The role of the left dorsolateral prefrontal cortex in the interplay between metacontrol and mind-wandering. Evidence from a HD-tDCS study

The role of the left dorsolateral prefrontal cortex in the interplay between metacontrol and mind-wandering. Evidence from a HD-tDCS study | 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 The role of the left dorsolateral prefrontal cortex in the interplay between metacontrol and mind-wandering. Evidence from a HD-tDCS study Víctor Martínez-Pérez, Lucía B. Palmero, Guillermo Campoy, Lorenza Colzato, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8938459/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The metacontrol framework claims that cognitive control operates along a continuum between persistence and flexibility. While spontaneous mind-wandering is often considered a failure of control, emerging evidence suggests that different types of mind-wandering, intentional versus unintentional, may reflect distinct metacontrol dynamics. We examined how high-definition transcranial direct current stimulation (HD-tDCS) over the left dorsolateral prefrontal cortex modulates the interplay between metacontrol strategies and mind-wandering, using a global-local task combined with intermittent thought probes. Ninety-two participants completed a global-local task while receiving either anodal or sham HD-tDCS at 1.5 mA over the left dorsolateral prefrontal cortex. Mind-wandering episodes were assessed using thought probes, distinguishing between intentional and unintentional mind-wandering. Metacontrol tendencies were inferred from global precedence effects observed in response accuracy and latency. HD-tDCS selectively enhanced accuracy in the local condition during the second half of the task, suggesting an increase in persistence-oriented control. Intentional mind-wandering was positively associated with cognitive flexibility (greater global precedence), while unintentional mind-wandering correlated with persistence. However, stimulation did not directly affect mind-wandering rate. Our findings support a double dissociation between types of mind-wandering and metacontrol styles. They provide causal evidence that HD-tDCS over the left dorsolateral prefrontal cortex can promote persistence without altering spontaneous thought frequency, thereby validating and extending the metacontrol framework. mind-wandering global-local task HD-tDCS metacontrol persistence flexibility Figures Figure 1 Figure 2 Introduction When individuals engage in cognitive control to complete tasks, their performance can be characterized by two meta-control styles that vary along a continuum, from extreme persistence to extreme flexibility (Hommel, 2015; Hommel & Colzato, 2017; Hommel, Colzato and Beste, 2024; Mekern et al., 2019). These styles dictate whether an individual maintains a focused and sustained approach to the task at hand (persistence) or fluidly shifts attention between various approaches, tasks, ideas, and thoughts (flexibility). Variations in these control styles have been observed both within individuals (Ashby et al., 1999; Colzato et al., 2012, 2017; Fischer & Hommel, 2012) and between different individuals (Colzato et al., 2008, 2014; Colzato, van Beest, et al., 2010; Colzato, Waszak, et al., 2010; McKone et al., 2010; Stock et al., 2014). This adaptability allows humans to adopt different control styles based on specific tasks, challenges, cultures, religions, moods, or genetic factors. Despite the human mind's capacity for complex cognitive tasks, it frequently experiences lapses in concentration, known as mind-wandering (hereafter MW). These episodes involve shifting attention away from the current task towards unrelated thoughts and internal reflections (Smallwood & Schooler, 2015). This phenomenon underscores the brain's dual capacity for intense focus and spontaneous diversion, reflecting a delicate balance between maintaining attention and engaging in introspective or imaginative thought. Consequently, mind-wandering not only exemplifies occasional lapses in focus but also highlights the intricate interplay between external task engagement and internal cognitive processes (Martínez-Pérez et al., 2023; Thomson et al., 2015). Emerging evidence suggests that mind-wandering can occur spontaneously (unintentional) or deliberately (intentional) (Seli et al., 2015) and studying the triggers of mind-wandering reveals significant distinctions (Martínez-Pérez et al., 2021, 2023; Seli et al., 2016, 2018). For instance, monotonous tasks like the Psychomotor Vigilance Task (PVT) tend to produce a higher rate of intentional mind-wandering compared to more demanding tasks like the Sustained Attention to Response Task (SART) (Martínez-Pérez et al., 2021), which are associated with higher rates of unintentional mind-wandering (Martínez-Pérez et al., 2021; Seli et al., 2016). Understanding the interplay between cognitive control styles (persistence-flexibility) and MW propensity is crucial, though rarely studied. The latter involves voluntary or involuntary shifts in attention from the task to self-generated thoughts and unrelated mental content, potentially influencing task performance depending on the predominance of persistence or flexibility. One can thus hypothesize that a tendency towards flexibility is linked to a higher propensity for MW or, more specifically, that the nature of MW (spontaneous or voluntary) correlates with the metacontrol strategies employed for task performance. Furthermore, these associations could have identifiable neural correlates. Recent advances in neuroscience have identified the dorsolateral prefrontal cortex (DLPFC) as a key regulator of both MW (Andrews-Hanna et al., 2014; Christoff et al., 2009; Stawarczyk et al., 2011) and metacognitive strategies (Beaty et al., 2015; Botvinick et al., 2001). Non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), have been instrumental in establishing causal links between DLPFC activity and cognitive control styles. Specifically, tDCS studies have shown that modulating DLPFC activity can influence individuals’ propensity for MW (Axelrod et al., 2015, 2018; Boayue et al., 2021; Filmer et al., 2019, 2021; Martínez-Pérez et al., 2023; and Nejati et al., 2023 with tACS) and their tendency towards persistence or flexibility in cognitive control (Andrews et al., 2011; Mayseless & Shamay-Tsoory, 2015; Zmigrod, 2014). Despite these insights, the temporal dynamics of MW and metacontrol strategies under DLPFC modulation remain poorly understood. Our objectives here were threefold. First, we aimed to investigate the temporal course of metacontrol strategies and MW within a single experiment. Second, we sought to assess the effect of high-definition tDCS (HD-tDCS) over the DLPFC on the propensity for MW and the development of persistence or flexibility strategies in task performance. Finally, we aimed to understand how these metacontrol tendencies correlate with one another, depending on whether MW is triggered intentionally or unintentionally, a distinction that is crucial to consider when investigating MW with transcranial electric brain stimulation (Chaieb et al., 2019). As a key objective of the present study was to track changes in time-on-task, mind-wandering (MW) and global-local performance under online HD-tDCS, we implemented a single continuous task stream beginning at stimulation onset rather than adding a separate baseline block. This was to avoid the possibility that an additional pre-stimulation block could induce practice and strategy-setting effects that would carry over to the stimulation period and complicate the interpretation of time-on-task dynamics. However, we acknowledge that the absence of a within-session baseline restricts our ability to unambiguously attribute observed group differences to stimulation-induced change versus pre-existing differences. Accordingly, we present stimulation-related findings as between-group differences expressed over time under randomized assignment, and treat mechanistic conclusions as tentative, pending replication with baseline and/or active-control montages. To address these objectives, we designed a study that evaluates MW through thought probes, in which participants are intermittently asked whether they are focused on the task or thinking about something else. Specifically, we incorporated thought probes at pseudo-random intervals to capture the temporal evolution of MW episodes. To examine metacontrol strategies, we used a version of the global-local task (Navon, 1977). We posited that people would tend to react more quickly to the broader global aspects compared to the smaller local details, a phenomenon referred to as the global precedence effect (GPE). Furthermore, this effect is more pronounced in certain individuals than in others and has been observed to vary within the same individual based on the circumstances. This variability has been linked to a flexible cognitive style, in contrast to a less prominent global precedence bias, which is associated with a more persistent and detail-oriented approach (Colzato, Hommel, et al., 2010; Colzato, van Beest, et al., 2010; Colzato, Van Hooidonk, et al., 2010; McKone et al., 2010). This approach allowed us to explore the effects of HD-tDCS over the DLPFC on the interplay between MW and metacognitive strategies within a single cognitive task. Method Participants All participants were students at the University of Murcia who participated for course credits, and none of them had previously participated in any study on transcranial electrical stimulation. We used G*Power software (Faul et al., 2007) to determine the required sample size for achieving 80% power to detect a small effect (Cohen’s d = 0.25) at an alpha level of 0.05. This calculation suggested that a sample of 46 participants would be sufficient to detect even a small effect of HD-tDCS on global-local task performance. However, given our additional interest in examining the effects of HD-tDCS on intentional and unintentional mind-wandering rates, we decided to collect data from a larger sample of 92 participants (mean age: 19.64, range: 18-29 years; 82 females), of which 45 were randomly assigned to the sham