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PreviousNext
Research ArticleNew Research, Cognition and Behavior

Learning Desire Is Predicted by Similar Neural Processing of Naturalistic Educational Materials

Yi Zhu (朱怡), Yafeng Pan (潘亚峰) and Yi Hu (胡谊)
eNeuro 19 August 2019, 6 (5) ENEURO.0083-19.2019; https://doi.org/10.1523/ENEURO.0083-19.2019
Yi Zhu (朱怡)
1School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, People’s Republic of China
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  • ORCID record for Yi Zhu (朱怡)
Yafeng Pan (潘亚峰)
1School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, People’s Republic of China
2Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), ULB Neuroscience Institute (UNI), Université Libre de Bruxelles, 1050 Bruxelles, Belgium
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  • ORCID record for Yafeng Pan (潘亚峰)
Yi Hu (胡谊)
1School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, People’s Republic of China
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Figures

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  • Figure 1.
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    Figure 1.

    Schematic illustration of the experimental procedure. A, Experimental setup. B, Events and time flows in a trial. C, Subjects ranked courses based on their learning desire form 1 (highest) to 15 (lowest). Note that in the following analyses, rankings were reversely coded.

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    Figure 2.

    Overview of the three-step ISC analysis. Neural responses are recorded on D electrodes from N subjects during the time (0–T s) of stimuli presentation. First, a few (first three in this study) maximally correlated components are extracted. Second, the spatial distribution of each component is visualized. Third, for each subject, ISC is measured as the sum of the averaged correlation coefficients between that subject and remaining subjects over the first three components. Ed, Electrode d; Sn, subject n. Ci, component i.

  • Figure 3.
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    Figure 3.

    Video-evoked versus chance-level ISC of each course clip. ISC evoked by each course clip significantly exceeded its chance level. Each dot represents one subject. Error bars indicate SEs. ***p < 0.001, FDR corrected.

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    Figure 4.

    ISC of high versus medium versus low effective course clips. A, ISCs of high and medium effective clips were respectively larger than that of low ones. B, Top, One representative illustration (i.e., the highest effective course clip) of the scalp projections of the first three maximally correlated components (i.e., C1, C2, and C3). Color indicates how strongly the component activity correlated with the EEG signals recorded on different electrodes across the scalp. Bottom, Subcomponent ISCs were also enhanced when the motivational effectiveness of course clips increased. Each dot represents one subject. Error bars indicate SEs. #p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001, FDR corrected.

  • Figure 5.
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    Figure 5.

    ISC predicted course-learning desire. A, Pearson correlation indicated ISC difference (high vs low) positively associated with the subject’s learning desire difference (high vs low; r(15) = 0.74, p = 0.002). B, ISC difference became a significant correlate of learning desire difference after ∼100 s of watching. The vertical red line with an asterisk indicates the earliest time (100.6 s) at which such a correlation reached the significance. The horizontal dashed line indicates the correlation coefficient (r(15) = 0.64, p < 0.05, FDR corrected). C, Pearson correlations indicated differences in subcomponent ISCs independently associated with the subject’s learning desire difference. D, For C1, the vertical purple line with an asterisk indicates the earliest time (85.5 s) at which correlation reached the significance. The horizontal dashed line indicates the correlation coefficient (r(15) = 0.60, p < 0.05, FDR corrected). C2 or C3 showed no such early prediction effect. Each dot represents one subject. #p < 0.1, *p < 0.05, **p < 0.01.

  • Figure 6.
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    Figure 6.

    ISC was associated with interest rather than value. A, Ratings of interest significantly exceeded those of value. B, C, For individual-level high effective course clips, the Spearman correlation indicated that ISC positively associated with the ratings of value (r(15) = 0.77, p = 0.0008; B), but not with ratings of interest (r(15) = 0.32, p = 0.25; C). Each dot represents one subject. Error bars indicate SEs. *p < 0.05, ***p < 0.001.

Tables

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    Table 1.

    Summary of course clips

    Onlineenrollment
    No.Course titleTopicDuration (s)URL-ending *MeanRank
    1.Psychological Health of College StudentsPsychology100NEU-1001930012#/info29915
    2.Taoist WisdomPhilosophy81XJTU-1001522001#/info35282
    3.Chinese Poetry ArtLiterature149SCU-21006#/info49371
    4.Silk Culture and ProductsArt129SUDA-1001754250#/info16015
    5.Managerial CommunicationManagement133NUEPU-292001#/info21686
    6.Economic Geography and Vicissitude of EnterprisesEconomics67ZNUEDU-1001615011#/info84810
    7.Culture of MathematicsMath123NANKAI-312001#/info33504
    8.Applied OpticsPhysics57BIT-1001606003#/info14819
    9.Medicinal ChemistryChemistry121CPU-1001570004#/info18317
    10.Engineering Materials and ManufacturingEngineering133SDU-306001#/info77612
    11.CytobiologyBiology132SCU-46011#/info15118
    12.First Aid General KnowledgeMedical Science170WHU-85001#/info33683
    13.Space Humanities and ArtsInterdiscipline215NUAA-1001764004#/info83611
    14.Medical EthicsInterdiscipline170XJTU-47022#/info38213
    15.Fantastic BionicsInterdiscipline129JLU-32007#/info18714
    • On-line enrollment (person-time/session) was recorded by the date of 2017/03/26.

    • *URL beginning with http://www.icourse163.org/course/.

Extended Data

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  • Extended Data 1

    This is the MATLAB code to compute EEG-derived ISC using correlated component analysis, specified for EEG data collected from the Compumedics NeuroScan system. You will need EEGLAB (version 14.1.0) and Curry7 format EEG data to run Step1_preprocess_demo.m. You will need the following files to run Step2_ISC_demo.m: (1) runisc.m (EEG-ISC specific code); (2) topoplot.m (stand-alone version of the EEGLAB popular display function); (3) Neuroscan64.loc (Neuroscan location file for topoplot); (4) notBoxPlot.m (stand-alone version of Rob Campbell’s scatter plot); (5) Data file (e.g., v12. mat, EEG data with 60 electrodes from 15 subjects while watching the course clip No. 12, time-points × channels × subjects). Download Extended Data 1, ZIP file.

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Learning Desire Is Predicted by Similar Neural Processing of Naturalistic Educational Materials
Yi Zhu (朱怡), Yafeng Pan (潘亚峰), Yi Hu (胡谊)
eNeuro 19 August 2019, 6 (5) ENEURO.0083-19.2019; DOI: 10.1523/ENEURO.0083-19.2019

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Learning Desire Is Predicted by Similar Neural Processing of Naturalistic Educational Materials
Yi Zhu (朱怡), Yafeng Pan (潘亚峰), Yi Hu (胡谊)
eNeuro 19 August 2019, 6 (5) ENEURO.0083-19.2019; DOI: 10.1523/ENEURO.0083-19.2019
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Keywords

  • electroencephalography (EEG)
  • intersubject correlation (ISC)
  • learning desire
  • motivational effectiveness
  • naturalistic stimuli
  • neural similarity

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