{"paper_id":"3bb95416-45d7-47fc-83eb-f0a2465fa208","body_text":"Relation of biology students’ metacognitive monitoring to neural activity during model-based scientific reasoning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Relation of biology students’ metacognitive monitoring to neural activity during model-based scientific reasoning Carrie Clark, McKenna Elliott, Joseph Dauer, Mei Grace Behrendt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2874829/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2024 Read the published version in npj Science of Learning → Version 1 posted 10 You are reading this latest preprint version Abstract Metacognitive calibration— the capacity to accurately self-assess one’s performance— forms the basis for error detection and self-monitoring, and a potential catalyst for conceptual change. Limited brain imaging research on authentic learning tasks implicates the lateral prefrontal and anterior cingulate brain regions in expert scientific reasoning. This study aimed to determine how variation in undergraduate life sciences students’ metacognitive calibration relates to their brain activity when evaluating the accuracy of biological models. Fifty undergraduate students enrolled in an introductory life sciences course completed a biology model reasoning task during fMRI. Findings suggest that students with higher metacognitive calibration recruit lateral prefrontal regions linked in prior research to expert STEM reasoning. Findings suggest that metacognition relates to important individual differences in undergraduates’ use of neural resources during an authentic educational task and underscore the importance of fostering metacognitive calibration in the classroom. Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience Metacognition educational neuroscience confidence error detection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Self-regulated learning involves the extent to which students are engaged, motivated and behaviorally active in their learning 1 . Self-regulated learners accomplish tasks or goals by continuously monitoring and correcting the effectiveness of their strategies and by evaluating past behaviors to better control future learning 2 . Metacognitive calibration, the match between a students’ objective performance and their subjective self-assessment of that performance 3,4 , is fundamental to self-regulated learning because it forms the basis for performance monitoring 5 . Students with more accurate calibration presumably have more reliable information on which to base their judgments regarding effortful, strategic, cognitive resources to optimize task performance 6 . Accordingly, metacognitive calibration has been linked to dorsomedial prefrontal systems thought to be involved in monitoring and detecting errors in performance, as well as to lateral PFC regions linked to effortful cognitive control 7 . These neuroimaging studies have used experimental tasks rather than tasks reflective of authentic educational experiences, leaving a gap in understanding of the role students’ metacognitive calibration plays in the learning process. Modeling and model evaluation is an authentic task in life sciences education, and expert scientists are skilled at detecting inaccuracies in models 8 . This study aimed to determine the relation of metacognitive calibration to student performance and neural activity when evaluating biological models. Defining Metacognition and its Relation to Learning Although a general definition of metacognition is “thinking about thinking” 9 , contemporary conceptualizations typically divide it into two distinct processes: knowledge about cognition, including self-monitoring and self-regulatory mechanisms 9–11 . Self-monitoring involves knowing how well one is performing and recognizing the likelihood of accuracy or inaccuracy in one’s judgments or behaviors. Conversely, metacognitive self-regulation concerns the process of organizing one’s cognition, planning, being aware of one’s comprehension and evaluating the efficacy of strategies during task performance 12,13 . Learners who develop high self-monitoring and self-regulation skills revise and reconstruct concepts to advance their knowledge and achieve high self-efficacy, persistence, and self-discipline in performing tasks 14–16 . Well-calibrated self-assessments also have been linked to students’ use of more effective study strategies, and to higher levels of academic growth over time 17–19 . Student ratings of confidence in their own performance offer one means of assessing self-awareness and calibration 20 . For well calibrated learners, confidence ratings should correspond with actual performance 21 . That is, students should have high confidence when they are accurate and low confidence when not. Although confidence estimates are higher for correct relative to incorrect trials 22 , lower-performing students tend to be more overconfident in their performance predictions, while higher-performing learners are accurate or underconfident in their self-assessments 11,23,24 . Additionally, learners tend to be overconfident when evaluating complex or difficult information and underconfident about relatively easier information 25 . Measuring metacognitive calibration Collecting multiple confidence ratings affords calculation of a simple phi coefficient (f), the correlation between students’ accuracy and confidence ratings across all trials of a task 26 . Although widely used in the education literature, this method is susceptible to bias based on the participant’s task performance and does not adequately parse students’ confidence bias – their general tendency to make high or low confidence ratings— from their capacity to discriminate correct from inaccurate responses 27,28 . Consequently, cognitive neuroscience researchers recommend using model-based Signal Detection Theory metrics to provide response-bias free measures of how precisely confidence ratings track task accuracy 26,29 . These measures include meta-d’ , a measure of student’s metacognition that is conditioned on their task performance distribution, and metacognitive efficiency , which reflects the difference between a student’s sensitivity to their performance and their actual task performance. An ‘ideal metacognitive observer’ should exhibit little difference between their meta- d and their task performance 26 . Such signal detection metrics parse students’ confidence biases and task performance from their capacity to discriminate optimal from less optimal performance, enabling deeper understanding of the implications of different aspects of metacognition for learning. Evaluating Metacognition in the Brain Metacognition has repeatedly been linked to the prefrontal cortex (PFC), with greater activity in dorsolateral and anterior medial PFC being associated with higher levels of self-awareness and metacognitive accuracy 30–32 . Several metacognition studies highlight the ventromedial PFC and posterior medial frontal cortex as central brain areas related to confidence estimates 33,34 and error detection processes 35–37 , while both the frontopolar cortex and the lateral PFC are involved in explicit metacognitive judgments 22,38 , metacognitive control, and subsequent behavioral regulation 39,40 . More generally, areas in the dorsal anterior cingulate cortex (ACC) are thought to monitor for conflict or errors in performance, whereas the lateral PFC presumably uses these inputs to bias behavior toward more adaptive cognitive strategies 28,41,42 . From these studies, it is reasonable to hypothesize that students with higher levels of metacognitive calibration will show higher levels of activity in medial and lateral PFC regions during academic tasks, reflecting optimal use of self-monitoring and regulatory processes to guide performance. Conceptual Change in Science Learning and its Relation to Metacognitive Calibration Model-based reasoning, a core emphasis area in STEM education, offers a particularly relevant context for understanding how metacognitive calibration relates to students’ deployment of prefrontal regions during authentic learning experiences 43 . From simple flowcharts and graphs to complex computer simulations, models pervade all areas of science and are how scientists reason, evaluate hypotheses, and convey ideas 44,45 . Undergraduate biology students encounter models through textbooks, lectures, note-taking, and classroom activities that convey foundational introductory principles regarding genetic, ecological and cellular systems 46 . Measuring biology students’ capacity to detect errors or misconceptions in models constitutes a powerful means to assess their understanding of essential educational content. Linking metacognitive calibration to model error detection offers potential for understanding how to elicit conceptual change to ultimately foster a deeper understanding of fundamental scientific concepts 47 . To learn scientific concepts, students must be able to actively refine their learning process, learning to integrate new concepts and relationships with their prior knowledge 48 . Learners must first become dissatisfied with their existing conceptions, embracing new conceptions as plausible 49 . However, conceptual alterations prove difficult when learners hold misconceptions 50,51 . Absent or poor metacognition may predict the extent to which learners both ignore new information and resist changing their minds, even when new information indicates errors in their original beliefs 31 . For learners who hold misconceptions, conceptual change is more predictable when an alternative scientific viewpoint initiates some cognitive conflict (e.g., error detection) 52 . Metacognitive calibration may be a critical initial step in this process of conceptual change. The relationship between metacognitive skills and conceptual change is implicit: individuals routinely recognize existing conceptions, evaluate them, and decide whether to reconstruct their understanding, which requires recognition of one’s knowledge limitations 53 . Students who are more metacognitively aware of misconceptions about a given topic are more likely to develop deeper understanding of a concept or topic (e.g., acquiring correct knowledge), decreasing the likelihood of entrenching misconceptions 54 . Conversely, students who are unaware of their misconceptions may become overly secure in those misconceptions (i.e., they do not know what they do not know) 54,55 and may consequently miss opportunities for knowledge revision 56 . Evaluating how students’ metacognitive calibration links to their error evaluation and ultimate learning success may offer critical clues as to how metacognition supports conceptual change. Using neuroimaging to understand metacognitive calibration’s role in science students’ model evaluation Limited literature on the neural regions involved in expert science cognition highlights its association with prefrontal regions linked in error detection, conflict monitoring, and inhibition 57 . Additionally, experts evaluating the accuracy of scientific circuits activate anterior cingulate cortex (ACC), ventrolateral PFC (VLPFC), and dorsolateral PFC (DLPFC) to a greater extent than novices, who tended to activate the DLPFC solely 58 . Brault Foisy et al. 59 theorized that the ACC and PFC play distinct roles in cognitive control during scientific reasoning. In their study, individuals activated the ACC when they provided both correct and incorrect responses to scientific conceptions, whereas only the dorsolateral PFC was activated when individuals gave correct answers. Potvin et al. 60 found that both the left DLPFC and left VLPFC exhibited greater activation when chemistry professors were presented with scientific statements containing misconceptions, supporting previous research linking DLPFC to the resolution of misconceptions. With respect to metacognition, novice students exhibited greater dorsomedial frontal activity when they were confident vs. not confident in their responses to physics electric circuit diagrams 61 . However, the brain regions associated with legitimate confidence, i.e., confidence that corresponded with accurate responses vs. overconfident responses, were more posterior and included inferior parietal, premotor and inferior temporal regions. Taken together, these studies implicate ACC and lateral PFC regions in the expert processing and resolution of science misconceptions. Assuming that students with higher metacognitive calibration are better equipped to engage in ‘expert’-like error detection and associated engagement of control, we expected such students to use these regions to a greater extent than their less calibrated peers when evaluating biological models. Summary Conceptual change theories identify metacognitive calibration as a critical process that enables students to detect and resolve gaps in their knowledge. However, neuroimaging studies typically examine the neural bases of metacognitive processing and calibration accuracy in the context of novel or self-reflection-based tasks, rather than educationally authentic tasks like those involving scientific reasoning 62,63 . The limited neuroimaging research on scientific cognition indicates that ‘expert’ scientists activate DLPFC, VLPFC and ACC brain regions to a greater extent than ‘novice’ undergraduates when reasoning about scientific phenomena, particularly when accurate reasoning involves detecting and inhibiting misconception 58,59 . The current study’s goal was to examine the relation of students’ metacognitive calibration to their neural activity during a biological model evaluation