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Behavioral and Self-Report Measures of Spontaneous Mind Wandering Predict Different Aspects of ADHD in Adults

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Title: Behavioral and Self-Report Measures of Spontaneous Mind Wandering Predict Different Aspects of ADHD in Adults
Language: English
Authors: Takehiro Minamoto (ORCID 0000-0001-8434-7009), Takuma Hayashi, Md Abdullah Al-Mamun
Source: Journal of Attention Disorders. 2026 30(9):1189-1204.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 16
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Attention Deficit Hyperactivity Disorder, Self Evaluation (Individuals), Attention Control, Symptoms (Individual Disorders), Reaction Time, Interference (Learning), Test Reliability, Replication (Evaluation), Prediction, Graduate Students, Undergraduate Students, Foreign Countries
Geographic Terms: Japan
Assessment and Survey Identifiers: Stroop Color Word Test
DOI: 10.1177/10870547261448262
ISSN: 1087-0547
1557-1246
Abstract: Objective: Studies have provided evidence that mind wandering (MW) is involved in the symptomatology of attention deficit/hyperactivity disorder (ADHD). One of the notable categorizations of MW is based on the dimension of intentionality, in which intentional MW is labeled as deliberate MW (D-MW) while the unintentional form is called spontaneous MW (SMW). S-MW has been reported to be associated with lapses of attention or impulsive behaviors in individuals with ADHD. Using self-report and behavioral measures, the present study aimed to replicate the finding that scores on the S-MW scale predict those on the ADHD Self-Report Scale (ASRS) and to examine whether task performance under the S-MW state similarly predicts the scores on the ASRS scores. Method: The participants completed the ASRS and the D-MW and S-MW scales several days before performing the laboratory task (i.e., the continuous counting Stroop task). During the Stroop task (576 trials × 2 blocks), experience sampling probes were presented (20/block), which requested the participants to report their attentional state (i.e., concentrated, D-MW, S-MW, or other). Results: A univariate analysis showed that the accuracy of the Stroop task was remarkably lower under S-MW than under D-MW. Multiple regression analyses showed that scores on the S-MW scale and the accuracy of the Stroop task in the S-MW state independently predicted the ASRS scores. Conclusion: These results may indicate that individuals with ADHD could suffer from two different types of interference stemming from S-MW.
Abstractor: As Provided
Entry Date: 20260101
Accession Number: EJ1513417
Database: ERIC
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  Value: <anid>AN0195587728;gs001sep.26;2026Jul27.01:37;v2.2.500</anid> <title id="AN0195587728-1">Behavioral and Self-Report Measures of Spontaneous Mind Wandering Predict Different Aspects of ADHD in Adults </title> <p>Objective: Studies have provided evidence that mind wandering (MW) is involved in the symptomatology of attention deficit/hyperactivity disorder (ADHD). One of the notable categorizations of MW is based on the dimension of intentionality, in which intentional MW is labeled as deliberate MW (D-MW) while the unintentional form is called spontaneous MW (S-MW). S-MW has been reported to be associated with lapses of attention or impulsive behaviors in individuals with ADHD. Using self-report and behavioral measures, the present study aimed to replicate the finding that scores on the S-MW scale predict those on the ADHD Self-Report Scale (ASRS) and to examine whether task performance under the S-MW state similarly predicts the scores on the ASRS scores. Method: The participants completed the ASRS and the D-MW and S-MW scales several days before performing the laboratory task (i.e., the continuous counting Stroop task). During the Stroop task (576 trials × 2 blocks), experience sampling probes were presented (20/block), which requested the participants to report their attentional state (i.e., concentrated, D-MW, S-MW, or other). Results: A univariate analysis showed that the accuracy of the Stroop task was remarkably lower under S-MW than under D-MW. Multiple regression analyses showed that scores on the S-MW scale and the accuracy of the Stroop task in the S-MW state independently predicted the ASRS scores. Conclusion: These results may indicate that individuals with ADHD could suffer from two different types of interference stemming from S-MW.</p> <p>Keywords: ADHD; spontaneous mind wandering; counting stroop task; experience sampling probe; default mode network</p> <hd id="AN0195587728-2">Introduction</hd> <p>Attention-deficit/hyperactivity disorder (ADHD) impairs many aspects of daily life activities, impacting 5% to 6% of children and 3% to 4% of adults worldwide. This is a neurodevelopmental disorder that is characterized by inattentive, hyperactive, and impulsive behaviors in patients compared with healthy individuals of the same age ([<reflink idref="bib15" id="ref1">15</reflink>]; [<reflink idref="bib37" id="ref2">37</reflink>]). Early studies hypothesized that deficits in executive function play a critical role in ADHD, and accumulating empirical findings support this hypothesis ([<reflink idref="bib6" id="ref3">6</reflink>]). For instance, [<reflink idref="bib3" id="ref4">3</reflink>] proposed an ADHD theory, emphasizing that deficits in inhibitory control impair neuropsychological abilities such as self-regulation of affect-motivation-arousal, which result in the appearance of poor sustained attention in patients with ADHD.</p> <hd id="AN0195587728-3">The Default Mode Network and Its Role in Cognitive Control</hd> <p>Following the discovery of the default mode network (DMN), research increasingly focused on offline brain states in neurological and psychiatric disorders ([<reflink idref="bib8" id="ref5">8</reflink>]). The DMN includes the ventral and dorsal medial prefrontal cortices, posterior cingulate cortex/precuneus, lateral parietal cortex, and entorhinal cortex ([<reflink idref="bib39" id="ref6">39</reflink>]). It was named after the observation that these regions decrease activity during tasks requiring new task-set formation, non–self-referential processing, or sustained goal maintenance, relative to resting states such as eyes-closed rest or fixation ([<reflink idref="bib40" id="ref7">40</reflink>]; [<reflink idref="bib47" id="ref8">47</reflink>]). Because DMN activity is suppressed during such tasks rather than enhanced during rest, early studies proposed that the brain maintains a structured "default" state rather than engaging in random activity ([<reflink idref="bib40" id="ref9">40</reflink>]).</p> <p>Later work identified an anticorrelation between the DMN and the task-positive dorsal attention network (DAN), which includes dorsal frontal and parietal regions ([<reflink idref="bib16" id="ref10">16</reflink>]). Greater cognitive demand increases DAN activity while proportionally decreasing DMN activity, a dynamic balance thought to be essential for efficient cognition ([<reflink idref="bib39" id="ref11">39</reflink>]). The salience network, centered on the anterior insula and anterior cingulate cortex, facilitates switching between these networks; when salient external events are detected, it suppresses the DMN and activates the DAN ([<reflink idref="bib49" id="ref12">49</reflink>]). Thus, efficient cognitive processing depends on coordinated interactions among the DMN, DAN, and salience network.</p> <p>Disrupted coordination among these networks appears to contribute to maladaptive behavior. For example, healthy individuals show DMN–DAN anticorrelation, whereas individuals with ADHD may exhibit positive correlations between DMN activity and dorsolateral prefrontal cortex activity ([<reflink idref="bib23" id="ref13">23</reflink>]). Similarly, adolescents with ADHD show reduced anticorrelation between the DMN and dorsal anterior cingulate cortex compared with healthy controls ([<reflink idref="bib50" id="ref14">50</reflink>]).</p> <hd id="AN0195587728-4">The Default Mode Network and Its Link to Mind Wandering</hd> <p>Building on the view that the DMN plays a central role in internally oriented cognition, its proposed functions include self-referential processing, social cognition, semantic and episodic memory, language, and mind wandering (MW; [<reflink idref="bib32" id="ref15">32</reflink>]). Because the present review focuses on attentional dynamics and their relation to DMN activity, we concentrate specifically on the link between the DMN and MW. Early evidence showed that increased DMN activity accompanies lapses of attention that interfere with goal-directed behavior ([<reflink idref="bib56" id="ref16">56</reflink>]), consistent with the DMN's anticorrelation with the task-positive attentional network described above. MW is a major contributor to such lapses, involving a shift from stimulus-driven, task-related processing to task-unrelated internal thought ([<reflink idref="bib43" id="ref17">43</reflink>]). Real-time experience-sampling studies demonstrated elevated DMN activity immediately before reports of MW ([<reflink idref="bib11" id="ref18">11</reflink>]), and medial prefrontal cortex activity has been associated with MW episodes marked by performance errors ([<reflink idref="bib30" id="ref19">30</reflink>]). Together, these findings suggest that MW reflects a DMN-dominant attentional state in which cognitive resources disengage from external task demands and shift toward internally generated thought.</p> <hd id="AN0195587728-5">Potential Contribution of MW to ADHD Symptomatology</hd> <p>Given the connection between DMN activity, MW, and attentional control, researchers have increasingly proposed that excessive or dysregulated MW may contribute to ADHD symptomatology ([<reflink idref="bib7" id="ref20">7</reflink>]). MW and ADHD share several behavioral features: both divert attention away from task-relevant information ([<reflink idref="bib48" id="ref21">48</reflink>]) undermine sustained focus by diffusing thought ([<reflink idref="bib42" id="ref22">42</reflink>]; [<reflink idref="bib44" id="ref23">44</reflink>]), and often arise impulsively as intrusive, task-irrelevant cognitions ([<reflink idref="bib10" id="ref24">10</reflink>]). In daily life, MW is associated with poorer academic and occupational performance ([<reflink idref="bib15" id="ref25">15</reflink>]; [<reflink idref="bib27" id="ref26">27</reflink>]; [<reflink idref="bib41" id="ref27">41</reflink>]), paralleling impairments common in ADHD ([<reflink idref="bib20" id="ref28">20</reflink>]).</p> <p>Since the mid-2010s, empirical work has examined whether MW accounts for variance in ADHD symptoms. In adults, self-report studies show that spontaneous MW (S-MW) correlates with ADHD symptoms in both clinical and non-clinical samples ([<reflink idref="bib43" id="ref29">43</reflink>]). Clinical studies have also demonstrated that the Mind Excessively Wandering Scale (MEWS) predicts ADHD severity beyond mood-related measures ([<reflink idref="bib22" id="ref30">22</reflink>]), and subsequent research has consistently confirmed links between S-MW and ADHD traits across adult cohorts (e.g., [<reflink idref="bib5" id="ref31">5</reflink>]; [<reflink idref="bib34" id="ref32">34</reflink>]).</p> <p>Parallel findings have emerged in younger populations. In adolescents, MEWS scores correlate strongly with ADHD symptoms and explain additional variance in general impairment and emotional dysregulation beyond standard ADHD scales ([<reflink idref="bib13" id="ref33">13</reflink>]). Studies with children have shown that those with ADHD report more MW and more frequent mind blanking during sustained-attention tasks ([<reflink idref="bib55" id="ref34">55</reflink>]), and MEWS scores reliably distinguish clinical from typically developing samples with moderate sensitivity and specificity ([<reflink idref="bib18" id="ref35">18</reflink>]). Together, these results indicate that excessive, spontaneous MW—likely reflecting DMN-dominant internal mentation—may be a core contributor to ADHD-related attentional difficulties across development.