The Relationship Between Mindfulness and Inhibitory Control in a Neutral and Reward Associated Context with a Focus on Individual Differences

The Relationship Between Mindfulness and Inhibitory Control in a Neutral and Reward Associated Context with a Focus on Individual Differences | 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 Relationship Between Mindfulness and Inhibitory Control in a Neutral and Reward Associated Context with a Focus on Individual Differences Atanas Tannous, Zsófia Logemann-Molnár, Zsolt Demetrovics, Alexander Logemann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9253829/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Empirical links between trait mindfulness and inhibitory control (IC) remain inconsistent, particularly in motivationally salient contexts. This study investigated how specific mindfulness facets relate to IC under varying reward structures and whether other dispositional traits modulate these associations. Methods: One hundred adults completed the Five Facet Mindfulness Questionnaire and validated measures of affective distress, self-regulation, emotion regulation, and broader dispositional traits. IC was assessed using a modified stop-signal task. Participants were randomly assigned to one of four between-subject conditions varying in reward structure to minimise carryover effects. Stop-signal reaction time was the primary outcome measure. Results: No significant main effects of total mindfulness or reward condition on stop-signal reaction time (SSRT) were observed. However, a significant interaction emerged between the FFMQ Describing facet and task condition ( p = .026), indicating that the relationship between the labelling of experience and IC is context dependent. Follow-up analyses revealed that this interaction was primarily driven by a significant positive association between Describing and SSRT in the fully reward-associated condition ( p = .046). In this 'saturated' reward environment, higher Describing capacity was linked to slower stopping performance. No other mindfulness facets or dispositional characteristics yielded significant moderating effects, although anxiety was independently associated with slower stopping across conditions. Conclusions: The findings indicate that mindfulness–IC relationships are facet-specific and context dependent. The Describing facet appears to differentially relate to inhibitory performance depending on reward structure, underscoring the importance of motivational context when examining mindfulness and executive control. Mindfulness Inhibitory Control Reward Context Executive Functions FFMQ Figures Figure 1 Figure 2 Figure 3 Introduction In the landscape of human cognition, inhibitory control (IC) stands as a fundamental executive function, defined as the ability to deliberately suppress dominant, automatic, or habitual responses to achieve a goal [1]. This mechanism is not merely a cognitive "brake"; it is integral to the successful maintenance of attention, stable emotion regulation, and conscious, adaptive decision-making [1]. Given its central role, deficits in IC are a common feature across diverse forms of psychopathology, including attention-deficit/hyperactivity disorder [2], substance use disorders, and various impulse-control issues [3]. Consequently, understanding the psychological traits that predict or enhance robust IC performance is a paramount concern in psychology. The brain regions involved in adequately executing this function are the right inferior frontal cortex (rIFC) and pre-supplementary motor area (pre-SMA) which are central to initiating and coordinating top-down suppression of prepotent responses, while the basal ganglia—particularly the subthalamic nucleus—modulate thalamocortical output to halt ongoing actions [4,5]. The anterior cingulate cortex (ACC) contributes by detecting conflict and signaling the need for control, and parietal regions assist in reorienting attention away from irrelevant stimuli [6]. Research also indicated that Default Mode Network (DMN) efficiency correlates with poorer inhibition performance as reviews of DMN functioning emphasize that failure to deactivate the DMN during goal-directed tasks is a hallmark of reduced cognitive control, including response inhibition [7–9]. The Dual Mechanisms of Control (DMC) framework distinguishes between proactive and reactive modes of inhibitory control [10]. Proactive control is characterized as an anticipatory, sustained form of “early selection,” in which goal-relevant information is actively maintained within the lateral prefrontal cortex to bias attention and action systems before cognitively demanding events occur. In contrast, reactive control functions as a “late correction” mechanism, mobilized only when interference or conflict is detected, and involves transient reactivation of task goals through lateral prefrontal cortex and broader neural networks. In real world contexts, reactive control is an efficient process that uses less cognitive resources and contributes to adaptive functioning. The Stop Signal Task (SST) [11] provides an objective, behavioral metric for reactive IC, yielding the Stop Signal Reaction Time (SSRT), which represents the estimated latency of the covert stopping process [12]. Performance in this task, particularly under conditions of heightened emotional or motivational salience, can help predict real-world self-regulatory success. According to estimates [13,14], inhibition is necessary for 80% to 90% of self-regulation attempts. A growing body of research has established trait mindfulness as a psychological disposition which can be a predictor of the extent to which individuals can benefit from mindfulness-based interventions [15,16]. Trait mindfulness is also strongly associated with superior executive function [17–21], including inhibitory performance [22,23], and with activations or altered structures in similar brain regions [24,25]. Specifically, research has demonstrated that during various emotion processing tasks, higher levels of trait mindfulness are primarily linked to enhanced pre-frontal cortex activation and decreased amygdala activation [16,26]. Defined as the non-judgemental awareness of present-moment experience [27–29], mindfulness can be fundamentally viewed as a form of self-regulation by cognitive and neuroscientific frameworks [16,30,31]. It has been shown to boost IC by fostering meta-awareness, an increased cognitive distance from internal stimuli, which facilitates the necessary disengagement required to cancel a planned action [16]. Additionally, mindfulness-based interventions have shown favorable outcomes in behavioral measures [18], as well as their underlying electrophysiological responses [17]. While there is evidence for the benefits of mindfulness training, and trait mindfulness [32], for inhibitory control and performance on go/no-go tasks [18,23,33–35], empirical evidence specifically linking trait mindfulness to IC performance in motivationally salient contexts remains mixed. This variability likely reflects the broader challenge of attributing behavioral effects to specific cognitive mechanisms, rather than fundamental shortcomings of inhibition paradigms specifically. Indeed, inhibitory control tasks capture the contribution of multiple processes, which contributes to modest correlations across tasks and complicates interpretation [36,37]. Importantly, the Stop-Signal Task (SST) is widely regarded as providing a relatively selective measure of motor inhibitory control when implemented and analyzed appropriately. As outlined by Verbruggen et al. [12], the use of a staircase tracking algorithm and the integration method for estimating SSRT controls for individual differences in response speed, thereby reducing the confounding influences of general processing speed and attentional factors. Consequently, the SSRT is considered a robust and theoretically grounded index of inhibitory control, supporting the use of the SST as a valid tool for examining individual differences in motor inhibition in the present study. Another point of nuance is that people do not operate in neutral environments, so inhibitory control must often be recruited in the presence of reward-associated cues such as appetitive food or monetary stimuli. These cues strongly activate the striatum and broader reward processing circuits, increase approach tendencies, and reliably challenge inhibition [38,39]. The evidence linking trait mindfulness to inhibitory control in neutral contexts is mixed, with some studies reporting positive associations [33,40,41] and others finding null or negative effects [42], and its role in reward contexts remains even less clear [33]. Neural work links mindfulness with reduced DMN activity and stronger connectivity within the salience network [24,25], which may heighten attentional capture via motivationally salient cues. Behavioral and ERP studies support this possibility: oddball paradigms show stronger early attention to salient stimuli among more mindful individuals [43], and mindfulness has been associated with poorer inhibition when the stimuli to-be-inhibited stimuli are reward related, including attractive faces, smoking cues, and food or money stimuli [22,42,44]. Yet other findings indicate that when reward cues appear before, and the subsequent stop signal is neutral, mindfulness can relate to better stopping performance [22]. This suggests that the timing of reward information is a critical but largely untested factor: inhibitory control may be impaired when mindfulness enhances attentional orienting during direct exposure to reward cues, but may be supported when inhibition is required after reward exposure, possibly due to reduced carry over effects and more efficient engagement of reactive control processes [45]. Taken together, these findings highlight the need to study how the temporal placement of reward-associated stimuli (i.e., before or during the required inhibition) could help shape the mindfulness-inhibition relationship. Recent work from Logemann-Molnár et al. [33], utilizing the Mindful Attention Awareness Scale (MAAS), confirmed a general association between trait mindfulness and IC performance but no interaction effect when reward-associated stimulus were introduced into the SST, relative to neutral. Interestingly, the previously observed main effect of condition, suggesting poorer inhibitory control in particular reward contexts, could not be confirmed in that study. This may suggest that the use of a within group design where each participant was exposed to all four variations of the SST introduced a possible carry-over effect of the three reward-associated conditions onto the single neutral condition. Another open question pertains to the role of different facets of mindfulness. In the previous study, the MAAS was utilized to assess a unidimensional aspect of present-moment attention. However, mindfulness is not a unitary construct; rather, it involves distinct, interacting components that collectively facilitate the mindful state [46]. The Five Facet Mindfulness Questionnaire (FFMQ) [47] is thought to provide a broader assessment of both the attentional and the attitudinal components (facets) of the construct, whereas the MAAS is predominantly focused on the attentional component [48,49]. The FFMQ facets include Observing, Describing, Nonjudging, Non-Reactivity, and Acting with Awareness (AWA), and they are theorized to categorize different cognitive and emotional processes, offering a structural perspective for the current investigation. The Describing and Nonjudging facets were found to be related to flexibility in the orienting attention network and neuroanatomical structures associated with orienting, attentional control and conflict detection [49,50]. Another study found a positive association between the Describing facet with attentional control performance [50]. The Non-Reactivity to Inner Experience facet is linked to fewer emotional regulation difficulties emotional regulation [51]. This skill could be particularly relevant in motivated contexts, as it allows individuals to register emotionally salient cues (e.g., potential monetary reward) without being captured by the affective response, thereby preventing interference with the stopping process. By assessing these specific facets, this study can determine if distinct mindfulness skills are differentially engaged by the cognitive demands of IC, particularly when motivational pressure is applied. Trait differences in negative affect and self-regulatory capacity may also shape when mindfulness relates to stopping performance, particularly in motivationally salient contexts. Attentional control theory posits that anxiety consumes cognitive resources by taxing working memory capacity, thereby