task. Specifically, we aimed to determine how individual differences in students’ calibration accuracy related to their activity in lateral prefrontal regions and ACC regions linked both to metacognition and to scientific expertise. Based on the theory that accurate calibration serves as a foundation for error detection and conceptual change, we hypothesized that students with higher metacognitive calibration would show higher levels of activity in prefrontal regions linked to these scientific processes. RESULTS Behavioral Results Table 1 presents descriptive statistics and correlations among the different behavioral variables. Students on average were correct on 65% of the model evaluation task trials, whereas they were confident approximately 75% of the time. Figure 1 illustrates the significant difference in student confidence levels for accurate vs. inaccurate trials of the model evaluation task, t (49) = 7.72, p < .001, although students still responded that they were confident more often than not for trials where they made an inaccurate response. The moderate correlation between total accurate responses and total confident responses ( r = .403, p = .004) indicated that as participants’ accuracy increased, their overall confidence levels also increased. This work is complemented by a study that highlights the importance of self-monitoring to evaluate models during the modeling process 64 . The different metrics for metacognitive calibration, sensitivity, and efficiency, as calculated from students’ confidence ratings and accuracy in the fMRI model evaluation task, were robustly inter-correlated ( p’s < .01). Neither student’s f scores nor their metacognitive efficiency scores correlated with their model evaluation accuracy, although meta- d ’ scores did, as expected ( p < .001). Final course grade in the introductory life science course correlated slightly with model evaluation task accuracy ( p = .008), but not with confidence, p = .39. Measures of metacognition showed minimal correlations with final course grades, except for meta- d’ , p = .05. Moreover, accuracy during the model evaluation task and metacognition metrics related to that task did not correlate with students’ biology self-efficacy, reading fluency, or task engagement scores, suggesting that these factors exerted no confounding effect on student performance. Therefore, we disregarded these variables in further analyses. Neuroimaging Results from the Model Evaluation Task Overall patterns of brain activity for confident and non-confident responses Table 2 and Figure 2 show that during trials where students indicated they were Confident > Not confident, widespread neural activation occurred across occipital regions, and in regions in the parietal cortex and lateral and medial PFC. We performed a further contrast of Error > No error models only for trials where students were confident, which indicated that students showed increased activity in a medial frontal cluster, in bilateral inferior prefrontal/insular regions and in the lingual gyrus (Table 2, supplementary Figure 1). Finally, we contrasted trials where students were confident and accurate in their response > confident but inaccurate (i.e., overconfident) in their response. This analysis revealed that students exhibited more activity in a single cluster in the left lingual gyrus when they were confident and accurate relative to confident and inaccurate (Table 2, supplementary Figure 2). Overall, when examined by trial type, students exhibited greater activity in lateral and medial PFC regions when they were confident, although PFC activity did not diverge for trials where students were overconfident vs. calibrated in their responses. Relation of individual differences in student confidence to neural response patterns Focusing on the Error > No error model contrast, we evaluated the relation of overall accuracy and confidence to students’ neural responses by including these variables as regressors in the group design matrix. There was no correlation of students’ model evaluation accuracy with their neural response patterns. Likewise, students’ overall confidence did not correlate with brain activity. However, students with lower confidence bias showed more activity in two clusters centered in the left inferior frontal gyrus (Table 3, Figure 3). Relation of student metacognitive calibration to neural response patterns Controlling for accuracy on the model evaluation task, a correlation emerged between students’ f values and their level of activity in the right middle frontal gyrus for the Error > No error model contrast (see Table 3). Students with higher f scores showed higher activity in this cluster, which spanned BA 8 and 9 (Figure 4). Comparable results emerged when we instead used the metacognitive efficiency score (i.e., d ’ – meta d ’), which correlated positively with activation in a right middle frontal cluster that overlapped with the cluster identified for f. Controlling for accuracy, students’ meta- d’ scores correlated with activity in the left inferior parietal and occipital gyri and in the postcentral gyrus (Figure 5). DISCUSSION Metacognition is central to science education: it is critical to developing and cultivating a deep conceptual understanding of scientific concepts 65 . Specifically, in biology education, students with higher awareness of the learning process and stronger ability to monitor, regulate, and control learning manifest a more meaningful understanding of targeted biology concepts and better scientific inquiry skills 66,67 . The current study evaluated the relation of students’ metacognitive calibration to their neural activity when evaluating errors in biology models. A major finding is that students with higher metacognitive calibration and efficiency recruit lateral prefrontal regions to a greater extent than their peers with lower metacognitive calibration when evaluating error-containing models. This finding has implications for understanding the role of metacognitive monitoring in students’ learning behavior and for approaches to STEM instruction. Controlling for task accuracy, higher metacognitive calibration, as measured using phi, was linked to higher levels of activity in the right dorsolateral PFC. The same effect held when we instead used students’ metacognitive efficiency scores, which more rigorously parse metacognitive calibration from confidence response biases and task performance 26 . Those few neuroimaging studies that have focused on STEM learning have linked the recruitment of lateral prefrontal and ACC brain regions to ‘expert’ scientific reasoning, with the lateral PFC being especially linked to accurate error detection within STEM experts’ domain of expertise 8,68 Therefore, the students in our study with higher levels of calibration may be more ‘expert-like’ in the regions they deploy when evaluating error-containing models. Brault-Fausy et al. 59 suggested that lateral prefrontal activity may be especially relevant for resolving interference when viewing scientific errors or misconceptions. Given that the DLPFC forms a core part of the frontal-parietal network associated with executive control 69 , findings may indicate that these students are more effectively deploying executive resources to resolve the errors present in these models. Surprisingly, students’ behavioral phi, meta-efficiency, and confidence bias scores were not linked significantly to their ultimate course grades, although both model evaluation accuracy and meta- d’ scores were significantly correlated with course grades. Notably, meta- d ’ reflects the predicted d’ of an individual, given their proportions of calibrated vs. non-calibrated confidence ratings 27 . The more meaningful indicator of a student’s metacognitive calibration relative to other students is thus the metacognitive efficiency score, which mathematically subtracts the individual’s task performance from their meta- d ’ to reveal the discrepancy between the two. The lack of correlation between meta-efficiency and course grades may indicate that, once we consider the individual’s accuracy in model error detection, calibration is less related to overall class performance. Thus, although higher calibration may be tied to the use of prefrontal brain regions associated with effortful resource deployment and, in previous studies, to students’ self-regulated learning and academic success 70–72 , it is students’ mastery of the concepts in these models and their ability to recognize accurate relative to inaccurate models that ultimately is reflected in their course performance. Students’ mean confidence was slightly higher than their accuracy (27 vs. 23) on the model task and students in general responded that they were confident more often than not for inaccurate trials. Indeed, some students had a bias score that exceeded 0, which means that they had more confident, inaccurate trials than confident, accurate trials. These descriptive findings support the notion that students tend to overestimate their performance, particularly when performance is low 23 . This overconfidence in one’s abilities suggests a lack of metacognitive awareness of their deficits, which may lead to ineffective self-regulation skills 11 . For example, an overconfident student, believing they know the material, might decide not to study for a test, thus increasing that student’s probability of doing poorly due to inadequate preparation. Although we did not find an association between confidence bias and performance or course grades, we did find that confidence bias correlated with lower metacognitive efficiency and with less left inferior frontal signal change when students viewed error-containing models. Bellon et al. (2020) 21 found that children showed activation in the left inferior frontal gyrus when rating their confidence in arithmetic problem solving and that the extent of children’s activity in this region was linked to their mathematics performance. Unlike Bellon et al., we examined neural activity during model evaluation as opposed to during the interval when students were making confidence ratings. However, the overlapping link to the left inferior frontal gyrus is possible and perhaps suggests that this region is important for modulating links between confidence and achievement. Interestingly, differences in neural activity for accurate and confident relative to inaccurate and confident trials were confined to the lingual gyrus; there were no differences in prefrontal activity for this contrast. Potvin et al. 61 similarly found that calibrated vs. overconfident trials in an electric circuit validation task were linked to parietal, premotor and fusiform regions rather than prefrontal regions. It is possible that this activity in posterior regions for correct and calibrated trials reflects the use of visual processing resources to support accurate processing and evaluation of the model. A significant study limitation is that one model evaluation task is not necessarily synonymous with overall academic performance or general metacognitive abilities. Additional assessments would be beneficial for determining and evaluating the neural and behavioral effects of metacognitive processes and of different forms of instruction. There is continued debate, for example, as to whether metacognitive calibration is task-specific or a general, trait-like characteristic 73,74 . Likewise, different instructional approaches can influence the neural correlates of learning 47 . Morgan-Short et al. 75,76 found that when adults learned an artificial language using either implicit or explicit instruction, they became equally proficient in the language. However, event-related potentials (ERPs) demonstrated fundamental differences at the neural level. Adults who were trained using implicit instruction exhibited patterns of brain activity more similar to native language speakers 75 , indicating that varied educational contexts, practices, and interventions may alter neural patterns even when this is not reflected at the behavioral level 47 . Another limitation of the study was that we had insufficient trial numbers to fully cross accuracy and confidence at the trial level to examine implications for brain activity: high levels of confidence meant that there were small numbers of trials where students were underconfident. Contrasts of confident vs. non-confident trials or calibrated vs. overconfident trials may also be less reliable because of unbalanced trial numbers for these conditions. Moreover, given the complexity of the task stimuli, each error containing model was equated in complexity to non-error containing models. Because the control for stimulus complexity was lost when we removed inaccurate trials, our primary contrast was based on all trials, regardless of students’ accuracy. Future studies should examine how accuracy modulates these relationships. Other limitations included a disproportionate representation of females and Euro-American Whites, as well as a small range in GPAs, which may have limited our power to capture the full extent of variation in students’ metacognitive monitoring performance. Future research is needed to determine how these individual differences in brain activity relate to ongoing academic achievement. One especially intriguing question is whether and how manipulations to instructional design, such as immediate feedback following errors, may impact students brain activity and, ultimately, help students become more self-regulated learners 77 . Likewise, future research should also examine how students might be encouraged to reflect on their calibration accuracy and whether such