</p> <hd id="AN0195587728-6">Deliberate MW and Spontaneous MW</hd> <p>As research on mind wandering (MW) progressed, it became evident that MW is multidimensional rather than a single construct ([<reflink idref="bib2" id="ref36">2</reflink>]). A prominent distinction concerns intentionality: <emph>deliberate</emph> MW (D-MW) and <emph>spontaneous</emph> MW (S-MW; [<reflink idref="bib9" id="ref37">9</reflink>]; [<reflink idref="bib46" id="ref38">46</reflink>]). D-MW can be adaptive, emerging during low-demand tasks and enabling planning or strategic reflection, whereas S-MW is intrusive and difficult to control, often disrupting ongoing performance. This distinction is relevant for ADHD, as [<reflink idref="bib43" id="ref39">43</reflink>] demonstrated that S-MW, but not D-MW, is associated with ADHD symptoms.</p> <p>Behavioral studies have further examined whether MW-related performance indices predict ADHD. [<reflink idref="bib17" id="ref40">17</reflink>], using a combination of sustained-attention tasks, reading with thought probes, and daily-life experience sampling, found that higher commission errors on the sustained-attention-to-response task (SART) and more frequent MW reports were associated with elevated ADHD symptom scores. They also showed that missed prompts in daily-life sampling predicted ADHD severity, indicating that MW-related performance and meta-awareness are linked to ADHD-related difficulties.</p> <p>Other work has tested how D-MW and S-MW vary with task difficulty. [<reflink idref="bib1" id="ref41">1</reflink>], manipulating SART difficulty and probing attentional states, reported that S-MW frequency in the difficult condition was predicted by inattention, hyperactivity, and impulsivity, whereas S-MW in the easy condition related only to hyperactivity and impulsivity. In contrast, D-MW showed no reliable association with ADHD symptoms. These findings suggest that spontaneous, intrusive thought—rather than intentional disengagement—captures the attentional dysregulation underlying ADHD, consistent with the broader view that ADHD involves difficulties controlling internally generated cognition.</p> <hd id="AN0195587728-7">The Aims of the Present Study</hd> <p>The present study aimed to extend prior work by [<reflink idref="bib43" id="ref42">43</reflink>] and [<reflink idref="bib1" id="ref43">1</reflink>] by directly examining on‑task performance during episodes of S‑MW and D‑MW. Most experimental research on MW has relied on experience‑sampling probes embedded in a primary task ([<reflink idref="bib57" id="ref44">57</reflink>]). Although the sustained‑attention‑to‑response task (SART) is commonly used for this purpose, meta‑analytic evidence suggests that task‑specific effects in SART‑based MW paradigms may be relatively modest ([<reflink idref="bib57" id="ref45">57</reflink>]). Recent work has therefore incorporated alternative tasks, such as the color Stroop task, paired with probe‑based MW assessment. For example, [<reflink idref="bib12" id="ref46">12</reflink>] asked participants to report whether they were on‑task, mind wandering, or experiencing elements of both, and found numerically, but not statistically, better performance during on‑task periods compared with MW episodes. Building on these findings, the present study sought to clarify whether on‑task performance differs reliably between S‑MW, D‑MW, and on‑task states when attentional state is probed in real time. By doing so, this work aims to provide a more precise behavioral characterization of intentional and spontaneous components of MW and their potential relevance to attentional dysregulation.</p> <p>In the present study, we refined the paradigm used by [<reflink idref="bib12" id="ref47">12</reflink>] to more reliably capture MW episodes and their behavioral consequences. First, we substantially increased task length (1,152 trials) and the number of experience‑sampling probes (40 probes), thereby enhancing the likelihood of detecting both S-WM and D-MW episodes. Second, instead of allowing broad or ambiguous reports, participants were required to classify their attentional state into four categories—on‑task, D‑MW, S‑MW, or other—providing a clearer distinction between intentional and unintentional forms of MW than in prior work. Third, we replaced the color Stroop task with a counting Stroop task and restricted the stimulus set to incongruent trials. In [<reflink idref="bib12" id="ref48">12</reflink>], the random mixture of neutral, congruent, and incongruent trials may have confounded attentional‑state comparisons. Participants could have encountered easier congruent trials disproportionately during MW or BOTH states, reducing the likelihood of detecting performance differences. Conversely, probes appearing after difficult incongruent trials may have elicited on‑task reports despite generally lower accuracy and slower responses in that condition, thereby underestimating on‑task performance. By using only incongruent trials, we controlled for trial‑type difficulty and eliminated these potential confounds. Finally, we ensured that the correct response key always differed from the previous trial to prevent accidental key repetitions from inflating accuracy. Taken together, these modifications provide a more sensitive and internally consistent assessment of how S‑MW and D‑MW influence performance on a demanding cognitive control task.</p> <p>Using a modified Stroop task, this study addressed three research questions. The first question concerned whether the self-reported measures of D-MW and S-MW were consistent with the on-task performance associated with each MW state. The second question asked whether we could replicate the study of [<reflink idref="bib43" id="ref49">43</reflink>], in which individual ADHD symptomatology was predicted by measurements of S-MW but not by D-MW. The third question asked whether individual ADHD symptomatology was commonly predicted by self-reported measures of S-MW and the task performance associated with S-MW or whether they predicted different aspects of ADHD.</p> <hd id="AN0195587728-8">Materials and Methods</hd> <p></p> <hd id="AN0195587728-9">Participants</hd> <p>A total of 67 graduate and undergraduate students at Shimane University participated in this study (41 females and 26 males). Their ages ranged from 18 to 30 years, with a mean of 20.36 (standard deviation [<emph>SD</emph>] = 2.52). The participants reported normal or corrected-to-normal vision and hearing. They were all fluent in Japanese. Before the experiment, the experimenter provided text guidelines and explained the details to each participant. The participants were allowed to freely ask questions during the instructions and provided written informed consent upon agreeing to participate in the experiment. The study protocol was approved by the Institutional Review Board of the Faculty of Shimane University. The participants received 1,500 yen for their participation for a duration of 90 min.</p> <hd id="AN0195587728-10">Apparatus</hd> <p>A 23-inch monitor (EV2316W, EIZO Corporation) was connected to a laptop PC (Dell Latitude E5540, Dell Inc.) through a VGA cable. A webcam (C920 V-U0028, Logicool Co. Ltd.) was placed on top of the monitor to record the participants' facial expressions and head movements. A keyboard (Microsoft Wired Keyboard 600, Microsoft Corporation) was connected to the PC via a USB cable. A chin rest (T.K.K.932, Takei Scientific Instruments Co., Ltd.) was fixed on a table and located 80 cm from the monitor. Stimulus presentation and response collection were controlled using PsychoPy ([<reflink idref="bib36" id="ref50">36</reflink>]), which was run on a Windows 10 operating system (Microsoft Corporation).</p> <hd id="AN0195587728-11">Self-Report Measures</hd> <p> <emph>ADHD symptomatology</emph>: Individual traits were assessed using the ASRS v1.1 ([<reflink idref="bib35" id="ref51">35</reflink>]), which was translated into Japanese ([<reflink idref="bib51" id="ref52">51</reflink>]). The measure comprises two parts: Part A has 6 items, and Part B has 12 items. The items in Part A are designed to evaluate the presence or absence of symptomatology among the participants. The items in Part B are typically used by physicians when diagnosing patients. All items are rated on a 5-point Likert scale (1 = Never to 5 = Very often), with higher scores indicating greater symptom severity.</p> <p> <emph>Deliberate or spontaneous MW traits</emph>: Individual traits of D-MW and S-MW were measured using the scales developed by [<reflink idref="bib9" id="ref53">9</reflink>]. The D-MW scale consists of four items assessing the individual efficiency of intentional MW. The D-MW scale consisted of 4 items for which a 7-point Likert scale was prepared. For items 1, 2, and 4, the Likert scale ranged from rarely (<reflink idref="bib1" id="ref54">1</reflink>) to a lot (<reflink idref="bib7" id="ref55">7</reflink>), while for item 3, the scale ranged from not at all true (<reflink idref="bib1" id="ref56">1</reflink>) to very true (<reflink idref="bib7" id="ref57">7</reflink>). By contrast, the S-MW scale comprises four items that are related to the involuntary emergence of MW or susceptibility to the sudden onset of MW. The S-MW scale also consisted of 4 items, and each had a 7-point Likert scale. For item 1, 2, and 4, the Likert scale ranged from rarely (<reflink idref="bib1" id="ref58">1</reflink>) to a lot (<reflink idref="bib7" id="ref59">7</reflink>), while for item 3, the scale ranged from almost never (<reflink idref="bib1" id="ref60">1</reflink>) to always (<reflink idref="bib7" id="ref61">7</reflink>).</p> <hd id="AN0195587728-12">Working Memory Capacity Measurement</hd> <p>Although we did not administer IQ tests in the present study, working memory capacity (WMC) was measured using an automated version of the operation span test ([<reflink idref="bib54" id="ref62">54</reflink>]). Given that WMC is closely tied to fluid intelligence ([<reflink idref="bib14" id="ref63">14</reflink>]), analyzing the scores of the operation span test may help us determine whether individual intellectual ability relates to the symptomatology of ADHD in the present sample.</p> <p>In the operation span test, a simple math problem was presented (e.g., (3 × 4) + 2 = ?), which was followed by a probe digit (e.g., 8) to which a participant needed to indicate whether the digit was the correct answer. Immediately after the probe, an alphabet was presented for 800 ms, and the participant was required to hold it in memory for the subsequent recognition. For instance, in a trial of set-size 3, the sequence was repeated three times, followed by a recognition phase. In the phase, a total of 12 alphabets (F, H, J, K, L, N, P, Q, R, S, T, Y) were simultaneously presented in a 4 × 3 matrix, and the participant needed to click three alphabets in the correct order as they had been presented. The participant had a choice to skip alphabet(s) in some position(s) if they were not able to recollect the item(s). Set sizes ranged from 3 to 7, which were randomly ordered, and each set size was repeated 3 times.</p> <hd id="AN0195587728-13">Procedure</hd> <p>The participants were recruited for three experimental sessions held on different days. In the first session, the participants performed four cognitive tasks, with the operation span test administered as the last task. The other four cognitive tasks were performed in the second session. At the end of the second session, the participants were required to complete the ASRS v1.1 and the D-MW and S-MW scales. The results of the cognitive tasks except for the operation span test are unrelated to those of the present study are presented elsewhere. In the third session, the participants were instructed to perform the counting Stroop task, in which the experience sampling probes were embedded (Figure 1). They performed two blocks of the task, which were separated by a 5-min break, and each block consisted of 576 Stroop trials.</p> <p>DIAGRAM: Figure 1. A schematic diagram of the counting Stroop task. Note. Each trial began with a fixation presented on a monitor for 1,000 ms, which was followed by a Stroop stimulus. The meaning of the stimulus was always incongruent with the number of items. The stimulus disappeared upon a key press by a participant or when 1,500 ms had elapsed. When responding to a Stroop stimulus, a participant was required to press one of the four keys that were associated with the number of items on the screen. That is, the z -key for one item, the x -key for two items, the n -key for three items, and the m -key for four items. Because these keys were arranged on the number line, the participants found it easier to learn the stimulus–response mapping. The inter-trial interval was jittered across trials, taking one of three values: 500, 750, or 1,000 ms. An experience sampling probe was intermittently presented, which prompted participants to report their attentional state just before the probe appeared. The probe had six choices as indicated in the figure.