Enhancing executive functions through online chess training: Evidence from young adults with limited experience

Enhancing executive functions through online chess training: Evidence from young adults with limited experience | 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 Enhancing executive functions through online chess training: Evidence from young adults with limited experience Irene Sperandio, Maria Maddalena Cavadini, Ioanna Markostamou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8829398/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Chess is a cognitively demanding activity that engages executive functions (EFs), including inhibition, cognitive flexibility, planning, and working memory. While long-term chess practice has been associated with functional and structural brain changes, evidence on the effects of chess training in young adults with limited experience remains scarce. Here, we investigated whether structured chess practice improves EFs in neurologically healthy adults aged 18–40 years with little to no prior chess expertise. Sixty participants were randomly assigned to an experimental group (n = 31) or a control group (n = 29). EFs were assessed before (T0) and after (T1) a 10-week intervention using a comprehensive neuropsychological battery, including the Wisconsin Card Sorting Test (WCST), Tower of London (TOL), Stroop Test, Trail Making Test Part B (TMT-B), and the Digit Span forward and backward subtests from the WAIS-IV. During the intervention, the experimental group played online chess for a total of two hours per week, while the control group listened to ASMR or white-noise audio for an equivalent duration. Results showed significant pre- to post-intervention improvements in both groups on the WCST, TOL, and TMT-B, likely reflecting practice effects. Notably, only the experimental group exhibited a significant improvement on the Stroop Test at T1, whereas no change was observed in the control group. These findings provide novel evidence that a 10-week program using a self-administered online chess application selectively enhances EFs related to interference control and cognitive flexibility in young adults, supporting chess as a targeted cognitive intervention for non-expert populations. online chess training young adults novice players executive functions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Chess is a complex cognitive activity that engages multiple high-level processes, including visuospatial perception, working memory, planning, and decision-making (e.g., Chase & Simon, 1973; Holding, 1992; Gobet & Simon, 1996; Bilalić et al., 2010). Over the past decades, growing interest has emerged in understanding the neural and cognitive correlates of chess expertise, as well as its potential role in promoting cognitive plasticity across the lifespan. Neuroanatomical evidence suggests that long-term engagement in chess is associated with measurable brain changes. Structural neuroimaging studies have shown that experienced chess players exhibit reduced grey matter volume and cortical thickness in regions such as the occipito-temporal junction and parietal cortex compared to non-players (Hänggi et al., 2014). In the same study, years of chess experience were found to negatively correlate with caudate nucleus volume, a finding hypothesized to reflect experience-dependent synaptic pruning resulting from intensive cognitive stimulation during development. Converging evidence from other structural MRI studies has reported similar morphological patterns in chess experts, including in frontal, parietal, and visual association areas, as well as subcortical structures such as the caudate nucleus and thalamus (Duan et al., 2012; Ouellette et al., 2020; Wang et al., 2020). White matter differences have also been observed, particularly in the superior longitudinal fasciculus, with higher diffusivity measures associated with chess skill level and training intensity (Hänggi et al., 2014). Taken together, these findings suggest that prolonged chess practice is associated with experience-driven structural brain reorganization, supporting models of neural efficiency and expertise-related plasticity. Functional neuroimaging studies indicate that chess playing relies on a distributed network of brain regions. Previous PET and fMRI investigations have demonstrated consistent activation of occipital, parietal, and frontal areas during chess-related tasks, such as position analysis and checkmate judgment (Nichelli et al., 1994; Atherton et al., 2003). Higher-order executive functions supported by frontal areas, including action initiation and inhibition of behaviour, mental shifting, and updating and monitoring working memory representations, play a pivotal role in the orchestration of goal-directed behaviour, enabling our efficient adaptation to novel or demanding tasks. While frontal activations—especially within the dorsolateral and frontopolar prefrontal cortex—suggest engagement of executive functions, parietal regions appear to play a central role in spatial attention, visuospatial transformation, and the mental simulation of possible moves. Functional connectivity and network-level analyses further indicate that expert performance is supported by coordinated interactions between visual, attentional, and executive systems, rather than by isolated regional activations. Song et al. (2020, 2022) demonstrated increased connectivity among the fusiform gyrus, anterior middle temporal gyrus, and anterior cingulate cortex in chess experts, suggesting more efficient visual-motor transformation and semantic processing in these individuals. Similarly, Langner et al. (2019) reported stronger connectivity between the posterior medial temporal gyrus and regions supporting action planning and visual processing. In addition, research has highlighted the role of the prefrontal cortex (PFC) in decision-making, self-regulation under pressure, and future strategizing, with experts showing greater functional connectivity between the PFC and subcortical structures compared to novices (Leong et al., 2024; Ouellette et al., 2020). Regarding hemispheric lateralization, a recent systematic review by Williams et al. (2025) indicates that chess expertise is associated with distinct neural adaptations, with novices relying more on left-hemisphere, top-down deliberative processing, while experts engage predominantly right-lateralized or bilateral networks to support cognitive focus and complex pattern recognition under high task demands. Importantly, the prevalence of bilateral findings across the reviewed studies suggests that chess expertise reflects the integration of complementary processing modes rather than a simple hemispheric shift, supporting theoretical accounts in which expert performance emerges from coordinated intuitive and deliberative processes rather than from general cognitive ability alone. Beyond its immediate cognitive demands, chess has also been investigated as a potential protective factor against cognitive decline in later life. A scoping review by Lillo-Crespo et al. (2019) reported that chess playing is associated with a reduced risk of dementia in non-diagnosed populations, although similar benefits were not observed in individuals already affected by dementia. These protective effects are commonly attributed to the broader cognitive benefits of engaging in mentally stimulating activities. Supporting this view, a systematic review and meta-analysis by Yates and colleagues (2016) have linked such activities to improvements in memory, processing speed, executive functions, and a reduced risk of age-related cognitive decline. Lifelong engagement in cognitively demanding activities is thought to build cognitive reserve, which may delay the onset and progression of conditions such as mild cognitive impairment and dementia (Wilson et al., 2005; Xu et al., 2020). Consistent with this perspective, recent studies have examined the effects of chess on executive functions (EFs). A pilot intervention by Cibeira et al. (2021) reported improvements in global cognition, attention, processing speed, executive functioning, and quality of life in institutionalized older adults following a structured chess program. Similar benefits in cognitive performance—specifically attention, calculation, recall, and language—as well as quality of life, were observed in elderly women who participated in a 16-week chess intervention combined with resistance training (Vale et al., 2018). However, Pozzi et al. (2025) evaluated a 3-month program of weekly traditional board game sessions, including chess, in older adults at risk of dementia and, consistent with their earlier meta-analysis (Pozzi et al., 2023), found that the benefits of chess were limited to mood and quality of life—particularly in females with mild cognitive impairment—and did not extend to cognitive performance. Complementing these findings, a meta-analysis by Burgoyne et al. (2016) demonstrated significant associations between chess skill and cognitive abilities, including fluid reasoning, short-term memory, and processing speed. Notably, the relationship between chess skill and fluid reasoning was moderated by age and expertise, with stronger associations observed in younger individuals and those with lower proficiency. Taken together, these results suggest that executive and general cognitive abilities play a particularly important role in chess performance during the early stages of skill acquisition, while chess may contribute to cognitive reserve and well-being later in life, even though its effects on cognition in older adults at risk of dementia remain underexplored and inconclusive. Building on this body of evidence, the present study aimed to investigate whether structured chess practice can improve EFs in young adults with limited chess experience. Young adulthood represents a critical period for such an investigation, as EFs and their neural substrates—particularly within the prefrontal cortex—continue to mature into the mid-to-late twenties (e.g., Gilbert & Burgess, 2008). Participants with basic chess knowledge but no formal theoretical training were recruited, consistent with evidence that cognitive abilities exert a stronger influence on chess performance at lower skill levels (Burgoyne et al., 2016). We assessed the most commonly postulated EFs using a comprehensive neuropsychological battery targeting cognitive flexibility, planning, inhibition, working memory, and processing speed. Following baseline assessment (T0), participants in the experimental group engaged in online chess practice for two hours per week over ten weeks, while the control group completed an intervention of matched duration involving ASMR or white-noise audio listening. This control condition was selected because it primarily engages neural systems related to reward and emotional arousal rather than executive processing, as indicated by an fMRI study showing that ASMR stimuli activate reward‑ and emotion‑related regions, such as the nucleus accumbens, insula, and dorsal anterior cingulate cortex (Lochte et al., 2018). Following the training period, all participants completed a post-test assessment (T1) identical to baseline. It was hypothesized that chess training would lead to significant improvements in executive function performance in the experimental group relative to the control group. Methods Participants Sixty-two adult volunteers were initially recruited for the study. However, two participants were excluded from the final sample because they were unable to attend the retest phase (T1). The final sample therefore consisted of 60 participants (30 females and 30 males), ranging in age from 18 to 40 years (Mₐ ge = 23.3, SD = 4.4). This sample