reflection might aid in the process of conceptual change in science. Lastly, additional research is needed to determine how metacognitive calibration affects students’ learning and academic performance throughout their educational trajectory. The present study’s finding that students with higher metacognitive monitoring and less confidence bias deployed more ‘expert like’ lateral prefrontal activity when they encountered error-containing models underscores a need to foster and nurture metacognition and self-awareness in the classroom. This is likely to require extensive instructional support both in terms of prompting reflection and in terms of encouraging students to act on their miscalibrations with effective learning strategies. Conceptual change is theorized to drive science learning and hinges on students’ willingness to engage with or experience conflict from new ideas 49,51,58,78–80 . Students who persistently learn from their own errors through self-reflection may be more motivated to continue to learn after failure, to correct their errors, and to recognize misconceptions 81,82 . Instructors might provide opportunities for students to reflect on their coursework by focusing on the learning process rather than on the content itself 83 , thereby engaging students in reflexive and adaptive thinking. They may also provide direct and immediate feedback, a core mechanism of self-regulated learning, which can positively impact academic achievement 84 . Finally, instructors may need to teach effective study habits to help students act on their metacognitive judgements, as students may lack this knowledge even when they are prepared to alter their strategies 85 . These instructional strategies may represent effective pathways for helping STEM students to be active, self-regulated learners and for moving them toward more expert model evaluation capacities. METHOD Participants Fifty-one undergraduate students were recruited, through class announcements, from five separate sections of one introductory life sciences course taught by two different instructors in two consecutive academic years at a large Midwestern university. First, we recruited from the sections taught by a professor who heavily used model-based instruction ( n = 35 students). To maximize our sample size, from the following academic year’s sections, we recruited more students from one section taught by the professor who used model-based instruction ( n = 6), and students from sections of the same course taught by an instructor who used less model-based instruction ( n = 10). Students were screened to ensure that none had a learning disability, Attention-Deficit/Hyperactivity Disorder, experience of concussion, or other neurological diagnosis that might impact neural response patterns and that none had contraindications to MRI. One student was excluded from analyses because they consistently gave the same response to every task trial. Of the final analytic sample (Valid N = 50, M age = 19.62, SD age = .90), 35 (70%) were first-year freshmen, 12 (24%) sophomores, and three (6%) juniors. Seven (14%) were first-generation college students. Forty-three (86%) were European American/White, three (6%) Hispanic, three (6%) Asian, and one (2%) identified as both European American/White and Hispanic. All but two students were native English speakers, and 38 (76%) were female, ten (20%) male, and two (4%) another gender. The average grade-point average (GPA), on a 4.0 scale and collected through a third party from the Registrar’s Office, was 3.54 ( SD = .53). The average final course grade from the introductory life sciences course, also on a 4.0 scale, was 3.55 ( SD = .62). For analyses, final course grades are used, rather than cumulative GPAs, because final course grades are a more concise measure of student biology knowledge. On average, participants’ combined aggregated standardized score on the Kaufman Brief Intelligence Test, Second Edition (KBIT-2) 86 was 103.37 ( SD = 11.07), with all students falling within two standard deviations of the normative mean. Procedure All procedures were approved by the university’s Institutional Review Board, and participants provided written, informed consent to participation. Scans were also sent to a radiologist for review and students were informed of any incidental findings, with all of these being limited to sinus congestion. Students were compensated with $50 cash after attending a 2-hour appointment at the university’s imaging center, where they completed the fMRI task. Study appointments occurred after the semester’s fourth week to allow students to become familiar with core course concepts and with the process of evaluating models or diagrams in biology. Students underwent MRI in a 3 Tesla Siemens Skyra scanner using a 32-channel head coil. After being fitted with ear protection, students reclined on the scanning table. First, a T1-weighted MPRAGE was acquired (TR = 1, TE =2.95ms, voxel size = 1mm3, flip angle = 9, field of view = 270, 176 sagittal slices) for registration purposes. This scan was followed by T2*-weighted echoplanar images (TR = 1s, TE = 25ms, 3 mm voxels, flip angle =90, FOV = 224mm) collected during the model evaluation task. fMRI task Model Evaluation Task. In the scanner, participants evaluated a series of models, formatted as flow charts, diagrams, or textbook-like images, that captured a breadth of content from the introduction to life sciences course (e.g., human evolution, the central dogma, genetic mutation). Stimuli were designed to examine the participants’ ability to detect errors and inhibit misconceptions (Figure 1). To provide context for the model, students first saw a two-second prompt, e.g., “Is there an error in the relationships?” They then viewed a model and the same prompt for ten seconds, after which they could take up to 30 seconds to indicate, via a response pad, whether the model was correct or incorrect. Lastly, participants were cued to indicate whether they were confident in their response (yes or no). Participants completed three separate, randomly-ordered runs lasting approximately five minutes each. Each run was composed of twelve randomly-ordered trials. Generally, there were two incorrect versions for every correct version of a model, with 14 total correct models and 22 error-containing trials. Trials were followed by a baseline rest period with a jittered interval of two to ten seconds, during which students saw random figures extracted from the model stimuli but without any words. Because students took varying times to respond, the number of volumes collected for each student varied between 231 and 424 volumes per run. Cognitive Assessments and Surveys After the MRI, participants completed a short electronic survey to collect basic demographics. In that survey, participants completed Baldwin et al.’s 87 College Biology Self-Efficacy Assessment to determine their self-reported confidence in using biology in classrooms and their daily lives, with 1 being “totally confident” and 5 being “not at all confident.” Participants also completed the Kaufman Educational Achievement Test Letter Word Recognition Test 88 to assess their reading in English. Last, participants filled out a modified version of the Positive and Negative Affect Scale questionnaire. This scale comprised 12 items, with eight items measuring positive affect and four items measuring negative affect. Participants were asked to indicate, on a scale of 1 to 5, their levels of each of the 12 emotions while completing the fMRI activities, with 1 indicating low levels of that emotion and 5 indicating high levels. From the survey, responses to the emotions ‘interested’, ‘motivated’, ‘attentive’, and ‘determined’ were summed to provide a measure of task engagement. Data Analysis Using SPSS 89 , we calculated our primary metric for calibration accuracy, the phi coefficient (f), which reflects the correlation between students’ accuracy and confidence scores. Two students were excluded from this analysis because they reported that they were confident on every trial; therefore, we could not calculate their f. We also calculated students’ total confidence and total accuracy across all runs of the task, as well as creating a bias score based on the proportion of inaccurate trials where students reported they were confident— the proportion of accurate trials on which they were confident 90 . We used MATLAB scripts developed by Fleming 29 to calculate a type 1 d ’ score, a type 2 meta- d ’ score, and a metacognitive efficiency score based on Bayesian estimation using Markov Chain Monte Carlo simulation implemented with the JAGS package. The advantage of this Bayesian approach to estimating these metacognitive metrics is that it is more robust with small trial numbers and can be used even when individuals have 0 responses in a particular cell. We used d’ – meta d’ as our estimate of metacognitive efficiency because the alternative ratio-based score resulted in some extreme values. Functional MRI data were processed and analyzed using the FMRIB Software Library 91 . Images were corrected for head motion, registered to the T1 image and normalized to the MNI 2mm template. In the first level models for each run, we regressed the fMRI signal on task onsets convolved with a double gamma hemodynamic response function. The interval of focus was between when participants viewed the model and prompt to when they responded whether the model was correct or incorrect, i.e., the ‘view model’ and ‘respond’ phases in Figure 1. The ‘confidence’ and prompt slides were treated as nuisance regressors in the design matrix, along with regressors for motion and temporal derivatives. Contrasts were performed for trials reflecting Error > No Error models. Furthermore, we contrasted trials where students responded they were Confident > Not-confident. Parameters were averaged in fixed effects models and passed to a group analysis conducted with a mixed effects ANOVA. In separate models, we examined whether students’ metacognitive scores correlated with their neural activity for the Error > No error model contrast. All contrasts were performed with a Z threshold of 3.1, p <.001 and a cluster-corrected p < .05. Declarations Data Availability Statistical maps are available on NeuroValut at https://identifiers.org/neurovault.collection:13902 and in the Supplementary Information. ACKNOWLEDGEMENTS This research is supported by NSF grant 2000549. We are grateful to the students who dedicated their time to participate in this study, as well as Eric Brewe, Cindy Hmelo-Silver, and Jonathan Fugelsang for their expert feedback and assistance throughout this project. AUTHOR CONTRIBUTIONS J.D. and C.C. designed and directed the project; J.D., C.C., M.B. and M.E. performed the experiments; C.C. analyzed the neural data; C.C. and M.B. developed the theoretical framework; M.B. wrote the manuscript, with significant input from C.C. and J.D. All authors provided critical feedback and helped shape the research, analyses, and manuscript. COMPETING INTERESTS All authors declare that they have no conflicts of interest. MATERIALS AND CORRESPONDENCE Mei Grace Behrendt, [email protected] References Zimmerman, B. J. & Martinez-Pons, M. Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology. 80, 284-290 (1988). Roebers, C. M. 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Meta d ’ .72 (.89) .54 ** .30 * .90 ** -.61 ** .57 ** 7. Meta-efficiency -.18 (.75) -.23 .03 .82 ** -.81 ** -.22 .67 ** 8. Biology self-efficacy 3.78 (.53) .23 .24 .11 .07 .20 .18 .03 9. KBIT Reading 106.36 (9.28) .13 -.01 .06 .02 .16 .07 -.06 .03 10. Engagement 4 (.47) -.02 -.09 -.12 .08 -.06 -.18 -.16 .12 -.01 11. Final Course Grade 3.55 (.62) .40 ** -.13 .23 -.13 .34 * .28 * .03 .21 .19 .23 * p < .05, ** p <.01, *** p <.001 † N = 47 Table 2: Maximum coordinates for neural clusters with significant BOLD signal change for different trial type contrasts MNI Contrast Voxels Max Z x y z Brain Region Confident > Not confident 48218 8.04 26 -98 4 R. Lingual gyrus (BA17) 322 4.98 46 48 -8 R. Middle frontal gyrus (BA 10) 187 4.38 6 -28 28 Cingulate gyrus 161 4.05 -40 44 -8 Inferior frontal gyrus (BA 45) Error model confident > No error model confident 201 4.68 -6 48 6 L. Anterior cingulate (BA 32) 165 3.84 -12 -80 2 L. Lingual gyrus (BA 17) 142 4.49 -32 20 -16 L. Inferior frontal gyrus (BA 47) 106 4.44 44 22 -18 R. Inferior frontal gyrus (BA 47) Confident accurate > Confident inaccurate response 333 3.93 -6 -84 -14 L. Lingual gyrus (BA 17) Table 3: Maximum coordinates for correlations of student metacognitive metrics with BOLD activity during model error detection Variable Voxels Max Z x y z Brain Region Confidence bias 265 4.3 -50 26 -4 L Inferior frontal gyrus (BA 47) 129 4.35 -4 16 6 L. Inferior frontal gyrus (BA 44) Phi 149 4.25 40 44 22 R. Middle frontal gyrus (B9) Meta- d ' 128 4.11 -46 -56 46 L. Inf parietal lobule (BA 40) 100 4.53 -24 -28 68 Postcentral gyrus (BA 3) 91 3.88 -32 -80 38 Superior occipital gyrus (BA 19) Meta-efficiency 105 4.31 -24 -28 68 L. Postcentral gyrus (BA 3) 89 4.09 40 42 20 R. Middle frontal gyrus (BA 9) Note: All correlations are with the contrast of Model Error > No Error; correlations with phi and confidence bias control for students’ error detection accuracy Additional Declarations (Not answered) Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2024 Read the published version in npj Science of Learning → Version 1 posted Editorial decision: revise 04 Jul, 2023 Review # 2 received at journal 27 Jun, 2023 Reviewer # 2 agreed at journal 26 Jun, 2023 Review # 1 received at journal 19 Jun, 2023 Reviewer # 1 agreed at journal 30 