</p> <p>In each trial of the Stroop task, one of four Japanese words representing digits (いち: one, に: two, さん: three, よん: four) was selected, and the number of the word to be presented was manipulated, which ranged from one to four. The words were vertically aligned, except when one item was presented. The task required participants to report the number of words presented while ignoring their meanings. When reporting, the participants pressed one of four keys—"<emph>z</emph>" for one item, "<emph>x</emph>" for two, "<emph>n</emph>" for three, and "<emph>m</emph>" for four—using the middle or index finger of the left or right hand. Because these keys were arranged on the number line, the participants found it easier to learn the stimulus–response mapping. They were instructed to respond as accurately and quickly as possible. In each trial, a cross fixation was presented for 1,000 ms, which was followed by a target stimulus. The target disappeared in response to a key press by the participant or when 1,500 ms had elapsed. One of the three inter-trial intervals (i.e., 500, 750, or 1,000 ms) was placed between the trials. The stimulus subtended a visual angle of 1.23° to 2.05°on the horizontal axis and 1.23° to 5.32° on the vertical axis.</p> <p>Two restrictions were introduced while preparing the stimulus list. First, the meaning of the selected digit was always incongruent with the number of words presented. Second, the correct response in the current trial was always different from that in the previous trial. For instance, if three items were presented in the current trial, one, two, or four items would have been presented in the previous trial.</p> <p>While participants continuously performed the Stroop task, experience sampling probes appeared intermittently to ask them to report their attentive state immediately before the probe was presented. The probe stimulus consisted of six sentences, and the participants had to select one sentence that matched their attentive state the most. The probe included the following sentences: (<reflink idref="bib1" id="ref64">1</reflink>) I fully concentrated on the task, (<reflink idref="bib2" id="ref65">2</reflink>) I performed the task while thinking about strategies or retrieving ideas to obtain higher scores, (<reflink idref="bib3" id="ref66">3</reflink>) I thought about unrelated things to the extent that my task performance was not affected, (<reflink idref="bib4" id="ref67">4</reflink>) I thought about unrelated things that could affect my task performance, (<reflink idref="bib5" id="ref68">5</reflink>) My mind wandered off somewhere, and (<reflink idref="bib6" id="ref69">6</reflink>) Others (e.g., I focused on a sound outside). The participants reported their attentive state by pressing one of six keys (1–6) on the keyboard. The probe stimulus was maintained until the participant pressed a key.</p> <p>While previous studies employed three options: "On task," "Intentionally mind wandering," or "spontaneously mind wandering" (e.g., [<reflink idref="bib1" id="ref70">1</reflink>]; [<reflink idref="bib46" id="ref71">46</reflink>]), the present study used six options for the following reasons. Option 1 (I fully concentrated on the task) corresponded to "On task" in the previous studies, in which participants focused on thoughts related to the ongoing task such as stimulus perception and response selection. For the choices of D-MW and S-MW, we prepared two choices for each, considering the functions of MW. [<reflink idref="bib33" id="ref72">33</reflink>] proposed two adaptive aspects of MW: autobiographical planning and creative problem solving. Following the proposal, we expected that participants could attempt to come up with a better way to solve the task, retrieving past experiences of cognitive exercises, which was assigned to option 2: I performed the task while thinking about strategies or retrieving ideas to obtain higher scores. Studies have hypothesized that D-MW and S-MW are similarly controlled by executive attentional control ([<reflink idref="bib2" id="ref73">2</reflink>]). In D-MW, attentional control is thought to help people intentionally shift from thinking about the task at hand to another train of thought ([<reflink idref="bib19" id="ref74">19</reflink>]). We assigned choice 3 (I thought about unrelated things to the extent that my task performance was not affected) to the intentional shift. In S-MW, two main causes are thought to be responsible: failure of attentional control and/or unintentional shifts in thought away from the task to be focused on ([<reflink idref="bib31" id="ref75">31</reflink>]; [<reflink idref="bib44" id="ref76">44</reflink>]). We assigned choice 4 (I thought about unrelated things that could affect my task performance) to the former and choice 5 (My mind wandered off somewhere) to the latter. We included choice 6 (Others) in case the experienced mental state was not applicable to the five candidates.</p> <p>The probe stimuli were presented 20 times in each block containing 576 Stroop trials. The stimuli were spaced by a minimum of 12 Stroop trials and a maximum of 64 (<emph>M</emph> = 29.75 trials, <emph>SD</emph> = 12.27). The participants were allowed to take a short break after responding to a probe stimulus. A Stroop trial restarted when the participants pressed one of four keys assigned to the Stroop task (i.e., "<emph>z</emph>," "<emph>x</emph>," "<emph>n</emph>," and "<emph>m</emph>"). Once the Stroop trials began, the participants were not allowed to pause unless they felt sick or experienced technical trouble. No feedback was provided on the performance of the Stroop task except during the practice trials. In the practice trial, the participants performed 51 trials, during which one probe stimulus was presented following the 19th trial and another was presented at the end. The participants were instructed to repeat the practice trials if their accuracy was < 50%. None of them performed below the criterion in the first practice.</p> <hd id="AN0195587728-14">Data Analysis</hd> <p> <emph>Measure scoring</emph>: For the ASRS v1.1, we scored Part A of the scale following the method proposed by [<reflink idref="bib25" id="ref77">25</reflink>]. We assigned a score of 1 to the first three items if a participant selected "sometimes," "often," or "very often," and 0 otherwise. For the last three items, the score was 1 if the participant selected "often" or "very often" but 0 otherwise. Then, we summed the scores for each participant. If the score of a participant is above 4, he/she was considered to have symptoms highly consistent in adults. Responses to the 12 items in Part B were not included in the analysis because standard guidelines have not been introduced for these items.</p> <p> <emph>Span score of the operation span test</emph>: For scoring the operation span test, we employed the absolute scoring procedure, where the set-size number was added only when the participants correctly recalled all the alphabets in the correct order in a given trial. For instance, in a trial of set-size 4, if the participants recalled all four alphabets in the correct order, 4 was added to their score, otherwise they received 0. The minimum possible score was 0 and the maximum was 75.</p> <p>For D-MW and S-MW, we followed the scoring methods described by [<reflink idref="bib9" id="ref78">9</reflink>]. The values of the four questions were summed by each participant. As proposed in [<reflink idref="bib9" id="ref79">9</reflink>], a higher score on the D-MW scale was interpreted as a greater tendency to deliberately engage in MW in everyday life. By contrast, a higher score on the S-MW scale was interpreted as a greater tendency to spontaneously engage in MW in everyday life.</p> <p> <emph>Dependent variables of the counting Stroop task</emph>: Four types of dependent variables were obtained for each participant: frequency, accuracy, response time, and response time variability in each attentive state (i.e., concentrated, D-MW, S-MW, and others). As previously stated, six choices were included in the probe stimulus. Choices (<reflink idref="bib1" id="ref80">1</reflink>) and (<reflink idref="bib6" id="ref81">6</reflink>) corresponded to the concentrated and other states, respectively. By contrast, D-MW included Choices (<reflink idref="bib2" id="ref82">2</reflink>) and (<reflink idref="bib3" id="ref83">3</reflink>), whereas S-MW included Choices (<reflink idref="bib4" id="ref84">4</reflink>) and (<reflink idref="bib5" id="ref85">5</reflink>).</p> <p>The frequency of each attentive state was quantified by counting the number of choices selected by the participants. When computing the accuracy, we selected five trials prior to each probe and assigned a value of 1 if the response was correct and 0 if it was incorrect or if no response was made. We then summed the scores for each attentive state. Finally, we divided the score by the number of probes for that attentive state multiplied by 5. As for the response time, we selected the correct response time of five trials prior to each probe. Subsequently, we averaged the response times for each attentive state. When obtaining response time variability, we calculated the standard deviations (<emph>SD</emph>s) of correct reaction times under deliberate and spontaneous MWs. When computing <emph>SD</emph>s, we included all the correct reaction times across two sessions under each MW state. We selected five trials prior to the probes, following the studies using a sustained attention task by [<reflink idref="bib44" id="ref86">44</reflink>]; [<reflink idref="bib45" id="ref87">45</reflink>], which measured the variability of the response times of five trials prior to the probes. It should be noted that this method yielded a significant effect of MW on task performance.</p> <p> <emph>Univariate analysis</emph>: Because our primary interest was the effect of D-MW and S-MW, we examined the difference in the means of each dependent variable (i.e., frequency, accuracy, response time, and response time variability) using the paired <emph>t</emph>-test. The statistical threshold for rejecting the null hypothesis was defined as <emph>p</emph> <.05 (two-tailed).</p> <p> <emph>Multivariate analyses</emph>: We computed Pearson's product-moment correlation coefficient for the following variables measured in each participant: scores on the ASRS v1.1 and D-MW and S-MW scales; the span score of the operation span test; frequency of D-MW and S-MW during the Stroop task; accuracy of the Stroop task under the D-MW and S-MW states; response time of the Stroop task under D-MW and S-MW; and response time variability of the Stroop task under D-MW and S-MW.</p> <p>To specify MW-related predictors of individual symptomatology of ADHD, a multiple regression analysis was performed, designating the ASRS v1.1 score as the objective variable. The explanatory variables are described in detail in the results section. All statistical analyses were performed using R (R Core[<reflink idref="bib52" id="ref88">52</reflink>]).</p> <hd id="AN0195587728-15">Results</hd> <p>Eight participants were excluded from the analysis for the following reasons. Two participants were excluded because of extremely low performance on the counting Stroop task under the D-MW or S-MW states (3.5 <emph>SD</emph>s away from the group mean). Four participants did not report any S-MW in response to the experience sampling probes, and two reported neither D-MW nor S-MW during the task; therefore, they were excluded from the main analysis (<emph>n</emph> = 59).