impairing executive functions like response inhibition [52]. Empirically, high anxiety is linked to worse cognitive control as it occupies mental capacity needed for goal-directed tasks [53], whereas mindfulness tends to enhance cognitive control. Thus, elevated anxiety or stress could alter how effectively mindfulness translates into improved Stop-Signal Reaction Time (SSRT) performance, since baseline cognitive control may be compromised [54]. In fact, anxious individuals often report more cognitive failures in daily life [55], highlighting anxiety’s toll on attention and inhibition. Indeed, high stress is generally thought to impair executive functions [1,56], potentially blunting the benefits of mindfulness on inhibitory control. Similarly, traits related to self-regulation and emotion regulation may also influence mindfulness–SSRT relationships. Mindfulness practice is fundamentally a training in self-regulation of attention and emotion [57], therefore it is plausible that Individuals with strong baseline self-regulatory skills (e.g., high trait self-regulation) might exhibit better inhibitory control under challenging conditions and thus gain less additional benefit from mindfulness, whereas those with poorer self-regulation could show greater improvements. Moreover, emotion regulation capacity is closely tied to inhibitory control; people who struggle to manage emotions tend to show deficits in response inhibition [58]. Mindfulness-based interventions are known to improve emotional regulation while reducing anxiety and bolstering stress resilience [57]. Consequently, variation in emotion regulation could alter the degree to which mindfulness training improves reactive inhibitory control, as those with poor emotion regulation might experience more pronounced gains in cognitive control from cultivating mindfulness. By integrating the multidimensional FFMQ, between-subject design, and testing the effects of individual differences, this study moves beyond simple trait correlations to help determine the boundary conditions of the mindfulness-IC relationship and identify key psychological factors that either facilitate or impede the cognitive benefits of present-moment awareness. Based on prior findings that reward cues disrupt inhibitory control and that mindfulness modulates sensitivity to motivational salience, we expected reward-associated contexts to produce slower stopping performance than fully neutral context. We further anticipated that mindfulness would facilitate inhibition when stopping followed exposure to reward, specifically when participants initiated responses to reward cues before encountering a neutral stop signal. In contrast, mindfulness was expected to impair inhibition when the stop signal itself was reward-associated, irrespective of the preceding go cue. As secondary, explorative aims, we also examined whether individual differences in self-regulation, negative affect, self-regulation, and other broader dispositional characteristics moderated these associations. Methods Sample The participants were recruited through social media platforms and local community advertisements. Individuals were eligible to take part if they were between 18 and 65 years old, reported no history of neurological or psychiatric disorders, and had not used psychoactive substances in the seven days prior to participation. All participants completed the study online. The target sample size was determined using G*Power [59], aiming to detect moderate effects with α = .05 and 80% power, yielding a required minimum of approximately 80 participants. A total of 209 individuals took part in the study and provided informed consent before any procedures. The participants received course credit for their participation. Consistent with prior recommendations for SST data quality, participants were excluded from analysis if they scored below the B1 level on the English proficiency self-assessment, produced an invalid (negative) SSRT estimate, demonstrated an inhibition rate above .75 or below .25, showed more than 10 percent omissions on go trials, or responded incorrectly on more than half of go trials. Data from participants who did not complete the experiment were also excluded from the analyses. After applying these criteria, the final dataset comprised 100 participants. Ages ranged from 18-51 years (M = 24.8, SD = 6.13). Materials Psytoolkit To facilitate online data collection, we employed the Psytoolkit framework [60,61], which allows for the integration of behavioral paradigms with psychometric self-report scales. This platform was selected due to its demonstrated reliability in replicating laboratory-standard results within a web-based environment [62]. Language Proficiency English reading proficiency is assessed via a single question on the Common European Framework of Reference for Languages (CEFR) Self-assessment grid [63]. Participants choose one of six response options, ranging from "A1 Basic user" to "C2 Proficient user." A minimum proficiency of "B1 Independent user" is required, which corresponds to the ability to understand texts using high frequency every day or job-related language. The response for B2 was “I can understand texts that primarily use high frequency every day or job-related language. I can also comprehend the description of events, feelings, and wishes in per-sonal letters." Five Facet Mindfulness Questionnaire (FFMQ) Dispositional mindfulness is also assessed using the 39-item FFMQ [47], which provides specific subscale scores on a 1 to 5-point Likert-type scale for Observing, Describing, Acting with Awareness, Non-Judging of Inner Experience, and Non-Reactivity to Inner Experience. A higher score on the FFMQ indicates higher levels of mindfulness. The FFMQ has demonstrated good internal consistency, factorial validity, and convergent validity with related constructs across both clinical and nonclinical populations [50; 67]. In the original validation study, Baer et al. [47] reported strong internal consistency for all FFMQ facets, with Cronbach’s alphas of .83 for Observing, .91 for Describing, .87 for Acting with Awareness, .87 for Nonjudging, and .75 for Nonreactivity. In the present sample, reliability estimates were acceptable to excellent across facets: Observe (α = .76), Describe (α = .92), Acting with Awareness (α = .91), Nonjudging (α = .89), and Nonreactive (α = .68). Importantly, prior work has highlighted that the facets capture partially distinct components of mindfulness and may show differential associations with cognitive and affective outcomes, particularly in nonmeditating samples [64]. Accordingly, analyses in the present study focused on individual FFMQ facets rather than a total score, in order to examine whether specific components of mindfulness were differentially associated with inhibitory control across task conditions. Emotion Regulation Questionnaire (ERQ) Emotional regulation strategies were assessed using the Emotion Regulation Questionnaire (ERQ) [65]. The ERQ is a widely used self-report measure that assesses two habitual emotion regulation strategies: cognitive reappraisal and expressive suppression. Cognitive reappraisal reflects the tendency to reinterpret emotionally evocative situations in order to alter their emotional impact, whereas expressive suppression reflects the tendency to inhibit the outward expression of emotions. The questionnaire consists of 10 items rated on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with higher scores indicating greater use of the respective strategy. The internal consistency reliability of the ERQ subscales was acceptable to excellent, with cognitive reappraisal demonstrating high reliability (Cronbach’s α = .89–.90) and expressive suppression demonstrating acceptable reliability (Cronbach’s α = .76–.80) [65–67]. In the present study, the ERQ demonstrated good internal consistency in the present sample for both reappraisal (α = .83, 95% CI [.77, .88]) and suppression (α = .80, 95% CI [.72, .86]). Reappraisal and suppression scores were examined as individual difference variables in exploratory analyses. Short Self-Regulation Questionnaire (SSRQ) The short version of the self-regulation questionnaire (SSRQ) [68] is a shortened version of the self-regulation questionnaire [69] which evaluates subjects’ dispositional self-regulation and their ability to regulate and plan their behavior in a flexible manner in accordance with the desired outcome. The SSRQ consists of 31 items and has demonstrated excellent internal consistency in the development sample (Cronbach’s α = .92) [68], with subsequent studies reporting similarly high reliability across independent samples [70]. Evidence for construct validity is provided by strong correlations with the original full length self-regulation questionnaire (r ≈ .96) [68]. The participants indicate the extent to which they agree with each item via a 5-point Likert scale: 1 (Strongly Disagree), 2 (Somewhat Disagree), 3 (Neutral), 4 (Somewhat Agree), and 5 (Strongly Agree). Examples of items include the following: “Once I have a goal, I can usually plan how to reach it,” “I have a lot of willpower” and “As soon as I see a problem or challenge, I start looking for possible solutions”. Depression Anxiety Stress Scale - 21 (DASS-21) The Depression Anxiety Stress Scales 21 (DASS‐21) [71] is a short form of the Lovibond & Lovibond [72] 42‐item self‐report measure of depression, anxiety, and stress (DASS). The DASS‐21 consists of three 7‐item self‐report scales taken from the full version of the DASS. The subjects are asked to use 4-point severity/frequency scales to rate the extent to which they have experienced each state over the past week. Scores for depression, anxiety and stress are calculated by summing the scores for the relevant items. The DASS‐21 subscales can validly be used to measure the dimensions of depression, anxiety, and stress. The subscales have demonstrated good to excellent internal consistency, with Cronbach’s α values of .88 for depression, .82 for anxiety, .90 for stress, and .93 for the total score [71]. Stop Signal Task (SST) The stop signal task (SST) was administered online following the standard paradigm outlined by Logan et al. [11] and adapted from Logemann-Molnár et al. [33]. In each trial, the participants responded to a centrally presented go stimulus with a left or right button, using their index finger. In a subset of trials, the same stimulus was followed by a stop signal, indicating that the initiated response should be inhibited. The stimuli were displayed for 150 ms, with intertrial intervals of 1500 ms for the go trials and 1700 ms for the stop trials. The participants first completed a practice phase of 48 go trials and 16 stop trials, with feedback for omission errors, commission errors, and incorrect responses. Following practice, each participant was randomly assigned to one of four conditions, which was consistent with the between-subject design. Across conditions, the go and stop stimuli were either neutral images (stones) or reward-associated images (money). This process produced four possible stimulus configurations: neutral go with a neutral stop, neutral go with a reward stop, reward go with a neutral stop, and reward go with a reward stop (see Fig. 1 for an example trial). The stop signal was indicated by the same stimulus used in the go trials but surrounded by 15 pixels narrow blue border, ensuring minimal visual change aside from the requirement to inhibit. Each participant completed 96 go trials and 32 stop trials. Stimuli appeared in portrait orientation (go: 100x200 pixels; stop (including border): 100x230 pixels) or landscape mode (go: 200x100 pixels; stop (including border): 230x100 pixels), and the orientation determined whether the left or right response key was correct. The trials were randomized, and the conditions were counterbalanced across participants. A one-up/one-down staircase procedure adjusts the stop-signal delay (SSD) to maintain an inhibition rate near 50% [73] using a tracking algorithm. The SSD began at 250ms and decreased by 50ms after failed inhibitions and increased by 50ms after successful ones, following recommended SST procedures [12,73]. SSRTs were estimated using the integration method in accordance with consensus guidelines [12]. Reaction times (RTs) below 150 ms or exceeding the intertrial interval were excluded. Omission trials were replaced with the maximum RT (1500ms) within the specific response window. Sorted go RTs were used to identify the integration point based on each participant’s proportion of failed stops. SSRT was calculated by subtracting the mean SSD from this integration-point RT, according to the latest consensus [12]. Procedure The study utilized a single-session, cross-sectional design via the online experimental