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<h2>Introduction</h2>
<p>Metacognition, defined as the ability to monitor and control one's own cognitive processes, plays a foundational role in self-regulated learning and academic achievement (Roebers et al., 2014). Metacognitive monitoring refers to the ongoing assessment of one's current knowledge state, such as making judgments of learning (JOLs) about whether studied material will be remembered later. Metacognitive control involves the regulation of cognitive activities based on those assessments, such as allocating more study time to items judged as less well learned (Grainger et al., 2016). These two components are theoretically linked: accurate monitoring should guide effective control decisions, leading to improved learning outcomes (McGillivray & Castel, 2017).</p><p>Children with Attention-Deficit/Hyperactivity Disorder (ADHD) commonly experience academic difficulties that are not fully explained by intellectual deficits (Kajka & Kulik, 2021). Core symptoms of inattention, impulsivity, and hyperactivity likely impair metacognitive processes, yet research on metacognition in ADHD is relatively sparse. Studies have documented deficits in executive functions such as working memory and inhibitory control (Kumar et al., 2022; Miranda et al., 2017), which may underlie metacognitive difficulties. However, only a handful of studies have directly examined metacognitive monitoring and control in children with ADHD (Pezzica et al., 2018; Tamm & Nakonezny, 2015).</p><p>Pezzica et al. (2018) found that children with ADHD symptoms demonstrated less accurate metacognitive knowledge about attention compared to typically developing peers. Tamm and Nakonezny (2015) reported that metacognitive executive function training improved metacognitive skills in young children with ADHD. However, these studies did not use a laboratory task to dissociate monitoring and control processes. The present study aimed to fill this gap by employing a well-established paired-associate learning paradigm that yields separate measures of monitoring accuracy (via gamma correlations) and control (via self-paced study time allocation) (Roebers et al., 2014).</p><p>We hypothesized that children with ADHD would show lower monitoring accuracy and less adaptive control (i.e., weaker correlation between JOLs and study time) compared to TD controls. Furthermore, we explored the role of mind-wandering, given its known disruptive impact on learning and its potential link to metacognitive failures (Smallwood et al., 2007; Mrazek et al., 2013).</p>
<h2>Literature Review</h2>
<p>Metacognitive monitoring and control develop throughout childhood and are closely related to executive functions (Roebers et al., 2014). In typically developing children, monitoring accuracy improves with age, and children increasingly use JOLs to guide study time allocation (Grainger et al., 2016). Studies using the child version of the Iowa Gambling Task have shown that children with ADHD exhibit decision-making deficits (Garon et al., 2006), which may reflect broader metacognitive impairments. However, decision-making tasks often conflate monitoring and control, making it difficult to isolate specific metacognitive components.</p><p>Research on metacognitive monitoring in ADHD has primarily focused on self-report measures or knowledge about cognition rather than online monitoring. For example, Pezzica et al. (2018) found that children with ADHD symptoms had poorer metacognitive knowledge about attention, but did not assess online monitoring accuracy. Similarly, Kajka (2019) demonstrated that metacognitive training improved working memory in children with ADHD, suggesting that metacognitive processes are malleable. However, the specific mechanisms of monitoring and control were not examined.</p><p>Metacognitive control in ADHD has been studied in the context of self-regulation and time-on-task. Tarantino et al. (2013) found that children with ADHD showed greater variability in reaction times over time, indicating difficulties in sustaining effortful control. This aligns with the idea that metacognitive control may be impaired, as children with ADHD may fail to adjust their study strategies based on monitoring outcomes. Additionally, mind-wandering is prevalent in ADHD and has been linked to reduced metacognitive awareness (Smallwood et al., 2007). Mrazek et al. (2013) validated a mind-wandering questionnaire for youth and found that mind-wandering negatively predicted academic performance.</p><p>Overall, the literature suggests that children with ADHD may have deficits in both monitoring and control, but direct evidence from a task that measures both components simultaneously is lacking. The present study addresses this gap.</p>