May, 2023 Reviewers invited by journal 22 May, 2023 Submission checks completed at journal 04 May, 2023 First submitted to journal 03 May, 2023 Unknown event 02 May, 2023 Editor assigned by journal 28 Apr, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-2874829\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":202760314,\"identity\":\"7897e5d3-1b53-4e29-9b41-fae7e37d069e\",\"order_by\":0,\"name\":\"Carrie Clark\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Nebraska-Lincoln\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Carrie\",\"middleName\":\"\",\"lastName\":\"Clark\",\"suffix\":\"\"},{\"id\":202760315,\"identity\":\"9d6668a1-bfad-4069-b339-a549baa76fd5\",\"order_by\":1,\"name\":\"McKenna Elliott\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Nebraska-Lincoln\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"McKenna\",\"middleName\":\"\",\"lastName\":\"Elliott\",\"suffix\":\"\"},{\"id\":202760316,\"identity\":\"2b81ed5b-74c7-4849-9c77-2b5e2caf922c\",\"order_by\":2,\"name\":\"Joseph Dauer\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Lincoln-Nebraska\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Joseph\",\"middleName\":\"\",\"lastName\":\"Dauer\",\"suffix\":\"\"},{\"id\":202760317,\"identity\":\"2b34903c-04bd-4ea6-9821-f90ba632b8ad\",\"order_by\":3,\"name\":\"Mei Grace Behrendt\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"https://orcid.org/0009-0009-3273-9778\",\"institution\":\"University of Nebraska-Lincoln\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mei\",\"middleName\":\"Grace\",\"lastName\":\"Behrendt\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2023-04-29 01:20:43\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-2874829/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-2874829/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s41539-024-00231-z\",\"type\":\"published\",\"date\":\"2024-03-04T05:00:00+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":37450638,\"identity\":\"188dd75e-c165-4bf8-bf9f-16c607f959d5\",\"added_by\":\"auto\",\"created_at\":\"2023-05-24 17:02:31\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":26060,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eProportion of accurate and inaccurate trials for which students responded that they were confident during the model evaluation task\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2874829/v1/d33e4d2a4c203990e3ea5c9e.png\"},{\"id\":37449624,\"identity\":\"d5d5eca1-fd21-4862-8a26-f68cfff85c22\",\"added_by\":\"auto\",\"created_at\":\"2023-05-24 16:46:31\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":500867,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eContrast of confident \\u0026gt; non-confident trials in full sample of students.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2874829/v1/6fbed4f93ddc2b15650bbfd2.png\"},{\"id\":37450114,\"identity\":\"39b02a91-68cf-42b8-9a0b-a4a6794f3d22\",\"added_by\":\"auto\",\"created_at\":\"2023-05-24 16:54:31\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":532836,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelation of confidence bias with activity in left inferior prefrontal cortex during evaluation of error \\u0026gt; no error models.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote:\\u003c/em\\u003e Graph is shown for illustrative purposes and reflects mean parameter estimates extracted from the cluster\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2874829/v1/b28a4f78ef179f1cc258d592.png\"},{\"id\":37450117,\"identity\":\"154fb835-2b1a-429b-ac29-534ae461bf49\",\"added_by\":\"auto\",\"created_at\":\"2023-05-24 16:54:31\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":533813,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelation of student\\u003cstrong\\u003e \\u003c/strong\\u003ef (orange) and metacognitive efficiency (purple) sores with activity in the right middle frontal gyrus during evaluation of error \\u0026gt; no error models.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote:\\u003c/em\\u003e Graph is shown for illustrative purposes and reflects mean parameter estimates extracted from the cluster\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2874829/v1/430da407c5346e6b494d0bda.png\"},{\"id\":37449630,\"identity\":\"62a954cd-8095-41b4-9167-b68d46f3aaa0\",\"added_by\":\"auto\",\"created_at\":\"2023-05-24 16:46:31\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":400223,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelation of student\\u003cstrong\\u003e \\u003c/strong\\u003e\\u003cem\\u003emeta-d’ \\u003c/em\\u003escores with left inferior parietal and precuneus activity during evaluation of error \\u0026gt; no error models.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNote:\\u003c/em\\u003e Graph is shown for illustrative purposes and reflects mean parameter estimates extracted from the cluster.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2874829/v1/f82d7d53499d020669f5728d.png\"},{\"id\":37449627,\"identity\":\"02122124-c7f3-43ad-b765-add562eb7039\",\"added_by\":\"auto\",\"created_at\":\"2023-05-24 16:46:31\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":74147,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe model evaluation task required participants to examine and determine whether the presented biology models contained errors, as well as to assess their confidence level for each model.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2874829/v1/11364d0fa3f12a630c0c0a62.png\"},{\"id\":52002110,\"identity\":\"c368ac5b-d7d8-41d2-85ea-918fc3e31b2b\",\"added_by\":\"auto\",\"created_at\":\"2024-03-05 08:12:01\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2360329,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2874829/v1/3d9aaaaa-06eb-4ea4-a178-41ebf4cff58a.pdf\"},{\"id\":37450116,\"identity\":\"570d33b5-399d-41a5-8687-1c35d8b7d91a\",\"added_by\":\"auto\",\"created_at\":\"2023-05-24 16:54:31\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":707609,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryMaterials.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2874829/v1/bd057beaa7451649b41f6d85.docx\"}],\"financialInterests\":\"(Not answered)\",\"formattedTitle\":\"Relation of biology students’ metacognitive monitoring to neural activity during model-based scientific reasoning\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eSelf-regulated learning involves the extent to which students are engaged, motivated and behaviorally active in their learning\\u003csup\\u003e1\\u003c/sup\\u003e. Self-regulated learners accomplish tasks or goals by continuously monitoring and correcting the effectiveness of their strategies and by evaluating past behaviors to better control future learning\\u003csup\\u003e2\\u003c/sup\\u003e. Metacognitive calibration, the match between a students\\u0026rsquo; objective performance and their subjective self-assessment of that performance\\u003csup\\u003e3,4\\u003c/sup\\u003e, is fundamental to self-regulated learning because it forms the basis for performance monitoring\\u003csup\\u003e5\\u003c/sup\\u003e. Students with more accurate calibration presumably have more reliable information on which to base their judgments regarding effortful, strategic, cognitive resources to optimize task performance\\u003csup\\u003e6\\u003c/sup\\u003e. Accordingly, metacognitive calibration has been linked to dorsomedial prefrontal systems thought to be involved in monitoring and detecting errors in performance, as well as to lateral PFC regions linked to effortful cognitive control\\u003csup\\u003e7\\u003c/sup\\u003e. These neuroimaging studies have used experimental tasks rather than tasks reflective of authentic educational experiences, leaving a gap in understanding of the role students\\u0026rsquo; metacognitive calibration plays in the learning process. Modeling and model evaluation is an authentic task in life sciences education, and expert scientists are skilled at detecting inaccuracies in models\\u003csup\\u003e8\\u003c/sup\\u003e. This study aimed to determine the relation of metacognitive calibration to student performance and neural activity when evaluating biological models.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eDefining Metacognition and its Relation to Learning\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough a general definition of metacognition is \\u0026ldquo;thinking about thinking\\u0026rdquo;\\u003csup\\u003e9\\u003c/sup\\u003e, contemporary conceptualizations typically divide it into two distinct processes: knowledge about cognition, including self-monitoring and self-regulatory mechanisms\\u003csup\\u003e9\\u0026ndash;11\\u003c/sup\\u003e. Self-monitoring involves knowing how well one is performing and recognizing the likelihood of accuracy or inaccuracy in one\\u0026rsquo;s judgments or behaviors. Conversely, metacognitive self-regulation concerns the process of organizing one\\u0026rsquo;s cognition, planning, being aware of one\\u0026rsquo;s comprehension and evaluating the efficacy of strategies during task performance\\u003csup\\u003e12,13\\u003c/sup\\u003e.\\u0026nbsp;Learners who develop high self-monitoring and self-regulation skills revise and reconstruct concepts to advance their knowledge and achieve high self-efficacy, persistence, and self-discipline in performing tasks\\u003csup\\u003e14\\u0026ndash;16\\u003c/sup\\u003e. Well-calibrated self-assessments also have been linked to students\\u0026rsquo; use of more effective study strategies, and to higher levels of academic growth over time\\u003csup\\u003e17\\u0026ndash;19\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eStudent ratings of confidence in their own performance offer one means of assessing self-awareness and calibration\\u003csup\\u003e20\\u003c/sup\\u003e. For well calibrated learners, confidence ratings should correspond with actual performance\\u003csup\\u003e21\\u003c/sup\\u003e. That is, students should have high confidence when they are accurate and low confidence when not. Although confidence estimates are higher for correct relative to incorrect trials\\u003csup\\u003e22\\u003c/sup\\u003e, lower-performing students tend to be more overconfident in their performance predictions, while higher-performing learners are accurate or underconfident in their self-assessments\\u003csup\\u003e11,23,24\\u003c/sup\\u003e. Additionally, learners tend to be overconfident when evaluating complex or difficult information and underconfident about relatively easier information\\u003csup\\u003e25\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eMeasuring metacognitive calibration\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCollecting multiple confidence ratings affords calculation of a simple phi coefficient (f),\\u0026nbsp;the correlation between students\\u0026rsquo; accuracy and confidence ratings across all trials of a task\\u003csup\\u003e26\\u003c/sup\\u003e. Although widely used in the education literature, this method is susceptible to bias based on the participant\\u0026rsquo;s task performance and does not adequately parse students\\u0026rsquo; confidence bias \\u0026ndash; their general tendency to make high or low confidence ratings\\u0026mdash; from their capacity to discriminate correct from inaccurate responses\\u003csup\\u003e27,28\\u003c/sup\\u003e. Consequently, cognitive neuroscience researchers recommend using model-based Signal Detection Theory metrics to provide response-bias free measures of how precisely confidence ratings track task accuracy\\u003csup\\u003e26,29\\u003c/sup\\u003e. These measures include \\u003cem\\u003emeta-d\\u0026rsquo;\\u003c/em\\u003e, a measure of student\\u0026rsquo;s metacognition that is conditioned on their task performance distribution, and \\u003cem\\u003emetacognitive efficiency\\u003c/em\\u003e, which reflects the difference between a student\\u0026rsquo;s sensitivity to their performance and their actual task performance. An \\u0026lsquo;ideal metacognitive observer\\u0026rsquo; should exhibit little difference between their meta-\\u003cem\\u003ed\\u003c/em\\u003e and their task performance\\u003csup\\u003e26\\u003c/sup\\u003e. Such signal detection metrics parse students\\u0026rsquo; confidence biases and task performance from their capacity to discriminate optimal from less optimal performance, enabling deeper understanding of the implications of different aspects of metacognition for learning.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eEvaluating Metacognition in the Brain\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMetacognition has repeatedly been linked to the prefrontal cortex (PFC), with greater activity in dorsolateral and anterior medial PFC being associated with higher levels of self-awareness and metacognitive accuracy\\u003csup\\u003e30\\u0026ndash;32\\u003c/sup\\u003e. Several metacognition studies highlight the ventromedial PFC and posterior medial frontal cortex as central brain areas related to confidence estimates\\u003csup\\u003e33,34\\u003c/sup\\u003e and error detection processes\\u003csup\\u003e35\\u0026ndash;37\\u003c/sup\\u003e, while both the frontopolar cortex and the lateral PFC are involved in explicit metacognitive judgments\\u003csup\\u003e22,38\\u003c/sup\\u003e, metacognitive control, and subsequent behavioral regulation\\u003csup\\u003e39,40\\u003c/sup\\u003e. More generally, areas in the dorsal anterior cingulate cortex (ACC) are thought to monitor for conflict or errors in performance, whereas the lateral PFC presumably uses these inputs to bias behavior toward more adaptive cognitive strategies\\u003csup\\u003e28,41,42\\u003c/sup\\u003e. From these studies, it is reasonable to hypothesize that students with higher levels of metacognitive calibration will show higher levels of activity in medial and lateral PFC regions during academic tasks, reflecting optimal use of self-monitoring and regulatory processes to guide performance.