platform Psytoolkit [60,61]. This tool is utilized for its high replicability in cognitive psychological experiments. After providing written informed consent, the participants first completed self-report questionnaires. Following the questionnaires, the participants were provided with instructions for the stop signal task (SST). Specifically, before the main task begins, participants were instructed to respond to the Go signal as fast and accurately as possible and to not wait for the stop signal. To aid in understanding the instructions, the participants were provided with feedback during the practice part of the task. After the participants performed the SST in the condition assigned to them via a balanced design, the experiment was completed. Statistical Analysis Analyses were conducted in R (version 4.5.2; R Core Team [74]). The data were screened to ensure valid estimation of the SSRT and adequate task performance. SSRT served as the primary dependent variable. The task condition was entered as a between-subject factor with four levels reflecting the reward association of the go and stop signals. Trait mindfulness was entered as a continuous predictor via the Five Facet Mindfulness Questionnaire (FFMQ) total score and individual facet scores, which were analyzed in separate models. SSRT was analyzed via linear regression–based analyses of covariance (ANCOVAs), with effects evaluated using Type III sums of squares. The primary effect of interest was the interaction between mindfulness and task condition. Significant interactions were followed up using model-based simple slope analyses to examine conditional associations within each condition. Exploratory models examined whether dispositional characteristics moderated the mindfulness–SSRT relationship. All continuous predictors were standardised prior to analysis. Statistical significance was evaluated using two-tailed tests with an alpha level of .05. Results Overall, the SSRT did not differ significantly across the four SST conditions, F (3, 96) = 1.02, p = .387 (descriptive SST performance data are shown in Table 1). SSRT values were nearly identical across the neutral and reward-go/neutral-stop and the neutral-go/reward stop conditions, with only the fully reward-associated condition showing slightly longer stopping latencies. Table 1. Summary of performance data in the SST. Condition SSRT Mean RT STD RT SOA Prop Correct Prop Omissions Prop Inhibition neutral go + neutral stop 229 735 208 491 .93 .03 .49 neutral go + money stop 229 718 200 479 .93 .03 .49 money go + neutral stop 230 708 212 465 .94 .03 .50 money go + money stop 252 686 237 442 .90 .04 .47 note : this table presents a summary of reaction time (RT) and performance metrics across conditions. Stop Signal Reaction Time (SSRT), Mean RT, Standard Deviation of RT (STD RT), Go-stop Stimulus Onset Asynchrony (SOA), and proportions of correct responses, omissions, and inhibitions are reported. Values are rounded for clarity, with SSRT, Mean RT, STD RT, and SOA rounded to the nea rest whole number, and proportions rounded to two decimal places. Primary analyses focused on whether mindfulness was associated with inhibitory control and whether these associations varied as a function of reward timing. Each FFMQ facet was tested separately using an ANCOVA with Condition as a four-level factor. Across models, none of the facets showed a significant main effect on SSRT (all p > .14), indicating that higher mindfulness facets did not correspond to faster stopping when collapsing across conditions. SSRT also did not differ across conditions in any model (all p > .25), suggesting that the experimental manipulation itself did not alter stopping performance while controlling for the different mindfulness facets. A significant interaction between and the Describing facet emerged, F (3,92) = 3.23, p = .026, indicating that the association between Describing and SSRT depended on reward placement within the trial. Follow-up simple-slope analyses were conducted to examine the association between the Describing facet and the SSRT within each task condition. The slope was not significant in the neutral go–neutral stop condition, b = 17.60, SE = 14.30, t (92) = 1.23, p = .22, nor in the neutral go–reward stop condition, b = −21.50, SE = 13.90, t (92) = −1.56, p = .12, or the reward go–neutral stop condition, b = −14.50, SE = 10.80, t (92) = −1.35, p = .18. In contrast, the slope was significant in the reward go–reward stop condition, with higher Describe scores associated with slower stopping, b = 19.20, SE = 9.52, t (92) = 2.02, p = .046. Simple-slope estimates showed that higher Describing scores were associated with faster stopping when reward cues appeared before the stop signal (reward go + neutral stop) and, potentially counterintuitive, when only the stop signal was reward-associated (neutral go + reward stop). In contrast, higher scores were associated with slower stopping in the fully neutral condition and when both the go and stop cues were reward associated (reward go + reward stop). No other facet demonstrated a similar interaction (all p > .10). A summary of these effects is shown in Table 2. Table 2. ANCOVA results for mindfulness facets predicting SSRT across conditions Facet Main effect of facet (p) Main effect of condition (p) Condition × facet interaction (p) Observing .146 .521 .143 Describing .221 .378 .026 Acting with Awareness .847 .258 .103 Non-Judging .834 .499 .828 Non-Reactivity .698 .347 .388 note. table displays Type III ANCOVA results for each mindfulness facet entered as a z-scored covariate, with Condition (Neutral Go + Neutral Stop; Neutral Go + Reward Stop; Reward Go + Neutral Stop; Reward Go + Reward Stop) as a between-subjects factor. SSRT served as the dependent variable. Significant Condition × Facet interactions indicate that the association between mindfulness and stopping performance differed across the four experimental conditions. For visualization purposes only, the Describing scores were median-split and plotted against the SSRT for each condition (Fig. 2), illustrating these divergent slope patterns across reward contexts. We also tested whether dispositional self-regulation, emotion regulation, and affective symptoms moderated the Describe x Condition effect. Across measures, the three way interactions were not significant, indicating that the primary interaction was not contingent on these individual differences. Anxiety was associated with overall stopping performance, such that higher anxiety predicted longer SSRTs, F (1, 84) = 7.18, p = .009. The Describe × Condition interaction remained significant after accounting for anxiety. Neither the Describe × Anxiety nor the Condition × Describe × Anxiety interaction reached statistical significance, indicating that anxiety did not modify the condition-specific association between Describe and SSRT. Discussion The present study examined how facets of dispositional mindfulness relate to reactive inhibitory control in a neutral and reward-associated contexts. A key finding was the interaction between the Describing facet (the tendency to put one’s experiences into words) and the SST conditions differing in reward cue placement. As shown in Figure 3, simple slope analyses revealed that higher descriptive scores were associated with markedly different stopping speeds (SSRTs) depending on the reward context. When only the go stimuli were reward-associated (i.e. presented before the neutral stop signal), higher Describing was associated with faster stopping (shorter SSRT). Individuals who were more adept at describing their internal experiences were better able to inhibit the prepotent go response even with a reward-associated go cue was present. Although only the slope in the Reward Go–Reward Stop condition reached statistical significance, the significant Describe × Condition interaction indicates that the association between mindfulness and SSRT differed across task contexts. Inspection of the simple slopes suggested that higher Describing was associated with longer SSRTs (i.e., reduced inhibitory control) in the fully neutral context, whereas in contexts in which either the go or the stop stimulus was reward-associated, higher Describing tended to be associated with shorter SSRTs (i.e., improved inhibitory control). However, these associations did not reach statistical significance. In contrast, in the fully reward-associated condition—where both the go and stop stimuli were reward-cued—higher Describing was significantly associated with longer SSRTs, indicating reduced inhibitory control. Notably, no other mindfulness facet demonstrated a similar interaction with condition (all p > .10). This finding suggests a unique role of the Describing facet in modulating inhibitory control across reward contexts. Other facets (observing, acting with awareness, nonjudging, and nonreactivity) did not have significant condition-specific effects on SSRT, underscoring that the ability to verbally label experiences (describing) was the primary facet driving these context-dependent differences. The finding that the relationship between Describing and SSRT varies as a function of condition highlights the importance of context in the mindfulness–inhibition relationship [33,75]. Describing appears to confer benefits when salient reward cues are present in only one part of the task (either prior to or at stopping) but may become a liability when the environment is either low in salience (neutral) or saturated with salience (dual reward cues). One possible explanation is that Describing reflects a form of cognitive processing or emotion- regulation that is optimally engaged only under certain levels of stimulus intensity. Individuals high in Describing tend to label and articulate their internal states; this trait is analogous to the process of affect labeling, known to dampen emotional reactivity by putting feelings into words [76]. Consistent with this, neuroscience research has shown that Describing is associated with greater involvement of the fronto-parietal control network, including prefrontal regions linked to attention regulation and reappraisal [77]. Thus, when a reward cue is present, high-Describing individuals may spontaneously name or contextualize the cue and their reaction to it, which could help maintain top-down focus on the goal of stopping. In support of this idea, prior work found that trait mindfulness (which encompasses Describing) correlates with better inhibitory performance specifically in high-reward contexts like monetary cues [22]. By contrast, in a neutral context with no particularly salient stimuli, that same tendency to introspect and label may offer no performance benefit – it might even divert cognitive resources or encourage a more reflective (slower) response style, leading to slower stopping. Interestingly, a parallel phenomenon is observed in emotion regulation research: affect labeling helps most under high-intensity emotional conditions but can be counterproductive under low-intensity conditions [76]. This aligns with the notion that certain mindfulness strategies or traits are most effective when there is something salient to regulate but may become superfluous or even detrimental when stimuli are neutral [42]. The case of the fully reward (reward-go + reward-stop) condition, where high Describing was linked to worse stopping, warrants consideration. Here, both go and stop stimuli are associated with reward, creating a scenario of ubiquitous salience. The surprising finding that participants high in the Describe facet had slower SSRTs in the Reward Go–Reward Stop condition suggests that simultaneously rewarding both going and stopping may heighten approach motivation and cognitive conflict, requiring greater cognitive control to balance competing goals [78]. Individuals high in Describe, who tend to consciously label and appraise their experiences, might be especially susceptible to this conflict; indeed, this facet has been linked to lower behavioral inhibition sensitivity (reduced automatic caution) [79], which could amplify approach tendencies toward reward cues. Consequently, when both go and stop signals are reward-linked, high-Describe individuals may not inhibit their go response quickly enough, yielding longer SSRTs. From a practical standpoint, these results hint that training programs or interventions (such as mindfulness-based trainings) might consider emphasizing the skill of noticing and labeling experiences to improve inhibitory control, particularly in environments rife with temptations or rewards. For instance, in addiction contexts or scenarios of monetary self-control, being able to mentally label cravings and contextualize reward cues might help individuals inhibit impulsive actions. However, our findings also warn