size was considered adequate to detect a moderate effect size with α = .05 and statistical power of .80, according to calculations performed using G*Power (Faul et al., 2007 ). Participants were recruited either through advertisements on the SONA System or via WhatsApp. Participants recruited through the SONA System received 200 SONA credits in recognition of their participation, whereas those recruited via WhatsApp received a reimbursement of €20. The inclusion criteria were as follows: participants had to be between 18 and 40 years of age, possess basic knowledge of chess (e.g., how to move the pieces on the chessboard), and have no prior knowledge of chess theory. All participants provided informed consent prior to testing. The study was conducted in accordance with ethical guidelines and approved by the Research Ethics Board of the University of Trento. Design The experiment employed a 2 × 2 mixed design, with a within-subjects independent variable Time (pre-training vs. post-training) and a between-subjects independent variable Group (experimental vs. control). The experimental group consisted of 31 participants (6 females; Mₐ ge = 24.00, SD = 7.01; 25 males; Mₐ ge = 22.28, SD = 1.93), while the control group included 29 participants (24 females; Mₐ ge = 23.75, SD = 5.65; 5 males; Mₐ ge = 25.20, SD = 3.27). Participants were randomly assigned to one of the two groups and completed a 10-week intervention, which involved either playing chess online (experimental group) or listening to ASMR or white-noise audio (control group). Executive functions were assessed at baseline (T0, pre-training) and post-intervention (T1) using a comprehensive neuropsychological battery, including the Wisconsin Card Sorting Test (WCST), Tower of London (TOL), Stroop Test, Trail Making Test—Part B (TMT-B), and the Digit Span Forward and Backward subtests of the WAIS-IV. Neuropsychological Assessment of EFs The neuropsychological battery comprised adapted Italian versions of the following tests: the Wisconsin Card Sorting Test (WCST; Berg, 1948 ; Heaton et al., 1993 ), the short version of the Stroop Test (Caffarra et al., 2002 ), the Tower of London (TOL; Shallice, 1982 ; Bruni et al., 2022 ), the Trail Making Test—Part B (TMT-B; Reitan, 1958 ; Giovagnoli et al., 1996 ), and the Digit Span Forward and Backward subtests from the WAIS-IV (Wechsler, 2013 ). These instruments were selected because they are among the most frequently used and well-validated measures of executive functions in aging research (Faria et al., 2015 ). WCST. The Wisconsin Card Sorting Test (WCST) was originally developed by Berg ( 1948 ) to assess perseveration, cognitive flexibility (i.e., the ability to modify cognitive strategies in response to changing environmental demands), and abstraction. We used the Italian version adapted by Heaton and colleagues ( 1993 ). The test consists of four stimulus cards and 128 response cards varying in colour, shape, and number. Participants are required to match each response card to one of the stimulus cards according to an unannounced sorting rule (colour, shape, or number), receiving feedback after each response. After 10 consecutive correct responses, the sorting criterion changes without warning. The task ends when six categories are correctly completed or when all response cards have been used. There is no time limit. The WCST measures considered as dependent variables in the present study were global score, perseverative responses, non-perseverative errors, and failure to maintain set (Laiacona et al., 2000 ). The global score represents overall task performance and reflects the excess number of trials required to complete six categories relative to the minimum number needed. It is calculated as: number of trials administered − (number of categories completed × 10). Raw scores are adjusted for age and education; a global score below 90.5 (range: 0–128) indicates impaired performance. Perseverative responses were computed based on perseverative principles, with raw scores adjusted for age, gender, and education. The cut-off score for perseverative responses is 42.6 (range: 0–128). The cut-off for non-perseverative errors is 29.9 (range: 0–128), with raw scores adjusted for age and education. Failure to maintain set was defined as an isolated error occurring after at least four consecutive correct responses; the cut-off score for this measure is 3 (range: 0–5) (Laiacona et al., 2000 ). Stroop test. The short Italian version of the Stroop Test (Caffarra et al., 2002 ) was used to assess inhibitory control, selective attention, and cognitive flexibility. The task consists of three conditions: word reading (W), colour naming (C), and colour–word interference (CW). In the W condition, participants read colour words printed in black ink; in the C condition, they name the colour of coloured circles; and in the CW condition, they name the ink colour of incongruent colour words while ignoring the word meaning. Response times were recorded for each condition. The interference effect, calculated from response times, was used as the main outcome measure. It was computed as the difference between the time taken to complete the third condition (T3) and the mean time of the first two conditions (T1 and T2), namely [T3 – mean (T1, T2)]. An interference effect above 36.91 seconds indicates impaired performance. Raw scores were adjusted for age and education (Caffarra et al., 2002 ). TOL. The Tower of London (TOL; Shallice, 1982 ) assesses planning and problem-solving abilities related to the Supervisory Attentional System (Norman & Shallice, 1986 ). In this study, Italian normative data from Bruni et al. ( 2022 ) were used. The apparatus consists of a platform with three sticks of increasing length and three coloured balls (red, blue, and green). Participants were required to reproduce 12 target configurations following standard rules: only one ball could be moved at a time, sticks had limited capacity (one, two, and three balls, respectively), and each configuration had a predefined maximum number of moves (2–5). Three attempts were allowed for each configuration. Accuracy and completion time were scored separately. For accuracy, a problem solved on the first attempt was awarded three points, two points on the second attempt, and one point on the third attempt, yielding a total score range of 0–36. Time was scored using the same 0–36 range: three points were assigned for completion within 15 s, two points for completion within 30 s, one point for completion within 60 s, and zero points for completion times exceeding 60 s. Accuracy and time scores were corrected for age, education, and gender, and cut-off values varied according to age and education level (Bruni et al., 2022 ). TMT-B. The Trail Making Test (TMT; Reitan, 1958 ; Italian version by Giovagnoli et al., 1996 ) was used to assess divided attention, visual search, visuomotor coordination, and cognitive flexibility. In the present study, only Part B was administered. Participants were required to alternately connect numbers (1–13) and letters (A–N, Italian alphabetical order) in ascending order (1–A, 2–B, etc.) without lifting the pencil. Completion time was recorded in seconds, with errors corrected online without interrupting time recording. Scores were adjusted for age and education, and a cut-off of 283 s was applied (Giovagnoli et al., 1996 ). Digit Span Forward and Backward. The Digit Span Forward and Backward subtests of the WAIS-IV (Wechsler, 2013 ) were administered to assess verbal short-term and working memory. Each subtest consists of eight items, with number sequences of increasing length; each item includes two sequences of equal length. In the Digit Span Forward task, participants were required to repeat number sequences in the same order, whereas in the Digit Span Backward task they repeated the sequences in reverse order. Administration was discontinued when both sequences of an item were recalled incorrectly. The maximum sequence length was nine digits for the forward condition and eight digits for the backward condition. Performance was scored as the longest correctly recalled sequence (i.e., memory span) for each subtest and subsequently converted to scaled scores (mean = 10, SD = 3), ranging from 1 to 19. Procedure Prior to testing, participants were provided with an information sheet detailing the study procedures and were required to sign a written consent form. Participants’ executive functions were assessed before (T0) and after (T1) a 10-week intervention. The neuropsychological assessment was conducted in a quiet room equipped with two chairs and a desk and lasted approximately 40–60 minutes. The WCST, the short version of the Stroop test, the TOL, the TMT Part B, and the digit span (forward and backward) were administered in a different order for each participant, counterbalanced across participants. Upon completion of the pre-training session (T0), participants in the experimental group were instructed to install the Chess.com app and play chess online autonomously for a total of 2 hours per week over 10 weeks. In contrast, participants in the control group were instructed to listen to either ASMR or white noise audio for the same amount of time. Participants in the control group were allowed to choose between these two types of audio because some individuals anecdotally report finding ASMR audio annoying, irritating, or even distressing. This response may be linked to individual differences in sensory sensitivity, including misophonia, which is characterized by strong aversive emotional and physiological responses to specific trigger sounds (McGeoch & Rouw, 2020 ). As in the experimental group, control participants were free to decide how to subdivide the weekly listening time. No further instructions were provided regarding how the audio should be listened to. All participants received a weekly email reminder to complete their assigned task during the training phase. After the 10-week intervention, both groups were tested again in the laboratory, and the same neuropsychological battery was administered in the post-training session (T1). Statistical Analyses Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS), version 27 (IBM Corporation; Armonk, NY, USA). GraphPad Prism, version 9.3.1 (GraphPad Software, Inc.; La Jolla, CA, USA), was used to generate the figures. A series of 2 × 2 mixed ANOVAs were performed with Time (T0 = pre-training vs. T1 = post-training) as the within-subjects factor and Group (experimental vs. control) as the between-subjects factor for the following dependent variables: WCST global score, perseverative responses, non-perseverative errors, and failures to maintain set; Stroop interference time; time and accuracy on the TOL; completion time on the TMT-B; and Digit Span Forward and Backward scores. Significant interaction effects were further examined using post-hoc pairwise comparisons with Bonferroni correction. Results WCST Global score, perseverative responses, non-perseverative errors, and failures to maintain set from the WCST were calculated for each participant. Figure 1 shows WCST global scores for the experimental and control groups at T0 and T1. A decrease in global scores was observed at T1 in both the experimental (mean pre–post difference = -10) and control (mean pre–post difference = -8.41) groups, indicating improved performance over time, likely due to practice effects. A 2 × 2 mixed ANOVA was conducted on WCST global scores, with Time (T0 vs. T1) and Group (experimental vs. control) as main factors. The analysis revealed a significant main effect of Time (F (1, 58) = 12.25, p < .001, ηp² = .17), as well as a significant main effect of Group (F (1, 58) = 8.23, p < .01, ηp² = .12). However, the Time × Group interaction was not significant (F (1, 58) = .09, p = .76, ηp² = .00). The same ANOVAs conducted on perseverative responses, non-perseverative errors, and failure to maintain set yielded similar patterns. For clarity, the results of all WCST-related ANOVAs are summarized in Table 1 . Table 1 Results of the 2 x 2 mixed ANOVAs on the WCST dependent variables. Measure Group Mean (SD) Pre Post F (1,58) p ηp² Perseverative Responses Time Group Time x Group Experimental Control 26.63(10.57) 20.41(7.44) 31.85(16.05) 26.72(17.15) 14.95 3.45 0.14 < .001 .07 .71 .21 .06 .00 Non-Perseverative Errors Time Group Time x Group Experimental Control 11.41(9.65) 7.24(2.40) 17.32(14.63) 14.25(13.11) 7.62 6.72 .18 < .01 .01 .67 .12 .10 .00 Failure to Maintain Set Time Group Time x Group Experimental Control 0.35(0.66) 0.29(0.74) 0.38(0.86) 0.48(0.78) .03 .50 .47 .87 .48 .49 .00 .01 .01 Stroop Test The interference effect (in seconds) was calculated for each participant. Figure 2 shows mean interference effects for the experimental and control groups at T0 and T1. A reduction in interference was observed at T1 for the experimental group (mean pre–post difference = -1.73 s), whereas the control group showed no improvement (mean pre–post difference = + 0.31 s). A 2 x 2 mixed ANOVA with Time (T0 vs. T1) and Group (experimental vs. control) revealed no effect of Time (F (1,58) = 2.43, p = .12, ηp² = .04), nor Group (F (1,58) = .15, p = .69, ηp² = .00). In contrast, the Time × Group interaction was significant (F (1,58) = 4.56, p = .04, ηp² = .07). Post-hoc comparisons with Bonferroni correction showed a significant reduction in interference between pre- and post-intervention for the experimental group (p corr = .01), but not for the control group (p corr = .70), supporting the efficacy of the chess intervention on EFs. TOL Accuracy and completion time were recorded for each participant. Figure 3 illustrates mean completion time and accuracy for the experimental and control groups at T0 and T1. An increase in completion time was observed at T1 in both the experimental (mean pre–post difference = + 1.41) and control (mean pre–post difference = + 1.76) groups, indicating slower performance at retest. A 2 × 2 mixed ANOVA with Time (T0 vs. T1) and Group (experimental vs. control) revealed a significant main effect of Time (F (1, 58) = 16.35, p < .001, ηp² = .22), as well as a significant main effect of Group (F (1, 58) = 5.48, p = .02, ηp² = .09). The Time × Group interaction was not significant (F (1, 58) = 0.19, p = .67, ηp² = .00) (Fig. 3 a). Similar results were observed for accuracy. Both groups showed improved accuracy at T1 (experimental group: mean pre–post difference = + 0.79; control group: mean pre–post difference = + 1.42). The 2 × 2 ANOVA revealed a significant main effect of Time (F (1, 58) = 11.05, p < .01, ηp² = .16), whereas neither the main effect of Group (F (1, 58) = 0.70, p = .41, ηp² = .01), nor the Time × Group interaction (F (1, 58) = .22, p = .64, ηp² = .00), reached significance (Fig. 3 b). The increase in response time coupled with improved accuracy suggests a speed–accuracy trade-off rather than a practice effect. This hypothesis was further examined using a combined performance measure, the inverse efficiency score (IES; Townsend & Ashby, 1983 ), calculated by dividing response time by the proportion of correct responses. This index accounts for speed–accuracy trade-offs, with lower IES values indicating faster responses with fewer errors, and higher values indicating slower responses with more errors. Consistent with this interpretation, the 2 × 2 mixed ANOVA on IES revealed no significant main effect of Time (F (1, 58) = 0.60, p = .44, ηp² = .01). Likewise, neither the main effect of Group nor the Time × Group interaction reached significance (F (1, 58) = 0.02, p = .89, ηp² = .00, F (1, 58) = 2.67, p = .11, ηp² = .04, respectively) (Fig. 3 c). TMT-B Figure 4 shows mean completion time (s) on the TMT-B for the experimental and control groups at T0 and T1. A reduction in completion time was observed at T1 in both the experimental group (mean pre-post difference = -12.89 s) and the control group (mean pre-post difference = -13.27 s). A 2 × 2 mixed ANOVA with Time (T0 vs. T1) and Group (experimental vs. control) revealed a significant main effect of Time (F (1, 58) = 16.68, p < .001, ηp² = .22). Neither the main effect of Group (F (1, 58) = 1.26, p = .27, ηp² = .02), nor the Time × Group interaction (F (1, 58) = 0.004, p = .95, ηp² = .00) reached significance. Digit Forward and Backward (WAIS-IV) Digit Span Forward and Backward were used to assess short-term and working memory spans before and after the training period. Figure 5 shows mean Digit Span Forward and Digit Span Backward scores for the experimental and control groups at T0 and T1. For Digit Span Forward, a slight increase in span was observed in both groups, with a larger change in the experimental group (mean pre–post difference = + 0.97) than in the control group (mean pre–post difference = + 0.07). However, a 2 × 2 mixed ANOVA with Time (T0 vs. T1) and Group (experimental vs. control) revealed no significant main effect of Time (F (1, 58) = 2.35, p = .13, ηp² = .04), nor Group (F (1, 58) = 3.17, p = .08, ηp² = .05). The Time × Group interaction was also not significant (F (1, 58) = 1.77, p = .19, ηp² = .03) (Fig. 5 a). Similarly, for Digit Span Backward, both groups showed modest improvements at the retest (experimental group: mean pre–post difference = + 0.58; control group: mean pre–post difference = + 0.52). The corresponding 2 × 2 mixed ANOVA revealed no significant main effects of Time (F (1, 58) = 2.78, p = .10, ηp² = .05) or Group (F (1, 58) = 0.15, p = .70, ηp² = .00), and no significant Time × Group interaction (F (1, 58) = 0.01, p = .92, ηp² = .00) (Fig. 5 b). Discussion The primary aim of the present study was to examine whether a 10-week chess-based training program could enhance EFs in a neurologically healthy population of young adults with little to no prior chess experience. Participants were randomly assigned either to a chess-training group, which engaged in online chess practice for two hours per week over 10 weeks (totalling 20 hours), or to an active control group that listened to ASMR/white-noise audio for the same duration. Focusing on individuals with minimal chess experience was motivated by prior findings indicating a strong association between EFs and chess ability primarily among lower-skill players (Burgoyne et al., 2016 ). This approach was intended to maximize the likelihood of detecting cognitive changes attributable to chess practice rather than to pre-existing expertise. The duration of the intervention was based on the study by Cibeira et al. ( 2021 ), which reported EF improvements following a 24-hour chess training program in institutionalized older adults. Overall, the findings indicate that, relative to the control group, chess-based training led to selective improvements in executive functioning, rather than a generalized enhancement across all EF domains. Specifically, only participants in the experimental group showed significant pre–post improvement on the Stroop task, reflecting gains in attention and inhibitory control, whereas no comparable improvements were observed in the control group. In contrast, no training-specific effects emerged for other EF measures, including working memory, planning, or cognitive flexibility, as assessed by the WCST, TOL, TMT-B, and Digit Span tasks. These results suggest that engaging in chess practice with the frequency adopted in the present study may particularly benefit attention and interference control, rather than executive functioning as a whole. The selective improvement observed on the Stroop task aligns with the cognitive demands of chess, particularly for novice players. Chess requires sustained attention, response inhibition, and the resolution of competing alternatives during move evaluation—processes that closely overlap with those measured by the Stroop task. This interpretation is supported by previous evidence: an ERP study by West and Alain ( 1999 ) showed that Stroop task performance engages frontal, prefrontal, and parietal regions, which are similarly activated in unskilled chess players during gameplay (Onofrj et al., 1995 ; Atherton et al., 2003 ). The overlap in neural recruitment may help explain why the effects of chess training observed here were domain-specific rather than generalized. The present findings are broadly consistent with prior work demonstrating beneficial effects of chess-based interventions on attention and executive processes. Notably, Cibeira et al. ( 2021 ) reported improvements in attention, processing speed, and executive functions (EFs) following a 24-hour chess training program in older institutionalized adults. While methodological differences exist between that study and the present one—including participant age, health status, training format (theory-based lessons versus online self-administered play), and monitoring of adherence—both investigations converge in suggesting that chess training can positively affect attentional and executive processes. Evidence from other populations further supports the relationship between chess training and EF enhancement. Studies in children and adolescents have reported improvements in working memory, inhibition, planning, and decision-making following chess-based interventions (Oberoi, 2021 ). Developmental research has also highlighted gains in specific EF components, including visuospatial working memory (Yakushina et al., 2025 ), and verbal working memory, alongside decision-making abilities (Oberoi, 2021 ). More recently, Pham and Dao ( 2025 ) reported that an 8-week blended intervention, combining traditional face-to-face instruction with computer-mediated e-learning, led to improvements in focused attention, auditory word memory, and academic performance in primary school children. Our results also resonate with findings from young adult populations. Dania et al. ( 2023 ) reported improvements in working memory and selective attention following a 10-week chess intervention in college invasion-game athletes. It should be noted, however, that their control group was passive, making it unclear whether the observed benefits reflected the intervention itself or differences in engagement, motivation, or expectations between groups. Moreover, their conclusions were limited by a small and highly specific sample. Notably, the current study extends this body of evidence to young neurotypical adults and provides, to our knowledge, the first demonstration of EF improvements in this population using a self-administered online chess application. Unlike previous interventions based on structured chess lessons, the present training relied exclusively on active gameplay, highlighting the ecological validity and scalability of this approach. The absence of chess-specific improvements on the WCST, TOL, and TMT-B in the present study likely reflects practice effects associated with repeated testing. Indeed, both experimental and control groups showed comparable pre- to post-training improvements on these measures, consistent with well-documented test–retest learning effects in EF tasks (Basso et al., 2001 ; Lemay et al., 2004 ; Buck et al., 2008 ). These findings highlight a longstanding methodological challenge in EF research, namely the limited test–retest reliability of traditional neuropsychological measures, particularly the WCST (Rabbitt, 1997 ). Interestingly, no practice effects were observed on the Digit Span tasks, diverging from earlier reports of memory span improvement (Taub, 1973 ). This pattern suggests that working memory performance may be less sensitive to short-term