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eConceptual Change in Science Learning and its Relation to Metacognitive Calibration\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eModel-based reasoning, a core emphasis area in STEM education, offers a particularly relevant context for understanding how metacognitive calibration relates to students\\u0026rsquo; deployment of prefrontal regions during authentic learning experiences\\u003csup\\u003e43\\u003c/sup\\u003e. From simple flowcharts and graphs to complex computer simulations, models pervade all areas of science and are how scientists reason, evaluate hypotheses, and convey ideas\\u003csup\\u003e44,45\\u003c/sup\\u003e. Undergraduate biology students encounter models through textbooks, lectures, note-taking, and classroom activities that convey foundational introductory principles regarding genetic, ecological and cellular systems\\u003csup\\u003e46\\u003c/sup\\u003e. Measuring biology students\\u0026rsquo; capacity to detect errors or misconceptions in models constitutes a powerful means to assess their understanding of essential educational content.\\u003c/p\\u003e\\n\\u003cp\\u003eLinking metacognitive calibration to model error detection offers potential for understanding how to elicit conceptual change to ultimately foster a deeper understanding of fundamental scientific concepts\\u003csup\\u003e47\\u003c/sup\\u003e. To learn scientific concepts, students must be able to actively refine their learning process, learning to integrate new concepts and relationships with their prior knowledge\\u003csup\\u003e48\\u003c/sup\\u003e. Learners must first become dissatisfied with their existing conceptions, embracing new conceptions as plausible\\u003csup\\u003e49\\u003c/sup\\u003e. However, conceptual alterations prove difficult when learners hold misconceptions\\u003csup\\u003e50,51\\u003c/sup\\u003e. Absent or poor metacognition may predict the extent to which learners both ignore new information and resist changing their minds, even when new information indicates errors in their original beliefs\\u003csup\\u003e31\\u003c/sup\\u003e. For learners who hold misconceptions, conceptual change is more predictable when an alternative scientific viewpoint initiates some cognitive conflict (e.g., error detection)\\u003csup\\u003e52\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eMetacognitive calibration may be a critical initial step in this process of conceptual change. The relationship between metacognitive skills and conceptual change is implicit: individuals routinely recognize existing conceptions, evaluate them, and decide whether to reconstruct their understanding, which requires recognition of one\\u0026rsquo;s knowledge limitations\\u003csup\\u003e53\\u003c/sup\\u003e. Students who are more metacognitively aware of misconceptions about a given topic are more likely to develop deeper understanding of a concept or topic (e.g., acquiring correct knowledge), decreasing the likelihood of entrenching misconceptions\\u003csup\\u003e54\\u003c/sup\\u003e. Conversely, students who are unaware of their misconceptions may become overly secure in those misconceptions (i.e., they do not know what they do not know)\\u003csup\\u003e54,55\\u003c/sup\\u003e and may consequently miss opportunities for knowledge revision\\u003csup\\u003e56\\u003c/sup\\u003e. Evaluating how students\\u0026rsquo; metacognitive calibration links to their error evaluation and ultimate learning success may offer critical clues as to how metacognition supports conceptual change.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eUsing neuroimaging to understand metacognitive calibration\\u0026rsquo;s role in science students\\u0026rsquo; model evaluation\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLimited literature on the neural regions involved in expert science cognition highlights its association with prefrontal regions linked in error detection, conflict monitoring, and inhibition\\u003csup\\u003e57\\u003c/sup\\u003e. Additionally, experts evaluating the accuracy of scientific circuits activate anterior cingulate cortex (ACC), ventrolateral PFC (VLPFC), and dorsolateral PFC (DLPFC) to a greater extent than novices, who tended to activate the DLPFC solely\\u003csup\\u003e58\\u003c/sup\\u003e. Brault Foisy et al.\\u003csup\\u003e59\\u003c/sup\\u003e theorized that the ACC and PFC play distinct roles in cognitive control during scientific reasoning. In their study, individuals activated the ACC when they provided \\u003cem\\u003eboth\\u003c/em\\u003e correct and incorrect responses to scientific conceptions, whereas only the dorsolateral PFC was activated when individuals gave correct answers. Potvin et al.\\u003csup\\u003e60\\u003c/sup\\u003e found that both the left DLPFC and left VLPFC exhibited greater activation when chemistry professors were presented with scientific statements containing misconceptions, supporting previous research linking DLPFC to the resolution of misconceptions.\\u0026nbsp;With respect to metacognition, novice students exhibited greater dorsomedial frontal activity when they were confident vs. not confident in their responses to physics electric circuit diagrams\\u003csup\\u003e61\\u003c/sup\\u003e. However, the brain regions associated with legitimate confidence, i.e., confidence that corresponded with accurate responses vs. overconfident responses, were more posterior and included inferior parietal, premotor and inferior temporal regions. Taken together, these studies implicate ACC and lateral PFC regions in the expert processing and resolution of science misconceptions. Assuming that students with higher metacognitive calibration are better equipped to engage in \\u0026lsquo;expert\\u0026rsquo;-like error detection and associated engagement of control, we expected such students to use these regions to a greater extent than their less calibrated peers when evaluating biological models.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eSummary\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConceptual change theories identify metacognitive calibration as a critical process that enables students to detect and resolve gaps in their knowledge.\\u0026nbsp;However,\\u0026nbsp;neuroimaging studies typically examine the neural bases of metacognitive processing and calibration accuracy in the context of novel or self-reflection-based tasks, rather than educationally authentic tasks like those involving scientific reasoning\\u003csup\\u003e62,63\\u003c/sup\\u003e. The limited neuroimaging research on scientific cognition indicates that \\u0026lsquo;expert\\u0026rsquo; scientists activate DLPFC, VLPFC and ACC brain regions to a greater extent than \\u0026lsquo;novice\\u0026rsquo; undergraduates when reasoning about scientific phenomena, particularly when accurate reasoning involves detecting and inhibiting misconception\\u003csup\\u003e58,59\\u003c/sup\\u003e. The current study\\u0026rsquo;s goal was to examine the relation of students\\u0026rsquo; metacognitive calibration to their neural activity during a biological model evaluation task. Specifically, we aimed to determine how individual differences in students\\u0026rsquo; calibration accuracy related to their activity in lateral prefrontal regions and ACC regions linked both to metacognition and to scientific expertise. Based on the theory that accurate calibration serves as a foundation for error detection and conceptual change, we hypothesized that students with higher metacognitive calibration would show higher levels of activity in prefrontal regions linked to these scientific processes.\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eBehavioral Results\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1 presents descriptive statistics and correlations among the different behavioral variables. Students on average were correct on 65% of the model evaluation task trials, whereas they were confident approximately 75% of the time. Figure 1 illustrates the significant difference in student confidence levels for accurate vs. inaccurate trials of the model evaluation task, \\u003cem\\u003et\\u003c/em\\u003e(49) = 7.72, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001, although students still responded that they were confident more often than not for trials where they made an inaccurate response. The moderate correlation between total accurate responses and total confident responses (\\u003cem\\u003er\\u003c/em\\u003e = .403, \\u003cem\\u003ep\\u003c/em\\u003e = .004) indicated that as participants\\u0026rsquo; accuracy increased, their overall confidence levels also increased. This work is complemented by a study that highlights the importance of self-monitoring to evaluate models during the modeling process\\u003csup\\u003e64\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eThe different metrics for metacognitive calibration, sensitivity, and efficiency, as calculated from students\\u0026rsquo; confidence ratings and accuracy in the fMRI model evaluation task, were robustly inter-correlated (\\u003cem\\u003ep\\u0026rsquo;s\\u003c/em\\u003e \\u0026lt; .01). Neither student\\u0026rsquo;s f scores nor their metacognitive efficiency scores correlated with their model evaluation accuracy, although meta-\\u003cem\\u003ed\\u003c/em\\u003e\\u0026rsquo; scores did, as expected (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001). Final course grade in the introductory life science course correlated slightly with model evaluation task accuracy (\\u003cem\\u003ep\\u003c/em\\u003e = .008), but not with confidence, \\u003cem\\u003ep\\u003c/em\\u003e = .39. Measures of metacognition showed minimal correlations with final course grades, except for meta-\\u003cem\\u003ed\\u0026rsquo;\\u003c/em\\u003e, \\u003cem\\u003ep\\u003c/em\\u003e = .05. Moreover, accuracy during the model evaluation task and metacognition metrics related to that task did not correlate with students\\u0026rsquo; biology self-efficacy, reading fluency, or task engagement scores, suggesting that these factors exerted no confounding effect on student performance. Therefore, we disregarded these variables in further analyses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNeuroimaging Results from the Model Evaluation Task\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOverall patterns of brain activity for confident and non-confident responses\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2 and Figure 2 show that during trials where students indicated they were Confident \\u0026gt; Not confident, widespread neural activation occurred across occipital regions, and in regions in the parietal cortex and lateral and medial PFC. We performed a further contrast of Error \\u0026gt; No error models only for trials where students were confident, which indicated that students showed increased activity in a medial frontal cluster, in bilateral inferior prefrontal/insular regions and in the lingual gyrus (Table 2, supplementary Figure 1). Finally, we contrasted trials where students were confident and accurate in their response \\u0026gt; confident but inaccurate (i.e., overconfident) in their response. This analysis revealed that students exhibited more activity in a single cluster in the left lingual gyrus when they were confident and accurate relative to confident and inaccurate (Table 2, supplementary Figure 2). Overall, when examined by trial type, students exhibited greater activity in lateral and medial PFC regions when they were confident, although PFC activity did not diverge for trials where students were overconfident vs. calibrated in their responses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRelation of individual differences in student confidence to neural response patterns\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFocusing on the Error \\u0026gt; No error model contrast, we evaluated the relation of overall accuracy and confidence to students\\u0026rsquo; neural responses by including these variables as regressors in the group design matrix. There was no correlation of students\\u0026rsquo; model evaluation accuracy with their neural response patterns. Likewise, students\\u0026rsquo; overall confidence did not correlate with brain activity. However, students with lower confidence bias showed more activity in two clusters centered in the left inferior frontal gyrus (Table 3, Figure 3).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRelation of student metacognitive calibration to neural response patterns\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eControlling for accuracy on the model evaluation task, a correlation emerged between students\\u0026rsquo; f values and their level of activity in the right middle frontal gyrus for the Error \\u0026gt; No error model contrast (see Table 3). Students with higher f scores showed higher activity in this cluster, which spanned BA 8 and 9 (Figure 4). Comparable results emerged when we instead used the metacognitive efficiency score (i.e., \\u003cem\\u003ed\\u003c/em\\u003e\\u0026rsquo; \\u0026ndash; meta \\u003cem\\u003ed\\u003c/em\\u003e\\u0026rsquo;), which correlated positively with activation in a right middle frontal cluster that overlapped with the cluster identified for\\u0026nbsp;f. Controlling for accuracy, students\\u0026rsquo; meta-\\u003cem\\u003ed\\u0026rsquo;\\u003c/em\\u003e scores correlated with activity in the left inferior parietal and occipital gyri and in the postcentral gyrus (Figure 5).