that context matters as the same mindfulness strategy might not universally improve performance and could even slow things down if applied inappropriately (for example, using a very introspective approach in a straightforward situation devoid of salient cues). Future research might explore whether these trait-level effects can also be seen through induced-mindfulness, how to titrate mindfulness strategies to task demands, and whether individuals can be taught to deploy the Describing skill when it is beneficial (e.g., high-conflict, high-salience moments) and perhaps dial it back when it is not needed. Limitations and Future Directions One important consideration is the distinction between naturally high mindfulness and experimentally induced mindfulness states. It might be tempting to assume that our trait-based findings would generalize to outcomes of mindfulness training or induction, but this is not guaranteed. Formal mindfulness interventions often include additional nonspecific factors (e.g., expectancies, group support) beyond simply raising one’s mindful awareness. Indeed, previous work by our group revealed that although trait mindfulness is generally related to better inhibitory control, it does not differentially predict performance across reward and neutral conditions in a within-subject design, which may have resulted from carry-over effects between conditions [33]. In that prior paradigm, each participant experienced three reward-laden blocks before a single neutral block, possibly contaminating performance in a neutral context. To address this, the current study employed a between-subjects design, with each participant randomly assigned to only one of the four cue conditions, thereby eliminating direct sequence effects between reward and neutral contexts. This approach sacrifices some statistical power (due to increased between-person variability) but prevents reward exposure in one block from influencing behavior in another, yielding a cleaner test of context-specific effects. Lastly, we acknowledge a technical limitation of the SSRT measure itself. The standard non-parametric method we used to compute SSRT assumes that whenever a stop trial fails, the participant initiated their response but could not stop in time. In reality, some failures may occur because the participant never triggered the stopping process at all – for example, if they momentarily didn’t notice the stop signal due to an attention lapse. Such “trigger failures” are not captured by the integration method and could lead to underestimating true stopping latency. If individuals high in mindfulness tend to have fewer lapses of attention, their shorter SSRTs might partly reflect better sustained attention (fewer missed stop signals) rather than purely faster inhibition. Newer parametric modeling approaches can estimate the contribution of trigger failures, but these require substantially larger number of trials to fit reliably, a demand that was not feasible in our online setting due to concerns about participant fatigue and attrition. We attempted to probe this issue indirectly by examining omission errors on go trials (since lapses of attention would likely increase missed responses). Encouragingly, trait mindfulness was not significantly associated with go-trial omissions in our data, and omission rates were uniformly low across the sample, suggesting attentional lapses were minimal. This makes it less likely that the mindfulness–SSRT relationship was driven by unnoticed stop signals. Nonetheless, because omissions were near floor levels, we had limited power to detect subtle differences on that metric. We therefore cannot entirely rule out the possibility that mindfulness might confer an advantage partly by reducing momentary attentional lapses. Future research with more granular stop-signal modeling or larger trial counts could shed further light on this issue. Conclusion In summary, this study provides further evidence that mindfulness is not a one-size-fits-all enhancer of self-control; instead, specific facets like Describing play a nuanced role that depends on context. The ability to describe present experience was uniquely associated with reduced inhibitory control in a context where both response-associated and stopping-associated stimuli were reward-associated. These findings underscore that, beyond general mindfulness or awareness, the reflective act of describing one’s experience can be a double-edged sword – potentially enhancing inhibitory control but attenuating it in particular reward-associated conditions. Moving forward, a deeper understanding of how and when to engage this “descriptive” mindfulness strategy could inform interventions for improving inhibitory control in the face of temptations, stressors, or rewarding distractions. Ultimately, our work suggests that helping individuals cultivate mindfulness skills in a context-sensitive way – knowing which facet to focus on at what time – may yield the greatest benefits for self-regulation and cognitive performance. The Describing facet’s selective efficacy invites further exploration into the mechanisms by which putting feelings into words can sometimes fortify inhibitory control, and other times, undermine it. Understanding this nuanced relationship will be key to harnessing the potential of mindfulness in enhancing executive functions in everyday life. Declarations Ethics approval All procedures were approved by the Research Ethics Committee of the Institute of Psychology, Eötvös Loránd University (ELTE), and conducted in accordance with the Declaration of Helsinki. Informed consent All persons gave their informed consent prior to their inclusion in the study. Consent for publication Not applicable Competing interest The authors declare that they have no conflict of interest. Author’s contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Atanas Tannous, Alxander Logemann, and Zsófia Logemann-Molnár. Atanas Tannous wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding ZD: The University of Gibraltar received funding from the Gibraltar Gambling Care Foundation, an independent, not-for-profit charity, and donations from gambling operators through the LCCPRET process supervised by the UK Gambling Commission. ZD is the Editor-in-Chief of the Journal of Behavioral Addictions. None of these funding sources are related to this study, and the funding institutions/organizations had no role in the study design, data collection, analysis, interpretation, manuscript writing, or decision to submit the paper for publication. The remaining authors declare no conflict of interest. AL, ZLM, AT: This work was funded by the Hungarian National Research, Development, and Innovation Office (https://nkfih.gov.hu; grant no. K131635). 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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-9253829","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618489953,"identity":"de13068b-f11d-4b5b-9c32-cdda936eb400","order_by":0,"name":"Atanas Tannous","email":"","orcid":"","institution":"Eötvös Loránd University","correspondingAuthor":false,"prefix":"","firstName":"Atanas","middleName":"","lastName":"Tannous","suffix":""},{"id":618489955,"identity":"7c879b7d-d211-40e3-9769-b770054c36bf","order_by":1,"name":"Zsófia Logemann-Molnár","email":"","orcid":"","institution":"Eötvös Loránd University","correspondingAuthor":false,"prefix":"","firstName":"Zsófia","middleName":"","lastName":"Logemann-Molnár","suffix":""},{"id":618489956,"identity":"b16b2fd7-f6ad-4212-b126-f6bda7d4deaa","order_by":2,"name":"Zsolt Demetrovics","email":"","orcid":"","institution":"Eötvös Loránd University","correspondingAuthor":false,"prefix":"","firstName":"Zsolt","middleName":"","lastName":"Demetrovics","suffix":""},{"id":618489958,"identity":"ea40b576-5c42-4c1e-a75d-40693e737bbe","order_by":3,"name":"Alexander Logemann","email":"data:image/png;base64,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","orcid":"","institution":"Eötvös Loránd University","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Logemann","suffix":""}],"badges":[],"createdAt":"2026-03-28 15:23:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9253829/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9253829/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106348028,"identity":"b60b72bb-1df4-4a64-af8a-9a65a1b5bfe3","added_by":"auto","created_at":"2026-04-07 16:44:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn example of a stop trial in the SST\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9253829/v1/71fb18d06614252a33018f24.png"},{"id":106348029,"identity":"0de7ccb7-dea7-4094-956b-02e35a717c42","added_by":"auto","created_at":"2026-04-07 16:44:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFFMQ-Describe score and SSRT for each condition\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9253829/v1/3790b2a34a2060a611a04403.png"},{"id":106403499,"identity":"b4edd009-f052-4ba2-a1cc-3d22ca926813","added_by":"auto","created_at":"2026-04-08 09:14:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137068,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel-estimated simple slopes for the FFMQ Describe facet and SSRT across conditions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9253829/v1/11a207160c31f166145cd22f.png"},{"id":106405583,"identity":"88088a0d-e106-4d40-a5ad-43dcc3d5f596","added_by":"auto","created_at":"2026-04-08 09:27:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1014298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9253829/v1/67b6f94d-d42a-431c-ac0f-41e74fe9025a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship Between Mindfulness and Inhibitory Control in a Neutral and Reward Associated Context with a Focus on Individual Differences","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the landscape of human cognition, inhibitory control (IC) stands as a fundamental executive function, defined as the ability to deliberately suppress dominant, automatic, or habitual responses to achieve a goal [1]. This mechanism is not merely a cognitive \u0026quot;brake\u0026quot;; it is integral to the successful maintenance of attention, stable emotion regulation, and conscious, adaptive decision-making [1]. Given its central role, deficits in IC are a common feature across diverse forms of psychopathology, including attention-deficit/hyperactivity disorder [2], substance use disorders, and various impulse-control issues [3]. Consequently, understanding the psychological traits that predict or enhance robust IC performance is a paramount concern in psychology. The brain regions involved in adequately executing this function are the right inferior frontal cortex (rIFC) and pre-supplementary motor area (pre-SMA) which are central to initiating and coordinating top-down suppression of prepotent responses, while the basal ganglia\u0026mdash;particularly the subthalamic nucleus\u0026mdash;modulate thalamocortical output to halt ongoing actions [4,5]. The anterior cingulate cortex (ACC) contributes by detecting conflict and signaling the need for control, and parietal regions assist in reorienting attention away from irrelevant stimuli [6]. Research also indicated that Default Mode Network (DMN) efficiency correlates with poorer inhibition performance as reviews of DMN functioning emphasize that failure to deactivate the DMN during goal-directed tasks is a hallmark of reduced cognitive control, including response inhibition\u0026nbsp;[7\u0026ndash;9].\u003c/p\u003e\n\u003cp\u003eThe Dual Mechanisms of Control (DMC) framework distinguishes between proactive and reactive modes of inhibitory control [10]. Proactive control is characterized as an anticipatory, sustained form of \u0026ldquo;early selection,\u0026rdquo; in which goal-relevant information is actively maintained within the lateral prefrontal cortex to bias attention and action systems before cognitively demanding events occur. In contrast, reactive control functions as a \u0026ldquo;late correction\u0026rdquo; mechanism, mobilized only when interference or conflict is detected, and involves transient reactivation of task goals through lateral prefrontal cortex and broader neural networks. In real world contexts, reactive control is an efficient process that uses less cognitive resources and contributes to adaptive functioning. The Stop Signal Task (SST) [11] provides an objective, behavioral metric for reactive IC, yielding the Stop Signal Reaction Time (SSRT), which represents the estimated latency of the covert stopping process [12]. Performance in this task, particularly under conditions of heightened emotional or motivational salience, can help predict real-world self-regulatory success. According to estimates [13,14], inhibition is necessary for 80% to 90% of self-regulation attempts.