5.472, p < 0.001, Cohen’s d = 0.876), as illustrated in Fig. 4 (b). Correlation analysis Pearson correlation analyses were conducted to examine associations between behavioral performance and brain activation within the intervention group at post-test (Fig. 5 ). Under the low cognitive load condition, correct RT was positively correlated with CH7 ( r = 0.634, p = 0.015), CH11 ( r = 0.619, p = 0.018), CH15 ( r = 0.595, p = 0.025), and negatively correlated with CH19 ( r = -0.793, p < 0.001). Under high cognitive load, correct RT was correlated with CH9 ( r = -0.608, p = 0.021), CH14 ( r = 0.718, p = 0.004), and CH18 ( r = 0.826, p < 0.001); and error RT was correlated with CH14 ( r = 0.737, p = 0.003). Since accuracy was at the ceiling, no correlation analysis was conducted for accuracy. Fig. 5. Open in a new tab Correlations between brain activation and behavioral performance in the intervention group at the post-test Discussion This study examined the effects of a 10-day mindfulness intervention on cognitive performance and neural processing in elite badminton athletes. Using fNIRS during a cognitively demanding task, we observed load-dependent changes in CH15(right vlPFC) engagement after training, suggesting a pattern of altered prefrontal recruitment that may be more economical under certain task demands. Notably, these neural effects occurred without robust behavioral gains, indicating that neural adaptation may precede observable performance changes. Mindfulness manipulation check The significant increase in total FFMQ scores following the intervention supports the effectiveness of the mindfulness program in enhancing athletes’ overall mindfulness. The FFMQ comprises five facets—Observing, Describing, Acting with Awareness, Non-Judging of Inner Experience, and Non-Reactivity to Inner Experience [ 4 ]. Although none of the individual facets changed significantly, their aggregated improvement yielded a reliable increase in the total score, indicating a more integrated shift in mindful awareness rather than selective gains in any single component. In an exploratory individual-differences analysis, changes in mindfulness were not reliably associated with changes in CH15 response under low cognitive load. This may partly reflect measurement and power considerations in a brief intervention with a small elite sample, where facet-level effects are likely smaller and harder to detect than the total score. Conceptually, it is also possible that short-term training first shifts a more global mindful orientation (state-like awareness) before facet-specific changes become differentiated and measurable with longer practice. This null association warrants replication in larger samples. Global mindful state and neural modulation This pattern is consistent with the characteristics of brief mindfulness interventions. Short-term training may be more likely to induce an overall change in attentional orientation and awareness (i.e., a global mindful state) than to produce detectable, facet-specific changes, which may be more differentiated and potentially more stable and thus require longer practice to shift. Accordingly, the absence of facet-level significance should not be interpreted as a lack of intervention effect; instead, it suggests that early-stage training may first alter general awareness before changes in specific facets become measurable. Importantly, this global shift in mindful awareness may provide a plausible psychological basis for the subsequent neural modulation observed in this study, such as altered response patterns in the vlPFC, reflecting changes in the way athletes allocate and regulate attentional resources under cognitive demand. This interpretation aligns with prior evidence that mindfulness-based programs enhance mental regulation and self-awareness. For instance, an eight-week Mindfulness-Based Stress Reduction program was associated with reduced stress in individuals with chronic medical conditions [ 4 ]. In sport contexts, the Mindful Sport Performance Enhancement (MSPE) program has been linked to performance optimization [ 65 ], and mindfulness practice has been associated with facilitating flow states during competition [ 1 ]. In the present study, the limited sample size and high baseline functioning in elite athletes may have further reduced sensitivity to detect subtle facet-specific changes. Overall, these findings suggest that even brief, structured mindfulness training can yield measurable psychological benefits and may be meaningfully incorporated into athletes’ routines to support psychological readiness and performance potential. vlPFC activation under cognitive load The Tower of London (TOL; Krikorian et al., 1994) assesses executive functions related to planning and problem-solving. In this study, under the low-load condition, the mindfulness group showed reduced activation in the right ventrolateral prefrontal cortex (vlPFC; CH15) at post-test relative to the control group. After FDR correction across 20 channels, CH15 was the only channel showing a significant Group × Time effect; thus, this result should be interpreted as localized and preliminary. This reduced CH15 engagement may reflect lower cognitive effort during task performance and may be compatible with a more economical prefrontal recruitment pattern. The right vlPFC plays a crucial role in inhibitory control, the selection of competing representations, and attentional regulation. These functions are particularly