This readme.txt file was generated on 20250210 by Colin Drexler Recommended citation for the data: ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset Reflection instructions influence 7- to 9-year-olds' metacognition and executive function at the levels of task performance and neural processing 2. Author Information Principal Investigator Contact Information Name: Philip David Zelazo Institution: University of Minnesota, Twin Cities Address: 51 E River Parkway, Minneapolis, MN 55455 Email: zelazo@umn.edu ORCID: 0000-0001-6017-2988 Associate or Co-investigator Contact Information Name: Colin Drexler Institution: University of Minnesota, Twin Cities Address: 51 E River Parkway, Minneapolis, MN 55455 Email: cdrexler@umn.edu ORCID: 0000-0001-6174-305X Associate or Co-investigator Contact Information Name: Institution: Address: Email: ORCID: 3. Date published or finalized for release: 4. Date of data collection (single date, range, approximate date) 20231014 to 20240826 5. Geographic location of data collection (where was data collected?): Minneapolis, Minnesota 6. Information about funding sources that supported the collection of the data: Shared Presence Foundation 7. Overview of the data (abstract): Though research on metacognitive development has historically remained independent from research on executive function (EF) skills, the two constructs share numerous theoretical similarities. Namely, the skill of reflection, or the ability to consciously reprocess information in real-time, may influence children’s awareness of their own use of EF skills. The present study examined the relations among implicit and explicit forms of metacognition in the Dimensional Change Card Sort (DCCS; Zelazo et al., 2012), while experimentally manipulating the propensity to reflect in 7- to 9-year-olds. Results showed that instructions to reflect led to improved task accuracy and better metacognitive control, but only younger children, as older children were likely reflecting spontaneously. Individual differences in trait mindfulness related to a similarly reflective mode of responding characterized by improved task accuracy and metacognitive control. In contrast, articulatory suppression impaired children’s task accuracy and metacognitive control. Additionally, simply asking children to make metacognitive judgments without extra instructions decreased the amplitude of neural indices of error monitoring, namely the error-related negativity (ERN) and N2 ERP components. Finally, individual differences in trait anxiety were related to larger Pe amplitudes. Taken together, the current findings reinforce theoretical frameworks integrating metacognition and EF, and highlight the shared influence of reflection across multiple levels of analysis. -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ 2. Links to publications that cite or use the data: 3. Was data derived from another source? If yes, list source(s): 4. Terms of Use: Data Repository for the U of Minnesota (DRUM) By using these files, users agree to the Terms of Use. https://conservancy.umn.edu/pages/policies/#drum-terms-of-use --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: MC-EF_dataset Short description: This dataset includes all finalized data from the DCCS task, questionnaires, and EEG recordings B. Filename: Short description: C. Filename: Short description: 2. Relationship between files: -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: A new Metacognitive DCCS was designed based on the mixed block of the NIH Toolbox version of the DCCS. Children were asked metacognitive monitoring and metacognitive control questions after a pseudo-randomly varying number of trials. Questionnaires included a Family Infromation Questionnaire for demographic variables, the Screen for Child Anxiety Related Disorders (SCARED) -Parent and -Child versions and the Mindful Attention Awareness Scale (MAAS) -Child version. EEG data were recorded using a 128-channel HydroCel Geodesic Sensor Net and EGI software during the metacognitive DCCS task. 2. Methods for processing the data: To prepare the behavioral data from the Metacognitive DCCS, we first calculated participants’ overall accuracy. Then, we calculated the median reaction time (RT) of correct trials in milliseconds, excluding trials with RTs faster than 100 ms. RT was measured as the latency between the presentation of the test stimulus and a participant’s key press response. To calculate post-error slowing, we subtracted participants’ RT on each pre-error trial from their RT on the associated post-error trial, and calculated the mean amount of slowing across all errors (Dutilh et al., 2012). A higher post-error slowing score (in ms) indicates that, on average, children responded slower on trials immediately following error trials than they did on trials immediately before error trials. We then computed two probability scores to assess metacognitive monitoring and metacognitive control (Fleming & Lau, 2014; Harvey, 1997). These were calculated as the squared difference between the probability rating and its actual occurrence. Immediately following completion of the DCCS, children were given an adapted version of the Metacognitive Knowledge Interview (McKI; Marulis et al., 2016). Data from each item (12 total) were scored on a scale from 0-2, corresponding to responses that were not at all metacognitive (0), partially metacognitive (1), and fully metacognitive (2). For questionnaires, we calculated an average of parent- and child-report SCARED scores for use in subsequent analyses. Item-level scores range from 0-2, with 41 total items, for a potential range of 0-82. There are 15 