\u003c/p\u003e\n\u003cp\u003eA growing body of research has established trait mindfulness as a psychological disposition which can be a predictor of the extent to which individuals can benefit from mindfulness-based interventions [15,16]. Trait mindfulness is also strongly associated with superior executive function\u0026nbsp;[17\u0026ndash;21], including inhibitory performance\u0026nbsp;[22,23], and with activations or altered structures in similar brain regions\u0026nbsp;[24,25]. Specifically, research has demonstrated that during various emotion processing tasks, higher levels of trait mindfulness are primarily linked to enhanced pre-frontal cortex activation and decreased amygdala activation\u0026nbsp;[16,26]. Defined as the non-judgemental awareness of present-moment experience\u0026nbsp;[27\u0026ndash;29], mindfulness can be fundamentally viewed as a form of self-regulation by cognitive and neuroscientific frameworks\u0026nbsp;[16,30,31]. It has been shown to boost IC by fostering meta-awareness, an increased cognitive distance from internal stimuli, which facilitates the necessary disengagement required to cancel a planned action\u0026nbsp;[16]. Additionally, mindfulness-based interventions have shown favorable outcomes in behavioral measures\u0026nbsp;[18], as well as their underlying electrophysiological responses\u0026nbsp;[17].\u003c/p\u003e\n\u003cp\u003eWhile there is evidence for the benefits of mindfulness training, and trait mindfulness [32], for inhibitory control and performance on go/no-go tasks\u0026nbsp;[18,23,33\u0026ndash;35], empirical evidence specifically linking trait mindfulness to IC performance in motivationally salient contexts remains mixed. This variability likely reflects the broader challenge of attributing behavioral effects to specific cognitive mechanisms, rather than fundamental shortcomings of inhibition paradigms specifically. Indeed, inhibitory control tasks capture the contribution of multiple processes, which contributes to modest correlations across tasks and complicates interpretation\u0026nbsp;[36,37]. Importantly, the Stop-Signal Task (SST) is widely regarded as providing a relatively selective measure of motor inhibitory control when implemented and analyzed appropriately. As outlined by Verbruggen et al.\u0026nbsp;[12], the use of a staircase tracking algorithm and the integration method for estimating SSRT controls for individual differences in response speed, thereby reducing\u0026nbsp;the\u0026nbsp;confounding influences of general processing speed and attentional factors. Consequently,\u0026nbsp;the\u0026nbsp;SSRT is considered a robust and theoretically grounded index of inhibitory control, supporting the\u0026nbsp;use of the\u0026nbsp;SST as a valid tool for examining individual differences in motor inhibition in the present study.\u003c/p\u003e\n\u003cp\u003eAnother point of nuance is that people do not operate in neutral environments, so inhibitory control must often be recruited in the presence of reward-associated cues such as appetitive food or monetary stimuli. These cues strongly activate the striatum and broader reward processing circuits, increase approach tendencies, and reliably challenge inhibition\u0026nbsp;[38,39]. The evidence linking trait mindfulness to inhibitory control in neutral contexts is mixed, with some studies reporting positive associations [33,40,41] and others finding null or negative effects [42], and its role in reward contexts remains even less clear [33]. Neural work links mindfulness with reduced DMN activity and stronger connectivity within the\u0026nbsp;salience network\u0026nbsp;[24,25], which may heighten attentional capture\u0026nbsp;via\u0026nbsp;motivationally salient cues. Behavioral and ERP studies support this possibility: oddball paradigms show stronger early attention to salient stimuli among more mindful individuals\u0026nbsp;[43], and mindfulness has been associated with poorer inhibition when the\u0026nbsp;stimuli\u0026nbsp;to-be-inhibited stimuli are reward related, including attractive faces, smoking cues, and food or money stimuli\u0026nbsp;[22,42,44]. Yet other findings indicate that when reward cues appear before, and the subsequent stop signal is neutral, mindfulness can relate to better stopping performance\u0026nbsp;[22]. This suggests that the timing of reward information is a critical but largely untested factor: inhibitory control may be impaired when mindfulness enhances attentional orienting during direct exposure to reward cues, but may be supported when inhibition is required after reward exposure, possibly due to reduced carry over effects and more efficient engagement of reactive control processes\u0026nbsp;[45]. Taken together, these findings highlight the need to study how the temporal placement of reward-associated stimuli (i.e.,\u0026nbsp;before or during the required inhibition) could help shape the mindfulness-inhibition relationship.\u003c/p\u003e\n\u003cp\u003eRecent work from Logemann-Moln\u0026aacute;r et al.\u0026nbsp;[33], utilizing the Mindful Attention Awareness Scale (MAAS), confirmed a general association between trait mindfulness and IC performance but no interaction effect when reward-associated stimulus were introduced into the SST, relative to neutral. Interestingly, the previously observed main effect of condition, suggesting poorer inhibitory control in particular reward contexts, could not be confirmed in that study. This may suggest that the use of a within group design where each participant was exposed to all four variations of the SST introduced a possible carry-over effect of the three reward-associated conditions onto the single neutral condition.\u003c/p\u003e\n\u003cp\u003eAnother open question pertains to the role of different facets of mindfulness. In the previous study, the MAAS was utilized to assess a unidimensional aspect of present-moment attention. However, mindfulness is not a unitary construct; rather, it involves distinct, interacting components that collectively facilitate the mindful state [46]. The Five Facet Mindfulness Questionnaire (FFMQ)\u0026nbsp;[47]\u0026nbsp;is thought to\u0026nbsp;provide\u0026nbsp;a broader assessment of both the attentional and\u0026nbsp;the\u0026nbsp;attitudinal components (facets) of the construct, whereas the MAAS is predominantly focused on the attentional component\u0026nbsp;[48,49]. The FFMQ facets include Observing, Describing,\u0026nbsp;Nonjudging, Non-Reactivity, and Acting with Awareness (AWA), and they are theorized to categorize different cognitive and emotional processes, offering a structural perspective for the current investigation. The Describing and\u0026nbsp;Nonjudging\u0026nbsp;facets were found to be related to flexibility in the orienting attention network and neuroanatomical structures associated with orienting, attentional control and conflict detection\u0026nbsp;[49,50]. Another study found a positive association between the Describing facet with attentional control performance\u0026nbsp;[50]. The Non-Reactivity to Inner Experience facet is linked to fewer emotional regulation difficulties emotional regulation\u0026nbsp;[51]. This skill could be particularly relevant in motivated contexts, as it allows individuals to register emotionally salient cues (e.g., potential monetary reward) without being captured by the affective response, thereby preventing interference with the stopping process. By assessing these specific facets, this study can determine if distinct mindfulness skills are differentially engaged by the cognitive demands of IC, particularly when motivational pressure is applied.\u003c/p\u003e\n\u003cp\u003eTrait differences in negative affect and self-regulatory capacity may also shape when mindfulness relates to stopping performance, particularly in motivationally salient contexts. Attentional control theory posits that anxiety consumes cognitive resources by taxing working memory capacity, thereby impairing executive functions like response inhibition [52]. Empirically, high anxiety is linked to worse cognitive control as it occupies mental capacity needed for goal-directed tasks [53], whereas mindfulness tends to enhance cognitive control. Thus, elevated anxiety or stress could alter how effectively mindfulness translates into improved Stop-Signal Reaction Time (SSRT) performance, since baseline cognitive control may be compromised [54]. In fact, anxious individuals often report more cognitive failures in daily life [55], highlighting anxiety\u0026rsquo;s toll on attention and inhibition. Indeed, high stress is generally thought to impair executive functions [1,56], potentially blunting the benefits of mindfulness on inhibitory control.\u003c/p\u003e\n\u003cp\u003eSimilarly, traits related to self-regulation and emotion regulation may also influence mindfulness\u0026ndash;SSRT relationships. Mindfulness practice is fundamentally a training in self-regulation of attention and emotion [57], therefore it is plausible that Individuals with strong baseline self-regulatory skills (e.g., high trait self-regulation) might exhibit better inhibitory control under challenging conditions and thus gain less additional benefit from mindfulness, whereas those with poorer self-regulation could show greater improvements. Moreover, emotion regulation capacity is closely tied to inhibitory control; people who struggle to manage emotions tend to show deficits in response inhibition [58]. Mindfulness-based interventions are known to improve emotional regulation while reducing anxiety and bolstering stress resilience [57]. Consequently, variation in emotion regulation could alter the degree to which mindfulness training improves reactive inhibitory control, as those with poor emotion regulation might experience more pronounced gains in cognitive control from cultivating mindfulness.\u003c/p\u003e\n\u003cp\u003eBy integrating the multidimensional FFMQ, between-subject design, and testing the effects of individual differences, this study moves beyond simple trait correlations to help determine the boundary conditions of the mindfulness-IC relationship and identify key psychological factors that either facilitate or impede the cognitive benefits of present-moment awareness. Based on prior findings that reward cues disrupt inhibitory control and that mindfulness modulates sensitivity to motivational salience, we expected reward-associated contexts to produce slower stopping performance than fully neutral context. We further anticipated that mindfulness would facilitate inhibition when stopping followed exposure to reward, specifically when participants initiated responses to reward cues before encountering a neutral stop signal. In contrast, mindfulness was expected to impair inhibition when the stop signal itself was reward-associated, irrespective of the preceding go cue. As secondary, explorative aims, we also examined whether individual differences in self-regulation, negative affect, self-regulation, and other broader dispositional characteristics moderated these associations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants were recruited through social media platforms and local community advertisements. Individuals were eligible to take part if they were between 18 and 65 years old, reported no history of neurological or psychiatric disorders, and had not used psychoactive substances in the seven days prior to participation. All participants completed the study online. The target sample size was determined using G*Power [59], aiming to detect moderate effects with \u0026alpha; = .05 and 80% power, yielding a required minimum of approximately 80 participants. A total of 209 individuals took part in the study and provided informed consent before any procedures. The participants received course credit for their participation. Consistent with prior recommendations for SST data quality, participants were excluded from analysis if they scored below the B1 level on the English proficiency self-assessment, produced an invalid (negative) SSRT estimate, demonstrated an inhibition rate above .75 or below .25, showed more than 10 percent omissions on go trials, or responded incorrectly on more than half of go trials. Data from participants who did not complete the experiment were also excluded from the analyses. After applying these criteria, the final dataset comprised 100 participants. Ages ranged from 18-51 years (M = 24.8, SD = 6.13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePsytoolkit\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo facilitate online data collection, we employed the Psytoolkit framework [60,61], which allows for the integration of behavioral paradigms with psychometric self-report scales. This platform was selected due to its demonstrated reliability in replicating laboratory-standard results within a web-based environment [62].