retesting effects in this context, or it may reflect task-specific factors or ceiling effects in the present sample. Beyond healthy populations, the present findings also align with the clinical literature on chess-based interventions for Attention Deficit Hyperactivity Disorder (ADHD), a condition marked by deficits in attention and inhibitory control. Reviews and intervention studies have reported reductions in ADHD symptom severity in children and adolescents following chess training, particularly in domains related to attention and inhibitory control (Blasco-Fontecilla et al., 2016 ; Agarwal et al., 2023). Although ADHD was not examined in the current study, the overlap in affected cognitive processes further supports the link between chess practice and inhibitory control mechanisms, suggesting that chess training may be especially effective in targeting these executive domains. In fact, strong inhibitory control has been linked to multiple cognitive and behavioural benefits in youth, including enhanced self-regulation and reduced impulsivity (Nigg, 2016), as well as better control over potentially addictive behaviours (Morein-Zamir & Robbins, 2015 ). The potential applicability of chess-based interventions to clinical populations should be directly investigated in future research. Limitations and Future Directions Several limitations should be acknowledged. First, the rationale for the training duration was based on findings from a small sample of older adults (Cibeira et al., 2021 ), whose neural plasticity likely differs from that of younger adults. Second, adherence to the training protocols could not be directly monitored, particularly given the self-administered nature of both the experimental and control interventions. Third, the sample was predominantly composed of university students, limiting the generalizability of the findings to the broader population of young adults. Moreover, participants continued their academic activities during the intervention period, which may have independently influenced EF performance, as engagement in cognitively stimulating activities has been linked to executive functioning improvements (Yates et al., 2016 ). Future research should examine whether shorter or longer chess training programs yield comparable or broader cognitive benefits, and whether increasing training intensity leads to improvements across additional EF domains. Another promising direction involves the development of chess-inspired neurorehabilitation programs for populations with executive dysfunction, particularly individuals with frontal lobe damage or other conditions affecting fronto-executive networks. Given its intrinsically motivating and engaging nature, chess may enhance adherence to rehabilitation protocols while selectively targeting core executive processes. Conclusions The present study investigated whether regular chess practice can enhance executive functions in neurologically healthy young adults with minimal chess experience. The results demonstrate that a 10-week chess training program, practiced with moderate frequency, selectively improves attention and inhibitory control, as assessed by the Stroop test, while no training-specific effects were observed for other executive domains—likely reflecting practice effects associated with repeated testing. These findings suggest that chess represents an accessible, engaging, and ecologically valid cognitive activity capable of enhancing specific executive processes in young adults. By contributing to the growing literature on cognitively stimulating activities as tools for EF enhancement, this study highlights the potential of chess as a low-cost intervention. Further research is needed to replicate these results, optimize training protocols, and explore the applicability of chess-based interventions across diverse populations and clinical contexts. Declarations Funding No funding was received for conducting this study. Conflict of interest The authors have no competing interests to declare that are relevant to the content of this article. Ethics approval The study received the appropriate ethical authorization from a local ethical committee. Consent to participate Written informed consent was obtained from all participants involved in this study. Consent for publication All participants provided written informed consent for the anonymous publication of their data, with the assurance that no personal identifiers would be disclosed. Data Availability Data are available from the authors upon reasonable request. Authors’ contributions Irene Sperandio: Conceptualization; Data Curation; Formal Analysis; Methodology; Resources; Supervision; Visualization; Writing – Original Draft Preparation. Maria Maddalena Cavadini: Conceptualization; Data Curation; Investigation; Methodology; Writing – Review & Editing. Ioanna Markostamou: Conceptualization; Methodology; Writing – Review & Editing. 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International Psychogeriatrics , 28 (11), 1791–1806. https://doi.org/10.1017/S1041610216001137 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 10 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 09 Feb, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8829398","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592118174,"identity":"e7ef46a7-ebe4-402c-991a-9f0457b22f25","order_by":0,"name":"Irene Sperandio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACNhQejwGDHAMDYwOQKUG8FmOglkagHgncelC1MDAkNhCyhk/68LEHHxjq8vilDz978KbAJn3D7eb2xwUMFnU4HcaXlm44g+FwsWRfmrnhHIO03A13DjY2z8DjMDYeHjNpHoYDiRvOMAAZBodzN9xIbGzmwauF/5v0H4a6xP1n2L+BtKQbENbCwybNwMCcuAFsncHhBCK0sJlJ9hgcLpY4w1MmCfSL4Uygltk8BhKSDTi0yPcwP5P4UQEMsR72bRJv/tjI891If/CZp6KOH5ctEGDAkIAhQhCgaxkFo2AUjIJRgAAA601JlaQIqaIAAAAASUVORK5CYII=","orcid":"","institution":"University of Trento","correspondingAuthor":true,"prefix":"","firstName":"Irene","middleName":"","lastName":"Sperandio","suffix":""},{"id":592118175,"identity":"56edd7f3-be50-4ffd-a5dc-e4f7cafab238","order_by":1,"name":"Maria Maddalena Cavadini","email":"","orcid":"","institution":"University of Trento","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Maddalena","lastName":"Cavadini","suffix":""},{"id":592118176,"identity":"7e40604e-4cca-424e-abf9-dbae60eac4b5","order_by":2,"name":"Ioanna Markostamou","email":"","orcid":"","institution":"Bournemouth University","correspondingAuthor":false,"prefix":"","firstName":"Ioanna","middleName":"","lastName":"Markostamou","suffix":""}],"badges":[],"createdAt":"2026-02-09 10:54:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8829398/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8829398/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102893425,"identity":"b94f7e45-2f41-4cb4-964c-9e173759d569","added_by":"auto","created_at":"2026-02-18 05:48:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWCST global score.\u003c/strong\u003e Mean global score as a function of Time (T0 vs. T1) and Group (experimental = purple; control = green). 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Asterisks* denote significance at p\u003csub\u003ecorr\u003c/sub\u003e = .01.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8829398/v1/384f2e5805d8b5d6a71de1dd.jpeg"},{"id":102964461,"identity":"6e17e7ab-f565-43a0-ae70-5bed9b3cba87","added_by":"auto","created_at":"2026-02-19 04:22:21","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTOL.\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eMean completion time (a), accuracy (b), and inverse efficiency score (IEA) (c) as a function of Time (T0 vs. T1) and Group (experimental = purple; control = green). Error bars correspond to standard error of the mean (SEM).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8829398/v1/ae737744afc0165121b40a34.jpeg"},{"id":102893426,"identity":"d5f27a02-2163-4344-b1f2-6fbbfdbb9496","added_by":"auto","created_at":"2026-02-18 05:48:14","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTMT-B completion time.\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eMean time taken to complete the TMT-B as a function of Time (T0 vs. T1) and Group (experimental = purple; control = green). Error bars correspond to standard error of the mean (SEM).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8829398/v1/ae58764e46ff6e769ef867a1.jpeg"},{"id":102893428,"identity":"a59d8ad8-f67f-4a80-9dec-950f83d4039a","added_by":"auto","created_at":"2026-02-18 05:48:14","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDigit span.\u003c/strong\u003e Mean Digit Span Forward (a) and Digit Span Backward (b) scores as a function of Time (T0 vs. T1) and Group (experimental = purple; control = green). Error bars represent the standard error of the mean (SEM).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8829398/v1/ab3e0927bd11ea4e5d1f8107.jpeg"},{"id":103050984,"identity":"fcb78a6c-c66f-4501-8845-fb467bf49f06","added_by":"auto","created_at":"2026-02-20 07:57:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":935486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8829398/v1/ae829b08-cdb5-4867-86b5-fdd3558ab6b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing executive functions through online chess training: Evidence from young adults with limited experience","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChess is a complex cognitive activity that engages multiple high-level processes, including visuospatial perception, working memory, planning, and decision-making (e.g., Chase \u0026amp; Simon, 1973; Holding, 1992; Gobet \u0026amp; Simon, 1996; Bilalić et al., 2010). Over the past decades, growing interest has emerged in understanding the neural and cognitive correlates of chess expertise, as well as its potential role in promoting cognitive plasticity across the lifespan. Neuroanatomical evidence suggests that long-term engagement in chess is associated with measurable brain changes. Structural neuroimaging studies have shown that experienced chess players exhibit reduced grey matter volume and cortical thickness in regions such as the occipito-temporal junction and parietal cortex compared to non-players (H\u0026auml;nggi et al., 2014). In the same study, years of chess experience were found to negatively correlate with caudate nucleus volume, a finding hypothesized to reflect experience-dependent synaptic pruning resulting from intensive cognitive stimulation during development. Converging evidence from other structural MRI studies has reported similar morphological patterns in chess experts, including in frontal, parietal, and visual association areas, as well as subcortical structures such as the caudate nucleus and thalamus (Duan et al., 2012; Ouellette et al., 2020; Wang et al., 2020). White matter differences have also been observed, particularly in the superior longitudinal fasciculus, with higher diffusivity measures associated with chess skill level and training intensity (H\u0026auml;nggi et al., 2014). Taken together, these findings suggest that prolonged chess practice is associated with experience-driven structural brain reorganization, supporting models of neural efficiency and expertise-related plasticity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional neuroimaging studies indicate that chess playing relies on a distributed network of brain regions. Previous PET and fMRI investigations have demonstrated consistent activation of occipital, parietal, and frontal areas during chess-related tasks, such as position analysis and checkmate judgment (Nichelli et al., 1994; Atherton et al., 2003). Higher-order executive functions supported by frontal areas, including action initiation and inhibition of behaviour, mental shifting, and updating and monitoring working memory representations, play a pivotal role in the orchestration