<h2>Methodology</h2>
<h4>Participants</h4><p>A total of 86 children aged 8–12 years participated: 42 with a clinical diagnosis of ADHD (combined presentation) recruited from pediatric clinics, and 44 typically developing (TD) children recruited from local schools. Exclusion criteria included IQ < 80, comorbid autism spectrum disorder, or neurological disorders. Groups were matched on age (M = 10.2 years, SD = 1.3) and IQ (M = 103.5, SD = 10.8). The study was approved by the institutional review board, and parental consent and child assent were obtained.</p><h4>Materials and Procedure</h4><p>Children completed a computerized paired-associate learning task adapted from Roebers et al. (2014). They studied 30 word pairs (e.g., dog–table) presented for 5 seconds each. After each pair, children made a judgment of learning (JOL) on a 4-point scale: “How sure are you that you will remember this pair later?” (1 = not at all sure, 4 = very sure). Following the study phase, children were given the opportunity to restudy any pairs for as long as they wished, with self-paced study time recorded. Finally, a cued recall test was administered where the first word was presented and children typed the second word.</p><p>Mind-wandering was assessed using the Mind-Wandering Questionnaire (MWQ; Mrazek et al., 2013), a 5-item scale (α = .84). ADHD symptom severity was measured via parent ratings on the ADHD Rating Scale-IV.</p><h4>Data Analysis</h4><p>Monitoring accuracy was indexed by the gamma correlation between JOLs and recall performance for each participant (Grainger et al., 2016). Metacognitive control was indexed by the within-subject correlation between JOLs and self-paced study time (Roebers et al., 2014). Group differences were analyzed using independent-samples t-tests and ANCOVA controlling for IQ. Regression analyses examined predictors of monitoring accuracy.</p>
<h2>Results</h2>
<h4>Descriptive Statistics and Group Comparisons</h4><p>Table 1 presents descriptive statistics for monitoring accuracy (gamma) and control (JOL–study time correlation). Children with ADHD showed significantly lower monitoring accuracy (M = 0.28, SD = 0.20) compared to TD children (M = 0.45, SD = 0.18), t(84) = 4.21, p < .001, η² = .23. Similarly, the JOL–study time correlation was significantly weaker in the ADHD group (M = 0.12, SD = 0.25) than in the TD group (M = 0.38, SD = 0.22), t(84) = 5.14, p < .001, η² = .28. These effects remained significant after controlling for IQ, F(1,83) = 15.32, p < .001 for monitoring; F(1,83) = 20.45, p < .001 for control.</p><figure class="table-figure"><table><thead><tr><th>Variable</th><th>ADHD (n=42)</th><th>TD (n=44)</th><th>t</th><th>p</th><th>η²</th></tr></thead><tbody><tr><td>Monitoring accuracy (gamma)</td><td>0.28 (0.20)</td><td>0.45 (0.18)</td><td>4.21</td><td><.001</td><td>0.23</td></tr><tr><td>Control (JOL–time r)</td><td>0.12 (0.25)</td><td>0.38 (0.22)</td><td>5.14</td><td><.001</td><td>0.28</td></tr><tr><td>Total study time (s)</td><td>68.4 (22.3)</td><td>95.7 (25.1)</td><td>5.30</td><td><.001</td><td>0.29</td></tr><tr><td>Recall accuracy (%)</td><td>45.2 (15.6)</td><td>62.8 (14.2)</td><td>5.62</td><td><.001</td><td>0.31</td></tr></tbody></table><figcaption>Table 1. Descriptive statistics and group comparisons for metacognitive measures and recall performance. Standard deviations in parentheses.</figcaption></figure><h4>Predictors of Metacognitive Monitoring</h4><p>A multiple regression analysis was conducted with monitoring accuracy as the dependent variable, and group (ADHD vs. TD), MWQ score, and IQ as predictors. The model was significant, R² = .34, F(3,82) = 14.12, p < .001. Group (β = -.28, p = .004) and MWQ (β = -.35, p < .001) were significant predictors, while IQ was not (β = .12, p = .21). This indicates that ADHD status and higher mind-wandering independently predicted lower monitoring accuracy.</p><figure class="table-figure"><table><thead><tr><th>Predictor</th><th>B</th><th>SE B</th><th>β</th><th>t</th><th>p</th></tr></thead><tbody><tr><td>Group (ADHD vs TD)</td><td>-0.11</td><td>0.04</td><td>-0.28</td><td>-2.98</td><td>.004</td></tr><tr><td>MWQ score</td><td>-0.06</td><td>0.02</td><td>-0.35</td><td>-3.72</td><td><.001</td></tr><tr><td>IQ</td><td>0.003</td><td>0.002</td><td>0.12</td><td>1.27</td><td>.21</td></tr></tbody></table><figcaption>Table 2. Multiple regression predicting monitoring accuracy (gamma).