Detecting Mind-Wandering with Machine Learning
Jin, Christina
DOI:
10.33612/diss.171835555
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Publication date: 2021
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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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DATA AND CODE AVAILABILITY STATEMENT
The data that support the findings of this thesis are openly available in
https://unishare.nl/index.php/s/T94LXPQqw5FEA4J. Analysis code is available in https://github.com/christina109.
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NEDERLANDSE SAMENVATTING
Heeft u zich ooit afgevraagd waarom u tijdens de uitvoering van uw werk ineens aan iets anders denkt, bijvoorbeeld vakantieplannen, het boek dat u aan het lezen bent of aan iets anders dat een sterke indruk heeft gemaakt? Deze ontkoppeling van onze mentale activiteit van de externe omgeving wordt vaak ‘mind wandering’ genoemd. In dit proefschrift onderzoeken we de onderliggende oorzaak en gerelateerde hersenstructuren van mind wandering.
De inleiding begint met onze definitie van mind wandering, en het wordt gecontrasteerd met verwante verschijnselen zoals afleiding. Vervolgens bespreken we hoe het afdwalen van gedachten afhangt van fundamentele cognitieve functies zoals aandacht. Een aanzienlijke hoeveelheid fysiologisch en neuraal bewijs suggereert dat mind wandering wordt ondersteund door een complex netwerk van hersengebieden, waaronder het ‘default mode network’ (DMN) en de visuele gebieden. Twee belangrijke cognitieve processen kunnen worden geïdentificeerd uit de studies die tot nu toe zijn gedaan – mind wandering omvat zowel sensorische ontkoppeling als geheugenprocessen (aangezien we tijdens het . Ten slotte hebben we meerdere factoren besproken die van invloed kunnen zijn op het optreden van mind wandering, zoals werkgeheugen, taakbelasting, metabewustzijn, persoonlijke zorgen, enz.
Om beter te begrijpen hoe en waarom onze gedachten afdwalen, zou het nuttig zijn om van moment tot moment te weten of dit het geval is. Het algemene doel van dit proefschrift is daarom het ontwikkelen van methoden om mind wandering te volgen met behulp van elektro-encefalografie (EEG) data en machine learning classifiers. Belangrijk is dat deze classificatiealgoritmes uiteindelijk in staat moeten zijn om te generaliseren over taken (binnen dezelfde studie), deelnemers en studies (experimenten). Behalve dat ze ons een moment-tot-moment beoordeling geven van de mind-wandering toestand van een individu, geven succesvolle classifiatiealgoritmes ons ook aanwijzingen over de hersengebieden die cruciaal zijn voor het optreden van mind wandering. Bovendien zou een efficiënte neurale indicator van mind wandering mogelijk kunnen worden gecombineerd met neurofeedback voor therapeutische doeleinden, zoals het
detecteren van de extreem piekergedrag bij depressie of het ondersteunen van aandachtstraining voor Attentional Deficit Hyperactivity Disorder (ADHD). Het onderzoek dat in dit proefschrift wordt gepresenteerd, bestaat uit drie experimenten met één gemeenschappelijk doel: het trainen van effectieve mind wandering classificatiesystemen met EEG met behulp van machine learning-modellen. EEG is geselecteerd als onze belangrijkste bron vanwege de hoge temporele resolutie - het werkt op een milliseconde schaal. Omdat onze mentale toestanden constant wisselen tussen mind wandering en taakgericht werken, stelt de temporele resolutie van EEG ons in staat om dergelijke dynamiek nauwkeuriger vast te leggen dan andere metingen van neurale activiteit, zoals neuroimaging. Een uitdaging bij het gebruik van externe EEG is dat het niet goed is in het lokaliseren van de relevante hersenregio's, vooral wanneer de neurale activiteit afkomstig is van diepere corticale structuren zoals het DMN of het limbisch systeem – gebieden waarvan we weten dat ze cruciaal zijn voor mind wandering. In Experiment 2 hebben we dit probleem aangepakt met bronlokalisatietechnieken waarmee we het signaal terug konden traceren naar de meest waarschijnlijke corticale generator.
De ontwikkeling van neurale indicatoren van mind wandering begint in hoofdstuk 2 met een eerste poging om mind wandering te voorspellen met EEG. We hebben dit gedaan op basis van verschillende kandidaat-EEG-indicatoren die zijn afgeleid van eerdere studies, waaronder ERPs, vermogen in specifieke frequentiebanden en functionele connectiviteit tussen kanalen. Deze functies werden gebruikt om een SVM-classificator (Support Vector Machine) te trainen om mind wandering te voorspellen binnen proefpersonen (intra-subjectieve modellering). Hierbij hebben we ons niet op één taak geconcentreerd zoals gebruikelijk is, maar in plaats daarvan twee cognitieve taken gebruikt. Het classificatiealgoritme werd getraind om op basis van data van de ene taak, mind wandering tijdens de andere taak te voorspellen. Het bleek inderdaad mogelijk om de algoritmes zo te trainen dat ze in staat waren om over de taken te generaliseren met een gemiddelde nauwkeurigheid van 60%. Van alle EEG-indicatoren was de alfa frequentieband het meest voorspellend voor mind wandering.