</p> <hd id="AN0195587728-16">Participants Attributes</hd> <p>Table 1 presents descriptive statistics for the ASRS (row 19) and the operation span test (row 20). Although our participants were not clinically diagnosed, some may exhibit ADHD‑related characteristics. Seventeen participants scored above 5 on the ASRS. According to the scale guidelines ([<reflink idref="bib25" id="ref89">25</reflink>]), scores greater than 4 indicate the possibility of ADHD symptoms and warrant further clinical evaluation. Thus, a subset of our sample may fall within a clinically relevant range. ADHD symptoms often become less noticeable after adolescence, as inattention persists while hyperactivity and impulsivity decline ([<reflink idref="bib4" id="ref90">4</reflink>]). Therefore, some participants may have been unaware of their symptoms despite experiencing attention-related difficulties.</p> <p>Table 1. Descriptive Statistics of Measured Variables.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variable label</th><th align="center"><italic>n</italic></th><th align="center">Mean</th><th align="center"><italic>SD</italic></th><th align="center">Median</th><th align="center">Min</th><th align="center">Max</th></tr></thead><tbody><tr><td>Frq Con</td><td>66</td><td>9.24</td><td>8.85</td><td>6</td><td>0</td><td>40</td></tr><tr><td>Frq DMW</td><td>66</td><td>15.74</td><td>7.7</td><td>15.5</td><td>0</td><td>36</td></tr><tr><td>Frq SMW</td><td>66</td><td>11.38</td><td>8.25</td><td>9.5</td><td>0</td><td>30</td></tr><tr><td>Frq Other</td><td>66</td><td>3.15</td><td>4.37</td><td>2</td><td>0</td><td>26</td></tr><tr><td>Acc Con</td><td>56</td><td>0.97</td><td>0.07</td><td>1</td><td>0.5</td><td>1</td></tr><tr><td>Acc DMW</td><td>65</td><td>0.95</td><td>0.07</td><td>0.98</td><td>0.5</td><td>1</td></tr><tr><td>Acc SMW</td><td>61</td><td>0.89</td><td>0.12</td><td>0.92</td><td>0.44</td><td>1</td></tr><tr><td>Acc Other</td><td>43</td><td>0.89</td><td>0.15</td><td>0.95</td><td>0.3</td><td>1</td></tr><tr><td>Rt Con</td><td>56</td><td>0.65</td><td>0.09</td><td>0.63</td><td>0.27</td><td>0.84</td></tr><tr><td>Rt DMW</td><td>65</td><td>0.66</td><td>0.09</td><td>0.66</td><td>0.29</td><td>0.87</td></tr><tr><td>Rt SMW</td><td>61</td><td>0.68</td><td>0.09</td><td>0.68</td><td>0.29</td><td>0.93</td></tr><tr><td>Rt Other</td><td>43</td><td>0.69</td><td>0.12</td><td>0.69</td><td>0.28</td><td>0.92</td></tr><tr><td>std Con</td><td>56</td><td>0.10</td><td>0.03</td><td>0.10</td><td>0.02</td><td>0.16</td></tr><tr><td>std DMW</td><td>65</td><td>0.12</td><td>0.03</td><td>0.12</td><td>0.05</td><td>0.20</td></tr><tr><td>std SMW</td><td>61</td><td>0.13</td><td>0.04</td><td>0.14</td><td>0.03</td><td>0.26</td></tr><tr><td>std Other</td><td>43</td><td>0.12</td><td>0.05</td><td>0.12</td><td>0.05</td><td>0.21</td></tr><tr><td>Scl DMW</td><td>67</td><td>5.8</td><td>1.18</td><td>6.25</td><td>2.25</td><td>7</td></tr><tr><td>Scl SMW</td><td>67</td><td>4.69</td><td>1.53</td><td>4.75</td><td>1</td><td>7</td></tr><tr><td>ASRSv</td><td>67</td><td>3.31</td><td>1.63</td><td>4</td><td>0</td><td>6</td></tr><tr><td>OSPAN</td><td>67</td><td>47.44</td><td>12.96</td><td>45</td><td>15</td><td>75</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>. Frq Con = Frequency of concentration in the counting Stroop task; Frq DMW = frequency of deliberate MW in the counting Stroop task; Frq SMW = frequency of spontaneous MW in the counting Stroop task; Frq Other = frequency of the other states in the counting Stroop task; Acc Con = Accuracy under concentration state in the counting Stroop task; Acc DMW = Accuracy under deliberate MW in the counting Stroop task; Acc SMW = Accuracy under spontaneous MW in the counting Stroop task; Acc Other = Accuracy under the other states in the counting Stroop task; RT Con = Response Time under concentration state in the counting Stroop task; RT DMW = Response Time under deliberate MW in the counting Stroop task; RT SMW = Response Time under spontaneous MW in the counting Stroop task; RT Other = Response Time under the other states in the counting Stroop task; std Con = response time variability under concentration state in the counting Stroop task; std DMW = response time variability under deliberate MW in the counting Stroop task; std SMW = response time variability under spontaneous MW in the counting Stroop task; std Other = response time variability under other states in the counting Stroop task; Scl DMW = Score of the deliberate MW scale; Scl SMW = Score of the spontaneous MW scale; ASRSv = Score of the ASRS v1.1; OSPAN = Score of the Operation Span Test.</p> <p>We also administered the operation span test to assess working memory capacity as a proxy for fluid intelligence. Because operation span scores were not correlated with ASRS scores, <emph>r</emph>(<reflink idref="bib57" id="ref91">57</reflink>) = −.08, <emph>p</emph> =.55, individual intellectual ability is unlikely to have influenced the main findings of this study.</p> <hd id="AN0195587728-17">Results of the Univariate Analysis</hd> <p>Table 1 summarizes the descriptive statistics of the frequency, performance accuracy, response time, and response time variability for the counting Stroop task under the D-MW and S-MW states. Overall, the participants reported more D-MW than S-MW during the Stroop task. A paired <emph>t</emph>-test showed a significant difference in frequency between the D-MW and S-MW states, <emph>t</emph>(<reflink idref="bib58" id="ref92">58</reflink>) = 2.35, <emph>p</emph> =.02. The effect size analysis showed a relatively low effect (Cohen's <emph>d</emph> = 0.31). The accuracy for the S-MW state was lower than that for the D-MW state (Figure 2). The paired <emph>t</emph>-test showed a significant difference in accuracy between the S-MW and D-MW states, <emph>t</emph>(<reflink idref="bib58" id="ref93">58</reflink>) = −5.56, <emph>p</emph> <.001. The effect size analysis showed a moderate effect (Cohen's <emph>d</emph> = 0.73). The response time under S-MW was greater than that under D-MW (Figure 2). The paired <emph>t</emph>-test showed a significant difference in response time between the S-MW and D-MW states, <emph>t</emph>(<reflink idref="bib58" id="ref94">58</reflink>) = 3.70, <emph>p</emph> <.001. The effect size analysis showed a moderate effect (Cohen's <emph>d</emph> = 0.48). It was also found that response time variability was greater under S-MW than under D-MW (Figure 2). A paired <emph>t</emph>-test showed significantly greater <emph>SD</emph>s under S-MW than D-MW: <emph>t</emph>(<reflink idref="bib58" id="ref95">58</reflink>) = 2.65, <emph>p</emph> =.01, Cohen's <emph>d =</emph> 0.34.</p> <p>Graph: Figure 2. The violin plot of accuracy (left), response time (middle) and response time variability (right) of the counting Stroop task under the D-MW and S-MW states. Note. The white dot represents the median of the data, and the thick black bar represents the interquartile range. Whiskers indicate 1.5 times the IQR from the edges of the box, and the violin-shaped area represents the estimated probability density of the data at that value.</p> <hd id="AN0195587728-18">Results of the Multivariate Analyses</hd> <p>Table 2 presents the correlation coefficients of the variables of interest. The notable correlations above the statistical threshold (<emph>p</emph> <.05, two-tailed) were as follows. First, the frequency of D-MW was negatively correlated with that of S-MW, <emph>r</emph>(<reflink idref="bib57" id="ref96">57</reflink>) = −.38, <emph>p</emph> =.003. Second, the frequency of S-WM during the counting Stroop task was positively correlated with response time under S-MW (<emph>r</emph>[<reflink idref="bib57" id="ref97">57</reflink>] =.40, <emph>p</emph> =.002) and response time variability under S-MW (<emph>r</emph>[<reflink idref="bib57" id="ref98">57</reflink>] =.34, <emph>p</emph> =.007). Third, the D-MW scale score was positively correlated with the accuracy of the Stroop task under D-MW, <emph>r</emph>(<reflink idref="bib57" id="ref99">57</reflink>) =.33, <emph>p</emph> =.01. By contrast, the score on the S-MW scale was not correlated with the accuracy of the Stroop task under S-MW, <emph>r</emph>(<reflink idref="bib57" id="ref100">57</reflink>) = −.19, <emph>p</emph> =.15. Fourth, in the D-MW state, accuracy was negatively correlated with response time, <emph>r</emph>(<reflink idref="bib57" id="ref101">57</reflink>) = −.47, <emph>p</emph> <.001. Similarly, in the S-MW state, accuracy was negatively correlated with response time, <emph>r</emph>(<reflink idref="bib57" id="ref102">57</reflink>) = −.49, <emph>p</emph> <.001. Fifth, accuracy of the counting Stroop task under D-MW was negatively correlated with response time variability under D-MW, <emph>r</emph>(<reflink idref="bib57" id="ref103">57</reflink>) = −.40, <emph>p</emph> <.005. Likewise, accuracy under the S-MW state was negatively correlated with response time variability under S-MW, <emph>r</emph>(<reflink idref="bib57" id="ref104">57</reflink>) = −.41, <emph>p</emph> <.005. Sixth, as shown in Figure 3, accuracy under S-MW was negatively correlated with the ASRS v1.1 score, <emph>r</emph>(<reflink idref="bib57" id="ref105">57</reflink>) = −.35, <emph>p</emph> =.006, whereas no such correlation was found between accuracy under D-MW and ASRS v1.1, <emph>r</emph>(<reflink idref="bib57" id="ref106">57</reflink>) <.01, <emph>p</emph> =.99. Finally, as shown in Figure 4, the S-MW score was positively correlated with the score on the ASRS v1.1, <emph>r</emph>(<reflink idref="bib57" id="ref107">57</reflink>) =.51, <emph>p</emph> <.001, whereas such a correlation was not found between the scores of D-MW and ASRS v1.1, <emph>r</emph>(<reflink idref="bib57" id="ref108">57</reflink>) =.08, <emph>p</emph> =.57.</p> <p>Table 2. Correlation Coefficients Between the Variables.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variable label</th><th align="center">2</th><th align="center">3</th><th align="center">4</th><th align="center">5</th><th align="center">6</th><th align="center">7</th><th align="center">8</th><th align="center">9</th><th align="center">10</th><th align="center">11</th><th align="center">12</th></tr></thead><tbody><tr><td> 1. Frq DMW</td><td>−.38<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−.03</td><td>−.03</td><td>−.10</td><td>−.06</td><td>−.06</td><td>.13</td><td>−.11</td><td>.20</td><td>.07</td><td>.01</td></tr><tr><td> 2. Frq SMW</td><td /><td>.08</td><td>−.17</td><td>.31*</td><td>.40<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>.24</td><td>.34<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−.08</td><td>.11</td><td>.20</td><td>.08</td></tr><tr><td> 3. Acc DMW</td><td /><td /><td>.38<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−.47<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−.40<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−.40<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−.27</td><td>.33*</td><td>.09</td><td>.00</td><td>−.05</td></tr><tr><td> 4. Acc SMW</td><td /><td /><td /><td>−.37<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−.49<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−.30*</td><td>−.41<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>.22</td><td>−.19</td><td>−.35*</td><td>−.08</td></tr><tr><td> 5. Rt DMW</td><td /><td /><td /><td /><td>.85<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>.79<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>.49<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−.21</td><td>.16</td><td>.18</td><td>.06</td></tr><tr><td> 6. Rt SMW</td><td /><td /><td /><td /><td /><td>.67<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>.75<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−.23</td><td>.20</td><td>.17</td><td>.09</td></tr><tr><td> 7. std DMW</td><td /><td /><td /><td /><td /><td /><td>.51<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−.05</td><td>.33*</td><td>.25</td><td>.03</td></tr><tr><td> 8. std SMW</td><td /><td /><td /><td /><td /><td /><td /><td>−.32*</td><td>.24</td><td>.23</td><td>.09</td></tr><tr><td> 9. Scl DMW</td><td /><td /><td /><td /><td /><td /><td /><td /><td>.16</td><td>.08</td><td>−.22</td></tr><tr><td>10. Scl SMW</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>.51<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−.08</td></tr><tr><td>11. ASRSv</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>−.08</td></tr><tr><td>12. OSPAN</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>1</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 <emph>Note</emph>. The abbreviations are identical to Table 1.