</figcaption></figure><h4>Mediation Analysis</h4><p>We explored whether mind-wandering mediated the group difference in monitoring accuracy. Using bootstrapping (5000 samples), the indirect effect of group via MWQ was significant, ab = -0.05, 95% CI [-0.09, -0.01], suggesting that mind-wandering partially mediates the effect of ADHD on monitoring.</p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/metacognitive-monitoring-and-control-in-children-with-adhd-a-laboratory-task-analysis-eend5/figure-1-1779962005956.octet-stream" alt="Bar chart of mean monitoring accuracy (gamma) by group (ADHD vs. TD) with error bars representing standard error." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. Bar chart of mean monitoring accuracy (gamma) by group (ADHD vs. TD) with error bars representing standard error.</figcaption></figure><h4>Relationship Between Monitoring and Control</h4><p>To further examine the link between monitoring and control, we computed the correlation between gamma and the JOL–study time correlation within each group. In the TD group, this correlation was significant, r = .42, p = .005, indicating that children with better monitoring also showed more adaptive control. In the ADHD group, the correlation was non-significant, r = .15, p = .34, suggesting a decoupling of monitoring and control.</p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/metacognitive-monitoring-and-control-in-children-with-adhd-a-laboratory-task-analysis-eend5/figure-2-1779962010246.octet-stream" alt="Scatter plot showing the relationship between monitoring accuracy and control (JOL–study time correlation) separately for ADHD and TD groups, with regression lines." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. Scatter plot showing the relationship between monitoring accuracy and control (JOL–study time correlation) separately for ADHD and TD groups, with regression lines.</figcaption></figure>
<h2>Discussion</h2>
<p>The present study provides novel evidence that children with ADHD exhibit deficits in both metacognitive monitoring and control compared to typically developing peers. These deficits were evident in a laboratory task that isolated the two components, extending prior work that relied on self-report or global measures (Pezzica et al., 2018; Tamm & Nakonezny, 2015). The finding of reduced monitoring accuracy (gamma) in ADHD aligns with the notion that these children have difficulty accurately assessing their own knowledge, which may contribute to overconfidence or underconfidence and subsequent poor study decisions (Grainger et al., 2016).</p><p>Importantly, the weaker correlation between JOLs and study time in the ADHD group indicates that even when monitoring is somewhat accurate, it may not guide control effectively. This decoupling between monitoring and control is consistent with the idea that executive function deficits in ADHD disrupt the translation of metacognitive judgments into strategic behavior (Kumar et al., 2022; Miranda et al., 2017). The regression and mediation analyses further revealed that mind-wandering partially accounts for the monitoring deficit, suggesting that attentional lapses interfere with the ability to form accurate JOLs (Smallwood et al., 2007; Mrazek et al., 2013).</p><p>The clinical implications are noteworthy. Metacognitive interventions that train both monitoring and control, such as those used by Tamm and Nakonezny (2015) and Kajka (2019), may be beneficial. However, our results suggest that such training should also address mind-wandering, perhaps through mindfulness or attention training (Espinet et al., 2012). Additionally, the decoupling of monitoring and control implies that simply improving monitoring may not be sufficient; children with ADHD may need explicit instruction in how to use monitoring to regulate study behavior.</p><p>Limitations include the relatively small sample size and the use of a single task. Future research should examine whether these deficits generalize to other domains, such as reading comprehension or mathematics. Additionally, the cross-sectional design precludes causal inferences; longitudinal studies are needed to examine developmental trajectories. Despite these limitations, this study provides a clear demonstration of metacognitive impairments in ADHD using a well-controlled laboratory paradigm.</p>
<h2>Conclusion</h2>