In Hoofdstuk 3 hebben we de ontwikkeling van de indicatoren geintegreerd in het bestuderen van drie samenhangende verschijnselen: mind wandering, onoplettendheid en lage taakvereisten. De gemeenschappelijke noemer onder deze drie verschijnselen is een soortgelijk proces van afname van aandacht, wat geïllustreerd wordt met verminderde gedragsprestaties en verhoogde alfa golven.
Dat is niet verwonderlijk want onoplettendheid gaat gepaard met meer mind wandering en het uitvoeren van taken met lage eisen veroorzaakt een grotere kans op mind wandering. Onze studie bewees echter dat een SVM-classificator onderscheid kan maken tussen mind wandering, onoplettendheid en lage taakvereisten. Daarnaast stelde bronlokalisatie ons in staat om met veel meer precisie te identificeren waar de relevante hersensignalen vandaan kwamen dan de analyse in hoofdstuk 2. Onze aanname is dat als onoplettendheid of lage taakvereisten echt lijken op mind wandering, we in staat zouden moeten zijn om mind wandering te voorspellen met een classificatiealgoritme dat is getraind op onoplettendheid of taakvereisten. We ontdekten echter dat er geen generalisatie was tussen deze processen. Een verdere vooruitgang die deze studie maakte, was dat we een vergelijkbare nauwkeurigheid (59%) van het voorspellen van mind wandering bereikten tussen proefpersonen, als de individuele-proefpersonen classificatoren in hoofdstuk 2. Daarnaast hebben we unieke alpha-activiteit waargenomen in de linker superieure gyrus, wat waarschijnlijk wijst op generalisatieprocessen van gedachten.
In Hoofdstuk 4 hebben we onderzocht of we mind-wandering voorspellingen verder konden verbeteren door gebruik te maken van diepe neurale netwerken. In het bijzonder hebben we een convolutioneel neuraal netwerk (CNN) gebruikt, een krachtig soort neuraal netwerk dat grotere gegevensgroottes aankan zonder dat er aannames over de inputdata gedaan moeten worden. Als input hebben we ruwe EEG data gebruikt, evenals de indicatoren die we in vorige hoofdstukken hadden gebruikt. Om de generaliseerbaarheid van het classificatiealgoritme verder te verifiëren, hebben we het netwerk getraind op de gegevens van experiment 1 en getest op de gegevens van experiment 2 - onafhankelijke experimenten met verschillende deelnemers en verschillende taken. We bereikten een nauwkeurigheid van 68% voor deze voorspelling.
In hoofdstuk 5 bespreek ik de belangrijkste conclusies van de drie onderzoeken. Dit hoofdstuk laat zien hoe onze bevindingen consistent zijn met één belangrijke theorie van over mind wandering: de perceptuele-ontkoppelings theorie. Deze conclusie wordt getrokken uit de resultaten in Hoofdstuk 2 dat pariëtale-occipitale alfa frequenties het meest voorspellend zijn voor mind wandering, evenals de bevindingen van Hoofdstuk 3 dat de alfabandactiviteit afkomstig was van de linker precuneus - een neurale correlaat voor visuele verwerking. Ik heb ook mogelijke richtingen aangedragen om te verkennen in toekomstig onderzoek, in het bijzonder het rekruteren van deelnemers met een goed inzicht in hun eigen
mentale staat om nauwkeurige zelfrapporten te verkrijgen, het gebruik van zogenoemde resting-state EEG als voorspeller, en het onderzoeken van de connectiviteit tussen de corticale bronnen.
Concluderend bevestigt het huidige proefschrift de mogelijkheid van het trainen van classificatiealgoritmes voor mind-wandering gebaseerd op EEG. Ten eerste hebben onze EEG-bevindingen het begrip van mind wandering vergroot, door grotendeels de perceptuele-ontkoppelings en de gedachtengeneratie-accounts van mind wandering te ondersteunen. Ten tweede hebben we classificatiealgoritmes ontwikkeld die kunnen generaliseren over taken, deelnemers en onderzoeken, wat bewijst dat vooraf getrainde algoritmes kunnen worden gebruikt om te voorspellen of nieuwe individuen een andere taak uitvoeren dan de getrainde taak. Dit biedt mogelijkheden voor mind wandering detectoren op basis van neurofysiologische gegevens in een dagelijks leven of in een klinisch scenario.