vital for suppressing irrelevant information and maintaining goal-directed behavior [ 1 , 3 , 44 ]. In contrast, dorsal and lateral prefrontal subregions (e.g., dlPFC) are more frequently linked to higher-order maintenance and manipulation of task goals, working memory demands, and multi-step planning processes [ 25 , 26 , 51 ]. Therefore, decreases in activation may carry different functional implications depending on the specific PFC subregion and the task context: for vlPFC under relatively low cognitive load, reduced activation may be compatible with a reduced need for inhibitory/selection control (e.g., less interference, less deliberate top-down filtering), whereas reduced recruitment in dlPFC under high demand could more plausibly reflect insufficient mobilization of control resources. At the same time, an alternative interpretation is that reduced vlPFC response could reflect a diminished capacity or tendency to recruit control when needed, which would be detrimental for performance under pressure. The present data cannot adjudicate between “reduced need” versus “reduced capacity,” particularly given the absence of robust behavioral gains and the low-load specificity of the effect. Future studies should test this distinction by incorporating stress manipulations or sport-relevant high-pressure tasks and examining whether mindfulness preserves performance while reducing vlPFC recruitment under increased demand. This subregional distinction is crucial for interpreting our findings localized to the right vlPFC (CH15) and observed under low load. Within a mindfulness-training context, a plausible mechanistic account is that brief intensive practice may improve attentional stability and reduce susceptibility to distraction or task-irrelevant processing, thereby lowering the need for reactive inhibitory/selection control supported by vlPFC during simpler planning demands [ 21 , 64 ]. Under this interpretation, reduced vlPFC activation would be consistent with a shift toward a more streamlined regulatory mode, in which comparable behavioral output is achieved with less reliance on overt control operations. The body-scan practice repeatedly trains sustained attention to a stable interoceptive target and the rapid reorientation of attention when distraction occurs. Within Lindsay and Creswell’s Mindfulness-to-Meaning (MAT) framework, mindfulness is proposed to strengthen attentional stability and reduce reactive, elaborative processing, thereby supporting more efficient top-down regulation. Applied to planning tasks, enhanced attentional stability may reduce the need for reactive inhibitory/selection control in the right vlPFC under relatively low cognitive load. In the context of this study, the reduced activation in the right vlPFC may reflect a more economical recruitment pattern. However, alternative explanations (e.g., strategy or engagement changes) remain plausible. Both pre- and post-intervention assessments occurred during the same pre-competition phase. All participants were elite athletes from a single training squad with highly regulated daily schedules. This controlled environment minimizes the impact of external variations (e.g., training load, routines) on group differences between measurements. Although motivation and task engagement were not directly assessed, these alternative explanations remain plausible. The attenuated hemodynamic response observed via fNIRS suggests reduced neural resource allocation for the task under low cognitive load, with no significant differences in behavioral performance (Group × Time effects on ACC or RT). This pattern is compatible with a more economical recruitment account, but alternative explanations (e.g., engagement or strategy changes) cannot be ruled out. This interpretation is consistent with prior research showing that mindfulness practice induces structural and functional adaptations that strengthen attentional control, working memory, and executive regulation [ 32 ] [ 31 , 63 ]. Therefore, the observed decrease in prefrontal activation during the TOL task may reflect mindfulness-related modulation of prefrontal processing. Taken together, these findings provide preliminary support for a region- and load-specific pattern that may be compatible with a more economical recruitment interpretation, while warranting caution given the absence of robust behavioral changes. Neural efficiency and brain–behavior links To better understand neural changes, we calculated neural efficiency (NE), a neurobehavioral metric integrating task performance and cortical activity. In neuroergonomics and fNIRS research, NE is commonly operationalized by combining performance metrics with prefrontal activation, such that higher efficiency reflects relatively better task performance achieved with lower cortical recruitment [ 13 , 49 ]. Here, NE was computed using a standardized change-score method, where task performance and CH15 activation were z-transformed into an efficiency score (higher scores indicating better performance–activation synergy) [ 11 ]. Under low cognitive load, CH15 showed reduced activation together with higher NE values in the mindfulness group compared with the control group, suggesting attenuated right vlPFC recruitment alongside preserved group-level performance and providing convergent evidence beyond activation change alone [ 13 ]. However, NE should be interpreted cautiously: because the Group × Time interaction was not significant for ACC/RT and