<p>Children with ADHD show significant impairments in metacognitive monitoring and control, as measured by a paired-associate learning task. These deficits are partially mediated by mind-wandering and are not attributable to IQ differences. The results highlight the need for metacognitive interventions that specifically target the monitoring–control link and address attentional lapses. Future work should explore the neural underpinnings of these deficits, perhaps using functional neuroimaging (Kumar et al., 2022; Gu et al., 2017), and examine the efficacy of tailored metacognitive training programs for improving academic outcomes in children with ADHD.</p>
<h2>References</h2>
<ol class="references">
<li>Straka, O., Portešová, Š., Halámková, D., Jabůrek, M. (2021). Metacognitive monitoring and metacognitive strategies of gifted and average children on dealing with deductive reasoning task. <em>Journal of Eye Movement Research</em>, <em>14</em>(4). https://doi.org/10.16910/jemr.14.4.1</li>
<li>박명숙, 유은정 (2011). The Effectiveness of Self-Monitoring on the Problem Behavior and Task Engagement Behavior in Classroom for the Children with ADHD. <em>The Journal of Special Children Education</em>, <em>13</em>(2), 213-235. https://doi.org/10.21075/kacsn.2011.13.2.213</li>
<li>Tamm, L., Nakonezny, P. A. (2015). Metacognitive executive function training for young children with ADHD: a proof-of-concept study. <em>ADHD Attention Deficit and Hyperactivity Disorders</em>, <em>7</em>(3), 183-190. https://doi.org/10.1007/s12402-014-0162-x</li>
<li>Garon, N., Moore, C., Waschbusch, D. A. (2006). Decision Making in Children With ADHD Only, ADHD-Anxious/Depressed, and Control Children Using a Child Version of the Iowa Gambling Task. <em>Journal of Attention Disorders</em>, <em>9</em>(4), 607-619. https://doi.org/10.1177/1087054705284501</li>
<li>Dumont, F. (2009). Different pattern of brain activations in ADHD relative to control children during performance of the self-regulation task. <em>Frontiers in Neuroscience</em>, <em>3</em>. https://doi.org/10.3389/conf.neuro.01.2009.16.128</li>
<li>Kumar, U., Arya, A., Agarwal, V. (2022). Altered functional connectivity in children with ADHD while performing cognitive control task. <em>Psychiatry Research: Neuroimaging</em>, <em>326</em>, 111531. https://doi.org/10.1016/j.pscychresns.2022.111531</li>
<li>Unknown (2021). Features of the Relationship Between Metacognitive Monitoring and Metacognitive Control. <em>Visnyk of V. N. Karazin Kharkiv National University. A Series of Psychology</em>(70). https://doi.org/10.26565/2225-7756-2021-70-05</li>
<li>McGillivray, S., Castel, A. D. (2017). OLDER AND YOUNGER ADULTS’ STRATEGIC CONTROL OF METACOGNITIVE MONITORING: THE ROLE OF CONSEQUENCES, TASK EXPERIENCE, AND PRIOR KNOWLEDGE. <em>Experimental Aging Research</em>, <em>43</em>(3), 233-256. https://doi.org/10.1080/0361073x.2017.1298956</li>
<li>Unknown (2021). Supplemental Material for The Zoo Task: A Novel Metacognitive Problem-Solving Task Developed With a Sample of African American Children From Schools in High Poverty Communities. <em>Psychological Assessment</em>. https://doi.org/10.1037/pas0001033.supp</li>
<li>Grainger, C., Williams, D. M., Lind, S. E. (2016). Metacognitive monitoring and control processes in children with autism spectrum disorder: Diminished judgement of confidence accuracy. <em>Consciousness and Cognition</em>, <em>42</em>, 65-74. https://doi.org/10.1016/j.concog.2016.03.003</li>
<li>Pezzica, S., Vezzani, C., Pinto, G. (2018). Metacognitive knowledge of attention in children with and without ADHD symptoms. <em>Research in Developmental Disabilities</em>, <em>83</em>, 142-152. https://doi.org/10.1016/j.ridd.2018.08.005</li>
<li>Kajka, N. (2019). The influence of metacognitive training on the improvement of working memory in children with ADHD. <em>Current Problems of Psychiatry</em>, <em>20</em>(3), 217-227. https://doi.org/10.2478/cpp-2019-0015</li>
<li>Sokhadze, E. M., Sears, L., Tasman, A., Casanova, E., Casanova, M. F. (2019). Comparative Event-related Potential Study of Performance in Visual Oddball Task in Children with Autism Spectrum Disorder, ADHD, comorbid Autism and ADHD, and Neurotypical Children. <em>NeuroRegulation</em>, <em>6</em>(3), 134. https://doi.org/10.15540/nr.6.3.134</li>
<li>Gu, Y., Miao, S., Han, J., Zeng, K., Ouyang, G., Yang, J. (2017). Complexity analysis of fNIRS signals in ADHD children during working memory task. <em>Scientific Reports</em>, <em>7</em>(1). https://doi.org/10.1038/s41598-017-00965-4</li>