APPENDIX
S1 Word list used in the sustained-attention-to-response task (SART)
pleasant number backward breath address lawyer agreed all
America American fear poor band bank bar thanks
shall aim business bone burial pretty both important
belt prepared message judgment bed discuss existence pay
mean better respect clouded turn like command move
property worried special shortly blue book bunch message
boat break write break desk believed roof close
thick love continue run running continue courage eternal
English single huge enormous behind experience party fact
guest guest hole soon area left building fuss
eat help lied sound commonly pleasure called caught
talk history written closed crazy stop prison feelings
conscience injured normal sit family search glass evening
throw joke half lord hero real angle high
hundred hope hotel marriage ice impression suddenly card
knowledge kitchen sale short cost newspaper kiss pillow
smile burden live age leather read army leading
class alive lying lies lift list left lot
lazy listening succeed size most girls middle minute
mist beautiful wall think thought nine neck above
call morning explore incredible immediately breakfast discovered receive
view uncle get hurry ears old seem survival
dad partner adjust suit place position beautiful hit
weird right accounting account relationship running drive risk
red call grey smoke quarrel appears shoes clean
bolt write second simple sleep battle keys hit
lock understand some special jump stuff insert steal
chair stop slice briefcase restraint drawing right back
much happy time consent coincidence total stairs pull
faith twelve twenty hours holiday often morning security
many change responsibility verb disappear forgive declaration solid
loss difference fresh chill departure left celebrate enemy
right meat airplane flight foot feet follow complete
adult peace boyfriend where to when which income
weekend desire under residential see sea looking sun
S2 Modelling performance based on three preceding trials from each probe
Figure S2 Modelling performance for both within-task leave-one-out cross-validation (LOOCV) and across-task prediction based on 3 preceding trials of each probe. The mean accuracy for the LOOCV was 0.62 (𝑆𝑆𝑆𝑆 = 0.09) in the SART and 0.69 (𝑆𝑆𝑆𝑆 = 0.10) in the visual search task (VS). For the across-task predictions, the mean accuracy was 0.58 (𝑆𝑆𝑆𝑆 = 0.11) for testing the SART model on the data of the visual search task (SART-VS) and 0.59 (𝑆𝑆𝑆𝑆 = 0.11) for testing the visual search task model on data of the SART (VS-SART). A t test conducted between the obtained accuracy and 0.5 confirmed this difference in the LOOCV: 𝑡𝑡(17)= 5.73, 𝑝𝑝 < 0.001, 𝑑𝑑 = 1.35 in the
SART and 𝑡𝑡(17)= 7.50, 𝑝𝑝 < 0.001, 𝑑𝑑 = 1.77 in the visual search task, as well as
in the across-task prediction: 𝑡𝑡(17)= 3.21, 𝑝𝑝 = 0.005, 𝑑𝑑 = 0.76 in SART-VS and
S3 Mind-wandering classification based on considering evaluation of task performance part of mind-wandering
Figure S3.1 Modelling performance based on the categorization that the on-task state referred to answer 1 and the mind-wandering state referred to answer 2, 3, and 5. Note based on this categorization, 19 participants were included. The mean accuracy for the LOOCV was 0.63 (𝑆𝑆𝑆𝑆 = 0.07) in the SART and 0.66 (𝑆𝑆𝑆𝑆 = 0.09) in the visual search task (VS). For the across-task predictions, the mean accuracy was 0.59 (𝑆𝑆𝑆𝑆 = 0.09) for testing the SART model on the visual search task data (SART-VS) and 0.59 (𝑆𝑆𝑆𝑆 = 0.07) for testing the visual search task model on data of the SART (VS-SART). A t test conducted comparing the obtained accuracy to the chance level of 0.5 confirmed a significant difference in both within-task LOOCV: 𝑡𝑡(18)=
7.81 , 𝑝𝑝 < 0.001, 𝑑𝑑 = 1.79 in the SART and 𝑡𝑡(18)= 7.70 , 𝑝𝑝 < 0.001, 𝑑𝑑 =
1.77 in the visual search task, as well as in the across-task prediction: 𝑡𝑡(18)= 4.19,
𝑝𝑝 < 0.001, 𝑑𝑑 = 0.96 in SART-VS and 𝑡𝑡(18)= 4.74, 𝑝𝑝 < 0.001, 𝑑𝑑 = 1.09 in
Figure S3.2 Marker testing results based on the categorization that the on-task state referred to answer 1 and the mind-wandering state referred to answer 2, 3, and 5. Paired t tests showed all the marker alone could predict above the chance level (𝑡𝑡𝑠𝑠 > 4.68, 𝑝𝑝𝑠𝑠 < 0.001). None of the markers outperformed the whole model (𝑡𝑡𝑠𝑠 < −2.96, 𝑝𝑝𝑠𝑠 < 0.008). Error bars indicate 95 percent confidence interval.