motivational/engagement factors were not directly assessed, reduced activation may also reflect alternative processes (e.g., strategy shifts, changes in task engagement, or effort allocation) rather than neural optimization per se. Future studies should include concurrent indices of engagement/effort (e.g., brief self-report ratings, comprehension checks, or pupillometry) to better differentiate these possibilities. Building on the neural efficiency findings, the correlation analysis provides additional exploratory information regarding the association between CH15 activation and behavioral performance within the intervention group. Specifically, under low cognitive load, CH15 activation was significantly correlated with correct reaction time, indicating that greater vlPFC activation in this channel was associated with slower responding. However, given the small sample size and the fact that this relationship was examined within a single group and a single significant channel, this correlation should be interpreted as descriptive and hypothesis-generating rather than confirmatory. Convergent neurobehavioral patterns suggest a 10-day mindfulness practice influences prefrontal engagement during tasks. This pattern—reduced right vlPFC, elevated NE, and a positive association between CH15 activation and correct RT under low load—aligns with a mindfulness-induced shift in the implementation of attentional control. Contemporary mechanistic accounts propose that mindfulness training primarily targets attention regulation/monitoring and reduces susceptibility to distraction, thereby decreasing the need for reactive inhibitory or selection control when task demands are relatively low (B. K. Hölzel et al., [ 31 ], Lutz, [ 47 ], Malinowski, [ 48 ], Tang, [ 64 ]. Reduced vlPFC activation may indicate fewer interference resolution instances or decreased dependence on effortful top-down filtering under low task demands; however, alternative explanations (e.g., strategy or engagement changes) cannot be excluded. In turn, the NE results suggest that this reduced recruitment occurred alongside preserved group-level performance, consistent with a more economical performance–activation configuration. At a process level, the present findings may therefore be interpreted as reflecting enhanced attentional stability and reduced reactive control demands during simpler planning operations—an early-stage regulatory change that could precede more overt behavioral gains with longer training or more sensitive behavioral measures [ 45 ]. Despite the significant neural changes observed following mindfulness training, no corresponding behavioral differences were detected between the mindfulness and control groups in the Tower of London (TOL) task. Notably, this neural–behavioral dissociation may reflect the temporal dynamics and regional specificity of mindfulness-related effects in elite athletes. This apparent dissociation between neural and behavioral outcomes is not uncommon and can be attributed to several factors. Neurophysiological adaptations often emerge earlier than measurable behavioral changes, reflecting a phase of neural reorganization rather than overt performance enhancement. For example, Picard et al., [ 57 ] demonstrated experience-dependent reductions in energy consumption in trained monkeys performing internally generated motor tasks despite comparable behavioral outputs. Similarly, the reduced activation observed in the right vlPFC in the present study may be compatible with a more economical recruitment interpretation before observable improvements in task performance. Comparable patterns have been reported in both motor learning and cognitive neuroscience research, in which early neural adaptations precede detectable behavioral gains [ 62 ]. From a neuroplasticity perspective, such dissociations may be particularly plausible following brief interventions, as neural regulation and resource allocation processes can be modulated before downstream behavioral gains become detectable [ 69 ]. Moreover, given that the participants were elite athletes with already high baseline cognitive performance, ceiling effects may have limited the sensitivity of behavioral measures to capture subtle improvements [ 15 , 20 ]. Although the Tower of London is a well-established measure of executive functioning, it may lack sufficient granularity to detect small performance changes in individuals operating near their optimal level. In this regard, mean-level RT/ACC may be insufficiently sensitive to detect subtle changes in highly trained populations; future studies could therefore consider complementary behavioral indices (e.g., response-time variability, strategy-related measures, or speed–accuracy trade-offs) to better capture potential adaptation. Laboratory-based planning tasks such as the Tower of London may not be sensitive to subtle cognitive changes that are transferable to sport-specific contexts, where mindfulness-related benefits may be more strongly expressed in attentional stability, emotional regulation, or decision-making under pressure. In addition, the timing of the intervention and testing sessions may have influenced behavioral outcomes, as mindfulness training was conducted in the evening following daily practice, whereas post-intervention testing took place in the morning. Nonetheless, this schedule was selected to balance ecological validity with experimental control, minimizing interference with the athletes’ regular