\\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eMetacognition is central to science education: it is critical to developing and cultivating a deep conceptual understanding of scientific concepts\\u003csup\\u003e65\\u003c/sup\\u003e. Specifically, in biology education, students with higher awareness of the learning process and stronger ability to monitor, regulate, and control learning manifest a more meaningful understanding of targeted biology concepts and better scientific inquiry skills\\u003csup\\u003e66,67\\u003c/sup\\u003e. The current study evaluated the relation of students\\u0026rsquo; metacognitive calibration to their neural activity when evaluating errors in biology models. A major finding is that students with higher metacognitive calibration and efficiency recruit lateral prefrontal regions to a greater extent than their peers with lower metacognitive calibration when evaluating error-containing models. This finding has implications for understanding the role of metacognitive monitoring in students\\u0026rsquo; learning behavior and for approaches to STEM instruction.\\u003c/p\\u003e\\n\\u003cp\\u003eControlling for task accuracy, higher metacognitive calibration, as measured using phi, was linked to higher levels of activity in the right dorsolateral PFC. The same effect held when we instead used students\\u0026rsquo; metacognitive efficiency scores, which more rigorously parse metacognitive calibration from confidence response biases and task performance\\u003csup\\u003e26\\u003c/sup\\u003e.\\u0026nbsp;Those few neuroimaging studies that have focused on STEM learning have linked the recruitment of lateral prefrontal and ACC brain regions to \\u0026lsquo;expert\\u0026rsquo; scientific reasoning, with the lateral PFC being especially linked to accurate error detection within STEM experts\\u0026rsquo; domain of expertise\\u003csup\\u003e8,68\\u003c/sup\\u003e Therefore, the students in our study with higher levels of calibration may be more \\u0026lsquo;expert-like\\u0026rsquo; in the regions they deploy when evaluating error-containing models. Brault-Fausy et al.\\u003csup\\u003e59\\u003c/sup\\u003e suggested that lateral prefrontal activity may be especially relevant for resolving interference when viewing scientific errors or misconceptions. Given that the DLPFC forms a core part of the frontal-parietal network associated with executive control\\u003csup\\u003e69\\u003c/sup\\u003e, findings may indicate that these students are more effectively deploying executive resources to resolve the errors present in these models.\\u003c/p\\u003e\\n\\u003cp\\u003eSurprisingly, students\\u0026rsquo; behavioral phi, meta-efficiency, and confidence bias scores were not linked significantly to their ultimate course grades, although both model evaluation accuracy and meta-\\u003cem\\u003ed\\u0026rsquo;\\u003c/em\\u003e scores were significantly correlated with course grades. Notably, meta-\\u003cem\\u003ed\\u003c/em\\u003e\\u0026rsquo; reflects the predicted \\u003cem\\u003ed\\u0026rsquo;\\u003c/em\\u003e of an individual, given their proportions of calibrated vs. non-calibrated confidence ratings\\u003csup\\u003e27\\u003c/sup\\u003e. The more meaningful indicator of a student\\u0026rsquo;s metacognitive calibration relative to other students is thus the metacognitive efficiency score, which mathematically subtracts the individual\\u0026rsquo;s task performance from their meta-\\u003cem\\u003ed\\u003c/em\\u003e\\u0026rsquo; to reveal the discrepancy between the two. The lack of correlation between meta-efficiency and course grades may indicate that, once we consider the individual\\u0026rsquo;s accuracy in model error detection, calibration is less related to overall class performance. Thus, although higher calibration may be tied to the use of prefrontal brain regions associated with effortful resource deployment and, in previous studies, to students\\u0026rsquo; self-regulated learning and academic success\\u003csup\\u003e70\\u0026ndash;72\\u003c/sup\\u003e, it is students\\u0026rsquo; mastery of the concepts in these models and their ability to recognize accurate relative to inaccurate models that ultimately is reflected in their course performance.\\u003c/p\\u003e\\n\\u003cp\\u003eStudents\\u0026rsquo; mean confidence was slightly higher than their accuracy (27 vs. 23) on the model task and students in general responded that they were confident more often than not for inaccurate trials. Indeed, some students had a bias score that exceeded 0, which means that they had more confident, inaccurate trials than confident, accurate trials. These descriptive findings support the notion that students tend to overestimate their performance, particularly when performance is low\\u003csup\\u003e23\\u003c/sup\\u003e. This overconfidence in one\\u0026rsquo;s abilities suggests a lack of metacognitive awareness of their deficits, which may lead to ineffective self-regulation skills\\u003csup\\u003e11\\u003c/sup\\u003e. For example, an overconfident student, believing they know the material, might decide not to study for a test, thus increasing that student\\u0026rsquo;s probability of doing poorly due to inadequate preparation.\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough we did not find an association between confidence bias and performance or course grades, we did find that confidence bias correlated with lower metacognitive efficiency and with less left inferior frontal signal change when students viewed error-containing models.\\u0026nbsp;Bellon et al. (2020)\\u003csup\\u003e21\\u003c/sup\\u003e found that children showed activation in the left inferior frontal gyrus when rating their confidence in arithmetic problem solving\\u0026nbsp;and that the extent of children\\u0026rsquo;s activity in this region was linked to their mathematics performance. Unlike Bellon et al., we examined neural activity during model evaluation as opposed to during the interval when students were making confidence ratings. However, the overlapping link to the left inferior frontal gyrus is possible and perhaps suggests that this region is important for modulating links between confidence and achievement.\\u003c/p\\u003e\\n\\u003cp\\u003eInterestingly, differences in neural activity for accurate and confident relative to inaccurate and confident trials were confined to the lingual gyrus; there were no differences in prefrontal activity for this contrast. Potvin et al.\\u003csup\\u003e61\\u003c/sup\\u003e similarly found that calibrated vs. overconfident trials in an electric circuit validation task were linked to parietal, premotor and fusiform regions rather than prefrontal regions. It is possible that this activity in posterior regions for correct and calibrated trials reflects the use of visual processing resources to support accurate processing and evaluation of the model.\\u003c/p\\u003e\\n\\u003cp\\u003eA significant study limitation is that one model evaluation task is not necessarily\\u0026nbsp;synonymous with overall\\u0026nbsp;academic performance or general metacognitive abilities. Additional assessments would be beneficial for determining and evaluating the neural and behavioral effects of metacognitive processes and of different forms of instruction. There is continued debate, for example, as to whether metacognitive calibration is task-specific or a general, trait-like characteristic\\u003csup\\u003e73,74\\u003c/sup\\u003e. Likewise, different instructional approaches can influence the neural correlates of learning\\u003csup\\u003e47\\u003c/sup\\u003e. Morgan-Short et al.\\u003csup\\u003e75,76\\u003c/sup\\u003e found that when adults learned an artificial language using either implicit or explicit instruction, they became equally proficient in the language. However, event-related potentials (ERPs) demonstrated fundamental differences at the neural level. Adults who were trained using implicit instruction exhibited patterns of brain activity more similar to native language speakers\\u003csup\\u003e75\\u003c/sup\\u003e, indicating that varied educational contexts, practices, and interventions may alter neural patterns even when this is not reflected at the behavioral level\\u003csup\\u003e47\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eAnother limitation of the study was that we had insufficient trial numbers to fully cross accuracy and confidence at the trial level to examine implications for brain activity: high levels of confidence meant that there were small numbers of trials where students were underconfident. Contrasts of confident vs. non-confident trials or calibrated vs. overconfident trials may also be less reliable because of unbalanced trial numbers for these conditions. Moreover, given the complexity of the task stimuli, each error containing model was equated in complexity to non-error containing models. Because the control for stimulus complexity was lost when we removed inaccurate trials, our primary contrast was based on all trials, regardless of students\\u0026rsquo; accuracy. Future studies should examine how accuracy modulates these relationships. Other limitations included a disproportionate representation of females and Euro-American Whites, as well as a small range in GPAs, which may have limited our power to capture the full extent of variation in students\\u0026rsquo; metacognitive monitoring performance.\\u003c/p\\u003e\\n\\u003cp\\u003eFuture research is needed to determine how these individual differences in brain activity relate to ongoing academic achievement. One especially intriguing question is whether and how manipulations to instructional design, such as immediate feedback following errors, may impact students brain activity and, ultimately, help students become more self-regulated learners\\u003csup\\u003e77\\u003c/sup\\u003e. Likewise, future research should also examine how students might be encouraged to reflect on their calibration accuracy and whether such reflection might aid in the process of conceptual change in science. Lastly, additional research is needed to determine how metacognitive calibration affects students\\u0026rsquo; learning and academic performance throughout their educational trajectory.\\u003c/p\\u003e\\n\\u003cp\\u003eThe present study\\u0026rsquo;s finding that\\u0026nbsp;students with higher metacognitive monitoring and less confidence bias deployed more \\u0026lsquo;expert like\\u0026rsquo; lateral prefrontal activity when they encountered error-containing models underscores a need to\\u0026nbsp;foster and nurture metacognition and self-awareness in the classroom.\\u0026nbsp;This is likely to require extensive instructional support both in terms of prompting reflection and in terms of encouraging students to act on their miscalibrations with effective learning strategies.\\u0026nbsp;Conceptual change is theorized to drive science learning and hinges on students\\u0026rsquo; willingness to engage with or experience conflict from new ideas\\u003csup\\u003e49,51,58,78\\u0026ndash;80\\u003c/sup\\u003e.\\u0026nbsp;Students who persistently learn from their own errors through self-reflection may be more motivated to continue to learn after failure, to correct their errors, and to recognize misconceptions\\u003csup\\u003e81,82\\u003c/sup\\u003e. Instructors might provide opportunities for students to reflect on their coursework by focusing on the learning process rather than on the content itself\\u003csup\\u003e83\\u003c/sup\\u003e, thereby engaging students in reflexive and adaptive thinking. They may also provide direct and immediate feedback, a core mechanism of self-regulated learning, which can positively impact academic achievement\\u003csup\\u003e84\\u003c/sup\\u003e. Finally, instructors may need to teach effective study habits to help students act on their metacognitive judgements, as students may lack this knowledge even when they are prepared to alter their strategies\\u003csup\\u003e85\\u003c/sup\\u003e. These instructional strategies may represent effective pathways for helping STEM students to be active, self-regulated learners and for moving them toward more expert model evaluation capacities.\\u003c/p\\u003e\"},{\"header\":\"METHOD\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eParticipants\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFifty-one undergraduate students were recruited, through class announcements, from five separate sections of one introductory life sciences course taught by two different instructors in two consecutive academic years at a large Midwestern university. First, we recruited from the sections taught by a professor who heavily used model-based instruction (\\u003cem\\u003en\\u003c/em\\u003e = 35 students). To maximize our sample size, from the following academic year\\u0026rsquo;s sections, we recruited more students from one section taught by the professor who used model-based instruction (\\u003cem\\u003en\\u003c/em\\u003e = 6), and students from sections of the same course taught by an instructor who used less model-based instruction (\\u003cem\\u003en\\u003c/em\\u003e = 10).