group and 47 to the stimulation (anodal) group. The study was approved by the ethical committee of the University of Murcia, and all participants gave their informed consent before the experiment. The study adhered to the ethical principles outlined in the Declaration of Helsinki. Apparatus and stimuli Behavioral measures The experiment was controlled using a Windows 10 equipped computer connected to a 22-inch TFT monitor with a resolution of 1920 × 1080 pixels, viewed at approximately 60 cm distance. Programming and data analysis were conducted using E-Prime 3 software, and responses were recorded with a 5-button Chronos device from Psychology Software Tools. The task was adapted from Navon's (1997) global-local paradigm. Participants were instructed to identify a target stimulus in each trial, which consisted of the letters "A" or "L." These letters could be presented either as a large letter composed of smaller letters (global condition) or as the smaller letters themselves forming a larger letter (local condition). Each trial began with a fixation point displayed for 1000 ms, followed by the target letter presented to the right or left of the fixation point for 150 ms. Participants then had 2000 ms to respond by pressing buttons 4 or 5 on the Chronos device. Feedback indicating the correctness of the response (correct/incorrect) was provided for 1500 ms. If no response was given within 2000 ms, it was recorded as an error, and the next trial commenced. Participants completed a total of 320 trials, encompassing 8 different combinations of global-local letters presented in randomized order, with the constraint that each combination occurred equally often within the entire session. For the global condition, we had 4 combinations: large A/small Ts, large A/small Hs, large L/small Ts, and large L/small Hs. For the local condition, we also had 4 combinations: small As/large T, small As/large H, small Ls/large T, and small Ls/large H. Participants ran 40 trials for each combination. As “Half” (first versus second) was a factor of interest, we also ensured that the distribution of trial types was balanced across halves, so that time-on-task effects could not be attributed to systematic changes in stimulus composition. We assessed MW using intermittent thought probes, which were presented at fixed trial-count intervals. One probe occurred after every 16 trials, making 20 probes in total across 320 trials. Within this constraint, the probes were not dependent on task events or performance. A fixed interval was chosen to ensure comparable sampling density across participants and between the first and second halves of the session, a key factor of interest. The probe asked: 'Which of the following responses best characterizes your state of mind just before the presentation of this display?', with the following response options: “On-task”, “Intentional MW” and “Unintentional MW”. Participants received verbal and written instructions both for answering the thought probe questions and for the task itself. For the thought probes, participants were informed that "on-task" meant thinking about something related to the task (e.g., how difficult or boring it was or the buttons they had to press). They were provided with a definition of MW and a brief explanation of the difference between intentional and unintentional MW. Intentional MW referred to moments when they voluntarily thought about things unrelated to the task (e.g., a shopping list), while unintentional MW referred to involuntary thoughts about unrelated matters (e.g., a past event suddenly coming to mind). Participants were explicitly told that there were no right or wrong answers and were encouraged to respond honestly. They used buttons 1, 2, and 3 on the Chronos device to choose between the three response alternatives: on-task, intentional MW, or unintentional MW with unlimited time to respond. HD-tDCS setup The HD-tDCS was administered online using a Starstim 8 device and Sponstim sponge-based electrodes with a circular area of 8 cm², controlled through NIC v2.0.11.7 software (Neuroelectrics®, Barcelona). The target brain area for anodal stimulation was the left dorsolateral prefrontal cortex (l-DLPFC), located at F3 according to the 10-20 system. Three return electrodes, T7, Cz, and Fp2, were positioned in a triangular configuration, each with a 33% current return (Figure 1A, 1B), and the intensity was set at 1.5 mA. This form of high-definition montage has been demonstrated to enhance the focality of stimulation (Kuo et al., 2013), and it has proven effective in modulating the DLPFC in previous studies conducted in our laboratory (Martínez-Pérez et al., 2019, 2022, 2023). Stimulation was delivered online for 15 minutes from the start of the task, including a 30-second ramp-up/ramp-down period. Sham stimulation consisted of these ramp periods only, to mimic scalp sensations. The 15-minute duration was chosen to align with standard HD-tDCS protocols targeting the prefrontal cortex, while also minimizing participant burden and ensuring sufficient time for stimulation during the initial stages of the task. As the main objective was to determine whether increasing the excitability of the left dorsolateral prefrontal cortex (l-DLPFC) influences the control policy towards persistence, an anodal-versus-sham comparison was implemented. The study employed a double-blind design, whereby neither the investigator nor the participant knew what type of tDCS stimulation was being delivered. This procedure was implemented using NIC2 software provided by Neuroelectrics to ensure that neither expectations nor biases influenced the outcomes. Importantly, after completing the task, participants were asked to complete a brief questionnaire about blinding, indicating whether they believed they had received active or sham stimulation. Their guesses did not differ from the level of accuracy expected by chance. Procedure After registering for the experiment via the university email distribution list, participants were scheduled to perform the experiment in situ, with sessions conducted between 10 AM and 4 PM. On the day of the experiment, participants were randomly assigned to either the sham or tDCS group using a double-blind design. Participants then performed the modified version of the global-local task in individual soundproof rooms. Upon completing the task, they were asked to finish the session by completing a brief transcranial electrical stimulation blinding efficacy survey. A detailed scheme of the procedure is depicted in Figure 1C. >> Insert Figure 1 here << Statistical analyses Data were analyzed using JASP 0.18.3, with a significance level set at α = 0.05. The primary objective of this study was to investigate the temporal course (first vs. second half) of the global-local task and the thought probes and to determine whether participants modulated their global-local scores and MW rates as a function of HD-tDCS stimulation. Additionally, we examined correlations between MW and global-local measures using Pearson correlation analysis. Three analyses of variance (ANOVAs) were conducted. The first ANOVA used MW rate as the dependent variable, while the other two utilized accuracy and response time (RT) in the global-local task as dependent variables. In the first analysis, Half (first half, second half) and MW Intentionality (intentional, unintentional) were the within-participant factors, and Stimulation (anodal, sham) was the between-participant factor. For the second and third ANOVAs, Half (first half, second half) and Target type (global, local) were the within-participant factors, with Stimulation (anodal, sham) as the between-participant factor. The Greenhouse-Geisser correction was applied to all tests, and further simple effects analyses were conducted only when interactions were significant. All post hoc tests were corrected using Tukey's method. Descriptive statistics were reported as the mean and standard error of the mean (SEM). Results Mind-wandering The rate of both intentional and unintentional MW, measured as the proportion of each MW probes among all thought probes, showed that the rate of unintentional MW was significantly higher than the rate of intentional MW, F (1, 90) = 69.06, p < 0.001, η 2 = 0.225 (M = 0.082, M = 0.268, respectively). The Half factor was also significant, F (1, 90) = 65.52, p < 0.001, η 2 = 0.048, indicating a greater MW rate during the second half of the task (M = 0.218) compared to the first half (M = 0.132). Importantly, the Half × Intentionality interaction was also significant, F (1, 90) = 7.145, p = 0.009, η 2 = 0.008. The interaction analysis revealed that, although both intentional and unintentional MW increased significantly from the first to the second half of the task, unintentional MW exhibited a greater relative increase (all p s < 0.001, see Figure 2A). None of the other factors or their interactions reached statistical significance (all F s < 1), indicating that the stimulation did not affect MW. Global-Local task (RT) The Half factor reached statistical significance, F (1, 90) = 103.86, p < 0.001, η 2 = 0.069, indicating that participants responded faster in the second half of the experiment (M = 422 ms) compared to the first half (M = 484 ms). The Target type factor was also statistically significant, F (1, 90) = 267.98, p < 0.001, η 2 = 0.94, with faster responses observed under the global condition (M = 417 ms) compared to the local condition (M = 489 ms). That is, we replicated the standard global precedence effect (GPE) (Navon, 1977) with this version of the global-local task. Additionally, the Half × Target type interaction was also significant, F (1, 90) = 7.45, p = 0.008, η 2 = 0.006. Post-hoc tests, corrected for multiple comparisons, revealed significant differences for all contrasts except the comparison between first-half-global and second-half-local responses. None of the other factors or their interactions reached statistical significance (all F s < 1), indicating that tDCS did not affect global-local RTs. Global-Local task (ACC) The Half factor reached statistical significance, F (1, 90) = 14.89, p < 0.001, η 2 = 0.012, indicating that participants were more