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLanguage Proficiency\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnglish reading proficiency is assessed via a single question on the Common European Framework of Reference for Languages (CEFR) Self-assessment grid [63]. Participants choose one of six response options, ranging from \u0026quot;A1 Basic user\u0026quot; to \u0026quot;C2 Proficient user.\u0026quot; A minimum proficiency of \u0026quot;B1 Independent user\u0026quot; is required, which corresponds to the ability to understand texts using high frequency every day or job-related language. The response for B2 was \u0026ldquo;I can understand texts that primarily use high frequency every day or job-related language. I can also comprehend the description of events, feelings, and wishes in per-sonal letters.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFive Facet Mindfulness Questionnaire (FFMQ)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDispositional mindfulness is also assessed using the 39-item FFMQ [47], which provides specific subscale scores on a 1 to 5-point Likert-type scale for Observing, Describing, Acting with Awareness, Non-Judging of Inner Experience, and Non-Reactivity to Inner Experience. A higher score on the FFMQ indicates higher levels of mindfulness. The FFMQ has demonstrated good internal consistency, factorial validity, and convergent validity with related constructs across both clinical and nonclinical populations [50; 67]. In the original validation study, Baer et al. [47] reported strong internal consistency for all FFMQ facets, with Cronbach\u0026rsquo;s alphas of .83 for Observing, .91 for Describing, .87 for Acting with Awareness, .87 for Nonjudging, and .75 for Nonreactivity. In the present sample, reliability estimates were acceptable to excellent across facets: Observe (\u0026alpha; = .76), Describe (\u0026alpha; = .92), Acting with Awareness (\u0026alpha; = .91), Nonjudging (\u0026alpha; = .89), and Nonreactive (\u0026alpha; = .68). Importantly, prior work has highlighted that the facets capture partially distinct components of mindfulness and may show differential associations with cognitive and affective outcomes, particularly in nonmeditating samples [64]. Accordingly, analyses in the present study focused on individual FFMQ facets rather than a total score, in order to examine whether specific components of mindfulness were differentially associated with inhibitory control across task conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEmotion Regulation Questionnaire (ERQ)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmotional regulation strategies were assessed using the Emotion Regulation Questionnaire (ERQ) [65]. The ERQ is a widely used self-report measure that assesses two habitual emotion regulation strategies: cognitive reappraisal and expressive suppression. Cognitive reappraisal reflects the tendency to reinterpret emotionally evocative situations in order to alter their emotional impact, whereas expressive suppression reflects the tendency to inhibit the outward expression of emotions. The questionnaire consists of 10 items rated on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with higher scores indicating greater use of the respective strategy. The internal consistency reliability of the ERQ subscales was acceptable to excellent, with cognitive reappraisal demonstrating high reliability (Cronbach\u0026rsquo;s \u0026alpha; = .89\u0026ndash;.90) and expressive suppression demonstrating acceptable reliability (Cronbach\u0026rsquo;s \u0026alpha; = .76\u0026ndash;.80)\u0026nbsp;[65\u0026ndash;67]. In the present study, the ERQ demonstrated good internal consistency in the present sample for both reappraisal (\u0026alpha; = .83, 95% CI [.77, .88]) and suppression (\u0026alpha; = .80, 95% CI [.72, .86]). Reappraisal and suppression scores were examined as individual difference variables in exploratory analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eShort Self-Regulation Questionnaire (SSRQ)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe short version of the self-regulation questionnaire (SSRQ) [68] is a shortened version of the self-regulation questionnaire [69] which evaluates subjects\u0026rsquo; dispositional self-regulation and their ability to regulate and plan their behavior in a flexible manner in accordance with the desired outcome. The SSRQ consists of 31 items and has demonstrated excellent internal consistency in the development sample (Cronbach\u0026rsquo;s \u0026alpha; = .92) [68], with subsequent studies reporting similarly high reliability across independent samples [70]. Evidence for construct validity is provided by strong correlations with the original full length self-regulation questionnaire (r \u0026asymp; .96) [68]. The participants indicate the extent to which they agree with each item via a 5-point Likert scale: 1 (Strongly Disagree), 2 (Somewhat Disagree), 3 (Neutral), 4 (Somewhat Agree), and 5 (Strongly Agree). Examples of items include the following: \u0026ldquo;Once I have a goal, I can usually plan how to reach it,\u0026rdquo; \u0026ldquo;I have a lot of willpower\u0026rdquo; and \u0026ldquo;As soon as I see a problem or challenge, I start looking for possible solutions\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDepression Anxiety Stress Scale - 21 (DASS-21)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Depression Anxiety Stress Scales 21 (DASS‐21) [71] is a short form of the Lovibond \u0026amp; Lovibond [72] 42‐item self‐report measure of depression, anxiety, and stress (DASS). The DASS‐21 consists of three 7‐item self‐report scales taken from the full version of the DASS. The subjects are asked to use 4-point severity/frequency scales to rate the extent to which they have experienced each state over the past week. Scores for depression, anxiety and stress are calculated by summing the scores for the relevant items. The DASS‐21 subscales can validly be used to measure the dimensions of depression, anxiety, and stress. The subscales have demonstrated good to excellent internal consistency, with Cronbach\u0026rsquo;s \u0026alpha; values of .88 for depression, .82 for anxiety, .90 for stress, and .93 for the total score [71].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStop Signal Task (SST)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stop signal task (SST) was administered online following the standard paradigm outlined by Logan et al. [11] and adapted from Logemann-Moln\u0026aacute;r et al. [33]. In each trial, the participants responded to a centrally presented go stimulus with a left or right button, using their index finger. In a subset of trials, the same stimulus was followed by a stop signal, indicating that the initiated response should be inhibited. The stimuli were displayed for 150 ms, with intertrial intervals of 1500 ms for the go trials and 1700 ms for the stop trials. The participants first completed a practice phase of 48 go trials and 16 stop trials, with feedback for omission errors, commission errors, and incorrect responses. Following practice, each participant was randomly assigned to one of four conditions, which was consistent with the between-subject design. Across conditions, the go and stop stimuli were either neutral images (stones) or reward-associated images (money). This process produced four possible stimulus configurations: neutral go with a neutral stop, neutral go with a reward stop, reward go with a neutral stop, and reward go with a reward stop (see Fig. 1 for an example trial). The stop signal was indicated by the same stimulus used in the go trials but surrounded by 15 pixels narrow blue border, ensuring minimal visual change aside from the requirement to inhibit. Each participant completed 96 go trials and 32 stop trials. Stimuli appeared in portrait orientation (go: 100x200 pixels; stop (including border): 100x230 pixels) or landscape mode (go: 200x100 pixels; stop (including border): 230x100 pixels), and the orientation determined whether the left or right response key was correct. The trials were randomized, and the conditions were counterbalanced across participants. A one-up/one-down staircase procedure adjusts the stop-signal delay (SSD) to maintain an inhibition rate near 50% [73] using a tracking algorithm. The SSD began at 250ms and decreased by 50ms after failed inhibitions and increased by 50ms after successful ones, following recommended SST procedures [12,73]. SSRTs were estimated using the integration method in accordance with consensus guidelines [12]. Reaction times (RTs) below 150 ms or exceeding the intertrial interval were excluded. Omission trials were replaced with the maximum RT (1500ms) within the specific response window. Sorted go RTs were used to identify the integration point based on each participant\u0026rsquo;s proportion of failed stops. SSRT was calculated by subtracting the mean SSD from this integration-point RT, according to the latest consensus [12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilized a single-session, cross-sectional design via the online experimental platform Psytoolkit [60,61]. This tool is utilized for its high replicability in cognitive psychological experiments. After providing written informed consent, the participants first completed self-report questionnaires. Following the questionnaires, the participants were provided with instructions for the stop signal task (SST). Specifically, before the main task begins, participants were instructed to respond to the Go signal as fast and accurately as possible and to not wait for the stop signal. To aid in understanding the instructions, the participants were provided with feedback during the practice part of the task. After the participants performed the SST in the condition assigned to them via a balanced design, the experiment was completed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted in R\u0026nbsp;(version 4.5.2; R Core Team [74]). The data were screened to ensure valid estimation of the SSRT and adequate task performance. SSRT served as the primary dependent variable. The task condition was entered as a between-subject factor with four levels reflecting the reward association of the go and stop signals. Trait mindfulness was entered as a continuous predictor via the Five Facet Mindfulness Questionnaire (FFMQ) total score and individual facet scores, which were analyzed in separate models.\u003c/p\u003e\n\u003cp\u003eSSRT was analyzed via linear regression\u0026ndash;based analyses of covariance (ANCOVAs), with effects evaluated using Type III sums of squares. The primary effect of interest was the interaction between mindfulness and task condition. Significant interactions were followed up using model-based simple slope analyses to examine conditional associations within each condition. Exploratory models examined whether dispositional characteristics moderated the mindfulness\u0026ndash;SSRT relationship. All continuous predictors were standardised prior to analysis. Statistical significance was evaluated using two-tailed tests with an alpha level of .05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOverall, the SSRT did not differ significantly across the four SST conditions, \u003cem\u003eF\u003c/em\u003e(3, 96) = 1.02, \u003cem\u003ep\u003c/em\u003e = .387 (descriptive SST performance data are shown in Table 1). SSRT values were nearly identical across the neutral and reward-go/neutral-stop and the neutral-go/reward stop conditions, with only the fully reward-associated condition showing slightly longer stopping latencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Summary of performance data in the SST.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eSSRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eMean RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eSTD RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eSOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eProp Correct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eProp Omissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eProp Inhibition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eneutral go + neutral stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eneutral go + money stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003emoney go + neutral stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003emoney go + money stop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003enote\u003c/strong\u003e: this table presents a summary of reaction time (RT) and performance metrics across conditions. Stop Signal Reaction Time (SSRT), Mean RT, Standard Deviation of RT (STD RT), Go-stop Stimulus Onset Asynchrony (SOA), and proportions of correct responses, omissions, and inhibitions are reported. Values are rounded for clarity, with SSRT, Mean RT, STD RT, and SOA rounded to the nea rest whole number, and proportions rounded to two decimal places.