</item> <item>3 <emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <p>Graph: Figure 3. The scatter plots between the accuracy of the counting Stroop task and the scores of the ADHD Self-Report Scale v1.1 (ASRS-v1.1). Note. The left figure shows the correlation between the task accuracy under the D-MW state and the scores of ASRS-v1.1. The right figure shows the correlation between the task accuracy under the S-MW state and the scores of ASRS-v1.1. The regression line is presented in each figure, and the shaded area represents the confidence interval around the line.</p> <p>Graph: Figure 4. The scatter plots between the scores of the MW scales and the scores of the ADHD Self-Report Scale v1.1 (ASRS-v1.1). Note. The left figure shows the correlation between the scores on the D-MW scale and the those on the ASRS-v1.1. The left figure shows correlation between the scores the S-MW scale and the those on the ASRS-v1.1.</p> <p>To specify the unique effects of the explanatory variables on the ASRS v1.1 score, multiple regression analyses were performed. In the first analysis, we included the following four explanatory variables in the linear model: accuracy of the counting Stroop task under the D-MW and S-MW states and the scores of the D-MW and S-MW scales. Table 3 shows the results of the analysis. A significance test showed that the multiple regression model fit the data sufficiently, <emph>F</emph>(<reflink idref="bib4" id="ref109">4</reflink>, 54) = 6.87, <emph>p</emph> <.001, <emph>R</emph><sups>2</sups><subs>adjust</subs> =.29. When looking at the standardized regression coefficients, the coefficient of accuracy under the S-MW state was significant, indicating that accuracy was negatively associated with the score of the ASRS v1.1, β<subs>std</subs> = −.31, <emph>t</emph> = −2.44, <emph>p</emph> =.017. By contrast, the regression coefficient of accuracy under the D-MW was not statistically significant, β<subs>std</subs> =.05, <emph>t</emph> = 0.42, <emph>p</emph> =.67. The regression coefficient of the S-MW score was statistically significant and positively associated with the ASRS v1.1 score, β<subs>std</subs> =.43, <emph>t</emph> = 3.74, <emph>p</emph> <.001. The regression coefficient of the D- MW score was not statistically significant, β<subs>std</subs> =.06, <emph>t</emph> = 0.49, <emph>p</emph> =.63.</p> <p>Table 3. Results of the Multiple Regression Analysis Explaining the Score of ASRS v1.1 by the Four Explanatory Variables.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Explanatory variable</th><th align="center">Standardized β</th><th align="center"><italic>S.E</italic>.</th><th align="center"><italic>t</italic>-Value</th><th align="center">Pr(>|<italic>t</italic>|)</th><th /></tr></thead><tbody><tr><td>(Intercept)</td><td><.001</td><td>0.11</td><td>0.00</td><td>1.000</td><td /></tr><tr><td>Acc D-MW</td><td>.05</td><td>0.13</td><td>0.42</td><td>0.673</td><td /></tr><tr><td>Acc S-MW</td><td>−.31</td><td>0.12</td><td>−2.44</td><td>0.018</td><td><xref ref-type="table-fn" rid="tfn5">*</xref></td></tr><tr><td>Scale D-MW</td><td>.06</td><td>0.12</td><td>0.49</td><td>0.626</td><td /></tr><tr><td>Scale S-MW</td><td>.44</td><td>0.12</td><td>3.74</td><td><.001</td><td><xref ref-type="table-fn" rid="tfn5">***</xref></td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 <emph>Note</emph>. Acc D-MW = Accuracy of deliberate MW in the counting Stroop task; Acc S-MW = Accuracy of spontaneous MW in the counting Stroop task; Scale D-MW = Score of the deliberate MW scale; Scale S-MW = Score of the spontaneous MW scale.</item> <item>5 <emph>p</emph> <.05. ***<emph>p</emph> <.001.</item> </ulist> <p>In the second analysis, we added two explanatory variables (frequency of D-MW and S-MW during the Stroop task) to the first regression model. A significance test showed that the multiple regression model fit the data sufficiently, <emph>F</emph>(<reflink idref="bib6" id="ref110">6</reflink>, 52) = 4.56, <emph>p</emph> <.001, <emph>R</emph><sups>2</sups><subs>adjust</subs> =.27. Similar to the first regression model, the standardized regression coefficient of accuracy under the S-MW state was significant and negatively associated with the ASRS v1.1 score, β<subs>std</subs> = −.28, <emph>t</emph> = −2.21, <emph>p</emph> =.031. The coefficient of the S-MW score was statistically significant and positively associated with the ASRS v1.1 score, β<subs>std</subs> =.42, <emph>t</emph> = 3.42, <emph>p</emph> =.001. The regression coefficients of the other variables were not significant, as presented in Table 4.</p> <p>Table 4. Results of the Multiple Regression Analysis Explaining the Score of ASRS v1.1 by the Six Explanatory Variables.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Explanatory variable</th><th align="center">Standardized β</th><th align="center"><italic>S.E</italic>.</th><th align="center"><italic>t</italic>-Value</th><th align="center">Pr(>|<italic>t</italic>|)</th><th /></tr></thead><tbody><tr><td>(Intercept)</td><td><.001</td><td>0.11</td><td>0</td><td>1</td><td /></tr><tr><td>Acc D-MW</td><td>.03</td><td>0.13</td><td>0.26</td><td>0.795</td><td /></tr><tr><td>Acc S-MW</td><td>−.29</td><td>0.13</td><td>−2.21</td><td>0.031</td><td><xref ref-type="table-fn" rid="tfn7">*</xref></td></tr><tr><td>Frq D-MW</td><td>.03</td><td>0.13</td><td>0.22</td><td>0.828</td><td /></tr><tr><td>Frq S-MW</td><td>.11</td><td>0.13</td><td>0.89</td><td>0.378</td><td /></tr><tr><td>Scale D-MW</td><td>.08</td><td>0.12</td><td>0.6</td><td>0.551</td><td /></tr><tr><td>Scale S-MW</td><td>.42</td><td>0.13</td><td>3.42</td><td>0.001</td><td><xref ref-type="table-fn" rid="tfn7">**</xref></td></tr></tbody></table> </ephtml> </p> <ulist> <item>6 <emph>Note</emph>. The abbreviations are identical to Table 3. Frq D-MW represents frequency of deliberate MW in the counting Stroop task, and Frq S-MW represents frequency of spontaneous MW in the counting Stroop task, respectively.</item> <item>7 <emph>p</emph> <.05. ***<emph>p</emph> <.01.</item> </ulist> <p>In the third analysis, we included accuracy under the S-MW state, the S-MW score, and their interaction term as explanatory variables to test whether the effect of one variable on the ASRS v1.1 score could change as a function of the other. While the multiple regression model fit the data sufficiently, <emph>F</emph>(<reflink idref="bib3" id="ref111">3</reflink>, 55) = 9.29, <emph>p</emph> <.001, <emph>R</emph><sups>2</sups><subs>adjust</subs> =.30, the effect of the interaction term was not significant, β<subs>std</subs> =.08, <emph>t</emph> =.84, <emph>p</emph> =.40. Similar to the first and second models, the standardized regression coefficient of accuracy under the S-MW state was statistically significant, β<subs>std</subs> = −.30, <emph>t</emph> = −2.54, <emph>p</emph> =.014, and that of the S-MW score was also significant, β<subs>std</subs> =.45, <emph>t</emph> = 4.00, <emph>p</emph> <.001. These results are summarized in Table 5.</p> <p>Table 5. Results of the Multiple Regression Analysis Explaining the Score of ASRS v1.1 by Acc S-MW (Accuracy of Spontaneous MW in the Counting Stroop Task), Scale S-MW (Score of the Spontaneous MW Scale), and Their Interaction.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Explanatory variable</th><th align="center">Standardized β</th><th align="center"><italic>S.E.</italic></th><th align="center"><italic>t</italic>-Value</th><th align="center">Pr(>|<italic>t</italic>|)</th><th /></tr></thead><tbody><tr><td>(Intercept)</td><td>.01</td><td>0.11</td><td>0.14</td><td>0.893</td><td /></tr><tr><td>Acc S-MW</td><td>−.30</td><td>0.12</td><td>−2.54</td><td>0.014</td><td><xref ref-type="table-fn" rid="tfn8">*</xref></td></tr><tr><td>Scale S-MW</td><td>.45</td><td>0.11</td><td>4.01</td><td>0.000</td><td><xref ref-type="table-fn" rid="tfn8">***</xref></td></tr><tr><td>Interaction</td><td>.08</td><td>0.10</td><td>0.84</td><td>0.402</td><td /></tr></tbody></table> </ephtml> </p> <p>8 <emph>p</emph> <.05. ***<emph>p</emph> <.001.</p> <p>We also performed a separate multiple regression analysis, including four explanatory variables: response time under the D-MW and S-MW states and the scores of the D-MW and S-MW scales. A significance test showed that the multiple regression model fits the data sufficiently, <emph>F</emph>(<reflink idref="bib4" id="ref112">4</reflink>, 54) = 4.95, <emph>p</emph> <.005, <emph>R</emph><sups>2</sups><subs>adjust =</subs>.21. However, the standardized regression coefficients were neither significant for response time under D-MW (β<subs>std</subs> =.13, <emph>t</emph> = 0.61, <emph>p</emph> =.55) nor that under S-MW (β<subs>std</subs> = −.04, <emph>t</emph> = 0.17, <emph>p</emph> =.87). The statistical results were summarized in Table S1.</p> <p>In addition, we performed a multiple regression analysis that included four explanatory variables: response time variability under the D-MW and S-MW states and the scores of the D-MW and S-MW scales. A significance test showed that the multiple regression model fit the data sufficiently, <emph>F</emph>(<reflink idref="bib4" id="ref113">4</reflink>, 54) = 5.09, <emph>p</emph> <.005, <emph>R</emph><sups>2</sups><subs>adjust</subs> =.22. However, the standardized regression coefficients were neither significant for response time variability under D-MW (β<subs>std</subs> =.04, <emph>t</emph> = 0.31, <emph>p</emph> =.76) nor for that under S-MW (β<subs>std</subs> =.11, <emph>t</emph> = 0.76, <emph>p</emph> =.45). The statistical results were summarized in Table S2.</p> <p>Finally, we performed the backward stepwise regression, designating the ASRS-v1.1 score as the objective variable and remaining 11 variables in Table 2 as explanatory variables. Backward stepwise regression is a method that starts with a comprehensive model containing all potential predictors, and then systematically removes variables one by one to arrive at a simpler model that still effectively captures the underlying patterns in the data. The results suggested that a model with two explanatory variables (accuracy under the S-MW in the counting Stroop task and the S-MW scale score) was the best to predict the ASRS-v1.1 score (AIC = 32.18).</p> <hd id="AN0195587728-19">Results of Supplementary Factor Analysis on Items of the ASRS and MW Scales</hd> <p>To examine commonality and discriminability of items across the three measures, we performed a factor analysis that included each item of the three surveys: 6 items of the ASRS, 4 items of the D-MW scale, and 4 items of the S-MW scale. The result supported the three-factor model: χ<sups>2</sups>(<reflink idref="bib52" id="ref114">52</reflink>) = 52.78, <emph>p</emph> =.44, where the first factor included the 4 items of the D-MW scale, the second factor included 5 items of the ASRS and 3 items of the S-MW scale, and the third factor included 4 items of the S-MW scale. Factor loading of each item and proportion of variance explained by each factor are summarized in Table S3. While ASRS and S-MW scale partially shared variance, there was additional variance explained solely by the S-WM scale. These results seem to indicate that three surveys measure different constructs.</p> <hd id="AN0195587728-20">Discussion</hd> <p>The present study investigated how subjective MW ratings and on-task performance associated with MW contribute to individual symptomatology of ADHD. Combining the experience sampling probe method with the counting Stroop task, we compared task performance in the D-MW state with that in the S-MW state. Several days before administering the task, we measured the individual traits of D-MW and S-MW using a subjective scale developed by [<reflink idref="bib9" id="ref115">9</reflink>]. Using the ASRS v1.1 score as the objective variable, a multiple regression analysis examined the unique contribution of each variable. The results showed that the ASRS v1.1 score was significantly predicted by the accuracy of the Stroop task in the S-MW state and the score of the S-MW scale. Notably, each variable explained a different portion the variances in the ASRS v1.1 scores.</p> <hd id="AN0195587728-21">Stroop Performance Under the D-MW and S-MW States</hd> <p>The deliberate or spontaneous account of MW emphasizes its beneficial and adverse aspects ([<reflink idref="bib9" id="ref116">9</reflink>]; [<reflink idref="bib46" id="ref117">46</reflink>]). Empirical studies support this account by showing that the S-MW score is positively correlated with individual variability in rhythmic response time in the metronome response task, an index of task lapses due to MW ([<reflink idref="bib44" id="ref118">44</reflink>]). By replacing the metronome task with the counting Stroop task, the present study compared task performance between the S-MW and D-MW states.