accurate in their responses in the second half of the experiment (M = 0.95) compared to the first half (M = 0.93). The Target type factor was also statistically significant, F (1, 90) = 90.31, p < 0.001, η 2 = 0.17, showing more accurate responses under the global condition (M = 0.96) compared to the local condition (M = 0.92). This outcome replicated the GPE in accuracy data, as observed with RTs. Importantly, the Half × Target type × Stimulation interaction was also significant, F (1, 90) = 9.81, p = 0.002, η 2 = 0.007 (see Figure 2B). To further explore this interaction, we conducted separate ANOVAs for the sham and active anodal conditions. For the sham condition, significant main effects were observed for both the Half factor, F (1, 44) = 6.5, p = 0.004, η 2 = 0.021, and the Target type factor, F (1, 44) = 41.89, p < 0.001, η 2 = 0.315 (demonstrating the GPE). However, the Half × Target type interaction was not significant, F (1, 44) = 1.94, p = 0.171, η 2 = 0.007. Conversely, in the tDCS condition, the ANOVA revealed a significant Half × Target type interaction, F (1, 44) = 9.29, p < 0.001, η 2 = 0.315. Inspection of Figure 2B suggests that, under anodal stimulation, accuracy for local targets increased from the first (M = 0.89) to the second half (M = 0.92) of the task, t (46) = 3.78, p < 0.001, Cohen’s d = 0.124. This is consistent with improved maintenance of detail-oriented processing under increasing time-on-task demands. In contrast, anodal stimulation had no significant effect on accuracy in the global condition over time, t (46) = 0.131, p = 0.89, Cohen’s d = 0.085 (M = 0.96, for the first and second halves). Correlation between MW and Global-Local task To explore the relationship between the propensity for MW and the biases toward persistence-flexibility, we constructed a Pearson correlation matrix encompassing measures of global intentional MW, unintentional MW, the Global Precedence Effect in response times (GPE-RT), and the Global Precedence Effect in accuracy (GPE-ACC). These analyses are exploratory and do not model either within-session temporal dynamics or group-specific coupling. Therefore, they should be interpreted as associations between participants rather than as evidence of co-fluctuations within individuals over time. The correlation matrix is presented in Figure 2-C (see Figure 2-D for illustrating the density graphs of those correlations). A positive correlation was observed between intentional MW and GPE-ACC ( ρ = .267, p = 0.01), while unintentional MW showed a negative correlation with GPE-ACC ( ρ = - .210, p > Insert Figure 2 here << Discussion This study offers new empirical evidence for the dynamic interplay between metacontrol modes and MW, and how this interaction can be modulated through HD-tDCS over the l-DLPFC. This study examined: (i) how intentional and unintentional motor work (MW) evolves over time; (ii) how global–local performance indices relate to MW intentionality; and (iii) whether anodal high-density transcranial direct current stimulation (HD-tDCS) targeting the left dorsolateral prefrontal cortex (DLPFC) modulates these patterns. Three key findings emerged. Firstly, both intentional and unintentional mind wandering (MW) increased from the first to the second half of the task, with a relatively larger increase for unintentional MW. Secondly, anodal stimulation did not alter overall MW rates. Thirdly, we observed stimulation-related modulation of accuracy selective to local processing in the second half of the task. Importantly, as the design did not include a pre-stimulation baseline block or an active-control stimulation site, we interpret the stimulation-related pattern as indicating a bias towards persistence-oriented processing under time-on-task demands according to the metacontrol framework proposed by Hommel and Colzato ( 2017 ). However, we refrain from making strong claims about region-specific mechanisms. Replication involving baseline measurements and an active control montage would strengthen anatomical and mechanistic inferences. One of the key contributions of our study is the demonstration of a double dissociation between intentional and unintentional MW and their relationship to metacontrol tendencies. Specifically, we observed that intentional MW was positively correlated with the GPE in accuracy, an index of cognitive flexibility, whereas unintentional MW was negatively associated with the same measure, indicative of a more persistence-oriented style. These results align with previous studies by Hommel, Colzato, and colleagues showing that flexible cognitive styles are associated with greater responsiveness to global-level information and broader attentional scopes (Colzato et al., 2010 ; Hommel & Colzato, 2017 ). This distinction further supports the view that intentional MW may reflect an adaptive form of internal cognition, akin to what Hommel and Colzato ( 2017 ) describe as the flexible mode of control, where internal thoughts are generated deliberately and may serve creative or future-oriented purposes (Smallwood & Schooler, 2015 ). In contrast, unintentional MW appears to emerge from lapses in metacontrol persistence, consistent with a loss of top-down regulation typical of a persistence-dominated control mode as time-on-task progresses (McVay & Kane, 2010 ; Seli et al., 2016 ). Despite these clear associations, HD-tDCS did not significantly affect the overall frequency of either form of MW. This absence of a direct stimulation effect on spontaneous thought aligns with previous mixed findings in the literature (Axelrod et al., 2015 ; Filmer et al., 2019 ; Martínez-Pérez et al., 2023 ; Rasmussen et al., 2024 ) and suggests that modulating MW propensity may require broader network-level stimulation, including medial prefrontal and default mode regions. Nonetheless, HD-tDCS over l-DLPFC did significantly improve task accuracy under local processing conditions, but only in the second half of the task, when performance typically declines. This selective improvement suggests a facilitation of persistence, helping participants maintain attention to detail as fatigue sets in, again consistent with the persistence mode described by the metacontrol framework (Hommel & Colzato, 2017 ). Stimulation was delivered online for 15 minutes from the start of the task, whereas the global-local task lasted approximately 25 minutes. Therefore, the accuracy benefit observed in the second half should not be interpreted as a strictly concurrent “online-only” effect when it emerges. Rather, it is consistent with delayed or sustained consequences of early stimulation (e.g., task-set maintenance that persists beyond stimulation offset), which become most apparent under increasing time-on-task demands. Additionally, the co-occurrence of increased MW rates with improved accuracy in the second half is not necessarily contradictory. Thought probes provide intermittent self-reports, whereas accuracy reflects continuous task performance. Thus, participants may report more off-task thoughts over time while still providing adequate task-set support on response-requiring trials. Furthermore, gains in stimulus-response mapping due to practice can improve accuracy even as subjective MW increases with fatigue. These interpretations remain tentative and highlight the value of future analyses that relate performance immediately preceding each probe to the reported mental state. These findings provide novel causal support for the metacontrol model, particularly the idea that stimulation can bias control states depending on task demands and temporal dynamics. Our prior work has shown similar increases in MW over time (Martínez-Pérez et al., 2023 ), and the current study extends this by demonstrating that HD-tDCS may mitigate decline in performance by reinforcing persistence-oriented control. Moreover, the observed increase in both intentional and unintentional MW across the session replicates our earlier findings (Martínez-Pérez et al., 2021 , 2023 ), reinforcing the idea that time-on-task effects reflect shifts in cognitive resources and control modes. These temporal dynamics are central to the metacontrol perspective, which argues that persistence and flexibility are not static traits but adaptable states responsive to internal and external pressures (Hommel, 2015 ). Limitations and future directions Several limitations qualify the strength and specificity of the conclusions. Most importantly, the study did not include a pre-stimulation baseline block and did not include an active-control stimulation site. Consequently, stimulation-related effects are best interpreted as between-group differences expressed over time under randomized assignment, and the present design does not allow strong claims about region-specific mechanisms uniquely attributable to l-DLPFC modulation. These constraints are common in early-stage neuromodulation work, but they underscore the need for replication using baseline assessment and an active-control montage to strengthen anatomical and mechanistic inference. A second set of limitations concerns measurement precision and temporal dynamics. MW estimates were derived from a limited number of probes per half, with fewer intentional than unintentional reports, reducing precision for fine-grained interaction tests involving intentionality. In addition, the stimulation window (15 minutes) was shorter than the overall task duration, which constrains inferences about the temporal locus of effects emerging in the second half; such effects may reflect delayed or sustained consequences of early stimulation rather than strictly concurrent online modulation. Future studies should increase probe density, incorporate probe-locked/time-resolved analyses of performance, and more tightly align stimulation timing with the critical task epochs (or systematically vary stimulation timing) to clarify when and how neuromodulation influences control-policy expression. Importantly, these limitations do not undermine the central contribution of the present work. The study identifies a theoretically diagnostic dissociation between intentional and unintentional mind-wandering in their relationship to global-local indices, and