MAAS items on a scale of 1 to 6, with an "I don't understand" as a response option to skip that question, so scores here represent the average rating among items that participants answered. For the EEG data, we performed preprocessing through the EEGLAB toolbox (Delorme & Makeig, 2004) and ERPLAB (Lopez-Calderon & Luck, 2014). All EEG data were preprocessed in MATLAB using the Harvard Automated Processing Pipeline for Electroencephalography (HAPPE; Gabard-Durnam et al., 2018). To calculate the ERN and Pe amplitudes, response-locked epochs were baseline corrected to the period -250 to -100 ms prior to the response. Participants who did not have at least 6 usable incorrect trials were not included in response-locked analyses (n = 11; Olvet & Hajcak, 2009). We defined the ERN as the mean difference in amplitude between correct and incorrect trials from 25 to 100 ms post-response, and the Pe as the mean difference in amplitude between correct and incorrect trials from 200 to 500 ms post-response. We measured the ERN at electrode clusters Fz (electrodes 11, 19, 18, 16, 10, and 4 on our 128-channel net) and FCz (electrodes 6, 13, 12, 5, and 112), and found that the ERN response was maximal at Fz, so further analyses were conducted on the Fz electrode cluster. The Pe was found to be maximal at the Pz electrode cluster (electrodes 62, 61, 67, 72, 77, and 78). To calculate the N2, stimulus-locked epochs were baseline corrected to the period -250 to 0 ms prior to the stimulus presentation. The N2 was calculated as the mean amplitude 275 to 350 ms post-stimulus presentation, and because it was not found to differ in our sample between switch and repeat trials, all trial types were averaged together. The N2 was found to be maximal at electrode cluster FCz. 3. Instrument- or software-specific information needed to interpret the data: 4. Standards and calibration information, if appropriate: 5. Environmental/experimental conditions: Children were randomly assigned to one of four instruction conditions for the Metacognitive DCCS. The first condition was the control condition, which consisted of the metacognitive task described above with no special instructions, only those necessary to understand the task. The second condition, reflection, consisted of the metacognitive task described above with extra instructions that children should “pause and think about which of the two games [they’re] playing” instead of “answering without thinking” (Espinet et al., 2013). The third condition, articulatory suppression, consisted of the metacognitive task described above with extra instructions that children should talk to themselves while playing, repeating aloud the syllable “da-” in tune with an 80-bpm metronome (Fatzer & Roebers, 2012). The fourth condition was a no-metacognition condition in which the metacognitive questions were replaced with simple factual questions (e.g., “What color is the text in this sentence?”). Finally, the same 80-bpm metronome sounded across all conditions, and trial lengths and counts remained identical across all conditions. 6. Describe any quality-assurance procedures performed on the data: We calculated internal consistency for all questionnaire measures, as well as SME (standard measurement error) for all EEG measures. 7. People involved with sample collection, processing, analysis and/or submission: Colin Drexler, Philip David Zelazo ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: MC-EF_dataset ----------------------------------------- 1. Number of variables: 17 2. Number of cases/rows: 119 3. Missing data codes: Code/symbol 'NA' Definition Code/symbol Definition 4. Variable List A. Name: ID Description: participants' ID number, determined by date of visit to the lab B. Name: Condition Description: represents which experimental condition participants belong to C. Name: Accuracy Description: participants' overall accuracy on the DCCS task D. Name: RT Description: participants' median reaction time on correct trials from the DCCS task E. Name: PES Description: participants' average post-error slowing on the DCCS task F. Name: MC_monitoring Description: participants' metacognitive monitoring score on the DCCS task G. Name: MC_control Description: participants' metacognitive control score on the DCCS task H. Name: scared Description: participants' score from the SCARED questionnaire I. Name: MAAS Description: participants' score from the MAAS questionnaire J. Name: McKI Description: participants' metacognitive knowledge score from the post-task interview K. Name: ERN Description: participants' error-related negativity score (EEG measure) L. Name: Pe Description: participants' error positivity score (EEG measure) M. Name: N2 Description: participants' N2 score (EEG measure) N. Name: age Description: participants' age in years at the date of testing O. Name: fiq_gender Description: participants' gender, as reported by parents in the family information questionnaire 0 = male, 1 = female P. Name: fiq_ethnicity Description: participants' ethnicity, as reported by parents in the family information questionnaire 1 = non-Hispanic White, 0 = all other ethnicities Q. Name: fiq_income Description: participants' household income, as reported by parents in the family information questionnaire 1 = <$25,000, 2 = $50,000 - $74,999, 3 = $75,000 - $99,999, 4 = $100,000 - $124,999, 5 = $125,000 - $149,999, 6 = $150,000 - $174,999, 7 = $175,000 - $199,999, 8 = $200,000 - $224,999, 9 = $225,000 - $249,999, 10 = $250,000 - $274,999, 11 = $275,000 - $299,999, 12 = $300,000 - $324,999, 13 = $325,000 - $349,999, 14 = $350,000 - $374,999, 15 = $375,000 - $399,999, 16 = $400,000 or more