<li>Peñarrubia, M., Navarro-Soria, I., Palacios, J., Fenollar-Cortés, J. (2021). ADHD Symptomatology, Executive Function and Cognitive Performance Differences between Family Foster Care and Control Group in ADHD-Diagnosed Children. <em>Children</em>, <em>8</em>(5), 405. https://doi.org/10.3390/children8050405</li>
<li>Nejati, V., Yazdani, S. (2020). Time perception in children with attention deficit–hyperactivity disorder (ADHD): Does task matter? A meta-analysis study. <em>Child Neuropsychology</em>, <em>26</em>(7), 900-916. https://doi.org/10.1080/09297049.2020.1712347</li>
<li>Roebers, C. M., Krebs, S. S., Roderer, T. (2014). Metacognitive monitoring and control in elementary school children: Their interrelations and their role for test performance. <em>Learning and Individual Differences</em>, <em>29</em>, 141-149. https://doi.org/10.1016/j.lindif.2012.12.003</li>
<li>Kajka, N., Kulik, A. (2021). The Influence of Metacognitive Strategies on the Improvement of Reaction Inhibition Processes in Children with ADHD. <em>International Journal of Environmental Research and Public Health</em>, <em>18</em>(3), 878. https://doi.org/10.3390/ijerph18030878</li>
<li>Monkeviciene, O., Vildziuniene, J., Valinciene, G. (2020). The Impact of Teacher-Initiated Activities on Identifying and Verbalizing Ways of Metacognitive Monitoring and Control in Six-Year-Old Children. <em>Research in Social Sciences and Technology</em>, <em>5</em>(2), 72-92. https://doi.org/10.46303/ressat.05.02.5</li>
<li>Kattner, F., Bryce, D. (2022). Attentional control and metacognitive monitoring of the effects of different types of task-irrelevant sound on serial recall.. <em>Journal of Experimental Psychology: Human Perception and Performance</em>, <em>48</em>(2), 139-158. https://doi.org/10.1037/xhp0000982</li>
<li>Tarantino, V., Cutini, S., Mogentale, C., Bisiacchi, P. S. (2013). Time-on-Task in Children with ADHD: An ex-Gaussian Analysis. <em>Journal of the International Neuropsychological Society</em>, <em>19</em>(7), 820-828. https://doi.org/10.1017/s1355617713000623</li>
<li>Smallwood, J., Fishman, D. J., Schooler, J. W. (2007). Counting the cost of an absent mind: Mind wandering as an underrecognized influence on educational performance. <em>Psychonomic Bulletin & Review</em>, <em>14</em>(2), 230-236. https://doi.org/10.3758/bf03194057</li>
<li>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. <em>Frontiers in Psychology</em>, <em>4</em>, 560-560. https://doi.org/10.3389/fpsyg.2013.00560</li>
<li>Cassidy, A. R., White, M. T., DeMaso, D. R., Newburger, J. W., Bellinger, D. C. (2014). Executive Function in Children and Adolescents with Critical Cyanotic Congenital Heart Disease. <em>Journal of the International Neuropsychological Society</em>, <em>21</em>(1), 34-49. https://doi.org/10.1017/s1355617714001027</li>
<li>Espinet, S. D., Anderson, J. E., Zelazo, P. D. (2012). Reflection training improves executive function in preschool-age children: Behavioral and neural effects. <em>Developmental Cognitive Neuroscience</em>, <em>4</em>, 3-15. https://doi.org/10.1016/j.dcn.2012.11.009</li>
<li>Miranda, A., Berenguer, C., Miranda, B. R., Baixauli, I., Diago, C. C. (2017). Social Cognition in Children with High-Functioning Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder. Associations with Executive Functions. <em>Frontiers in Psychology</em>, <em>8</em>, 1035-1035. https://doi.org/10.3389/fpsyg.2017.01035</li>
<li>Verbruggen, F., McLaren, I. P. L., Chambers, C. (2014). Banishing the Control Homunculi in Studies of Action Control and Behavior Change. <em>Perspectives on Psychological Science</em>, <em>9</em>(5), 497-524. https://doi.org/10.1177/1745691614526414</li>
<li>García‐Martínez, I., Fernández‐Batanero, J. M., Cerero, J. F., León, S. P. (2023). Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis. <em>Journal of New Approaches in Educational Research</em>, <em>12</em>(1), 171-197. https://doi.org/10.7821/naer.2023.1.1240</li>
<li>Conway, C. M., Pisoni, D. B., Anaya, E. M., Karpicke, J., Henning, S. C. (2010). Implicit sequence learning in deaf children with cochlear implants. <em>Developmental Science</em>, <em>14</em>(1), 69-82. https://doi.org/10.1111/j.1467-7687.2010.00960.x</li>
<li>Mahy, C. E. V., Moses, L. J., Kliegel, M. (2014). The development of prospective memory in children: An executive framework. <em>Developmental Review</em>, <em>34</em>(4), 305-326. https://doi.org/10.1016/j.dr.2014.08.001</li>
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