it provides initial evidence consistent with the idea that prefrontal neuromodulation can bias task-expressed persistence under increasing time-on-task demands. These findings sharpen testable predictions for future experiments designed to establish specificity while building on the diagnostic patterns observed here. Overall, the present study identifies a dissociation relevant to metacontrol between intentional and unintentional MW, and provides evidence consistent with the view that these forms of mental engagement are related differently to persistence-flexibility in task performance. Additionally, while the design does not permit robust region-specific mechanistic inference, the observed stimulation-related pattern indicates that HD-tDCS targeting the left prefrontal cortex can modulate task-based control in a manner that selectively enhances persistence-oriented performance under escalating time-on-task demands. Together, these findings offer empirical support for key predictions of the metacontrol framework and generate clear, testable hypotheses for future neuromodulation studies incorporating baseline assessment, active-control stimulation, and time-resolved modelling to establish anatomical specificity and temporal dynamics. Declarations Acknowledgements This study was supported by grant PID2021-125408NB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”. Conflict of interest The authors declare no competing interests. Data availability statement The datasets generated and analyzed during the current study are available from the first/corresponding author upon request. Author Contributions Víctor Martínez-Pérez: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. Lucía B. Palmero: Investigation, Methodology, Formal analysis, Writing – original draft. Guillermo Campoy: Investigation, Formal analysis, Writing – review & editing. Lorenza Colzato: Conceptualization, Writing – review & editing. Bernhard Hommel: Conceptualization, Methodology, Writing – review & editing. Luis J. Fuentes: Conceptualization, Methodology, Writing – original draft, Writing – reviewing and editing, Funding acquisition. References Alexandersen, A., Csifcsák, G., Groot, J., & Mittner, M. (2022). 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Psychological Review , 108 (3), 624-652. https://doi.org/10.1037/0033-295X.108.3.624 Chaieb, L., Antal, A., Derner, M., Leszczyński, M., & Fell, J. (2019). New perspectives for the modulation of mind-wandering using transcranial electric brain stimulation. Neuroscience , 409 , 69-80. https://doi.org/10.1016/j.neuroscience.2019.04.032 Christoff, K., Gordon, A. M., Smallwood, J., Smith, R., & Schooler, J. W. (2009). Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proceedings of the National Academy of Sciences , 106 (21), 8719-8724. https://doi.org/10.1073/pnas.0900234106 Colzato, L. S., Szapora, A., & Hommel, B. (2012). Meditate to Create: The Impact of Focused-Attention and Open-Monitoring Training on Convergent and Divergent Thinking. Frontiers in Psychology , 3 . https://doi.org/10.3389/fpsyg.2012.00116 Colzato, L. S., Szapora, A., Lippelt, D., & Hommel, B. (2017). 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Transcranial direct current stimulation and methylphenidate interact to increase cognitive persistence as a core component of metacontrol: Evidence from aperiodic activity analyses. Brain Stimulation , 18 (3), 720-729. https://doi.org/10.1016/j.brs.2025.03.024 Hommel, B. (2015). Chapter Two - Between Persistence and Flexibility: The Yin and Yang of Action Control. En A. J. Elliot (Ed.), Advances in Motivation Science (Vol. 2, pp. 33-67). Elsevier. https://doi.org/10.1016/bs.adms.2015.04.003 Hommel, B., Colzato, L., & Beste, C. (2024). No convincing evidence for the independence of persistence and flexibility. Nature Reviews Psychology , 3 (9), 638-638. https://doi.org/10.1038/s44159-024-00353-6 Hommel, B., & Colzato, L. S. (2017). The social transmission of metacontrol policies: Mechanisms underlying the interpersonal transfer of persistence and flexibility. Neuroscience & Biobehavioral Reviews , 81 , 43-58. https://doi.org/10.1016/j.neubiorev.2017.01.009 Kam, J. W. Y., Mittner, M., & Knight, R. T. (2022). Mind-wandering: Mechanistic insights from lesion, tDCS, and iEEG. Trends in Cognitive Sciences , 26 (3), 268-282. https://doi.org/10.1016/j.tics.2021.12.005 Kuo, H.-I., Bikson, M., Datta, A., Minhas, P., Paulus, W., Kuo, M.-F., & Nitsche, M. A. (2013). Comparing Cortical Plasticity Induced by Conventional and High-Definition 4 × 1 Ring tDCS: A Neurophysiological Study. Brain Stimulation , 6 (4), 644-648. https://doi.org/10.1016/j.brs.2012.09.010 Martínez-Pérez, V., Andreu, A., Sandoval-Lentisco, A., Tortajada, M., Palmero, L. B., Castillo, A., Campoy, G., & Fuentes, L. J. (2023). Vigilance decrement and mind-wandering in sustained attention tasks: Two sides of the same coin? Frontiers in Neuroscience , 17 . https://doi.org/10.3389/fnins.2023.1122406 Martínez-Pérez, V., Baños, D., Andreu, A., Tortajada, M., Palmero, L. B., Campoy, G., & Fuentes, L. J. (2021). Propensity to intentional and unintentional mind-wandering differs in arousal and executive vigilance tasks. PLOS ONE , 16 (10), e0258734. https://doi.org/10.1371/journal.pone.0258734 Martínez-Pérez, V., Castillo, A., Sánchez-Pérez, N., Vivas, A. B., Campoy, G., & Fuentes, L. J. (2019). Time course of the inhibitory tagging effect in ongoing emotional processing. A HD-tDCS study. Neuropsychologia , 135 , 107242. https://doi.org/10.1016/j.neuropsychologia.2019.107242 Martínez-Pérez, V., Tortajada, M., Palmero, L. B., Campoy, G., & Fuentes, L. J. (2022). Effects of transcranial alternating current stimulation over right-DLPFC on vigilance tasks depend on the arousal level. Scientific Reports , 12 (1). https://doi.org/10.1038/s41598-021-04607-8 Mayseless, N., & Shamay-Tsoory, S. G. (2015). Enhancing verbal creativity: Modulating creativity by altering the balance between right and left inferior frontal gyrus with tDCS. Neuroscience , 291 , 167-176. https://doi.org/10.1016/j.neuroscience.2015.01.061 McKone, E., Aimola Davies, A., Fernando, D., Aalders, R., Leung, H., Wickramariyaratne, T., & Platow, M. J. (2010). Asia has the global advantage: Race and visual attention. Vision Research , 50 (16), 1540-1549. https://doi.org/10.1016/j.visres.2010.05.010 McVay, J. C., & Kane, M. J. (2010). Does Mind Wandering Reflect Executive Function or Executive Failure? Comment on and. Psychological bulletin , 136 (2), 188-207. https://doi.org/10.1037/a0018298 Mekern, V. N., Sjoerds, Z., & Hommel, B. (2019). How metacontrol biases and adaptivity impact performance in cognitive search tasks. Cognition , 182 , 251-259. https://doi.org/10.1016/j.cognition.2018.10.001 Navon, D. (1977). Forest before trees: The precedence of global features in visual perception. Cognitive Psychology , 9 (3), 353-383. https://doi.org/10.1016/0010-0285(77)90012-3 Nawani, H., Mittner, M., & Csifcsák, G. (2023). Modulation of mind wandering using transcranial direct current stimulation: A meta-analysis based on electric field modeling. NeuroImage , 272 , 120051. https://doi.org/10.1016/j.neuroimage.2023.120051 Nejati, V., Zamiran, B., & Nitsche, M. A. (2023). The Interaction of the Dorsolateral and Ventromedial Prefrontal Cortex During Mind Wandering. Brain Topography , 36 (4), 535-544. https://doi.org/10.1007/s10548-023-00970-z Rasmussen, T., Filmer, H. L., & Dux, P. E. (2024). On the role of prefrontal and parietal cortices in mind wandering and dynamic thought. Cortex , 178 , 249-268. https://doi.org/10.1016/j.cortex.2024.06.017 Seli, P., Carriere, J. S. A., & Smilek, D. (2015). Not all mind wandering is created equal: Dissociating deliberate from spontaneous mind wandering. Psychological Research , 79 (5), 750-758. https://doi.org/10.1007/s00426-014-0617-x Seli, P., Konishi, M., Risko, E. F., & Smilek, D. (2018). The role of task difficulty in theoretical accounts of mind wandering. Consciousness and Cognition , 65 , 255-262. https://doi.org/10.1016/j.concog.2018.08.005 Seli, P., Risko, E. F., & Smilek, D. (2016). On the Necessity of Distinguishing Between Unintentional and Intentional Mind Wandering. Psychological Science , 27 (5), 685-691. https://doi.org/10.1177/0956797616634068 Smallwood, J., & Schooler, J. W. (2015). The Science of Mind Wandering: Empirically Navigating the Stream of Consciousness. Annual Review of Psychology , 66 (1), 487-518. https://doi.org/10.1146/annurev-psych-010814-015331 Stawarczyk, D., Majerus, S., Maquet, P., & D’Argembeau, A. (2011). Neural Correlates of Ongoing Conscious Experience: Both Task-Unrelatedness and Stimulus-Independence Are Related to Default Network Activity. PLOS ONE , 6 (2), e16997. https://doi.org/10.1371/journal.pone.0016997 Stock, A.