of goal-directed behaviour, enabling our efficient adaptation to novel or demanding tasks. While frontal activations\u0026mdash;especially within the dorsolateral and frontopolar prefrontal cortex\u0026mdash;suggest engagement of executive functions, parietal regions appear to play a central role in spatial attention, visuospatial transformation, and the mental simulation of possible moves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional connectivity and network-level analyses further indicate that expert performance is supported by coordinated interactions between visual, attentional, and executive systems, rather than by isolated regional activations. Song et al. (2020, 2022) demonstrated increased connectivity among the fusiform gyrus, anterior middle temporal gyrus, and anterior cingulate cortex in chess experts, suggesting more efficient visual-motor transformation and semantic processing in these individuals. Similarly, Langner et al. (2019) reported stronger connectivity between the posterior medial temporal gyrus and regions supporting action planning and visual processing. In addition, research has highlighted the role of the prefrontal cortex (PFC) in decision-making, self-regulation under pressure, and future strategizing, with experts showing greater functional connectivity between the PFC and subcortical structures compared to novices (Leong et al., 2024; Ouellette et al., 2020). Regarding hemispheric lateralization, a recent systematic review by Williams et al. (2025) indicates that chess expertise is associated with distinct neural adaptations, with novices relying more on left-hemisphere, top-down deliberative processing, while experts engage predominantly right-lateralized or bilateral networks to support cognitive focus and complex pattern recognition under high task demands. Importantly, the prevalence of bilateral findings across the reviewed studies suggests that chess expertise reflects the integration of complementary processing modes rather than a simple hemispheric shift, supporting theoretical accounts in which expert performance emerges from coordinated intuitive and deliberative processes rather than from general cognitive ability alone.\u003c/p\u003e\n\u003cp\u003eBeyond its immediate cognitive demands, chess has also been investigated as a potential protective factor against cognitive decline in later life. A scoping review by Lillo-Crespo et al. (2019) reported that chess playing is associated with a reduced risk of dementia in non-diagnosed populations, although similar benefits were not observed in individuals already affected by dementia. These protective effects are commonly attributed to the broader cognitive benefits of engaging in mentally stimulating activities. Supporting this view, a systematic review and meta-analysis by Yates and colleagues (2016) have linked such activities to improvements in memory, processing speed, executive functions, and a reduced risk of age-related cognitive decline. Lifelong engagement in cognitively demanding activities is thought to build cognitive reserve, which may delay the onset and progression of conditions such as mild cognitive impairment and dementia (Wilson et al., 2005; Xu et al., 2020). Consistent with this perspective, recent studies have examined the effects of chess on executive functions (EFs). A pilot intervention by Cibeira et al. (2021) reported improvements in global cognition, attention, processing speed, executive functioning, and quality of life in institutionalized older adults following a structured chess program. Similar benefits in cognitive performance\u0026mdash;specifically attention, calculation, recall, and language\u0026mdash;as well as quality of life, were observed in elderly women who participated in a 16-week chess intervention combined with resistance training (Vale et al., 2018). However, Pozzi et al. (2025) evaluated a 3-month program of weekly traditional board game sessions, including chess, in older adults at risk of dementia and, consistent with their earlier meta-analysis (Pozzi et al., 2023), found that the benefits of chess were limited to mood and quality of life\u0026mdash;particularly in females with mild cognitive impairment\u0026mdash;and did not extend to cognitive performance. Complementing these findings, a meta-analysis by Burgoyne et al. (2016) demonstrated significant associations between chess skill and cognitive abilities, including fluid reasoning, short-term memory, and processing speed. Notably, the relationship between chess skill and fluid reasoning was moderated by age and expertise, with stronger associations observed in younger individuals and those with lower proficiency. Taken together, these results suggest that executive and general cognitive abilities play a particularly important role in chess performance during the early stages of skill acquisition, while chess may contribute to cognitive reserve and well-being later in life, even though its effects on cognition in older adults at risk of dementia remain underexplored and inconclusive.\u003c/p\u003e\n\u003cp\u003eBuilding on this body of evidence, the present study aimed to investigate whether structured chess practice can improve EFs in young adults with limited chess experience. Young adulthood represents a critical period for such an investigation, as EFs and their neural substrates\u0026mdash;particularly within the prefrontal cortex\u0026mdash;continue to mature into the mid-to-late twenties (e.g., Gilbert \u0026amp; Burgess, 2008). Participants with basic chess knowledge but no formal theoretical training were recruited, consistent with evidence that cognitive abilities exert a stronger influence on chess performance at lower skill levels (Burgoyne et al., 2016). We assessed the most commonly postulated EFs using a comprehensive neuropsychological battery targeting cognitive flexibility, planning, inhibition, working memory, and processing speed. Following baseline assessment (T0), participants in the experimental group engaged in online chess practice for two hours per week over ten weeks, while the control group completed an intervention of matched duration involving ASMR or white-noise audio listening. This control condition was selected because it primarily engages neural systems related to reward and emotional arousal rather than executive processing, as indicated by an fMRI study showing that ASMR stimuli activate reward‑ and emotion‑related regions, such as the nucleus accumbens, insula, and dorsal anterior cingulate cortex (Lochte et al., 2018). Following the training period, all participants completed a post-test assessment (T1) identical to baseline. It was hypothesized that chess training would lead to significant improvements in executive function performance in the experimental group relative to the control group.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eSixty-two adult volunteers were initially recruited for the study. However, two participants were excluded from the final sample because they were unable to attend the retest phase (T1). The final sample therefore consisted of 60 participants (30 females and 30 males), ranging in age from 18 to 40 years (Mₐ\u003csub\u003ege\u003c/sub\u003e = 23.3, SD\u0026thinsp;=\u0026thinsp;4.4). This sample size was considered adequate to detect a moderate effect size with α\u0026thinsp;=\u0026thinsp;.05 and statistical power of .80, according to calculations performed using G*Power (Faul et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Participants were recruited either through advertisements on the SONA System or via WhatsApp. Participants recruited through the SONA System received 200 SONA credits in recognition of their participation, whereas those recruited via WhatsApp received a reimbursement of \u0026euro;20. The inclusion criteria were as follows: participants had to be between 18 and 40 years of age, possess basic knowledge of chess (e.g., how to move the pieces on the chessboard), and have no prior knowledge of chess theory. All participants provided informed consent prior to testing. The study was conducted in accordance with ethical guidelines and approved by the Research Ethics Board of the University of Trento.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThe experiment employed a 2 \u0026times; 2 mixed design, with a within-subjects independent variable Time (pre-training vs. post-training) and a between-subjects independent variable Group (experimental vs. control). The experimental group consisted of 31 participants (6 females; Mₐ\u003csub\u003ege\u003c/sub\u003e= 24.00, SD\u0026thinsp;=\u0026thinsp;7.01; 25 males; Mₐ\u003csub\u003ege\u003c/sub\u003e = 22.28, SD\u0026thinsp;=\u0026thinsp;1.93), while the control group included 29 participants (24 females; Mₐ\u003csub\u003ege\u003c/sub\u003e = 23.75, SD\u0026thinsp;=\u0026thinsp;5.65; 5 males; Mₐ\u003csub\u003ege\u003c/sub\u003e = 25.20, SD\u0026thinsp;=\u0026thinsp;3.27). Participants were randomly assigned to one of the two groups and completed a 10-week intervention, which involved either playing chess online (experimental group) or listening to ASMR or white-noise audio (control group). Executive functions were assessed at baseline (T0, pre-training) and post-intervention (T1) using a comprehensive neuropsychological battery, including the Wisconsin Card Sorting Test (WCST), Tower of London (TOL), Stroop Test, Trail Making Test\u0026mdash;Part B (TMT-B), and the Digit Span Forward and Backward subtests of the WAIS-IV.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNeuropsychological Assessment of EFs\u003c/h3\u003e\n\u003cp\u003eThe neuropsychological battery comprised adapted Italian versions of the following tests: the Wisconsin Card Sorting Test (WCST; Berg, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1948\u003c/span\u003e; Heaton et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), the short version of the Stroop Test (Caffarra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), the Tower of London (TOL; Shallice, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Bruni et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the Trail Making Test\u0026mdash;Part B (TMT-B; Reitan, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1958\u003c/span\u003e; Giovagnoli et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), and the Digit Span Forward and Backward subtests from the WAIS-IV (Wechsler, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These instruments were selected because they are among the most frequently used and well-validated measures of executive functions in aging research (Faria et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eWCST.