\\u003c/p\\u003e\\n\\u003cp\\u003eStudents were screened to ensure that none had a learning disability, Attention-Deficit/Hyperactivity Disorder, experience of concussion, or other neurological diagnosis that might impact neural response patterns and that none had contraindications to MRI. One student was excluded from analyses because they consistently gave the same response to every task trial. Of the final analytic sample (Valid \\u003cem\\u003eN\\u003c/em\\u003e = 50, \\u003cem\\u003eM\\u003csub\\u003eage\\u0026nbsp;\\u003c/sub\\u003e\\u003c/em\\u003e= 19.62, \\u003cem\\u003eSD\\u003csub\\u003eage\\u003c/sub\\u003e\\u0026nbsp;\\u003c/em\\u003e= .90), 35 (70%) were first-year freshmen, 12 (24%) sophomores, and three (6%) juniors. Seven (14%) were first-generation college students. Forty-three (86%) were European American/White, three (6%) Hispanic, three (6%) Asian, and one (2%) identified as both European American/White and Hispanic. All but two students were native English speakers, and 38 (76%) were female, ten (20%) male, and two (4%) another gender. The average grade-point average (GPA), on a 4.0 scale and collected through a third party from the Registrar\\u0026rsquo;s Office, was 3.54 (\\u003cem\\u003eSD\\u003c/em\\u003e = .53). The average final course grade from the introductory life sciences course, also on a 4.0 scale, was 3.55 (\\u003cem\\u003eSD\\u003c/em\\u003e = .62). For analyses, final course grades are used, rather than cumulative GPAs, because final course grades are a more concise measure of student biology knowledge. On average, participants\\u0026rsquo; combined aggregated standardized score on the\\u0026nbsp;Kaufman Brief Intelligence Test, Second Edition (KBIT-2)\\u003csup\\u003e86\\u003c/sup\\u003e was 103.37 (\\u003cem\\u003eSD\\u003c/em\\u003e = 11.07), with all students falling within two standard deviations of the normative mean.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eProcedure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll procedures were approved by the university\\u0026rsquo;s Institutional Review Board, and participants provided written, informed consent to participation. Scans were also sent to a radiologist for review and students were informed of any incidental findings, with all of these being limited to sinus congestion. Students were compensated with $50 cash after attending a 2-hour appointment at the university\\u0026rsquo;s imaging center, where they completed the fMRI task. Study appointments occurred after the semester\\u0026rsquo;s fourth week to allow students to become familiar with core course concepts and with the process of evaluating models or diagrams in biology.\\u003c/p\\u003e\\n\\u003cp\\u003eStudents underwent MRI in a 3 Tesla Siemens Skyra scanner using a 32-channel head coil. After being fitted with ear protection, students reclined on the scanning table. First, a T1-weighted MPRAGE was acquired (TR = 1, TE =2.95ms, voxel size = 1mm3, flip angle = 9, field of view = 270, 176 sagittal slices) for registration purposes. This scan was followed by T2*-weighted echoplanar images (TR = 1s, TE = 25ms, 3 mm voxels, flip angle =90, FOV = 224mm) collected during the model evaluation task.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003efMRI task\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eModel Evaluation Task.\\u0026nbsp;\\u003c/em\\u003eIn the scanner, participants evaluated a series of models, formatted as flow charts, diagrams, or textbook-like images, that captured a breadth of content from the introduction to life sciences course (e.g., human evolution, the central dogma, genetic mutation). Stimuli were designed to examine the participants\\u0026rsquo; ability to detect errors and inhibit misconceptions (Figure 1). To provide context for the model, students first saw a two-second prompt, e.g., \\u0026ldquo;Is there an error in the relationships?\\u0026rdquo; They then viewed a model and the same prompt for ten seconds, after which they could take up to 30 seconds to indicate, via a response pad, whether the model was correct or incorrect. Lastly, participants were cued to indicate whether they were confident in their response (yes or no). Participants completed three separate, randomly-ordered runs lasting approximately five minutes each. Each run was composed of twelve randomly-ordered trials. Generally, there were two incorrect versions for every correct version of a model, with 14 total correct models and 22 error-containing trials. Trials were followed by a baseline rest period with a jittered interval of two to ten seconds, during which students saw random figures extracted from the model stimuli but without any words. Because students took varying times to respond, the number of volumes collected for each student varied between 231 and 424 volumes\\u0026nbsp;per run.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCognitive Assessments and Surveys\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAfter the MRI, participants completed a short electronic survey to collect basic demographics. In that survey, participants completed Baldwin et al.\\u0026rsquo;s\\u003csup\\u003e87\\u003c/sup\\u003e College Biology Self-Efficacy Assessment to determine their self-reported confidence in using biology in classrooms and their daily lives, with 1 being \\u0026ldquo;totally confident\\u0026rdquo; and 5 being \\u0026ldquo;not at all confident.\\u0026rdquo; Participants also completed the Kaufman Educational Achievement Test Letter Word Recognition Test\\u003csup\\u003e88\\u003c/sup\\u003e to assess their reading in English.\\u0026nbsp;Last, participants filled out a modified version of the Positive and Negative Affect Scale questionnaire. This scale comprised 12 items, with eight items measuring positive affect and four items measuring negative affect. Participants were asked to indicate, on a scale of 1 to 5, their levels of each of the 12 emotions while completing the fMRI activities, with 1 indicating low levels of that emotion and 5 indicating high levels. From the survey, responses to the emotions \\u0026lsquo;interested\\u0026rsquo;, \\u0026lsquo;motivated\\u0026rsquo;, \\u0026lsquo;attentive\\u0026rsquo;, and \\u0026lsquo;determined\\u0026rsquo; were summed to provide a measure of task engagement.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsing SPSS\\u003csup\\u003e89\\u003c/sup\\u003e, we calculated our primary metric for calibration accuracy, the phi coefficient (f), which reflects the correlation between students\\u0026rsquo; accuracy and confidence scores.\\u0026nbsp;Two students were excluded from this analysis because they reported that they were confident on every trial; therefore, we could not calculate their\\u0026nbsp;f. We also calculated students\\u0026rsquo; total confidence and total accuracy across all runs of the task, as well as creating a bias score based on the proportion of inaccurate trials where students reported they were confident\\u0026mdash; the proportion of accurate trials on which they were confident\\u003csup\\u003e90\\u003c/sup\\u003e. We used MATLAB scripts developed by Fleming\\u003csup\\u003e29\\u003c/sup\\u003e to calculate a type 1 \\u003cem\\u003ed\\u003c/em\\u003e\\u0026rsquo; score, a type 2 meta-\\u003cem\\u003ed\\u003c/em\\u003e\\u0026rsquo; score, and a metacognitive efficiency score based on Bayesian estimation using Markov Chain Monte Carlo simulation implemented with the JAGS package. The advantage of this Bayesian approach to estimating these metacognitive metrics is that it is more robust with small trial numbers and can be used even when individuals have 0 responses in a particular cell. We used d\\u0026rsquo; \\u0026ndash; meta d\\u0026rsquo; as our estimate of metacognitive efficiency because the alternative ratio-based score resulted in some extreme values.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFunctional MRI data were processed and analyzed using the FMRIB Software Library\\u003csup\\u003e91\\u003c/sup\\u003e. Images were corrected for head motion, registered to the T1 image and normalized to the MNI 2mm template. In the first level models for each run, we regressed the fMRI signal on task onsets convolved with a double gamma hemodynamic response function. The interval of focus was between when participants viewed the model and prompt to when they responded whether the model was correct or incorrect, i.e., the \\u0026lsquo;view model\\u0026rsquo; and \\u0026lsquo;respond\\u0026rsquo; phases in Figure 1. The \\u0026lsquo;confidence\\u0026rsquo; and prompt slides were treated as nuisance regressors in the design matrix, along with regressors for motion and temporal derivatives.\\u0026nbsp;Contrasts were performed for trials reflecting Error \\u0026gt; No Error models. Furthermore, we contrasted trials where students responded they were Confident \\u0026gt; Not-confident.\\u0026nbsp;Parameters were averaged in fixed effects models and passed to a group analysis conducted with a mixed effects ANOVA. In separate models, we examined whether students\\u0026rsquo; metacognitive scores correlated with their neural activity for the Error \\u0026gt; No error model contrast. All contrasts were performed with a \\u003cem\\u003eZ\\u003c/em\\u003e threshold of 3.1, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt;.001 and a cluster-corrected \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .05.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eStatistical maps are available on NeuroValut at https://identifiers.org/neurovault.collection:13902 and in the Supplementary Information.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eACKNOWLEDGEMENTS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research is supported by NSF grant 2000549. We are grateful to the students who dedicated their time to participate in this study, as well as Eric Brewe, Cindy Hmelo-Silver, and Jonathan Fugelsang for their expert feedback and assistance throughout this project.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAUTHOR CONTRIBUTIONS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJ.D. and C.C. designed and directed the project; J.D., C.C., M.B. and M.E. performed the experiments; C.C. analyzed the neural data; C.C. and M.B. developed the theoretical framework; M.B. wrote the manuscript, with significant input from C.C. and J.D. All authors provided critical feedback and helped shape the research, analyses, and manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCOMPETING INTERESTS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors declare that they have no conflicts of interest.\\u003c/p\\u003e\\n\\u003cp\\u003eMATERIALS AND CORRESPONDENCE\\u003c/p\\u003e\\n\\u003cp\\u003eMei Grace Behrendt, mei-grace.behrendt@huskers.unl.edu\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eZimmerman, B. J. \\u0026amp; Martinez-Pons, M. Construct validation of a strategy model of student self-regulated learning. \\u003cem\\u003eJournal of Educational Psychology.\\u003c/em\\u003e \\u003cstrong\\u003e80,\\u003c/strong\\u003e 284-290 (1988).\\u003c/li\\u003e\\n\\u003cli\\u003eRoebers, C. M. Executive function and metacognition: Towards a unifying framework of cognitive self-regulation. \\u003cem\\u003eDev. 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Educ.\\u003c/em\\u003e \\u003cstrong\\u003e86,\\u003c/strong\\u003e 548-571 (2002).\\u003c/li\\u003e\\n\\u003cli\\u003ediSessa, A. A. \\u0026amp; Sherin, B. L. What changes in conceptual change? \\u003cem\\u003eInt. J. Sci. Educ.\\u003c/em\\u003e \\u003cstrong\\u003e20, \\u003c/strong\\u003e1155-1191 (1998).\\u003c/li\\u003e\\n\\u003cli\\u003ediSessa, A. A. A \\u0026ldquo;theory bite\\u0026rdquo; on the meaning of scientific inquiry: A companion to kuhn and pease. \\u003cem\\u003eCogn. Instr.\\u003c/em\\u003e \\u003cstrong\\u003e26,\\u003c/strong\\u003e 560-566 (2008).\\u003c/li\\u003e\\n\\u003cli\\u003eZimmerman, B. J. Self-regulation involves more than metacognition: A social cognitive perspective. \\u003cem\\u003eEducational Psychologist.\\u003c/em\\u003e \\u003cstrong\\u003e30,\\u003c/strong\\u003e 217-221 (1995).\\u003c/li\\u003e\\n\\u003cli\\u003eTulis, M., Steuer, G., \\u0026amp; Dresel, M. 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Differences in metacognitive regulation in introductory biology students: When prompts are not enough. \\u003cem\\u003eCBE\\u0026mdash;Life Sci. Educ.\\u003c/em\\u003e \\u003cstrong\\u003e14,\\u003c/strong\\u003e 1-12 (2015).\\u003c/li\\u003e\\n\\u003cli\\u003eKaufman, A .S., \\u0026amp; Kaufman, N. L. (with Breaux, K. C.) Technical \\u0026amp; interpretive manual. \\u003cem\\u003eKaufman test of educational achievement brief form\\u003c/em\\u003e (3rd ed.). Bloomington, MN: NCS Pearson. (2014).\\u003c/li\\u003e\\n\\u003cli\\u003eBaldwin, J. A., Ebert‐May, D. \\u0026amp; Burns, D. J. The development of a college biology self‐efficacy instrument for nonmajors. \\u003cem\\u003eSci. Educ.\\u003c/em\\u003e \\u003cstrong\\u003e83,\\u003c/strong\\u003e 397-408 (1999).\\u003c/li\\u003e\\n\\u003cli\\u003eKaufman, A. S., \\u0026amp; Kaufman, N. L. \\u003cem\\u003eKaufman test of educational achievement brief form \\u003c/em\\u003e(3rd ed.). Bloomington, MN: NCS Pearson (2015).\\u003c/li\\u003e\\n\\u003cli\\u003eIBM Corp. Released 2020. IBM SPSS Statistics for Macintosh, Version 27.0. Armonk, NY: IBM Corp.\\u003c/li\\u003e\\n\\u003cli\\u003eMoritz, S., Woodward, T. S., Whitman, J. C. \\u0026amp; Cuttler, C. Confidence in errors as a possible basis for delusions in schizophrenia. \\u003cem\\u003eJ. Nerv. Ment. Dis.\\u003c/em\\u003e \\u003cstrong\\u003e193,\\u003c/strong\\u003e 9-16 (2005).\\u003c/li\\u003e\\n\\u003cli\\u003eJenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., \\u0026amp; Smith, S. M. FSL. NeuroImage. \\u003cstrong\\u003e62,\\u003c/strong\\u003e 782-790 (2012).\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"643\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"96.42301710730949%\\\" colspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDescriptive Statistics and Correlations Among the Behavioral Variables\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003eVariable M (SD)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e1. Total accuracy\\u003c/p\\u003e\\n \\u003cp\\u003e23.40 (4.