-K., Arning, L., Epplen, J. T., & Beste, C. (2014). DRD1 and DRD2 Genotypes Modulate Processing Modes of Goal Activation Processes during Action Cascading. Journal of Neuroscience , 34 (15), 5335-5341. https://doi.org/10.1523/JNEUROSCI.5140-13.2014 Thomson, D. R., Besner, D., & Smilek, D. (2015). A resource-control account of sustained attention: Evidence from mind-wandering and vigilance paradigms. Perspectives on psychological science , 10 (1), 82-96. Zmigrod, S. (2014). The Role of the Parietal Cortex in Multisensory and Response Integration: Evidence from Transcranial Direct Current Stimulation (tDCS). Multisensory Research , 27 (2), 161-172. https://doi.org/10.1163/22134808-00002449 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8938459","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596942230,"identity":"a15f853a-9f8b-42d6-9647-3bda5e84b4f6","order_by":0,"name":"Víctor Martínez-Pérez","email":"","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"Víctor","middleName":"","lastName":"Martínez-Pérez","suffix":""},{"id":596942232,"identity":"054f7343-7d13-48bd-a53c-fd1117f76e97","order_by":1,"name":"Lucía B. Palmero","email":"","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"Lucía","middleName":"B.","lastName":"Palmero","suffix":""},{"id":596942233,"identity":"1008cc32-aa4d-40fa-a6cc-1bd8f40f262e","order_by":2,"name":"Guillermo Campoy","email":"","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Campoy","suffix":""},{"id":596942234,"identity":"bb9beb36-8050-43ef-a367-d32db4b08e88","order_by":3,"name":"Lorenza Colzato","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lorenza","middleName":"","lastName":"Colzato","suffix":""},{"id":596942246,"identity":"8bc7c15e-b7b3-4133-87ba-5cd327d2aac9","order_by":4,"name":"Bernhard Hommel","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Bernhard","middleName":"","lastName":"Hommel","suffix":""},{"id":596942249,"identity":"08174179-9f86-42bb-be98-736546916e4c","order_by":5,"name":"Luis J. Fuentes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBACPuYDEAY/0VrY2BKAJBBLNpCsxeAA8Vp4DB8X/rCxN76RncDw4Q9xWoyNZySkJW67kbuBcWYbMVrke8ykeRIOJ5gBtTDzNhBni/lvnoT/9sYzgFr+EOkwM2aehAOMGySAWhjYiNLCVizNk5acOOPM2w0He4nxCz8b88bPPDZ29vztuRsf/CDGYSjgAKkaRsEoGAWjYBTgAAC3Ti7fHtmIxAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad de Murcia","correspondingAuthor":true,"prefix":"","firstName":"Luis","middleName":"J.","lastName":"Fuentes","suffix":""}],"badges":[],"createdAt":"2026-02-22 10:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8938459/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8938459/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104398293,"identity":"164b9947-419c-4bb3-bde6-9631f577906f","added_by":"auto","created_at":"2026-03-11 12:01:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1582968,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Electrodes setup. (B) Resulting E-field stimulation. (C) Experimental procedure.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8938459/v1/8ab2eb1b130d4daec745a11a.png"},{"id":103568088,"identity":"ce87fdd8-0e17-4bca-a979-830a324b19b7","added_by":"auto","created_at":"2026-02-27 07:36:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":781192,"visible":true,"origin":"","legend":"\u003cp\u003eResults resume: (A) Temporal course of the MW over the task splitting by intentional and unintentional. (B) Temporal course of the Global-Local task (ACC) and its interaction with the HD-tDCS stimulation. (C) Matrix of correlations between Intentional/Unintentional MW, Global Precedence (RT) and Global Precedence (ACC). (D) Density graphs of significant correlations between Intentional MW and Global Precedence (negative correlation), and Unintentional MW and Unintentional MW and Global Precedence (positive correlation).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8938459/v1/72c162aa859c9d1a74077aae.png"},{"id":106959175,"identity":"6d302099-2f3e-4c3c-ab4c-b033cbb9f762","added_by":"auto","created_at":"2026-04-15 08:52:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2760632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8938459/v1/e81e2b7a-ef89-426d-9ece-7459ca077bb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of the left dorsolateral prefrontal cortex in the interplay between metacontrol and mind-wandering. Evidence from a HD-tDCS study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhen individuals engage in cognitive control to complete tasks, their performance can be characterized by two meta-control styles that vary along a continuum, from extreme persistence to extreme flexibility (Hommel, 2015; Hommel \u0026amp; Colzato, 2017; Hommel, Colzato and Beste, 2024; Mekern et al., 2019). These styles dictate whether an individual maintains a focused and sustained approach to the task at hand (persistence) or fluidly shifts attention between various approaches, tasks, ideas, and thoughts (flexibility). Variations in these control styles have been observed both within individuals (Ashby et al., 1999; Colzato et al., 2012, 2017; Fischer \u0026amp; Hommel, 2012) and between different individuals (Colzato et al., 2008, 2014; Colzato, van Beest, et al., 2010; Colzato, Waszak, et al., 2010; McKone et al., 2010; Stock et al., 2014). This adaptability allows humans to adopt different control styles based on specific tasks, challenges, cultures, religions, moods, or genetic factors.\u003c/p\u003e\n\u003cp\u003eDespite the human mind\u0026apos;s capacity for complex cognitive tasks, it frequently experiences lapses in concentration, known as mind-wandering (hereafter MW). These episodes involve shifting attention away from the current task towards unrelated thoughts and internal reflections (Smallwood \u0026amp; Schooler, 2015). This phenomenon underscores the brain\u0026apos;s dual capacity for intense focus and spontaneous diversion, reflecting a delicate balance between maintaining attention and engaging in introspective or imaginative thought. Consequently, mind-wandering not only exemplifies occasional lapses in focus but also highlights the intricate interplay between external task engagement and internal cognitive processes (Mart\u0026iacute;nez-P\u0026eacute;rez et al., 2023; Thomson et al., 2015). Emerging evidence suggests that mind-wandering can occur spontaneously (unintentional) or deliberately (intentional) (Seli et al., 2015) and studying the triggers of mind-wandering reveals significant distinctions (Mart\u0026iacute;nez-P\u0026eacute;rez et al., 2021, 2023; Seli et al., 2016, 2018). For instance, monotonous tasks like the Psychomotor Vigilance Task (PVT) tend to produce a higher rate of intentional mind-wandering compared to more demanding tasks like the Sustained Attention to Response Task (SART) (Mart\u0026iacute;nez-P\u0026eacute;rez et al., 2021), which are associated with higher rates of unintentional mind-wandering (Mart\u0026iacute;nez-P\u0026eacute;rez et al., 2021; Seli et al., 2016).\u003c/p\u003e\n\u003cp\u003eUnderstanding the interplay between cognitive control styles (persistence-flexibility) and MW propensity is crucial, though rarely studied. The latter involves voluntary or involuntary shifts in attention from the task to self-generated thoughts and unrelated mental content, potentially influencing task performance depending on the predominance of persistence or flexibility. One can thus hypothesize that a tendency towards flexibility is linked to a higher propensity for MW or, more specifically, that the nature of MW (spontaneous or voluntary) correlates with the metacontrol strategies employed for task performance. Furthermore, these associations could have identifiable neural correlates. Recent advances in neuroscience have identified the dorsolateral prefrontal cortex (DLPFC) as a key regulator of both MW (Andrews-Hanna et al., 2014; Christoff et al., 2009; Stawarczyk et al., 2011) and metacognitive strategies (Beaty et al., 2015; Botvinick et al., 2001). Non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), have been instrumental in establishing causal links between DLPFC activity and cognitive control styles. Specifically, tDCS studies have shown that modulating DLPFC activity can influence individuals\u0026rsquo; propensity for MW (Axelrod et al., 2015, 2018; Boayue et al., 2021; Filmer et al., 2019, 2021; Mart\u0026iacute;nez-P\u0026eacute;rez et al., 2023; and Nejati et al., 2023 with tACS) and their tendency towards persistence or flexibility in cognitive control (Andrews et al., 2011; Mayseless \u0026amp; Shamay-Tsoory, 2015; Zmigrod, 2014).\u003c/p\u003e\n\u003cp\u003eDespite these insights, the temporal dynamics of MW and metacontrol strategies under DLPFC modulation remain poorly understood. Our objectives here were threefold. First, we aimed to investigate the temporal course of metacontrol strategies and MW within a single experiment. Second, we sought to assess the effect of\u0026nbsp;high-definition tDCS (HD-tDCS) over the DLPFC on the propensity for MW and the development of persistence or flexibility strategies in task performance. Finally, we aimed to understand how these metacontrol tendencies correlate with one another, depending on whether MW is triggered intentionally or unintentionally, a distinction that is crucial to consider when investigating MW with transcranial electric brain stimulation (Chaieb et al., 2019).\u003c/p\u003e\n\u003cp\u003eAs a key objective of the present study was to track changes in time-on-task, mind-wandering (MW) and global-local performance under online HD-tDCS, we implemented a single continuous task stream beginning at stimulation onset rather than adding a separate baseline block. This was to avoid the possibility that an additional pre-stimulation block could induce practice and strategy-setting effects that would carry over to the stimulation period and complicate the interpretation of time-on-task dynamics. However, we acknowledge that the absence of a within-session baseline restricts our ability to unambiguously attribute observed group differences to stimulation-induced change versus pre-existing differences. Accordingly, we present stimulation-related findings as between-group differences expressed over time under randomized assignment, and treat mechanistic conclusions as tentative, pending replication with baseline and/or active-control montages.\u003c/p\u003e\n\u003cp\u003eTo address these objectives, we designed a study that evaluates MW through thought probes, in which participants are intermittently asked whether they are focused on the task or thinking about something else. Specifically, we incorporated thought probes at pseudo-random intervals to capture the temporal evolution of MW episodes. To examine metacontrol strategies, we used a version of the global-local task (Navon, 1977). We posited that people would tend to react more quickly to the broader global aspects compared to the smaller local details, a phenomenon referred to as the global precedence effect (GPE). Furthermore, this effect is more pronounced in certain individuals than in others and has been observed to vary within the same individual based on the circumstances. This variability has been linked to a flexible cognitive style, in contrast to a less prominent global precedence bias, which is associated with a more persistent and detail-oriented approach (Colzato, Hommel, et al., 2010; Colzato, van Beest, et al., 2010; Colzato, Van Hooidonk, et al., 2010; McKone et al., 2010). This approach allowed us to explore the effects of HD-tDCS over the DLPFC on the interplay between MW and metacognitive strategies within a single cognitive task.