</p> <p>The frequency of D-MW during the Stroop task was higher than that of S-MW. In previous studies that used the standard SART, participants reported more D-MW when the SART was difficult than when it was easy. By contrast, participants reported less S-MW when the SART was difficult than when it was easy ([<reflink idref="bib1" id="ref119">1</reflink>]; [<reflink idref="bib46" id="ref120">46</reflink>]). Although the participants reported greater S-MW than D-MW, regardless of task difficulty, the difference was smaller in the easy condition. The discrepancy between the previous studies and the present study is attributed to differences in probe questions rather than to differences in the tasks used (i.e., SART and counting Stroop task). In previous studies ([<reflink idref="bib1" id="ref121">1</reflink>]; [<reflink idref="bib46" id="ref122">46</reflink>]), participants were instructed to report MW when their thoughts before the probe were unrelated to the task. The criterion for separating D-MW and S-MW was whether the appearance or persistence of MW was intentional or spontaneous. However, the present study focused on the beneficial aspects of D-MW and classified accessing past episodes to construct a better strategy for D-MW. Given that both access to episodic memory and MW depend on neural activity in the DMN regions ([<reflink idref="bib26" id="ref123">26</reflink>]; [<reflink idref="bib32" id="ref124">32</reflink>]), the present definition of D-MW seems acceptable. In the correlation analysis, we found a significant negative correlation between the frequency of D-MW and S-MW (<emph>r</emph> = −.38), which was similar to the correlations reported by [<reflink idref="bib1" id="ref125">1</reflink>]: <emph>r</emph> = −.40 in the difficult SART condition and <emph>r</emph> = −.30 in the easy SART condition. These consistent results seem to justify our classification of D-MW and S-MW.</p> <p>Significant differences between D-MW and S-MW were found in three behavioral indices: accuracy, response time, and response time variability. Accuracy under D-MW was statistically higher than that under S-MW, with a moderate-to-high effect size (Cohen's <emph>d</emph> = 0.73). Response time under S-MW was slower than that under D-MW, with a moderate effect size (Cohen's <emph>d</emph> = 0.48). The standard deviation of response time under S-MW was larger than that under D-MW, with a small effect size (Cohen's <emph>d</emph> = 0.34). While studies have identified indices differentiating performance on a task from that under MW (e.g., [<reflink idref="bib29" id="ref126">29</reflink>]; [<reflink idref="bib44" id="ref127">44</reflink>]), few have reported indices separating task performance under D-MW from that under S-MW. The present results indicate that three behavioral indices of the continuous counting Stroop task under the D-MW and S-MW states can be used as a sensitive measure to discriminate task involvement in each MW state. Some task parameters may be critical, such as including only incongruent trials, alternating correct response keys on every trial, and jittering inter-trial intervals. Further studies are required to address these issues to improve task sensitivity.</p> <p>When looking at correlation coefficients between behavioral indices, there were several consistencies between them. First, the frequency of S-MW in the Stroop task was positively correlated with response time and response time variability under S-MW. That is, participants reporting more S-MW during the Stroop task required more time to respond and their response time varied more when their minds spontaneously wandered. Second, task accuracy under D-MW was negatively correlated with response time as well as response time variability, meaning that participants with greater accuracy under D-MW made faster responses and their response time varied less. These results seem to reject the tradeoff between accuracy and response speed as well as response time variability. Third, similar to D-MW, task accuracy under S-MW was negatively correlated with response time and response time variability. Participants with lower accuracy under S-MW made slower responses and their response time varied more. The results may suggest that participants with lower accuracy experienced deeper S-MW, which led them to take longer to respond and made it harder for them to produce stable responses. These results also seem to reject the tradeoff between accuracy and response speed as well as response time variability. Collectively, the present results suggest that the continuous counting Stroop task can be used as a valuable method to capture behavioral changes during D-MW and S-MW.</p> <hd id="AN0195587728-22">Agreement Between Subjective Evaluation of the MW Trait and Behavioral Performance</hd> <p>One of the main goals of the present study was to assess the agreement between subjective evaluations of MW traits and behavioral measurements associated with MWs. In a previous study by [<reflink idref="bib42" id="ref128">42</reflink>], the proportion of MW during the metronome response task was positively correlated with the D-MW (<emph>r =</emph>.40) and S-MW (<emph>r =</emph>.49) scores. Such a correlation was not observed in the present study, where the frequency of D-MW during the Stroop task was uncorrelated with the score of the D-MW scale (<emph>r</emph> = −.11) and the frequency of S-MW was uncorrelated with the score of the S-MW scale (<emph>r</emph> =.11). One possible explanation for this discrepancy is the timing of scale administration. While [<reflink idref="bib42" id="ref129">42</reflink>] administered the scales just before the participants performed the metronome task, our participants completed the scales a few days before the Stroop task. The significant correlations obtained in the study of [<reflink idref="bib42" id="ref130">42</reflink>] may have been partially due to exposure to the scales just before task administration, such that participants who indicated that they possessed strong traits of MW could be prone to follow their self-assessments and reported more MW in the metronome task. Further consideration is required to examine the mutual influence between subjective evaluations and experimental tasks, which tap into common psychological constructs.</p> <p>Accuracy in the D-MW state was positively correlated with the score on the D-MW scale. By contrast, accuracy in the S-MW state was not correlated with the S-MW score. It has been argued that individual-difference correlations between the frequency of MW and task performance under MW states are fragile ([<reflink idref="bib28" id="ref131">28</reflink>]), and the statistical significance of the correlations can disappear with subtle disturbances ([<reflink idref="bib53" id="ref132">53</reflink>]). This argument may apply to the null correlation between the S-MW score and accuracy in the S-MW state. Regarding the significant correlation of D-MW, we assume that the nature of controllability embedded in D-MW plays an important role. Because D-MW, by definition, is the intentional employment of MW to relieve boredom or let the mind wander to get inspiration, participants may easily realize whether they are good at it or not by looking back on their daily lives. This significant correlation suggests that their belief in controllability over D-MW was endorsed by their actual implementation of D-MW during the task. By contrast, S-MW occurs without intention, which could make it difficult to precisely gauge the trait by oneself.</p> <hd id="AN0195587728-23">Significant Contribution of the S-MW to the ADHD Symptomatology</hd> <p>The present study aimed to replicate and advance the findings of [<reflink idref="bib43" id="ref133">43</reflink>]. A multiple regression analysis showed that the S-MW score significantly predicted that the ASRS v1.1 score, whereas the D-MW score did not. The predictive ability of the S-MW scale remained even after controlling for the effects of behavioral measurements. When looking at the standardized regression coefficient, the value assigned to the S-MW scale in the present study (β<subs>std</subs> =.42 in the first model) was similar to those reported by Seli et al. (β<subs>std</subs> =.47 for the non-clinical sample and β<subs>std</subs> =.40 for the clinical sample). The coefficient assigned to the D-MW scale (β<subs>std</subs> =.06 in the first model) was also similar to those in the previous study (β<subs>std</subs> =.03 for the non-clinical sample and β<subs>std</subs> =.07 for the clinical sample). These consistent results may allow us to generalize the idiosyncratic contribution of the S-MW trait to individual ADHD propensity in Asian populations in addition to Westerners.</p> <p>Besides the self-report measure, we also found that individual symptomatology of ADHD was predicted by the accuracy of the counting Stroop task in the S-MW state but not in the D-MW state. The significant prediction remained after controlling for the frequency of S-MW during the Stroop task as well as the score of the S-MW scale. Furthermore, we found no interaction between the accuracy in the S-MW state and the score on the S-MW scale when predicting the ADHD symptomatology score. These results indicate that the accuracy of the task in the S-MW state explains a different portion of the variances in the ADHD score compared with the S-MW scale scores. Given that the S-MW scale comprises questions regarding daily tendencies or difficulties associated with S-MW, the variance of ADHD scores explained by the S-MW score seems to be due to the macroscopic interference of S-MW; that is, individuals are distracted by unrelated thoughts and fail to remain absorbed in their primary task. Therefore, inattentive or hyperactive symptoms in ADHD can be partially attributed to task-unrelated thoughts that emerge spontaneously and are experienced daily. By contrast, the accuracy of the Stroop task in the S-MW state could explain the variance in ADHD scores in terms of microscopic interference. Specifically, individuals with greater ADHD symptomatology may suffer from moment-to-moment interference by S-MW, which makes them easily distracted by disturbances such as Stroop interference.</p> <p>Two different interferences caused by the S-MW (i.e., macroscopic and microscopic), which may result in the symptomatology of ADHD, may stem from different neural underpinnings. Regarding macroscopic interference, individuals with ADHD may suffer from daily inattentive and hyperactive behaviors due to an unbalanced anti-correlation between the DMN and the frontoparietal network (FPN). The anti-correlation between the DMN and FPN is attenuated in children and adults with ADHD ([<reflink idref="bib38" id="ref134">38</reflink>]), supporting our account. Because this anti-correlation between the two networks is required for a smooth transition from the cognitive control state to the idling state and vice versa ([<reflink idref="bib24" id="ref135">24</reflink>]), a disrupted transition in individuals with ADHD may induce inappropriate S-MW while they are engaged in cognitive tasks, such as studying in a classroom or working in an office. Regarding microscopic interference, S-MW may distract individuals with greater ADHD symptomatology by hampering the function of the salience network (SAN). Because the SAN plays an important role in switching dominance between the DMN and FPN ([<reflink idref="bib21" id="ref136">21</reflink>]), dysfunction of the SAN may make it difficult for ADHD individuals to recover cognitive control when MW spontaneously arises while they are engaged in an ongoing task. An instantaneous response is required to cope with the sudden rise in S-MW, and the rapid recruitment of the SAN, which can be impaired in ADHD, is critical. However, the results of a single study need to be cautiously interpreted and should not be overstated. Therefore, we note that the hypotheses above are speculative and need further investigation.