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e2. Total confidence\\u003c/p\\u003e\\n \\u003cp\\u003e27.28 (4.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e.40\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e3. Phi\\u003csup\\u003e\\u0026dagger;\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e.10 (.19)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e4. Confidence bias\\u003c/p\\u003e\\n \\u003cp\\u003e-.27 (.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e.39\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e-.84\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e5. Type 1 d\\u0026rsquo;\\u003c/p\\u003e\\n \\u003cp\\u003e.90 (.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e.97\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e.36\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e6. Meta \\u003cem\\u003ed\\u003c/em\\u003e\\u0026rsquo;\\u003c/p\\u003e\\n \\u003cp\\u003e.72 (.89)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e.54\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e.30\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e.90\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e-.61\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e.57\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e7. Meta-efficiency\\u003c/p\\u003e\\n \\u003cp\\u003e-.18 (.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e-.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e.82\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e-.81\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e-.22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e.67\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e8. Biology self-efficacy\\u003c/p\\u003e\\n \\u003cp\\u003e3.78 (.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e9. KBIT Reading\\u003c/p\\u003e\\n \\u003cp\\u003e106.36 (9.28)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e-.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e-.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e10. Engagement\\u003c/p\\u003e\\n \\u003cp\\u003e4 (.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e-.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e-.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e-.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e-.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e-.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e-.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e-.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"23.79471228615863%\\\"\\u003e\\n \\u003cp\\u003e11. Final Course Grade\\u003c/p\\u003e\\n \\u003cp\\u003e3.55 (.62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.57542768273717%\\\"\\u003e\\n \\u003cp\\u003e.40\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"5.598755832037325%\\\"\\u003e\\n \\u003cp\\u003e-.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.331259720062208%\\\"\\u003e\\n \\u003cp\\u003e.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.065318818040436%\\\"\\u003e\\n \\u003cp\\u003e-.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.797822706065318%\\\"\\u003e\\n \\u003cp\\u003e.34\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.376360808709176%\\\"\\u003e\\n \\u003cp\\u003e.28\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"10.419906687402799%\\\"\\u003e\\n \\u003cp\\u003e.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.531881804043546%\\\"\\u003e\\n \\u003cp\\u003e.21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.9315707620528775%\\\"\\u003e\\n \\u003cp\\u003e.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"3.576982892690513%\\\"\\u003e\\n \\u003cp\\u003e.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;*\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .05, **\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt;.01, ***\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt;.001\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e\\u0026dagger;\\u003c/sup\\u003e N = 47\\u003cbr\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2:\\u003c/strong\\u003e Maximum coordinates for neural clusters with significant BOLD signal change for different trial type contrasts\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"627\\\" style=\\\"margin-right: calc(42%); width: 58%;\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"19.45773524720893%\\\" colspan=\\\"4\\\" style=\\\"width: 47.4624%;\\\"\\u003e\\n \\u003cp\\u003eMNI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003eContrast\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003eVoxels\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003eMax Z\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003ex\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003ey\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003ez\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eBrain Region\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003eConfident \\u0026gt; Not confident\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e48218\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e8.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e-98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eR. Lingual gyrus (BA17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e322\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e4.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e-8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eR. Middle frontal gyrus (BA 10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e187\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e4.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e-28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eCingulate gyrus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e161\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e4.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e-40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e-8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eInferior frontal gyrus (BA 45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003eError model confident \\u0026gt; No error model confident\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e201\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e4.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e-6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eL. Anterior cingulate (BA 32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e165\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e3.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e-12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e-80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eL. Lingual gyrus (BA 17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e142\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e4.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e-32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e-16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eL. Inferior frontal gyrus (BA 47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e106\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e4.44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e-18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eR. Inferior frontal gyrus (BA 47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"24.720893141945773%\\\" style=\\\"width: 22.1883%;\\\"\\u003e\\n \\u003cp\\u003eConfident accurate \\u0026gt; Confident inaccurate response\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.569377990430622%\\\" style=\\\"width: 8.5227%;\\\"\\u003e\\n \\u003cp\\u003e333\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"12.280701754385966%\\\" style=\\\"width: 10.8737%;\\\"\\u003e\\n \\u003cp\\u003e3.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.017543859649122%\\\" style=\\\"width: 6.3185%;\\\"\\u003e\\n \\u003cp\\u003e-6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.0606060606060606%\\\" style=\\\"width: 5.4369%;\\\"\\u003e\\n \\u003cp\\u003e-84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.379585326953748%\\\" style=\\\"width: 5.7308%;\\\"\\u003e\\n \\u003cp\\u003e-14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"33.49282296650718%\\\" style=\\\"width: 29.9763%;\\\"\\u003e\\n \\u003cp\\u003eL. Lingual gyrus (BA 17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3:\\u003c/strong\\u003e Maximum coordinates for correlations of student metacognitive metrics with BOLD \\u0026nbsp;activity during model error detection \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"612\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003eVoxels\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003eMax Z\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003ex\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003ey\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003ez\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003eBrain Region\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003eConfidence bias\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003e265\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003e4.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003e-50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003e26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003e-4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003eL Inferior frontal gyrus (BA 47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003e129\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003e4.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003e-4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003eL. Inferior frontal gyrus (BA 44)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003ePhi\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003e149\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003e4.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003e44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003eR. Middle frontal gyrus (B9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003eMeta-\\u003cem\\u003ed\\u003c/em\\u003e\\u0026apos;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e128\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e4.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003eL. Inf parietal lobule (BA 40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e4.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003ePostcentral gyrus (BA 3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e3.88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\" valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003eSuperior occipital gyrus (BA 19)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003eMeta-efficiency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003e105\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003e4.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003e-24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003e-28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003e68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003eL. Postcentral gyrus (BA 3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25.49019607843137%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"9.313725490196079%\\\"\\u003e\\n \\u003cp\\u003e89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"8.49673202614379%\\\"\\u003e\\n \\u003cp\\u003e4.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"7.189542483660131%\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.209150326797386%\\\"\\u003e\\n \\u003cp\\u003e42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"6.862745098039215%\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"36.43790849673203%\\\"\\u003e\\n \\u003cp\\u003eR. Middle frontal gyrus (BA 9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eNote: All correlations are with the contrast of Model Error \\u0026gt; No Error; correlations with phi and confidence bias control for students\\u0026rsquo; error detection accuracy\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-science-of-learning\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"npjscilearn\",\"sideBox\":\"Learn more about [npj Science of Learning](http://www.nature.com/npjscilearn/)\",\"snPcode\":\"41539\",\"submissionUrl\":\"https://mts-npjscilearn.nature.com/cgi-bin/main.plex\",\"title\":\"npj Science of Learning\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Metacognition, educational neuroscience, confidence, error detection\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-2874829/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-2874829/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eMetacognitive calibration\\u0026mdash; the capacity to accurately self-assess one\\u0026rsquo;s performance\\u0026mdash; forms the basis for error detection and self-monitoring, and a potential catalyst for conceptual change. Limited brain imaging research on authentic learning tasks implicates the lateral prefrontal and anterior cingulate brain regions in expert scientific reasoning. This study aimed to determine how variation in undergraduate life sciences students\\u0026rsquo; metacognitive calibration relates to their brain activity when evaluating the accuracy of biological models. Fifty undergraduate students enrolled in an introductory life sciences course completed a biology model reasoning task during fMRI. Findings suggest that students with higher metacognitive calibration recruit lateral prefrontal regions linked in prior research to expert STEM reasoning. 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