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were students at the University of Murcia who participated for course credits, and none of them had previously participated in any study on transcranial electrical stimulation. We used G*Power software (Faul et al., 2007) to determine the required sample size for achieving 80% power to detect a small effect (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.25) at an alpha level of 0.05. This calculation suggested that a sample of 46 participants would be sufficient to detect even a small effect of HD-tDCS on global-local task performance. However, given our additional interest in examining the effects of HD-tDCS on intentional and unintentional mind-wandering rates, we decided to collect data from a larger sample of 92 participants (mean age: 19.64, range: 18-29 years; 82 females), of which 45 were randomly assigned to the sham group and 47 to the stimulation (anodal) group.\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethical committee of the University of Murcia, and all participants gave their informed consent before the experiment. The study adhered to the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApparatus and stimuli\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBehavioral measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment was controlled using a Windows 10 equipped computer connected to a 22-inch TFT monitor with a resolution of 1920 \u0026times; 1080 pixels, viewed at approximately 60 cm distance. Programming and data analysis were conducted using E-Prime 3 software, and responses were recorded with a 5-button Chronos device from Psychology Software Tools. The task was adapted from Navon\u0026apos;s (1997) global-local paradigm. Participants were instructed to identify a target stimulus in each trial, which consisted of the letters \u0026quot;A\u0026quot; or \u0026quot;L.\u0026quot; These letters could be presented either as a large letter composed of smaller letters (global condition) or as the smaller letters themselves forming a larger letter (local condition).\u003c/p\u003e\n\u003cp\u003eEach trial began with a fixation point displayed for 1000 ms, followed by the target letter presented to the right or left of the fixation point for 150 ms. Participants then had 2000 ms to respond by pressing buttons 4 or 5 on the Chronos device. Feedback indicating the correctness of the response (correct/incorrect) was provided for 1500 ms. If no response was given within 2000 ms, it was recorded as an error, and the next trial commenced. Participants completed a total of 320 trials, encompassing 8 different combinations of global-local letters presented in randomized order, with the constraint that each combination occurred equally often within the entire session. For the global condition, we had 4 combinations: large A/small Ts, large A/small Hs, large L/small Ts, and large L/small Hs. For the local condition, we also had 4 combinations: small As/large T, small As/large H, small Ls/large T, and small Ls/large H.\u0026nbsp;Participants ran 40 trials for each combination. As \u0026ldquo;Half\u0026rdquo; (first versus second) was a factor of interest, we also ensured that the distribution of trial types was balanced across halves, so that time-on-task effects could not be attributed to systematic changes in stimulus composition.\u003c/p\u003e\n\u003cp\u003eWe assessed MW using intermittent thought probes, which were presented at fixed trial-count intervals. One probe occurred after every 16 trials, making 20 probes in total across 320 trials. Within this constraint, the probes were not dependent on task events or performance. A fixed interval was chosen to ensure comparable sampling density across participants and between the first and second halves of the session, a key factor of interest. The probe asked: \u0026apos;Which of the following responses best characterizes your state of mind just before the presentation of this display?\u0026apos;, with the following response options: \u0026ldquo;On-task\u0026rdquo;, \u0026ldquo;Intentional MW\u0026rdquo; and \u0026ldquo;Unintentional MW\u0026rdquo;. Participants received verbal and written instructions both for answering the thought probe questions and for the task itself. For the thought probes, participants were informed that \u0026quot;on-task\u0026quot; meant thinking about something related to the task (e.g., how difficult or boring it was or the buttons they had to press). They were provided with a definition of MW and a brief explanation of the difference between intentional and unintentional MW. Intentional MW referred to moments when they voluntarily thought about things unrelated to the task (e.g., a shopping list), while unintentional MW referred to involuntary thoughts about unrelated matters (e.g., a past event suddenly coming to mind). Participants were explicitly told that there were no right or wrong answers and were encouraged to respond honestly. They used buttons 1, 2, and 3 on the Chronos device to choose between the three response alternatives: on-task, intentional MW, or unintentional MW with unlimited time to respond.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHD-tDCS setup\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe HD-tDCS was administered online using a Starstim 8 device and Sponstim sponge-based electrodes with a circular area of 8 cm\u0026sup2;, controlled through NIC v2.0.11.7 software (Neuroelectrics\u0026reg;, Barcelona). The target brain area for anodal stimulation was the left dorsolateral prefrontal cortex (l-DLPFC), located at F3 according to the 10-20 system. Three return electrodes, T7, Cz, and Fp2, were positioned in a triangular configuration, each with a 33% current return\u0026nbsp;(Figure 1A, 1B), and the intensity was set at 1.5 mA. This form of high-definition montage has been demonstrated to enhance the focality of stimulation (Kuo et al., 2013), and it has proven effective in modulating the DLPFC in previous studies conducted in our laboratory (Mart\u0026iacute;nez-P\u0026eacute;rez et al., 2019, 2022, 2023). Stimulation was delivered online for 15 minutes from the start of the task, including a 30-second ramp-up/ramp-down period. Sham stimulation consisted of these ramp periods only, to mimic scalp sensations. The 15-minute duration was chosen to align with standard HD-tDCS protocols targeting the prefrontal cortex, while also minimizing participant burden and ensuring sufficient time for stimulation during the initial stages of the task. As the main objective was to determine whether increasing the excitability of the left dorsolateral prefrontal cortex (l-DLPFC) influences the control policy towards persistence, an anodal-versus-sham comparison was implemented.\u003c/p\u003e\n\u003cp\u003eThe study employed a double-blind design, whereby neither the investigator nor the participant knew what type of tDCS stimulation was being delivered. This procedure was implemented using NIC2 software provided by Neuroelectrics to ensure that neither expectations nor biases influenced the outcomes. Importantly, after completing the task, participants were asked to complete a brief questionnaire about blinding, indicating whether they believed they had received active or sham stimulation. Their guesses did not differ from the level of accuracy expected by chance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u003cem\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter registering for the experiment via the university email distribution list, participants were scheduled to perform the experiment in situ, with sessions conducted between 10 AM and 4 PM. On the day of the experiment, participants were randomly assigned to either the sham or tDCS group using a double-blind design. Participants then performed the modified version of the global-local task in individual soundproof rooms. Upon completing the task, they were asked to finish the session by completing a brief transcranial electrical stimulation blinding efficacy survey. A detailed scheme of the procedure is depicted in Figure 1C.\u003c/p\u003e\n\u003cp\u003e\u0026gt;\u0026gt; Insert Figure 1 here \u0026lt;\u0026lt;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using JASP 0.18.3, with a significance level set at \u0026alpha; = 0.05. The primary objective of this study was to investigate the temporal course (first vs. second half) of the global-local task and the thought probes and to determine whether participants modulated their global-local scores and MW rates as a function of HD-tDCS stimulation. Additionally, we examined correlations between MW and global-local measures using\u0026nbsp;Pearson correlation analysis.\u003c/p\u003e\n\u003cp\u003eThree analyses of variance (ANOVAs) were conducted. The first ANOVA used MW rate as the dependent variable, while the other two utilized accuracy and response time (RT) in the global-local task as dependent variables. In the first analysis, Half (first half, second half) and MW Intentionality (intentional, unintentional) were the within-participant factors, and Stimulation (anodal, sham) was the between-participant factor. For the second and third ANOVAs, Half (first half, second half) and Target type (global, local) were the within-participant factors, with Stimulation (anodal, sham) as the between-participant factor. The Greenhouse-Geisser correction was applied to all tests, and further simple effects analyses were conducted only when interactions were significant. All post hoc tests were corrected using Tukey\u0026apos;s method. Descriptive statistics were reported as the mean and standard error of the mean (SEM).