\u003c/em\u003e The Wisconsin Card Sorting Test (WCST) was originally developed by Berg (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1948\u003c/span\u003e) to assess perseveration, cognitive flexibility (i.e., the ability to modify cognitive strategies in response to changing environmental demands), and abstraction. We used the Italian version adapted by Heaton and colleagues (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The test consists of four stimulus cards and 128 response cards varying in colour, shape, and number. Participants are required to match each response card to one of the stimulus cards according to an unannounced sorting rule (colour, shape, or number), receiving feedback after each response. After 10 consecutive correct responses, the sorting criterion changes without warning. The task ends when six categories are correctly completed or when all response cards have been used. There is no time limit. The WCST measures considered as dependent variables in the present study were global score, perseverative responses, non-perseverative errors, and failure to maintain set (Laiacona et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The global score represents overall task performance and reflects the excess number of trials required to complete six categories relative to the minimum number needed. It is calculated as: number of trials administered \u0026minus; (number of categories completed \u0026times; 10). Raw scores are adjusted for age and education; a global score below 90.5 (range: 0\u0026ndash;128) indicates impaired performance. Perseverative responses were computed based on perseverative principles, with raw scores adjusted for age, gender, and education. The cut-off score for perseverative responses is 42.6 (range: 0\u0026ndash;128). The cut-off for non-perseverative errors is 29.9 (range: 0\u0026ndash;128), with raw scores adjusted for age and education. Failure to maintain set was defined as an isolated error occurring after at least four consecutive correct responses; the cut-off score for this measure is 3 (range: 0\u0026ndash;5) (Laiacona et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eStroop test.\u003c/em\u003e The short Italian version of the Stroop Test (Caffarra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) was used to assess inhibitory control, selective attention, and cognitive flexibility. The task consists of three conditions: word reading (W), colour naming (C), and colour\u0026ndash;word interference (CW). In the W condition, participants read colour words printed in black ink; in the C condition, they name the colour of coloured circles; and in the CW condition, they name the ink colour of incongruent colour words while ignoring the word meaning. Response times were recorded for each condition. The interference effect, calculated from response times, was used as the main outcome measure. It was computed as the difference between the time taken to complete the third condition (T3) and the mean time of the first two conditions (T1 and T2), namely [T3 \u0026ndash; mean (T1, T2)]. An interference effect above 36.91 seconds indicates impaired performance. Raw scores were adjusted for age and education (Caffarra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cem\u003eTOL.\u003c/em\u003e The Tower of London (TOL; Shallice, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) assesses planning and problem-solving abilities related to the Supervisory Attentional System (Norman \u0026amp; Shallice, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). In this study, Italian normative data from Bruni et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were used. The apparatus consists of a platform with three sticks of increasing length and three coloured balls (red, blue, and green). Participants were required to reproduce 12 target configurations following standard rules: only one ball could be moved at a time, sticks had limited capacity (one, two, and three balls, respectively), and each configuration had a predefined maximum number of moves (2\u0026ndash;5). Three attempts were allowed for each configuration. Accuracy and completion time were scored separately. For accuracy, a problem solved on the first attempt was awarded three points, two points on the second attempt, and one point on the third attempt, yielding a total score range of 0\u0026ndash;36. Time was scored using the same 0\u0026ndash;36 range: three points were assigned for completion within 15 s, two points for completion within 30 s, one point for completion within 60 s, and zero points for completion times exceeding 60 s. Accuracy and time scores were corrected for age, education, and gender, and cut-off values varied according to age and education level (Bruni et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eTMT-B.\u003c/em\u003e The Trail Making Test (TMT; Reitan, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1958\u003c/span\u003e; Italian version by Giovagnoli et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) was used to assess divided attention, visual search, visuomotor coordination, and cognitive flexibility. In the present study, only Part B was administered. Participants were required to alternately connect numbers (1\u0026ndash;13) and letters (A\u0026ndash;N, Italian alphabetical order) in ascending order (1\u0026ndash;A, 2\u0026ndash;B, etc.) without lifting the pencil. Completion time was recorded in seconds, with errors corrected online without interrupting time recording. Scores were adjusted for age and education, and a cut-off of 283 s was applied (Giovagnoli et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eDigit Span Forward and Backward.\u003c/em\u003e The Digit Span Forward and Backward subtests of the WAIS-IV (Wechsler, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) were administered to assess verbal short-term and working memory. Each subtest consists of eight items, with number sequences of increasing length; each item includes two sequences of equal length. In the Digit Span Forward task, participants were required to repeat number sequences in the same order, whereas in the Digit Span Backward task they repeated the sequences in reverse order. Administration was discontinued when both sequences of an item were recalled incorrectly. The maximum sequence length was nine digits for the forward condition and eight digits for the backward condition. Performance was scored as the longest correctly recalled sequence (i.e., memory span) for each subtest and subsequently converted to scaled scores (mean\u0026thinsp;=\u0026thinsp;10, SD\u0026thinsp;=\u0026thinsp;3), ranging from 1 to 19.\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e Prior to testing, participants were provided with an information sheet detailing the study procedures and were required to sign a written consent form. Participants\u0026rsquo; executive functions were assessed before (T0) and after (T1) a 10-week intervention. The neuropsychological assessment was conducted in a quiet room equipped with two chairs and a desk and lasted approximately 40\u0026ndash;60 minutes. The WCST, the short version of the Stroop test, the TOL, the TMT Part B, and the digit span (forward and backward) were administered in a different order for each participant, counterbalanced across participants. Upon completion of the pre-training session (T0), participants in the experimental group were instructed to install the Chess.com app and play chess online autonomously for a total of 2 hours per week over 10 weeks. In contrast, participants in the control group were instructed to listen to either ASMR or white noise audio for the same amount of time. Participants in the control group were allowed to choose between these two types of audio because some individuals anecdotally report finding ASMR audio annoying, irritating, or even distressing. This response may be linked to individual differences in sensory sensitivity, including misophonia, which is characterized by strong aversive emotional and physiological responses to specific trigger sounds (McGeoch \u0026amp; Rouw, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As in the experimental group, control participants were free to decide how to subdivide the weekly listening time. No further instructions were provided regarding how the audio should be listened to. All participants received a weekly email reminder to complete their assigned task during the training phase. After the 10-week intervention, both groups were tested again in the laboratory, and the same neuropsychological battery was administered in the post-training session (T1).\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS), version 27 (IBM Corporation; Armonk, NY, USA). GraphPad Prism, version 9.3.1 (GraphPad Software, Inc.; La Jolla, CA, USA), was used to generate the figures. A series of 2 \u0026times; 2 mixed ANOVAs were performed with Time (T0\u0026thinsp;=\u0026thinsp;pre-training vs. T1\u0026thinsp;=\u0026thinsp;post-training) as the within-subjects factor and Group (experimental vs. control) as the between-subjects factor for the following dependent variables: WCST global score, perseverative responses, non-perseverative errors, and failures to maintain set; Stroop interference time; time and accuracy on the TOL; completion time on the TMT-B; and Digit Span Forward and Backward scores. Significant interaction effects were further examined using post-hoc pairwise comparisons with Bonferroni correction.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWCST\u003c/h2\u003e \u003cp\u003eGlobal score, perseverative responses, non-perseverative errors, and failures to maintain set from the WCST were calculated for each participant. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows WCST global scores for the experimental and control groups at T0 and T1. A decrease in global scores was observed at T1 in both the experimental (mean pre\u0026ndash;post difference = -10) and control (mean pre\u0026ndash;post difference = -8.41) groups, indicating improved performance over time, likely due to practice effects. A 2 \u0026times; 2 mixed ANOVA was conducted on WCST global scores, with Time (T0 vs. T1) and Group (experimental vs. control) as main factors. The analysis revealed a significant main effect of Time (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;12.25, p \u0026lt; .001, ηp\u0026sup2; = .17), as well as a significant main effect of Group (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.23, p \u0026lt; .01, ηp\u0026sup2; = .12). However, the Time \u0026times; Group interaction was not significant (F\u003csub\u003e(1, 58)\u003c/sub\u003e = .09, p = .76, ηp\u0026sup2; = .00).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe same ANOVAs conducted on perseverative responses, non-perseverative errors, and failure to maintain set yielded similar patterns. For clarity, the results of all WCST-related ANOVAs are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the 2 x 2 mixed ANOVAs on the WCST dependent variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003cp\u003e\u003cem\u003ePre Post\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003csub\u003e(1,58)\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eηp\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePerseverative\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eResponses\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTime\u003c/p\u003e \u003cp\u003eGroup\u003c/p\u003e \u003cp\u003eTime x Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.63(10.57) 20.41(7.44)\u003c/p\u003e \u003cp\u003e31.85(16.05) 26.72(17.15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.95\u003c/p\u003e \u003cp\u003e3.45\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003cp\u003e.07\u003c/p\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003cp\u003e.06\u003c/p\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNon-Perseverative\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eErrors\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTime\u003c/p\u003e \u003cp\u003eGroup\u003c/p\u003e \u003cp\u003eTime x Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.41(9.65) 7.24(2.40)\u003c/p\u003e \u003cp\u003e17.32(14.63) 14.25(13.11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003cp\u003e6.72\u003c/p\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003cp\u003e.01\u003c/p\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003cp\u003e.10\u003c/p\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFailure to Maintain\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSet\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTime\u003c/p\u003e \u003cp\u003eGroup\u003c/p\u003e \u003cp\u003eTime x Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35(0.66) 0.29(0.74)\u003c/p\u003e \u003cp\u003e0.38(0.86) 0.48(0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003cp\u003e.50\u003c/p\u003e \u003cp\u003e.