\u003c/p\u003e\n\u003cp\u003ePrimary analyses focused on whether mindfulness was associated with inhibitory control and whether these associations varied as a function of reward timing. Each FFMQ facet was tested separately using an ANCOVA with Condition as a four-level factor. Across models, none of the facets showed a significant main effect on SSRT (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.14), indicating that higher mindfulness facets did not correspond to faster stopping when collapsing across conditions. SSRT also did not differ across conditions in any model (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.25), suggesting that the experimental manipulation itself did not alter stopping performance while controlling for the different mindfulness facets.\u003c/p\u003e\n\u003cp\u003eA significant interaction between \u0026nbsp;and the Describing facet emerged, \u003cem\u003eF\u003c/em\u003e(3,92) = 3.23, \u003cem\u003ep\u003c/em\u003e = .026, indicating that the association between Describing and SSRT depended on reward placement within the trial. Follow-up simple-slope analyses were conducted to examine the association between the Describing facet and the SSRT within each task condition. The slope was not significant in the neutral go\u0026ndash;neutral stop condition, \u003cem\u003eb\u003c/em\u003e = 17.60, \u003cem\u003eSE\u003c/em\u003e = 14.30, \u003cem\u003et\u003c/em\u003e(92) = 1.23, \u003cem\u003ep\u003c/em\u003e = .22, nor in the neutral go\u0026ndash;reward stop condition, \u003cem\u003eb\u003c/em\u003e = \u0026minus;21.50, \u003cem\u003eSE\u003c/em\u003e = 13.90, \u003cem\u003et\u003c/em\u003e(92) = \u0026minus;1.56, \u003cem\u003ep\u003c/em\u003e = .12, or the reward go\u0026ndash;neutral stop condition, \u003cem\u003eb\u003c/em\u003e = \u0026minus;14.50, \u003cem\u003eSE\u003c/em\u003e = 10.80, \u003cem\u003et\u003c/em\u003e(92) = \u0026minus;1.35, \u003cem\u003ep\u003c/em\u003e = .18. In contrast, the slope was significant in the reward go\u0026ndash;reward stop condition, with higher Describe scores associated with slower stopping, \u003cem\u003eb\u003c/em\u003e = 19.20, \u003cem\u003eSE\u003c/em\u003e = 9.52, \u003cem\u003et\u003c/em\u003e(92) = 2.02, \u003cem\u003ep\u003c/em\u003e = .046.\u0026nbsp;Simple-slope estimates showed that higher Describing scores were associated with faster stopping when reward cues appeared before the stop signal (reward go + neutral stop) and, potentially counterintuitive, when only the stop signal was reward-associated (neutral go + reward stop). In contrast, higher scores were associated with slower stopping in the fully neutral condition and when both the go and stop cues were reward\u0026nbsp;associated (reward go + reward stop). No other facet demonstrated a similar interaction (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.10). A summary of these effects is shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. ANCOVA results for mindfulness facets predicting SSRT across\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econditions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eFacet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMain effect of facet (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMain effect of condition (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eCondition \u0026times; facet interaction (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eObserving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eDescribing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eActing with Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eNon-Judging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eNon-Reactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003enote.\u003c/strong\u003e table displays Type III ANCOVA results for each mindfulness facet entered as a z-scored covariate, with Condition (Neutral Go + Neutral Stop; Neutral Go + Reward Stop; Reward Go + Neutral Stop; Reward Go + Reward Stop) as a between-subjects factor. SSRT served as the dependent variable. Significant Condition \u0026times; Facet interactions indicate that the association between mindfulness and stopping performance differed across the four experimental conditions.\u003c/p\u003e\n\u003cp\u003eFor visualization purposes only, the Describing scores were median-split and plotted against the SSRT for each condition (Fig. 2), illustrating these divergent slope patterns across reward contexts.\u003c/p\u003e\n\u003cp\u003eWe also tested whether dispositional self-regulation, emotion regulation, and affective symptoms moderated the Describe x Condition effect. Across measures, the three way interactions were not significant, indicating that the primary interaction was not contingent on these individual differences. Anxiety was associated with overall stopping performance, such that higher anxiety predicted longer SSRTs, \u003cem\u003eF\u003c/em\u003e(1, 84) = 7.18, \u003cem\u003ep\u003c/em\u003e = .009. The Describe \u0026times; Condition interaction remained significant after accounting for anxiety. Neither the Describe \u0026times; Anxiety nor the Condition \u0026times; Describe \u0026times; Anxiety interaction reached statistical significance, indicating that anxiety did not modify the condition-specific association between Describe and SSRT.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study examined how facets of dispositional mindfulness relate to reactive inhibitory control in a neutral and reward-associated contexts. A key finding was the interaction between the Describing facet (the tendency to put one\u0026rsquo;s experiences into words) and the SST conditions differing in reward cue placement. As shown in Figure 3, simple slope analyses revealed that higher descriptive scores were associated with markedly different stopping speeds (SSRTs) depending on the reward context. When only the go stimuli were reward-associated (i.e. presented before the neutral stop signal), higher Describing was associated with faster stopping (shorter SSRT). Individuals who were more adept at describing their internal experiences were better able to inhibit the prepotent go response even with a reward-associated go cue was present. Although only the slope in the Reward Go\u0026ndash;Reward Stop condition reached statistical significance, the significant Describe \u0026times; Condition interaction indicates that the association between mindfulness and SSRT differed across task contexts. Inspection of the simple slopes suggested that higher Describing was associated with longer SSRTs (i.e., reduced inhibitory control) in the fully neutral context, whereas in contexts in which either the go or the stop stimulus was reward-associated, higher Describing tended to be associated with shorter SSRTs (i.e., improved inhibitory control). However, these associations did not reach statistical significance. In contrast, in the fully reward-associated condition\u0026mdash;where both the go and stop stimuli were reward-cued\u0026mdash;higher Describing was significantly associated with longer SSRTs, indicating reduced inhibitory control.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, no other mindfulness facet demonstrated a similar interaction with condition (all \u003cem\u003ep\u003c/em\u003e \u0026gt; .10). This finding suggests a unique role of the Describing facet in modulating inhibitory control across reward contexts. Other facets (observing, acting with awareness, nonjudging, and nonreactivity) did not have significant condition-specific effects on SSRT, underscoring that the ability to verbally label experiences (describing) was the primary facet driving these context-dependent differences.\u003c/p\u003e\n\u003cp\u003eThe finding that the relationship between Describing and SSRT varies as a function of condition highlights the importance of context in the mindfulness\u0026ndash;inhibition relationship [33,75]. Describing appears to confer benefits when salient reward cues are present in only one part of the task (either prior to or at stopping) but may become a liability when the environment is either low in salience (neutral) or saturated with salience (dual reward cues). One possible explanation is that Describing reflects a form of cognitive processing or emotion- regulation that is optimally engaged only under certain levels of stimulus intensity. Individuals high in Describing tend to label and articulate their internal states; this trait is analogous to the process of affect labeling, known to dampen emotional reactivity by putting feelings into words [76]. Consistent with this, neuroscience research has shown that Describing is associated with greater involvement of the fronto-parietal control network, including prefrontal regions linked to attention regulation and reappraisal [77]. Thus, when a reward cue is present, high-Describing individuals may spontaneously name or contextualize the cue and their reaction to it, which could help maintain top-down focus on the goal of stopping. In support of this idea, prior work found that trait mindfulness (which encompasses Describing) correlates with better inhibitory performance specifically in high-reward contexts like monetary cues [22]. By contrast, in a neutral context with no particularly salient stimuli, that same tendency to introspect and label may offer no performance benefit \u0026ndash; it might even divert cognitive resources or encourage a more reflective (slower) response style, leading to slower stopping. Interestingly, a parallel phenomenon is observed in emotion regulation research: affect labeling helps most under high-intensity emotional conditions but can be counterproductive under low-intensity conditions [76]. This aligns with the notion that certain mindfulness strategies or traits are most effective when there is something salient to regulate but may become superfluous or even detrimental when stimuli are neutral [42].\u003c/p\u003e\n\u003cp\u003eThe case of the fully reward (reward-go + reward-stop) condition, where high Describing was linked to worse stopping, warrants consideration. Here, both go and stop stimuli are associated with reward, creating a scenario of ubiquitous salience. The surprising finding that participants high in the Describe facet had slower SSRTs in the Reward Go\u0026ndash;Reward Stop condition suggests that simultaneously rewarding both going and stopping may heighten approach motivation and cognitive conflict, requiring greater cognitive control to balance competing goals [78]. Individuals high in Describe, who tend to consciously label and appraise their experiences, might be especially susceptible to this conflict; indeed, this facet has been linked to lower behavioral inhibition sensitivity (reduced automatic caution) [79], which could amplify approach tendencies toward reward cues. Consequently, when both go and stop signals are reward-linked, high-Describe individuals may not inhibit their go response quickly enough, yielding longer SSRTs.\u003c/p\u003e\n\u003cp\u003eFrom a practical standpoint, these results hint that training programs or interventions (such as mindfulness-based trainings) might consider emphasizing the skill of noticing and labeling experiences to improve inhibitory control, particularly in environments rife with temptations or rewards. For instance, in addiction contexts or scenarios of monetary self-control, being able to mentally label cravings and contextualize reward cues might help individuals inhibit impulsive actions. However, our findings also warn that context matters as the same mindfulness strategy might not universally improve performance and could even slow things down if applied inappropriately (for example, using a very introspective approach in a straightforward situation devoid of salient cues). Future research might explore whether these trait-level effects can also be seen through induced-mindfulness, how to titrate mindfulness strategies to task demands, and whether individuals can be taught to deploy the Describing skill when it is beneficial (e.g., high-conflict, high-salience moments) and perhaps dial it back when it is not needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne important consideration is the distinction between naturally high mindfulness and experimentally induced mindfulness states. It might be tempting to assume that our trait-based findings would generalize to outcomes of mindfulness training or induction, but this is not guaranteed. Formal mindfulness interventions often include additional nonspecific factors (e.g., expectancies, group support) beyond simply raising one\u0026rsquo;s mindful awareness. Indeed, previous work by our group revealed that although trait mindfulness is generally related to better inhibitory control, it does not differentially predict performance across reward and neutral conditions in a within-subject design, which may have resulted from carry-over effects between conditions [33]. In that prior paradigm, each participant experienced three reward-laden blocks before a single neutral block, possibly contaminating performance in a neutral context. To address this, the current study employed a between-subjects design, with each participant randomly assigned to only one of the four cue conditions, thereby eliminating direct sequence effects between reward and neutral contexts. This approach sacrifices some statistical power (due to increased between-person variability) but prevents reward exposure in one block from influencing behavior in another, yielding a cleaner test of context-specific effects.