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eMind-wandering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rate of both intentional and unintentional MW, measured as the proportion of each MW probes among all thought probes, showed that the rate of unintentional MW was significantly higher than the rate of intentional MW, \u003cem\u003eF\u003c/em\u003e(1, 90) = 69.06, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.225 (M = 0.082, M\u003csub\u003e\u0026nbsp;\u003c/sub\u003e= 0.268, respectively). The Half factor was also significant, \u003cem\u003eF\u003c/em\u003e(1, 90) = 65.52, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.048, indicating a greater MW rate during the second half of the task (M = 0.218) compared to the first half (M = 0.132). Importantly, the Half \u0026times; Intentionality interaction was also significant, \u003cem\u003eF\u003c/em\u003e(1, 90) = 7.145, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.009, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.008. The interaction analysis revealed that, although both intentional and unintentional MW increased significantly from the first to the second half of the task, unintentional MW exhibited a greater relative increase (all \u003cem\u003ep\u003c/em\u003es \u0026lt; 0.001, see Figure 2A). None of the other factors or their interactions reached statistical significance (all \u003cem\u003eF\u003c/em\u003es \u0026lt; 1), indicating that the stimulation did not affect MW.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal-Local task (RT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Half factor reached statistical significance, \u003cem\u003eF\u003c/em\u003e(1, 90) = 103.86, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.069, indicating that participants responded faster in the second half of the experiment (M\u003csub\u003e\u0026nbsp;\u003c/sub\u003e= 422 ms) compared to the first half (M = 484 ms). The Target type factor was also statistically significant, \u003cem\u003eF\u003c/em\u003e(1, 90) = 267.98, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.94, with faster responses observed under the global condition (M = 417 ms) compared to the local condition (M = 489 ms). That is, we replicated the standard global precedence effect (GPE) (Navon, 1977) with this version of the global-local task. Additionally, the Half \u0026times; Target type interaction was also significant, \u003cem\u003eF\u003c/em\u003e(1, 90) = 7.45, \u003cem\u003ep\u003c/em\u003e = 0.008, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.006. Post-hoc tests, corrected for multiple comparisons, revealed significant differences for all contrasts except the comparison between first-half-global and second-half-local responses. None of the other factors or their interactions reached statistical significance (all \u003cem\u003eF\u003c/em\u003es \u0026lt; 1), indicating that tDCS did not affect global-local RTs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal-Local task (ACC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Half factor reached statistical significance, \u003cem\u003eF\u003c/em\u003e(1, 90) = 14.89, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.012, indicating that participants were more accurate in their responses in the second half of the experiment (M = 0.95) compared to the first half (M = 0.93). The Target type factor was also statistically significant, \u003cem\u003eF\u003c/em\u003e(1, 90) = 90.31, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.17, showing more accurate responses under the global condition (M = 0.96) compared to the local condition (M = 0.92). This outcome replicated the GPE in accuracy data, as observed with RTs.\u003c/p\u003e\n\u003cp\u003eImportantly, the Half \u0026times; Target type \u0026times; Stimulation interaction was also significant, \u003cem\u003eF\u003c/em\u003e(1, 90) = 9.81, \u003cem\u003ep\u003c/em\u003e = 0.002, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.007 (see Figure 2B). To further explore this interaction, we conducted separate ANOVAs for the sham and active anodal conditions. For the sham condition, significant main effects were observed for both the Half factor, \u003cem\u003eF\u003c/em\u003e(1, 44) = 6.5, \u003cem\u003ep\u003c/em\u003e = 0.004, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.021, and the Target type factor, \u003cem\u003eF\u003c/em\u003e(1, 44) = 41.89, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.315 (demonstrating the GPE). However, the Half \u0026times; Target type interaction was not significant, \u003cem\u003eF\u003c/em\u003e(1, 44) = 1.94, \u003cem\u003ep\u003c/em\u003e = 0.171, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.007.\u003c/p\u003e\n\u003cp\u003eConversely, in the tDCS condition, the ANOVA revealed a significant Half \u0026times; Target type interaction, \u003cem\u003eF\u003c/em\u003e(1, 44) = 9.29, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.315. Inspection of Figure 2B suggests that, under anodal stimulation, accuracy for local targets increased from the first (M = 0.89) to the second half (M = 0.92) of the task, \u003cem\u003et\u003c/em\u003e(46) = 3.78, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.124. This is consistent with improved maintenance of detail-oriented processing under increasing time-on-task demands. In contrast, anodal stimulation had no significant effect on accuracy in the global condition over time, \u003cem\u003et\u003c/em\u003e(46) = 0.131, \u003cem\u003ep\u003c/em\u003e = 0.89, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.085 (M = 0.96, for the first and second halves).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between MW and Global-Local task\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between the propensity for MW and the biases toward persistence-flexibility, we constructed a Pearson correlation matrix encompassing measures of global intentional MW, unintentional MW, the Global Precedence Effect in response times (GPE-RT), and the Global Precedence Effect in accuracy (GPE-ACC). These analyses are exploratory and do not model either within-session temporal dynamics or group-specific coupling. Therefore, they should be interpreted as associations between participants rather than as evidence of co-fluctuations within individuals over time. The correlation matrix is presented in Figure 2-C (see Figure 2-D for illustrating the density graphs of those correlations). A positive correlation was observed between intentional MW and GPE-ACC (\u003cem\u003e\u0026rho;\u003c/em\u003e = .267, \u003cem\u003ep\u003c/em\u003e = 0.01), while unintentional MW showed a negative correlation with GPE-ACC (\u003cem\u003e\u0026rho;\u003c/em\u003e = - .210, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). None of the other correlation pairs reached statistical significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026gt;\u0026gt; Insert Figure 2 here \u0026lt;\u0026lt;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers new empirical evidence for the dynamic interplay between metacontrol modes and MW, and how this interaction can be modulated through HD-tDCS over the l-DLPFC. This study examined: (i) how intentional and unintentional motor work (MW) evolves over time; (ii) how global\u0026ndash;local performance indices relate to MW intentionality; and (iii) whether anodal high-density transcranial direct current stimulation (HD-tDCS) targeting the left dorsolateral prefrontal cortex (DLPFC) modulates these patterns. Three key findings emerged. Firstly, both intentional and unintentional mind wandering (MW) increased from the first to the second half of the task, with a relatively larger increase for unintentional MW. Secondly, anodal stimulation did not alter overall MW rates. Thirdly, we observed stimulation-related modulation of accuracy selective to local processing in the second half of the task.\u003c/p\u003e \u003cp\u003eImportantly, as the design did not include a pre-stimulation baseline block or an active-control stimulation site, we interpret the stimulation-related pattern as indicating a bias towards persistence-oriented processing under time-on-task demands according to the metacontrol framework proposed by Hommel and Colzato (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, we refrain from making strong claims about region-specific mechanisms. Replication involving baseline measurements and an active control montage would strengthen anatomical and mechanistic inferences.\u003c/p\u003e \u003cp\u003eOne of the key contributions of our study is the demonstration of a double dissociation between intentional and unintentional MW and their relationship to metacontrol tendencies. Specifically, we observed that intentional MW was positively correlated with the GPE in accuracy, an index of cognitive flexibility, whereas unintentional MW was negatively associated with the same measure, indicative of a more persistence-oriented style. These results align with previous studies by Hommel, Colzato, and colleagues showing that flexible cognitive styles are associated with greater responsiveness to global-level information and broader attentional scopes (Colzato et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hommel \u0026amp; Colzato, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis distinction further supports the view that intentional MW may reflect an adaptive form of internal cognition, akin to what Hommel and Colzato (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) describe as the flexible mode of control, where internal thoughts are generated deliberately and may serve creative or future-oriented purposes (Smallwood \u0026amp; Schooler, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In contrast, unintentional MW appears to emerge from lapses in metacontrol persistence, consistent with a loss of top-down regulation typical of a persistence-dominated control mode as time-on-task progresses (McVay \u0026amp; Kane, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Seli et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these clear associations, HD-tDCS did not significantly affect the overall frequency of either form of MW. This absence of a direct stimulation effect on spontaneous thought aligns with previous mixed findings in the literature (Axelrod et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Filmer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mart\u0026iacute;nez-P\u0026eacute;rez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rasmussen et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and suggests that modulating MW propensity may require broader network-level stimulation, including medial prefrontal and default mode regions. Nonetheless, HD-tDCS over l-DLPFC did significantly