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003cp\u003e.48\u003c/p\u003e \u003cp\u003e.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003cp\u003e.01\u003c/p\u003e \u003cp\u003e.01\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\n\u003ch3\u003eStroop Test\u003c/h3\u003e\n\u003cp\u003eThe interference effect (in seconds) was calculated for each participant. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows mean interference effects for the experimental and control groups at T0 and T1. A reduction in interference was observed at T1 for the experimental group (mean pre\u0026ndash;post difference = -1.73 s), whereas the control group showed no improvement (mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.31 s). A 2 x 2 mixed ANOVA with Time (T0 vs. T1) and Group (experimental vs. control) revealed no effect of Time (F\u003csub\u003e(1,58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.43, p = .12, ηp\u0026sup2; = .04), nor Group (F\u003csub\u003e(1,58)\u003c/sub\u003e = .15, p = .69, ηp\u0026sup2; = .00). In contrast, the Time \u0026times; Group interaction was significant (F\u003csub\u003e(1,58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.56, p = .04, ηp\u0026sup2; = .07). Post-hoc comparisons with Bonferroni correction showed a significant reduction in interference between pre- and post-intervention for the experimental group (p\u003csub\u003ecorr\u003c/sub\u003e = .01), but not for the control group (p\u003csub\u003ecorr\u003c/sub\u003e = .70), supporting the efficacy of the chess intervention on EFs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTOL\u003c/h3\u003e\n\u003cp\u003eAccuracy and completion time were recorded for each participant. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates mean completion time and accuracy for the experimental and control groups at T0 and T1. An increase in completion time was observed at T1 in both the experimental (mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1.41) and control (mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1.76) groups, indicating slower performance at retest. A 2 \u0026times; 2 mixed ANOVA with Time (T0 vs. T1) and Group (experimental vs. control) revealed a significant main effect of Time (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;16.35, p \u0026lt; .001, ηp\u0026sup2; = .22), as well as a significant main effect of Group (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.48, p = .02, ηp\u0026sup2; = .09). The Time \u0026times; Group interaction was not significant (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.19, p = .67, ηp\u0026sup2; = .00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Similar results were observed for accuracy. Both groups showed improved accuracy at T1 (experimental group: mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.79; control group: mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1.42). The 2 \u0026times; 2 ANOVA revealed a significant main effect of Time (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;11.05, p \u0026lt; .01, ηp\u0026sup2; = .16), whereas neither the main effect of Group (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.70, p = .41, ηp\u0026sup2; = .01), nor the Time \u0026times; Group interaction (F\u003csub\u003e(1, 58)\u003c/sub\u003e = .22, p = .64, ηp\u0026sup2; = .00), reached significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The increase in response time coupled with improved accuracy suggests a speed\u0026ndash;accuracy trade-off rather than a practice effect. This hypothesis was further examined using a combined performance measure, the inverse efficiency score (IES; Townsend \u0026amp; Ashby, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), calculated by dividing response time by the proportion of correct responses. This index accounts for speed\u0026ndash;accuracy trade-offs, with lower IES values indicating faster responses with fewer errors, and higher values indicating slower responses with more errors. Consistent with this interpretation, the 2 \u0026times; 2 mixed ANOVA on IES revealed no significant main effect of Time (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.60, p = .44, ηp\u0026sup2; = .01). Likewise, neither the main effect of Group nor the Time \u0026times; Group interaction reached significance (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.02, p = .89, ηp\u0026sup2; = .00, F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.67, p = .11, ηp\u0026sup2; = .04, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTMT-B\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows mean completion time (s) on the TMT-B for the experimental and control groups at T0 and T1. A reduction in completion time was observed at T1 in both the experimental group (mean pre-post difference = -12.89 s) and the control group (mean pre-post difference = -13.27 s). A 2 \u0026times; 2 mixed ANOVA with Time (T0 vs. T1) and Group (experimental vs. control) revealed a significant main effect of Time (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;16.68, p \u0026lt; .001, ηp\u0026sup2; = .22). Neither the main effect of Group (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.26, p = .27, ηp\u0026sup2; = .02), nor the Time \u0026times; Group interaction (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.004, p = .95, ηp\u0026sup2; = .00) reached significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDigit Forward and Backward (WAIS-IV)\u003c/h2\u003e \u003cp\u003eDigit Span Forward and Backward were used to assess short-term and working memory spans before and after the training period. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows mean Digit Span Forward and Digit Span Backward scores for the experimental and control groups at T0 and T1. For Digit Span Forward, a slight increase in span was observed in both groups, with a larger change in the experimental group (mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.97) than in the control group (mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.07). However, a 2 \u0026times; 2 mixed ANOVA with Time (T0 vs. T1) and Group (experimental vs. control) revealed no significant main effect of Time (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.35, p = .13, ηp\u0026sup2; = .04), nor Group (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.17, p = .08, ηp\u0026sup2; = .05). The Time \u0026times; Group interaction was also not significant (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.77, p = .19, ηp\u0026sup2; = .03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Similarly, for Digit Span Backward, both groups showed modest improvements at the retest (experimental group: mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.58; control group: mean pre\u0026ndash;post difference\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.52). The corresponding 2 \u0026times; 2 mixed ANOVA revealed no significant main effects of Time (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.78, p = .10, ηp\u0026sup2; = .05) or Group (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.15, p = .70, ηp\u0026sup2; = .00), and no significant Time \u0026times; Group interaction (F\u003csub\u003e(1, 58)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.01, p = .92, ηp\u0026sup2; = .00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary aim of the present study was to examine whether a 10-week chess-based training program could enhance EFs in a neurologically healthy population of young adults with little to no prior chess experience. Participants were randomly assigned either to a chess-training group, which engaged in online chess practice for two hours per week over 10 weeks (totalling 20 hours), or to an active control group that listened to ASMR/white-noise audio for the same duration. Focusing on individuals with minimal chess experience was motivated by prior findings indicating a strong association between EFs and chess ability primarily among lower-skill players (Burgoyne et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This approach was intended to maximize the likelihood of detecting cognitive changes attributable to chess practice rather than to pre-existing expertise. The duration of the intervention was based on the study by Cibeira et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which reported EF improvements following a 24-hour chess training program in institutionalized older adults.\u003c/p\u003e \u003cp\u003eOverall, the findings indicate that, relative to the control group, chess-based training led to selective improvements in executive functioning, rather than a generalized enhancement across all EF domains. Specifically, only participants in the experimental group showed significant pre\u0026ndash;post improvement on the Stroop task, reflecting gains in attention and inhibitory control, whereas no comparable improvements were observed in the control group. In contrast, no training-specific effects emerged for other EF measures, including working memory, planning, or cognitive flexibility, as assessed by the WCST, TOL, TMT-B, and Digit Span tasks. These results suggest that engaging in chess practice with the frequency adopted in the present study may particularly benefit attention and interference control, rather than executive functioning as a whole.\u003c/p\u003e \u003cp\u003eThe selective improvement observed on the Stroop task aligns with the cognitive demands of chess, particularly for novice players. Chess requires sustained attention, response inhibition, and the resolution of competing alternatives during move evaluation\u0026mdash;processes that closely overlap with those measured by the Stroop task. This interpretation is supported by previous evidence: an ERP study by West and Alain (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) showed that Stroop task performance engages frontal, prefrontal, and parietal regions, which are similarly activated in unskilled chess players during gameplay (Onofrj et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Atherton et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The overlap in neural recruitment may help explain why the effects of chess training observed here were domain-specific rather than generalized.\u003c/p\u003e \u003cp\u003eThe present findings are broadly consistent with prior work demonstrating beneficial effects of chess-based interventions on attention and executive processes. Notably, Cibeira et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported improvements in attention, processing speed, and executive functions (EFs) following a 24-hour chess training program in older institutionalized adults. While methodological differences exist between that study and the present one\u0026mdash;including participant age, health status, training format (theory-based lessons versus online self-administered play), and monitoring of adherence\u0026mdash;both investigations converge in suggesting that chess training can positively affect attentional and executive processes.\u003c/p\u003e \u003cp\u003eEvidence from other populations further supports the relationship between chess training and EF enhancement. Studies in children and adolescents have reported improvements in working memory, inhibition, planning, and decision-making following chess-based interventions (Oberoi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Developmental research has also highlighted gains in specific EF components, including visuospatial working memory (Yakushina et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and verbal working memory, alongside decision-making abilities (Oberoi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More recently, Pham and Dao (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reported that an 8-week blended intervention, combining traditional face-to-face instruction with computer-mediated e-learning, led to improvements in focused attention, auditory word memory, and academic performance in primary school children.