training routines. Overall, the observed dissociation suggests that short-term mindfulness training may primarily influence prefrontal regulatory processes during an early stage of adaptation, and that longer training periods and/or more sensitive behavioral metrics may be required to detect downstream performance changes. Taken together, these factors may explain the absence of significant behavioral effects despite neural indicators potentially compatible with a more economical prefrontal recruitment pattern. If replicated, reduced right vlPFC recruitment under low cognitive load may indicate that athletes require less top-down inhibitory control to maintain task goals in relatively routine cognitive states, potentially reflecting greater attentional steadiness and lower cognitive effort. In applied settings, this could be relevant to pre-competition preparation and between-play moments where athletes must sustain focus and resist distraction. However, transfer to actual sport performance cannot be inferred from the present data; future studies should directly test sport-specific decision-making under pressure and competition-relevant outcomes. Limitations and future directions Even with these valuable insights, several limitations should be noted. First, the sample size was small, which may reduce statistical power and contribute to the relatively large variability observed in reaction time (particularly in the control group at pre-test), potentially reflecting inter-individual differences in response strategies among elite athletes. Second, the 10-day intervention was brief; longer interventions may be required for detectable behavioral improvements. Third, stress/anxiety was not assessed using validated self-report or physiological measures during testing, limiting our ability to examine whether the observed neural modulation was mediated by acute stress reduction. Fourth, potential ceiling effects in elite athletes may have reduced the sensitivity of the Tower of London task to detect subtle behavioral changes. Fifth, although control sessions were supervised, engagement during video viewing was not formally quantified; future work could include brief manipulation checks (e.g., engagement ratings or a short content recall after viewing) to better rule out differential engagement as an alternative explanation. Sixth, while the evening training and morning testing schedule was ecologically valid, it may introduce circadian-related variability in cognitive performance, potentially reducing behavioral sensitivity and contributing to null behavioral findings. Finally, because participants were drawn from a single sport (elite badminton), the generalizability of the findings to other athletic domains remains uncertain. Future studies should recruit larger samples, incorporate concurrent stress/anxiety measures, and consider more demanding or sport-relevant cognitive paradigms. In addition, future work should retain trial-level RT to quantify intra-individual RT variability (e.g., RT SD/ICV), which may be more sensitive than mean RT for detecting cognitive changes in high-performing athletes. Replication across different sports and performance levels will also help establish the robustness and generalizability of these effects. Conclusions This study suggests that a 10-day intensive mindfulness training may be associated with changes in prefrontal hemodynamic responses in elite badminton athletes, reflected in a lower hemodynamic response in the right vlPFC during cognitive task performance under low cognitive load. These findings may be broadly consistent with a neural efficiency interpretation that is specific to the right vlPFC, although the absence of significant behavioral changes warrants cautious interpretation. The results add to evidence on mindfulness-related modulation of task-evoked hemodynamic responses in the right vlPFC (indexed by CH15) during executive task performance. Acknowledgements We want to give special thanks to all the athletes who participated in this research. Abbreviations TOL Tower of London ROI Region of Interest Authors’ contributions Y.T.S: investigation, analysis, data collection, visualization, writing – original draft, writing – review & editing. J.X.Z: data collection, visualization, writing – review & editing.S.G, J.Z, and R.M: data visualization. P.Z: resources and technical support. Y.Z.C: data collection. M.Q.X: supervision, writing – review & editing. Funding The author(s) received no financial support for this article’s research, authorship, and/or publication. Data availability The data supporting the findings of this study are available from the corresponding author upon reasonable request, due to privacy considerations involving the athlete participants. Declarations Ethics approval and consent to participate In accordance with the Declaration of Helsinki, the Ethics Committee of the Guangzhou Sport University approved the study, and written informed consent was obtained from the participants. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Yunting Song and Jiaxiu Zhang contributed equally to this study. References 1. Aherne C, Moran AP, Lonsdale C. The effect of mindfulness training on athletes’ flow: An initial investigation. 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[ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request, due to privacy considerations involving the athlete participants. 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