</p> <p>While task accuracy under the S-MW state predicted the scores of the ASRS, the other behavioral indices (i.e., response time and response time variability) did not. The null results may be attributed to the construction of the task. Because the task was designed to require pressing different keys on every trial, some participants experienced trials with small movements (e.g., from the middle finger of the left hand to the index finger of the same hand) while others experienced large movements (e.g., from the middle finger of the left hand to the middle finger of the right hand). Because we could not predict when a participant experienced deliberate MW and spontaneous MW, it was not possible to experimentally control finger movements. Such differences in finger movements may mask the relationship between symptomatology of ADHD and on-task response time and response time variability under S-MW. Further investigations are needed to test the predictability of on-task response time and response time variability under S-MW for symptomatology of ADHD.</p> <p>In conclusion, the present study showed that the individual symptomatology of ADHD can be explained by two independent indices of S-MW. One was the self-reflection of episodes associated with S-MW, measured using the S-MW scale. The other was the lapse of attention during the ongoing cognitive task, which was assessed using the continuous counting Stroop task. These methods may be helpful in diagnosing different aspects of ADHD or in identifying the distinctive neural mechanisms responsible for ADHD.</p> <hd id="AN0195587728-24">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-jad-10.1177_10870547261448262 for Behavioral and Self-Report Measures of Spontaneous Mind Wandering Predict Different Aspects of ADHD in Adults by Takehiro Minamoto, Takuma Hayashi and Md Abdullah Al-Mamun in Journal of Attention Disorders</p> <ref id="AN0195587728-25"> <title> References </title> <blist> <bibl id="bib1" idref="ref41" type="bt">1</bibl> <bibtext> Arabaci G., Parris B. A. (2018). Probe-caught spontaneous and deliberate mind wandering in relation to self-reported inattentive, hyperactive and impulsive traits in adults. Scientific Reports, 8(1), Article 4113.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref36" type="bt">2</bibl> <bibtext> Banks J. B., Welhaf M. S. (2022). Individual differences in dimensions of mind wandering: The mediating role of emotional valence and intentionality. Psychological Research, 86(5), 1495–1517.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref4" type="bt">3</bibl> <bibtext> Barkley R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121(1), 65–94.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref67" type="bt">4</bibl> <bibtext> Becker S. P., Langberg J. M. (2013). Sluggish cognitive tempo among young adolescents with ADHD: Relations to mental health, academic, and social functioning. Journal of Attention Disorders, 17(8), 681–689.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref31" type="bt">5</bibl> <bibtext> Biederman J., Lanier J., DiSalvo M., Noyes E., Fried R., Woodworth K. Y., Biederman I., Faraone S. V. (2019). Clinical correlates of mind wandering in adults with ADHD. Journal of Psychiatric Research, 117, 15–23.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref3" type="bt">6</bibl> <bibtext> Boonstra A. M., Oosterlaan J., Sergeant J. A., Buitelaar J. K. (2005). Executive functioning in adult ADHD: A meta-analytic review. Psychological Medicine, 35(8), 1097–1108.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref20" type="bt">7</bibl> <bibtext> Bozhilova N. S., Michelini G., Kuntsi J., Asherson P. (2018). Mind wandering perspective on attention-deficit/hyperactivity disorder. Neuroscience and Biobehavioral Reviews, 92, 464–476.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref5" type="bt">8</bibl> <bibtext> Broyd S. J., Demanuele C., Debener S., Helps S. K., James C. J., Sonuga-Barke E. J. (2009). Default-mode brain dysfunction in mental disorders: A systematic review. Neuroscience and Biobehavioral Reviews, 33(3), 279–296.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref37" type="bt">9</bibl> <bibtext> Carriere J. S., Seli P., Smilek D. (2013). Wandering in both mind and body: Individual differences in mind wandering and inattention predict fidgeting. Canadian Journal of Experimental Psychology, 67(1), 19–31.</bibtext> </blist> <blist> <bibtext> Cheyne A. J., Solman G. J., Carriere J. S., Smilek D. (2009). Anatomy of an error: A bidirectional state model of task engagement/disengagement and attention-related errors. Cognition, 111(1), 98–113.</bibtext> </blist> <blist> <bibtext> Christoff K., Gordon A. M., Smallwood J., Smith R., Schooler J. W. (2009). Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proceedings of the National Academy of Sciences of the United States of America, 106(21), 8719–8724.</bibtext> </blist> <blist> <bibtext> Compton R. J., Gearinger D., Wild H. (2019). The wandering mind oscillates: EEG alpha power is enhanced during moments of mind-wandering. Cognitive, Affective, & Behavioral Neuroscience, 19(5), 1184–1191.</bibtext> </blist> <blist> <bibtext> Dekkers T. J., Flisar A., Karami Motaghi A., Karl A., Frick M. A., Boyer B. E. (2025). Does Mind-Wandering Explain ADHD-Related Impairment in Adolescents? Child Psychiatry & Human Development, 56(2), 346–357.</bibtext> </blist> <blist> <bibtext> Engle R. W., Tuholski S. W., Laughlin J. E., Conway A. R. A. (1999). Working memory, short-term memory, and general fluid intelligence: A latent-variable approach. Journal of Experimental Psychology. General, 128(3), 309–331.</bibtext> </blist> <blist> <bibtext> Fayyad J., De Graaf R., Kessler R., Alonso J., Angermeyer M., Demyttenaere K., De Girolamo G., Haro J. M., Karam E. G., Lara C., Lépine J. P., Ormel J., Posada-Villa J., Zaslavsky A. M., Jin R. (2007). Cross-national prevalence and correlates of adult attention-deficit hyperactivity disorder. The British Journal of Psychiatry: The Journal of Mental Science, 190, 402–409.</bibtext> </blist> <blist> <bibtext> Fox M. D., Snyder A. Z., Vincent J. L., Corbetta M., Van Essen D. C., Raichle M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673–9678.</bibtext> </blist> <blist> <bibtext> Franklin M. S., Mrazek M. D., Anderson C. L., Johnston C., Smallwood J., Kingstone A., Schooler J. W. (2017). Tracking Distraction. Journal of Attention Disorders, 21(6), 475–486.</bibtext> </blist> <blist> <bibtext> Frick M. A., Asherson P., Brocki K. C. (2020). Mind-wandering in children with and without ADHD. The British Journal of Clinical Psychology, 59(2), 208–223.</bibtext> </blist> <blist> <bibtext> Giambra L. M. (1995). A laboratory method for investigating influences on switching attention to task-unrelated imagery and thought. Consciousness and Cognition, 4(1), 1–21.</bibtext> </blist> <blist> <bibtext> Gordon C. T., Fabiano G. A. (2019). The transition of youth with ADHD into the workforce: Review and future directions. Clinical Child and Family Psychology Review, 22(3), 316–347.</bibtext> </blist> <blist> <bibtext> Goulden N., Khusnulina A., Davis N. J., Bracewell R. M., Bokde A. L., McNulty J. P., Mullins P. G. (2014). The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM. Neuroimage, 99, 180–190.</bibtext> </blist> <blist> <bibtext> Helfer B., Cooper R. E., Bozhilova N., Maltezos S., Kuntsi J., Asherson P. (2019). The effects of emotional lability, mind wandering and sleep quality on ADHD symptom severity in adults with ADHD. European Psychiatry, 55, 45–51.</bibtext> </blist> <blist> <bibtext> Hoekzema E., Carmona S., Ramos-Quiroga J. A., Richarte Fernandez V., Bosch R., Soliva J. C., Rovira M., Bulbena A., Tobeña A., Casas M., Vilarroya O. (2014). An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD. Human Brain Mapping, 35(4), 1261–1272.</bibtext> </blist> <blist> <bibtext> Keller J. B., Hedden T., Thompson T. W., Anteraper S. A., Gabrieli J. D., Whitfield-Gabrieli S. (2015). Resting-state anticorrelations between medial and lateral prefrontal cortex: Association with working memory, aging, and individual differences. Cortex, 64, 271–280.</bibtext> </blist> <blist> <bibtext> Kessler R. C., Adler L., Ames M., Barkley R. A., Birnbaum H., Greenberg P., Johnston J. A., Spencer T., Ustün T. B. (2005). The prevalence and effects of adult attention deficit/hyperactivity disorder on work performance in a nationally representative sample of workers. Journal of Occupational and Environmental Medicine, 47(6), 565–572.</bibtext> </blist> <blist> <bibtext> Kim H. (2010). Dissociating the roles of the default-mode, dorsal, and ventral networks in episodic memory retrieval. Neuroimage, 50(4), 1648–1657.</bibtext> </blist> <blist> <bibtext> Knowles D., Tay R. (2002). Driver inattention: More risky than the fatal four? [Paper presentation]. Road Safety Research, Policing and Education Conference, Adelaide, SA, Australia.</bibtext> </blist> <blist> <bibtext> Laflamme P., Seli P., Smilek D. (2018). Validating a visual version of the metronome response task. Behavior Research Methods, 50(4), 1503–1514.</bibtext> </blist> <blist> <bibtext> Levinson D. B., Stoll E. L., Kindy S. D., Merry H. L., Davidson R. J. (2014). A mind you can count on: Validating breath counting as a behavioral measure of mindfulness. Frontiers in Psychology, 5, Article 1202.</bibtext> </blist> <blist> <bibtext> Mason M. F., Norton M. I., Van Horn J. D., Wegner D. M., Grafton S. T., Macrae C. N. (2007). Wandering minds: The default network and stimulus-independent thought. Science, 315(5810), 393–395.</bibtext> </blist> <blist> <bibtext> McVay J. C., Kane M. J. (2010). Does mind wandering reflect executive function or executive failure? Comment on Smallwood and Schooler (2006) and Watkins (2008). Psychological Bulletin, 136(2), 188–197.</bibtext> </blist> <blist> <bibtext> Menon V. (2023). 20 years of the default mode network: A review and synthesis. Neuron, 111(16), 2469–2487.</bibtext> </blist> <blist> <bibtext> Mooneyham B. W., Schooler J. W. (2013). The costs and benefits of mind-wandering: A review. Canadian Journal of Experimental Psychology = Revue Canadienne de Psychologie Experimentale, 67(1), 11–18.</bibtext> </blist> <blist> <bibtext> Mrazek M. D., Phillips D. T., Franklin M. S., Broadway J. M., Schooler J. W. (2013). Young and restless: Validation of the Mind-Wandering Questionnaire (MWQ) reveals disruptive impact of mind-wandering for youth. Frontiers in Psychology, 4, Article 560.</bibtext> </blist> <blist> <bibtext> Murphy K. R., Adler L. A. (2004). Assessing attention-deficit/hyperactivity disorder in adults: Focus on rating scales. The Journal of Clinical Psychiatry, 65(Suppl 3), 12–17.</bibtext> </blist> <blist> <bibtext> Peirce J., Gray J. R., Simpson S., MacAskill M., Hochenberger R., Sogo H., Kastman E., Lindeløv J. K. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51(1), 195–203.</bibtext> </blist> <blist> <bibtext> Polanczyk G., de Lima M. S., Horta B. L., Biederman J., Rohde L. A. (2007). The worldwide prevalence of ADHD: A systematic review and metaregression analysis. The American Journal of Psychiatry, 164(6), 942–948.</bibtext> </blist> <blist> <bibtext> Posner J., Park C., Wang Z. (2014). Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder. Neuropsychology Review, 24(1), 3–15.</bibtext> </blist> <blist> <bibtext> Raichle M. E. (2015). The brain's default mode network. Annual Review of Neuroscience, 38, 433–447.</bibtext> </blist> <blist> <bibtext> Raichle M. E., MacLeod A. M., Snyder A. Z., Powers W. J., Gusnard D. A., Shulman G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682.</bibtext> </blist> <blist> <bibtext> Risko E. F., Anderson N., Sarwal A., Engelhardt M., Kingstone A. (2012). Everyday attention: Variation in mind wandering and memory in a lecture. Applied Cognitive Psychology, 26(2), 234–242.</bibtext> </blist> <blist> <bibtext> Seli P., Carriere J. S., Levene M., Smilek D. (2013). How few and far between? Examining the effects of probe rate on self-reported mind wandering. Frontiers in Psychology, 4, Article 430.</bibtext> </blist> <blist> <bibtext> Seli P., Carriere J. S., Smilek D. (2015). Not all mind wandering is created equal: Dissociating deliberate from spontaneous mind wandering. Psychological Research, 79(5), 750–758.</bibtext> </blist> <blist> <bibtext> Seli P., Cheyne J. A., Smilek D. (2013). Wandering minds and wavering rhythms: Linking mind wandering and behavioral variability. Journal of Experimental Psychology. Human Perception and Performance, 39(1), 1–5.</bibtext> </blist> <blist> <bibtext> Seli P., Jonker T. R., Cheyne J. A., Smilek D. (2013). Enhancing SART validity by statistically controlling speed-accuracy trade-offs. Frontiers in Psychology, 4, Article 265.