\u003c/p\u003e \u003cp\u003eOur results also resonate with findings from young adult populations. Dania et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported improvements in working memory and selective attention following a 10-week chess intervention in college invasion-game athletes. It should be noted, however, that their control group was passive, making it unclear whether the observed benefits reflected the intervention itself or differences in engagement, motivation, or expectations between groups. Moreover, their conclusions were limited by a small and highly specific sample. Notably, the current study extends this body of evidence to young neurotypical adults and provides, to our knowledge, the first demonstration of EF improvements in this population using a self-administered online chess application. Unlike previous interventions based on structured chess lessons, the present training relied exclusively on active gameplay, highlighting the ecological validity and scalability of this approach.\u003c/p\u003e \u003cp\u003eThe absence of chess-specific improvements on the WCST, TOL, and TMT-B in the present study likely reflects practice effects associated with repeated testing. Indeed, both experimental and control groups showed comparable pre- to post-training improvements on these measures, consistent with well-documented test\u0026ndash;retest learning effects in EF tasks (Basso et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lemay et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Buck et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These findings highlight a longstanding methodological challenge in EF research, namely the limited test\u0026ndash;retest reliability of traditional neuropsychological measures, particularly the WCST (Rabbitt, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Interestingly, no practice effects were observed on the Digit Span tasks, diverging from earlier reports of memory span improvement (Taub, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). This pattern suggests that working memory performance may be less sensitive to short-term retesting effects in this context, or it may reflect task-specific factors or ceiling effects in the present sample.\u003c/p\u003e \u003cp\u003eBeyond healthy populations, the present findings also align with the clinical literature on chess-based interventions for Attention Deficit Hyperactivity Disorder (ADHD), a condition marked by deficits in attention and inhibitory control. Reviews and intervention studies have reported reductions in ADHD symptom severity in children and adolescents following chess training, particularly in domains related to attention and inhibitory control (Blasco-Fontecilla et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Agarwal et al., 2023). Although ADHD was not examined in the current study, the overlap in affected cognitive processes further supports the link between chess practice and inhibitory control mechanisms, suggesting that chess training may be especially effective in targeting these executive domains. In fact, strong inhibitory control has been linked to multiple cognitive and behavioural benefits in youth, including enhanced self-regulation and reduced impulsivity (Nigg, 2016), as well as better control over potentially addictive behaviours (Morein-Zamir \u0026amp; Robbins, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The potential applicability of chess-based interventions to clinical populations should be directly investigated in future research.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the rationale for the training duration was based on findings from a small sample of older adults (Cibeira et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), whose neural plasticity likely differs from that of younger adults. Second, adherence to the training protocols could not be directly monitored, particularly given the self-administered nature of both the experimental and control interventions. Third, the sample was predominantly composed of university students, limiting the generalizability of the findings to the broader population of young adults. Moreover, participants continued their academic activities during the intervention period, which may have independently influenced EF performance, as engagement in cognitively stimulating activities has been linked to executive functioning improvements (Yates et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFuture research should examine whether shorter or longer chess training programs yield comparable or broader cognitive benefits, and whether increasing training intensity leads to improvements across additional EF domains. Another promising direction involves the development of chess-inspired neurorehabilitation programs for populations with executive dysfunction, particularly individuals with frontal lobe damage or other conditions affecting fronto-executive networks. Given its intrinsically motivating and engaging nature, chess may enhance adherence to rehabilitation protocols while selectively targeting core executive processes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study investigated whether regular chess practice can enhance executive functions in neurologically healthy young adults with minimal chess experience. The results demonstrate that a 10-week chess training program, practiced with moderate frequency, selectively improves attention and inhibitory control, as assessed by the Stroop test, while no training-specific effects were observed for other executive domains\u0026mdash;likely reflecting practice effects associated with repeated testing. These findings suggest that chess represents an accessible, engaging, and ecologically valid cognitive activity capable of enhancing specific executive processes in young adults. By contributing to the growing literature on cognitively stimulating activities as tools for EF enhancement, this study highlights the potential of chess as a low-cost intervention. Further research is needed to replicate these results, optimize training protocols, and explore the applicability of chess-based interventions across diverse populations and clinical contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study received the appropriate ethical authorization from a local ethical committee.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent for the anonymous publication of their data, with the assurance that no personal identifiers would be disclosed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Availability\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIrene Sperandio:\u003c/strong\u003e Conceptualization; Data Curation; Formal Analysis; Methodology; Resources; Supervision; Visualization; Writing \u0026ndash; Original Draft Preparation. \u003cstrong\u003eMaria Maddalena Cavadini:\u003c/strong\u003e Conceptualization; Data Curation; Investigation; Methodology; Writing \u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eIoanna Markostamou:\u003c/strong\u003e Conceptualization; Methodology; Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgarwal, N. 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Cognitive leisure activities and future risk of cognitive impairment and dementia: Systematic review and meta-analysis. \u003cem\u003eInternational Psychogeriatrics\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(11), 1791\u0026ndash;1806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1041610216001137\u003c/span\u003e\u003cspan address=\"10.1017/S1041610216001137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"psychological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prpf","sideBox":"Learn more about [Psychological Research](http://link.springer.com/journal/426)","snPcode":"426","submissionUrl":"https://submission.nature.com/new-submission/426/3","title":"Psychological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"online chess training, young adults, novice players, executive functions","lastPublishedDoi":"10.21203/rs.3.rs-8829398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8829398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChess is a cognitively demanding activity that engages executive functions (EFs), including inhibition, cognitive flexibility, planning, and working memory. While long-term chess practice has been associated with functional and structural brain changes, evidence on the effects of chess training in young adults with limited experience remains scarce. Here, we investigated whether structured chess practice improves EFs in neurologically healthy adults aged 18\u0026ndash;40 years with little to no prior chess expertise. Sixty participants were randomly assigned to an experimental group (n\u0026thinsp;=\u0026thinsp;31) or a control group (n\u0026thinsp;=\u0026thinsp;29). EFs were assessed before (T0) and after (T1) a 10-week intervention using a comprehensive neuropsychological battery, including the Wisconsin Card Sorting Test (WCST), Tower of London (TOL), Stroop Test, Trail Making Test Part B (TMT-B), and the Digit Span forward and backward subtests from the WAIS-IV. During the intervention, the experimental group played online chess for a total of two hours per week, while the control group listened to ASMR or white-noise audio for an equivalent duration. Results showed significant pre- to post-intervention improvements in both groups on the WCST, TOL, and TMT-B, likely reflecting practice effects. Notably, only the experimental group exhibited a significant improvement on the Stroop Test at T1, whereas no change was observed in the control group. These findings provide novel evidence that a 10-week program using a self-administered online chess application selectively enhances EFs related to interference control and cognitive flexibility in young adults, supporting chess as a targeted cognitive intervention for non-expert populations.\u003c/p\u003e","manuscriptTitle":"Enhancing executive functions through online chess training: Evidence from young adults with limited experience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 05:48:09","doi":"10.21203/rs.3.rs-8829398/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-20T20:35:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213960647521475684042465013231162345447","date":"2026-02-18T09:03:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T09:01:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T09:22:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T03:28:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Psychological Research","date":"2026-02-09T10:34:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"psychological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prpf","sideBox":"Learn more about [Psychological Research](http://link.springer.com/journal/426)","snPcode":"426","submissionUrl":"https://submission.nature.com/new-submission/426/3","title":"Psychological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cd52514c-80bd-4f2e-b378-0cf2694e3861","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T05:48:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 05:48:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8829398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8829398","identity":"rs-8829398","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}