University of Groningen
Detecting Mind-Wandering with Machine Learning
Jin, Christina
DOI:
10.33612/diss.171835555
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Publication date: 2021
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Jin, C. (2021). Detecting Mind-Wandering with Machine Learning: Discovering the Neural Correlates of Mind-Wandering Through Generalizable Machine Learning Classifiers with EEG. University of Groningen. https://doi.org/10.33612/diss.171835555
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Detecting Mind-Wandering
with Machine Learning
Discovering the Neural Correlates of
Mind-Wandering Through Generalizable
Machine Learning Classifiers with EEG
C O V E R D E S I G N
Christina Yi Jin
P R I N T I N G
ProefschriftMaken
CONTENTS
1 I N T R O D U C T I O N 1
1.1 Concept of mind-wandering 2
1.1.1 Heterogeneity and definition in the current research 2 1.1.2 (Mal)functions of mind-wandering 2
1.2 Cognitive frameworks of mind-wandering 3
1.3 Behavioral and neural evidence for mind-wandering 4
1.3.1 Experimental paradigms and associated behavioral
patterns 4
1.3.2 Eye-tracking studies 6
1.3.3 EEG markers 6
1.3.4 Brain regions and networks 7
1.4 Contributing factors 10 1.5 Control of mind-wandering 14 1.6 This thesis 15 2 P R E D I C T I N G T A S K - G E N E R A L M I N D - W A N D E R I N G W I T H E E G 19 2.1 Introduction 20 2.2 Methods 23 2.2.1 Subjects 23 2.2.2 Procedure 23 2.2.3 Stimuli 25
2.2.4 Experience-sampling thought probes 25
2.2.5 EEG recording and offline processing 26
2.2.6 Trial classification 26 2.2.7 Behavioral measures 27 2.2.8 Single-trial ERP 27 2.2.9 Time-frequency analysis 30 2.2.10 Machine learning 31 2.3 Results 32 2.3.1 Behavioral results 32 2.3.2 Classification results 33
2.3.3 Contributions of individual markers 36
ii CONTENTS
Acknowledgements 45
3 V I G I L A N C E , T A S K D E M A N D S A N D M I N D
-W A N D E R I N G 47
3.1 Introduction 48
3.1.1 Mind-wandering and vigilance decrement 49 3.1.2 Mind-wandering and task demands 50
3.1.3 Alpha oscillation as a neural marker 51
3.1.4 Current study 52
3.2 Methods 54
3.2.1 Participants 54
3.2.2 Experimental procedure 54
3.2.3 Trial classification 57
3.2.4 Behavioral data analysis 58
3.2.5 EEG recording and offline preprocessing 58
3.2.6 Feature computation 59
3.2.7 Classifier training and testing 60
3.2.8 Feature testing and source localization 61
3.3 Results 63
3.3.1 Behavioral results 63
3.3.2 Classification results 66
3.3.3 Feature testing and dipole fitting 66
3.4 Discussion 70
3.4.1 Mind-wandering and vigilance decrement 73 3.4.2 Mind-wandering and task demands 74
3.4.3 Limitations and future work 76
3.5 Conclusion 78 Acknowledgements 78 4 D E C O D I N G S T U D Y I N D E P E N D E N T M I N D -W A N D E R I N G 79 4.1 Introduction 80 4.2 Methods 84 4.2.1 Datasets 84 4.2.2 EEG preparation 85 4.3 Experiment 1 89 4.3.1 Methods 91 4.3.2 Results 92 4.3.3 Discussion 93
CONTENTS iii 4.4 Experiment 2 96 4.4.1 Methods 96 4.4.2 Results 97 4.4.3 Discussion 100 4.5 General Discussion 102 4.6 Conclusion 104 5 S U M M A R Y A N D D I S C U S S I O N 105 5.1 Overall summary 105 5.2 Modelling mind-wandering 108 5.3 Future directions 110 5.4 Conclusion 111 D A T A A N D C O D E A V A I L A B I L I T Y S T A T E M E N T 113 B I B L I O G R A P H Y 114 N E D E R L A N D S E S A M E N V A T T I N G 135 A P P E N D I X 139 L I S T O F P U B L I C A T I O N S 149 A C K N O W L E D G E M E N T S 151