improve task accuracy under local processing conditions, but only in the second half of the task, when performance typically declines. This selective improvement suggests a facilitation of persistence, helping participants maintain attention to detail as fatigue sets in, again consistent with the persistence mode described by the metacontrol framework (Hommel \u0026amp; Colzato, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Stimulation was delivered online for 15 minutes from the start of the task, whereas the global-local task lasted approximately 25 minutes. Therefore, the accuracy benefit observed in the second half should not be interpreted as a strictly concurrent \u0026ldquo;online-only\u0026rdquo; effect when it emerges. Rather, it is consistent with delayed or sustained consequences of early stimulation (e.g., task-set maintenance that persists beyond stimulation offset), which become most apparent under increasing time-on-task demands. Additionally, the co-occurrence of increased MW rates with improved accuracy in the second half is not necessarily contradictory. Thought probes provide intermittent self-reports, whereas accuracy reflects continuous task performance. Thus, participants may report more off-task thoughts over time while still providing adequate task-set support on response-requiring trials. Furthermore, gains in stimulus-response mapping due to practice can improve accuracy even as subjective MW increases with fatigue. These interpretations remain tentative and highlight the value of future analyses that relate performance immediately preceding each probe to the reported mental state.\u003c/p\u003e \u003cp\u003eThese findings provide novel causal support for the metacontrol model, particularly the idea that stimulation can bias control states depending on task demands and temporal dynamics. Our prior work has shown similar increases in MW over time (Mart\u0026iacute;nez-P\u0026eacute;rez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the current study extends this by demonstrating that HD-tDCS may mitigate decline in performance by reinforcing persistence-oriented control.\u003c/p\u003e \u003cp\u003eMoreover, the observed increase in both intentional and unintentional MW across the session replicates our earlier findings (Mart\u0026iacute;nez-P\u0026eacute;rez et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), reinforcing the idea that time-on-task effects reflect shifts in cognitive resources and control modes. These temporal dynamics are central to the metacontrol perspective, which argues that persistence and flexibility are not static traits but adaptable states responsive to internal and external pressures (Hommel, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eSeveral limitations qualify the strength and specificity of the conclusions. Most importantly, the study did not include a pre-stimulation baseline block and did not include an active-control stimulation site. Consequently, stimulation-related effects are best interpreted as between-group differences expressed over time under randomized assignment, and the present design does not allow strong claims about region-specific mechanisms uniquely attributable to l-DLPFC modulation. These constraints are common in early-stage neuromodulation work, but they underscore the need for replication using baseline assessment and an active-control montage to strengthen anatomical and mechanistic inference.\u003c/p\u003e \u003cp\u003eA second set of limitations concerns measurement precision and temporal dynamics. MW estimates were derived from a limited number of probes per half, with fewer intentional than unintentional reports, reducing precision for fine-grained interaction tests involving intentionality. In addition, the stimulation window (15 minutes) was shorter than the overall task duration, which constrains inferences about the temporal locus of effects emerging in the second half; such effects may reflect delayed or sustained consequences of early stimulation rather than strictly concurrent online modulation. Future studies should increase probe density, incorporate probe-locked/time-resolved analyses of performance, and more tightly align stimulation timing with the critical task epochs (or systematically vary stimulation timing) to clarify when and how neuromodulation influences control-policy expression.\u003c/p\u003e \u003cp\u003eImportantly, these limitations do not undermine the central contribution of the present work. The study identifies a theoretically diagnostic dissociation between intentional and unintentional mind-wandering in their relationship to global-local indices, and it provides initial evidence consistent with the idea that prefrontal neuromodulation can bias task-expressed persistence under increasing time-on-task demands. These findings sharpen testable predictions for future experiments designed to establish specificity while building on the diagnostic patterns observed here.\u003c/p\u003e \u003cp\u003eOverall, the present study identifies a dissociation relevant to metacontrol between intentional and unintentional MW, and provides evidence consistent with the view that these forms of mental engagement are related differently to persistence-flexibility in task performance. Additionally, while the design does not permit robust region-specific mechanistic inference, the observed stimulation-related pattern indicates that HD-tDCS targeting the left prefrontal cortex can modulate task-based control in a manner that selectively enhances persistence-oriented performance under escalating time-on-task demands. Together, these findings offer empirical support for key predictions of the metacontrol framework and generate clear, testable hypotheses for future neuromodulation studies incorporating baseline assessment, active-control stimulation, and time-resolved modelling to establish anatomical specificity and temporal dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grant PID2021-125408NB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by \u0026ldquo;ERDF A way of making Europe\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the first/corresponding author upon request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eV\u0026iacute;ctor Mart\u0026iacute;nez-P\u0026eacute;rez: Conceptualization, Methodology, Formal analysis, Investigation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Visualization. Luc\u0026iacute;a B. Palmero: Investigation, Methodology, Formal analysis, Writing \u0026ndash; original draft.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eGuillermo Campoy: Investigation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing. Lorenza Colzato: Conceptualization, Writing \u0026ndash; review \u0026amp; editing. Bernhard Hommel: Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing. Luis J. Fuentes: Conceptualization, Methodology, Writing \u0026ndash; original draft, Writing \u0026ndash; reviewing and editing, Funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlexandersen, A., Csifcs\u0026aacute;k, G., Groot, J., \u0026amp; Mittner, M. (2022). 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DRD1 and DRD2 Genotypes Modulate Processing Modes of Goal Activation Processes during Action Cascading. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(15), 5335-5341. https://doi.org/10.1523/JNEUROSCI.5140-13.2014\u003c/li\u003e\n \u003cli\u003eThomson, D. R., Besner, D., \u0026amp; Smilek, D. (2015). A resource-control account of sustained attention: Evidence from mind-wandering and vigilance paradigms. \u003cem\u003ePerspectives on psychological science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 82-96.\u003c/li\u003e\n \u003cli\u003eZmigrod, S. (2014). The Role of the Parietal Cortex in Multisensory and Response Integration: Evidence from Transcranial Direct Current Stimulation (tDCS). \u003cem\u003eMultisensory Research\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(2), 161-172. https://doi.org/10.1163/22134808-00002449\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mind-wandering, global-local task, HD-tDCS, metacontrol, persistence, flexibility","lastPublishedDoi":"10.21203/rs.3.rs-8938459/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8938459/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe metacontrol framework claims that cognitive control operates along a continuum between persistence and flexibility. While spontaneous mind-wandering is often considered a failure of control, emerging evidence suggests that different types of mind-wandering, intentional versus unintentional, may reflect distinct metacontrol dynamics. We examined how high-definition transcranial direct current stimulation (HD-tDCS) over the left dorsolateral prefrontal cortex modulates the interplay between metacontrol strategies and mind-wandering, using a global-local task combined with intermittent thought probes. Ninety-two participants completed a global-local task while receiving either anodal or sham HD-tDCS at 1.5 mA over the left dorsolateral prefrontal cortex. Mind-wandering episodes were assessed using thought probes, distinguishing between intentional and unintentional mind-wandering. Metacontrol tendencies were inferred from global precedence effects observed in response accuracy and latency. HD-tDCS selectively enhanced accuracy in the local condition during the second half of the task, suggesting an increase in persistence-oriented control. Intentional mind-wandering was positively associated with cognitive flexibility (greater global precedence), while unintentional mind-wandering correlated with persistence. However, stimulation did not directly affect mind-wandering rate. Our findings support a double dissociation between types of mind-wandering and metacontrol styles. They provide causal evidence that HD-tDCS over the left dorsolateral prefrontal cortex can promote persistence without altering spontaneous thought frequency, thereby validating and extending the metacontrol framework.\u003c/p\u003e","manuscriptTitle":"The role of the left dorsolateral prefrontal cortex in the interplay between metacontrol and mind-wandering. 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