\u003c/p\u003e\n\u003cp\u003eLastly, we acknowledge a technical limitation of the SSRT measure itself. The standard non-parametric method we used to compute SSRT assumes that whenever a stop trial fails, the participant initiated their response but could not stop in time. In reality, some failures may occur because the participant never triggered the stopping process at all \u0026ndash; for example, if they momentarily didn\u0026rsquo;t notice the stop signal due to an attention lapse. Such \u0026ldquo;trigger failures\u0026rdquo; are not captured by the integration method and could lead to underestimating true stopping latency. If individuals high in mindfulness tend to have fewer lapses of attention, their shorter SSRTs might partly reflect better sustained attention (fewer missed stop signals) rather than purely faster inhibition. Newer parametric modeling approaches can estimate the contribution of trigger failures, but these require substantially larger number of trials to fit reliably, a demand that was not feasible in our online setting due to concerns about participant fatigue and attrition. We attempted to probe this issue indirectly by examining omission errors on go trials (since lapses of attention would likely increase missed responses). Encouragingly, trait mindfulness was not significantly associated with go-trial omissions in our data, and omission rates were uniformly low across the sample, suggesting attentional lapses were minimal. This makes it less likely that the mindfulness\u0026ndash;SSRT relationship was driven by unnoticed stop signals. Nonetheless, because omissions were near floor levels, we had limited power to detect subtle differences on that metric. We therefore cannot entirely rule out the possibility that mindfulness might confer an advantage partly by reducing momentary attentional lapses. Future research with more granular stop-signal modeling or larger trial counts could shed further light on this issue.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study provides further evidence that mindfulness is not a one-size-fits-all enhancer of self-control; instead, specific facets like Describing play a nuanced role that depends on context. The ability to describe present experience was uniquely associated with reduced inhibitory control in a context where both response-associated and stopping-associated stimuli were reward-associated. These findings underscore that, beyond general mindfulness or awareness, the reflective act of describing one\u0026rsquo;s experience can be a double-edged sword \u0026ndash; potentially enhancing inhibitory control but attenuating it in particular reward-associated conditions. Moving forward, a deeper understanding of how and when to engage this \u0026ldquo;descriptive\u0026rdquo; mindfulness strategy could inform interventions for improving inhibitory control in the face of temptations, stressors, or rewarding distractions. Ultimately, our work suggests that helping individuals cultivate mindfulness skills in a context-sensitive way \u0026ndash; knowing which facet to focus on at what time \u0026ndash; may yield the greatest benefits for self-regulation and cognitive performance. The Describing facet\u0026rsquo;s selective efficacy invites further exploration into the mechanisms by which putting feelings into words can sometimes fortify inhibitory control, and other times, undermine it. Understanding this nuanced relationship will be key to harnessing the potential of mindfulness in enhancing executive functions in everyday life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003cstrong\u003eapproval\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were approved by the Research Ethics Committee of the Institute of Psychology, E\u0026ouml;tv\u0026ouml;s Lor\u0026aacute;nd University (ELTE), and conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed consent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll persons gave their informed consent prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interest\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor\u0026rsquo;s contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Atanas Tannous, Alxander Logemann, and Zs\u0026oacute;fia Logemann-Moln\u0026aacute;r. Atanas Tannous wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZD: The University of Gibraltar received funding from the Gibraltar Gambling Care Foundation, an independent, not-for-profit charity, and donations from gambling operators through the LCCPRET process supervised by the UK Gambling Commission. ZD is the Editor-in-Chief of the Journal of Behavioral Addictions.\u003c/p\u003e\n\u003cp\u003eNone of these funding sources are related to this study, and the funding institutions/organizations had no role in the study design, data collection, analysis, interpretation, manuscript writing, or decision to submit the paper for publication. The remaining authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eAL, ZLM, AT: This work was funded by the Hungarian National Research, Development, and Innovation Office (https://nkfih.gov.hu; grant no. K131635).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Open Science Framework (OSF) repository at https://osf.io/96gx2/overview?view_only=58116f899a594100b4089adcd4d75772\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors utilized Grammarly for advanced grammatical and stylistic consistency [80], and ChatGPT for refining the manuscript\u0026rsquo;s linguistic clarity and readability [81]. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the accuracy and integrity of the final published work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDiamond A. 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Construct validity of the five facet mindfulness questionnaire in meditating and nonmeditating samples. Assessment. 2008;15:329\u0026ndash;42. https://doi.org/10.1177/1073191107313003\u003c/li\u003e\n\u003cli\u003eGross JJ, John OP. Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. J Pers Soc Psychol. US: American Psychological Association; 2003;85:348\u0026ndash;62. https://doi.org/10.1037/0022-3514.85.2.348\u003c/li\u003e\n\u003cli\u003eJohn OP, Gross JJ. Healthy and Unhealthy Emotion Regulation: Personality Processes, Individual Differences, and Life Span Development. J Pers [Internet]. 2004 [cited 2026 Mar 11]; https://doi.org/10.1111/j.1467-6494.2004.00298.x\u003c/li\u003e\n\u003cli\u003ePreece D, Becerra R, Robinson K, Gross J. The Emotion Regulation Questionnaire: Psychometric Properties in General Community Samples. J Pers Assess. 2020;102:348\u0026ndash;56. https://doi.org/10.1080/00223891.2018.1564319\u003c/li\u003e\n\u003cli\u003eCarey KB, Neal DJ, Collins SE. A psychometric analysis of the self-regulation questionnaire. Addict Behav. 2004;29:253\u0026ndash;60. https://doi.org/10.1016/j.addbeh.2003.08.001\u003c/li\u003e\n\u003cli\u003eBrown JM, Miller WR, Lawendowski LA. The self-regulation questionnaire. Innov Clin Pract Source Book Vol 17. Sarasota, FL, US: Professional Resource Press/Professional Resource Exchange; 1999. p. 281\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eNeal DJ, Carey KB. A Follow-Up Psychometric Analysis of the Self-Regulation Questionnaire. Psychol Addict Behav. 2005;19:414\u0026ndash;22. https://doi.org/10.1037/0893-164X.19.4.414\u003c/li\u003e\n\u003cli\u003eHenry JD, Crawford JR. The short-form version of the Depression Anxiety Stress Scales (DASS-21): construct validity and normative data in a large non-clinical sample. 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US: American Psychological Association; 2010;36:430\u0026ndash;47. https://doi.org/10.1037/a0016802\u003c/li\u003e\n\u003cli\u003eRocher AR du. Re-examining the relationship between mindfulness facets, attentional control, and dispositional reinforcement sensitivity. Int J Personal Psychol. 2022;8:58\u0026ndash;66. https://doi.org/10.21827/ijpp.8.38453\u003c/li\u003e\n\u003cli\u003eGrammarly [Internet]. San Francisco: Grammarly Inc.; [cited 2026 Feb 11]. Available from: https://app.grammarly.com/\u003c/li\u003e\n\u003cli\u003eChatGPT [Internet]. San Francisco: OpenAI; [cited 2026 Feb 11]. Available from: https://chatgpt.com/\u003c/li\u003e\n\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":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mindfulness, Inhibitory Control, Reward Context, Executive Functions, FFMQ","lastPublishedDoi":"10.21203/rs.3.rs-9253829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9253829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEmpirical links between trait mindfulness and inhibitory control (IC) remain inconsistent, particularly in motivationally salient contexts. This study investigated how specific mindfulness facets relate to IC under varying reward structures and whether other dispositional traits modulate these associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eOne hundred adults completed the Five Facet Mindfulness Questionnaire and validated measures of affective distress, self-regulation, emotion regulation, and broader dispositional traits. IC was assessed using a modified stop-signal task. Participants were randomly assigned to one of four between-subject conditions varying in reward structure to minimise carryover effects. Stop-signal reaction time was the primary outcome measure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eNo significant main effects of total mindfulness or reward condition on stop-signal reaction time (SSRT) were observed. However, a significant interaction emerged between the FFMQ Describing facet and task condition (\u003cem\u003ep\u003c/em\u003e = .026), indicating that the relationship between the labelling of experience and IC is context dependent. Follow-up analyses revealed that this interaction was primarily driven by a significant positive association between Describing and SSRT in the fully reward-associated condition (\u003cem\u003ep\u003c/em\u003e= .046). In this 'saturated' reward environment, higher Describing capacity was linked to slower stopping performance. No other mindfulness facets or dispositional characteristics yielded significant moderating effects, although anxiety was independently associated with slower stopping across conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe findings indicate that mindfulness–IC relationships are facet-specific and context dependent. The Describing facet appears to differentially relate to inhibitory performance depending on reward structure, underscoring the importance of motivational context when examining mindfulness and executive control.\u003c/p\u003e","manuscriptTitle":"The Relationship Between Mindfulness and Inhibitory Control in a Neutral and Reward Associated Context with a Focus on Individual Differences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:43:59","doi":"10.21203/rs.3.rs-9253829/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-22T19:21:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202636132691424582985441735162143252535","date":"2026-04-15T12:13:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17004906707781876183864505472091551286","date":"2026-04-04T09:43:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T02:16:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-01T10:16:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T08:02:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T08:01:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-03-28T15:14:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f5e8014-4a1b-4938-8424-bedb1a915cdc","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:43:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:43:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9253829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9253829","identity":"rs-9253829","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}