</bibtext> </blist> <blist> <bibtext> Seli P., Risko E. F., Smilek D. (2016). On the necessity of distinguishing between unintentional and intentional mind wandering. Psychological Science, 27(5), 685–691.</bibtext> </blist> <blist> <bibtext> Shulman G. L., Fiez J. A., Corbetta M., Buckner R. L., Miezin F. M., Raichle M. E., Petersen S. E. (1997). Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. Journal of Cognitive Neuroscience, 9(5), 648–663.</bibtext> </blist> <blist> <bibtext> Smallwood J., Schooler J. W. (2006). The restless mind. Psychological Bulletin, 132(6), 946–958.</bibtext> </blist> <blist> <bibtext> Sridharan D., Levitin D. J., Menon V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the United States of America, 105(34), 12569–12574.</bibtext> </blist> <blist> <bibtext> Sun L., Cao Q., Long X., Sui M., Cao X., Zhu C., Zuo X., An L., Song Y., Zang Y., Wang Y. (2012). Abnormal functional connectivity between the anterior cingulate and the default mode network in drug-naive boys with attention deficit hyperactivity disorder. Psychiatry Research, 201(2), 120–127.</bibtext> </blist> <blist> <bibtext> Takeda T., Tsuji Y., Kurita H. (2017). Psychometric properties of the Japanese version of the Adult Attention-deficit hyperactivity disorder (ADHD) Self-Report Scale (ASRS-J) and its short scale in accordance with DSM-5 diagnostic criteria. Research in Developmental Disabilities, 63, 59–66.</bibtext> </blist> <blist> <bibtext> Team R. C. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing.</bibtext> </blist> <blist> <bibtext> Thomson D. R., Seli P., Besner D., Smilek D. (2014). On the link between mind wandering and task performance over time. Consciousness and Cognition, 27, 14–26.</bibtext> </blist> <blist> <bibtext> Unsworth N., Heitz R. P., Schrock J. C., Engle R. W. (2005). An automated version of the operation span task. Behavior Research Methods, 37(3), 498–505.</bibtext> </blist> <blist> <bibtext> Van den Driessche C., Bastian M., Peyre H., Stordeur C., Acquaviva E., Bahadori S., Delorme R., Sackur J. (2017). Attentional lapses in attention-deficit/hyperactivity disorder: Blank Rather than wandering thoughts. Psychological Science, 28(10), 1375–1386.</bibtext> </blist> <blist> <bibtext> Weissman D. H., Roberts K. C., Visscher K. M., Woldorff M. G. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience, 9(7), 971–978.</bibtext> </blist> <blist> <bibtext> Zanesco A. P., Denkova E., Jha A. P. (2025). Mind-wandering increases in frequency over time during task performance: An individual-participant meta-analytic review. Psychological Bulletin, 151(2), 217–239.</bibtext> </blist> </ref> <ref id="AN0195587728-26"> <title> Footnotes </title> <blist> <bibtext> Takehiro Minamoto</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0001-8434-7009</bibtext> </blist> <blist> <bibtext> The study protocol was approved by the Institutional Review Board of the Faculty of Human Sciences, Shimane University.</bibtext> </blist> <blist> <bibtext> The participants provided written informed consent upon agreeing to participate in the experiment.</bibtext> </blist> <blist> <bibtext> The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by research grant from Japan Society for Promotions of Science to TM (#21K03130 and 24K00507).</bibtext> </blist> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> Data and Codes are only to be made available via request to the Authors, meeting the following restrictions. One needs to submit a formal data sharing agreement. One also needs to obtain approval from the requesting researcher's local ethics committee.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Takehiro Minamoto; Takuma Hayashi and Md Abdullah Al-Mamun</p> <p>Reported by Author; Author; Author</p> <p></p> <p>Takehiro Minamoto is an Associate Professor of Cognitive Neuroscience whose work explores how the brain controls attention. Combining experimental psychology and neurophysiological techniques, he has investigated neural sources generating individual differences in deliberate attentional control.</p> <p>Takuma Hayashi earned a bachelor's degree in Human Sciences, completing research projects on ADHD and mind wandering. Although he currently works outside academia, he maintains a strong interest in neurodevelopmental disorders.</p> <p>Md Abdullah Al-Mamun is currently a Ph.D. student at Shimane University, Japan. He completed his Master of Engineering in the Faculty of Mechanical, Electrical, and Electronics Engineering at the same university in 2023. His research focuses on mind wandering detection, attention analysis, and AI-driven cognitive state modelling. His work has included studies on detecting attention and different mind wandering states using blink frequency and emotion analysis, as well as research related to behavioural and self-report measures of spontaneous mind wandering and ADHD in adults. His research is supported by the Japan Science and Technology Agency under the S-SPRING project.</p> </aug> <nolink nlid="nl1" bibid="bib15" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib37" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib39" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib40" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib47" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib16" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib49" firstref="ref12"></nolink> <nolink nlid="nl8" bibid="bib23" firstref="ref13"></nolink> <nolink nlid="nl9" bibid="bib50" firstref="ref14"></nolink> <nolink nlid="nl10" bibid="bib32" firstref="ref15"></nolink> <nolink nlid="nl11" bibid="bib56" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib43" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib11" firstref="ref18"></nolink> <nolink nlid="nl14" bibid="bib30" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib48" firstref="ref21"></nolink> <nolink nlid="nl16" bibid="bib42" firstref="ref22"></nolink> <nolink nlid="nl17" bibid="bib44" firstref="ref23"></nolink> <nolink nlid="nl18" bibid="bib10" firstref="ref24"></nolink> <nolink nlid="nl19" bibid="bib27" firstref="ref26"></nolink> <nolink nlid="nl20" bibid="bib41" firstref="ref27"></nolink> <nolink nlid="nl21" bibid="bib20" firstref="ref28"></nolink> <nolink nlid="nl22" bibid="bib22" firstref="ref30"></nolink> <nolink nlid="nl23" bibid="bib34" firstref="ref32"></nolink> <nolink nlid="nl24" bibid="bib13" firstref="ref33"></nolink> <nolink nlid="nl25" bibid="bib55" firstref="ref34"></nolink> <nolink nlid="nl26" bibid="bib18" firstref="ref35"></nolink> <nolink nlid="nl27" bibid="bib46" firstref="ref38"></nolink> <nolink nlid="nl28" bibid="bib17" firstref="ref40"></nolink> <nolink nlid="nl29" bibid="bib57" firstref="ref44"></nolink> <nolink nlid="nl30" bibid="bib12" firstref="ref46"></nolink> <nolink nlid="nl31" bibid="bib36" firstref="ref50"></nolink> <nolink nlid="nl32" bibid="bib35" firstref="ref51"></nolink> <nolink nlid="nl33" bibid="bib51" firstref="ref52"></nolink> <nolink nlid="nl34" bibid="bib54" firstref="ref62"></nolink> <nolink nlid="nl35" bibid="bib14" firstref="ref63"></nolink> <nolink nlid="nl36" bibid="bib33" firstref="ref72"></nolink> <nolink nlid="nl37" bibid="bib19" firstref="ref74"></nolink> <nolink nlid="nl38" bibid="bib31" firstref="ref75"></nolink> <nolink nlid="nl39" bibid="bib25" firstref="ref77"></nolink> <nolink nlid="nl40" bibid="bib45" firstref="ref87"></nolink> <nolink nlid="nl41" bibid="bib52" firstref="ref88"></nolink> <nolink nlid="nl42" bibid="bib58" firstref="ref92"></nolink> <nolink nlid="nl43" bibid="bib26" firstref="ref123"></nolink> <nolink nlid="nl44" bibid="bib29" firstref="ref126"></nolink> <nolink nlid="nl45" bibid="bib28" firstref="ref131"></nolink> <nolink nlid="nl46" bibid="bib53" firstref="ref132"></nolink> <nolink nlid="nl47" bibid="bib38" firstref="ref134"></nolink> <nolink nlid="nl48" bibid="bib24" firstref="ref135"></nolink> <nolink nlid="nl49" bibid="bib21" firstref="ref136"></nolink>
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  Label: Title
  Group: Ti
  Data: Behavioral and Self-Report Measures of Spontaneous Mind Wandering Predict Different Aspects of ADHD in Adults
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  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Takehiro+Minamoto%22">Takehiro Minamoto</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8434-7009">0000-0001-8434-7009</externalLink>)<br /><searchLink fieldCode="AR" term="%22Takuma+Hayashi%22">Takuma Hayashi</searchLink><br /><searchLink fieldCode="AR" term="%22Md+Abdullah+Al-Mamun%22">Md Abdullah Al-Mamun</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Attention+Disorders%22"><i>Journal of Attention Disorders</i></searchLink>. 2026 30(9):1189-1204.
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  Label: Availability
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
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  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Evaluation+%28Individuals%29%22">Self Evaluation (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Control%22">Attention Control</searchLink><br /><searchLink fieldCode="DE" term="%22Symptoms+%28Individual+Disorders%29%22">Symptoms (Individual Disorders)</searchLink><br /><searchLink fieldCode="DE" term="%22Reaction+Time%22">Reaction Time</searchLink><br /><searchLink fieldCode="DE" term="%22Interference+%28Learning%29%22">Interference (Learning)</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Reliability%22">Test Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Replication+%28Evaluation%29%22">Replication (Evaluation)</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+Students%22">Graduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Japan%22">Japan</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Stroop+Color+Word+Test%22">Stroop Color Word Test</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/10870547261448262
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1087-0547<br />1557-1246
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: Studies have provided evidence that mind wandering (MW) is involved in the symptomatology of attention deficit/hyperactivity disorder (ADHD). One of the notable categorizations of MW is based on the dimension of intentionality, in which intentional MW is labeled as deliberate MW (D-MW) while the unintentional form is called spontaneous MW (SMW). S-MW has been reported to be associated with lapses of attention or impulsive behaviors in individuals with ADHD. Using self-report and behavioral measures, the present study aimed to replicate the finding that scores on the S-MW scale predict those on the ADHD Self-Report Scale (ASRS) and to examine whether task performance under the S-MW state similarly predicts the scores on the ASRS scores. Method: The participants completed the ASRS and the D-MW and S-MW scales several days before performing the laboratory task (i.e., the continuous counting Stroop task). During the Stroop task (576 trials × 2 blocks), experience sampling probes were presented (20/block), which requested the participants to report their attentional state (i.e., concentrated, D-MW, S-MW, or other). Results: A univariate analysis showed that the accuracy of the Stroop task was remarkably lower under S-MW than under D-MW. Multiple regression analyses showed that scores on the S-MW scale and the accuracy of the Stroop task in the S-MW state independently predicted the ASRS scores. Conclusion: These results may indicate that individuals with ADHD could suffer from two different types of interference stemming from S-MW.
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  Group: Date
  Data: 20260101
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  Data: EJ1513417
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        Value: 10.1177/10870547261448262
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        PageCount: 16
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      – SubjectFull: Attention Deficit Hyperactivity Disorder
        Type: general
      – SubjectFull: Self Evaluation (Individuals)
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      – TitleFull: Behavioral and Self-Report Measures of Spontaneous Mind Wandering Predict Different Aspects of ADHD in Adults
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