S4 Performance of logistic regression classifiers
Figure S4.1 Modelling performance of logistic regression classifiers. The mean accuracy for within-task LOOCV was 0.56 (𝑆𝑆𝑆𝑆 = 0.06) in the SART and 0.57 (𝑆𝑆𝑆𝑆 = 0.08) in the visual search task (VS). For the across-task predictions, the mean accuracy was 0.56 (𝑆𝑆𝑆𝑆 = 0.05) for testing the SART model on the visual search task data (SART-VS) and 0.55 (𝑆𝑆𝑆𝑆 = 0.05) for testing the visual search task model on data of the SART (VS-SART). A t test comparing the obtained accuracy to the chance level of 0.5 confirmed this difference in the LOOCV: 𝑡𝑡(17)= 3.89, 𝑝𝑝 = 0.001,
𝑑𝑑 = 0.92 in the SART and 𝑡𝑡(17)= 3.78, 𝑝𝑝 = 0.002, 𝑑𝑑 = 0.89 in the visual
search task, as well as in the across-task prediction: 𝑡𝑡(17)= 4.70, 𝑝𝑝 < 0.001, 𝑑𝑑 =
1.11 in SART-VS and 𝑡𝑡(17)= 4.20, 𝑝𝑝 < 0.001, 𝑑𝑑 = 0.99 in VS-SART. Paired t
test results showed the accuracy of logistic regression models was lower than the SVM models in the cross-validation process (𝑡𝑡𝑠𝑠 > 6.70, 𝑝𝑝𝑠𝑠 < 0.001), but no statistically significant difference was found in the across-task predictions.
Figure S4.2 Marker testing results using logistic regression as classifiers. Paired t tests showed only 11 markers out of 30 performed above chance level as marked in the graph (∗ 𝑝𝑝 < 0.05,∗∗ 𝑝𝑝 < 0.01,∗∗∗ 𝑝𝑝 < 0.001). Paired t tests showed the performance of single-marker models built using logistic regression were generally worse that built using SVM (𝑡𝑡𝑠𝑠 > 3.37, 𝑝𝑝𝑠𝑠 < 0.004). Error bars indicate 95 percent confidence interval.
S5 Performance with classifiers trained on three trials before each probe
Figure S5 Performance with classifiers trained on three trials before each probe. This graph is the result of a supplementary analysis as to the main results showed in Figure 5. All the classifiers performed above the chance level (0.5214) during the 10-fold CV and LOPOCV (𝑡𝑡s > 2.08, 𝑝𝑝s < 0.046). When predicting the self-reported mental states in both tasks, the task demands or the vigilance classifier did not surpass the chance level (𝑡𝑡s < .37, 𝑝𝑝s > 0.164). The error bar reflects one between-subject standard error (SE). Asterisks indicate the difference between the accuracy and chancel level is significant using one-sample t tests (*** p <0.001, ** p<0.01).
S6 Trial count in each condition for each participant during the behavioral analysis
Table S6 Compensatory data to Chapter 3.3.1. Shaded areas indicate that a participant was removed from the behavioral analysis in the corresponding task due to the small amount of data in one of the two classes. Note that this procedure was only performed during the behavioral analysis. Because in the machine learning analysis we trained classifiers on 29 participants and tested them on the remaining left-out participant (LOPOCV), this “missing one class” dilemma is not a problem since the training data sample is guaranteed to have data from two classes and the test sample was allowed to be of data from a single class.
Visual Search Task SART
Participant On-task Mind-wandering On-task Mind-wandering
1 21 30 36 0 2 15 45 33 3 3 54 3 27 9 4 60 0 36 0 5 15 45 9 27 6 57 0 36 0 7 24 15 33 0 8 51 9 36 0 9 60 0 36 0 10 15 42 6 27 11 24 33 9 12 12 36 24 36 0 13 51 0 36 0 14 57 3 36 0 15 30 30 21 6 16 42 15 30 0 17 39 15 9 24 18 12 24 15 15 19 39 21 30 3 20 54 3 36 0 21 15 33 6 30 22 51 6 36 0 23 18 39 24 6 24 3 57 0 36 25 39 15 24 6 26 54 0 36 0 27 42 9 36 0 28 33 12 27 3 29 24 24 9 12 30 30 6 18 0
S7 Sensitivity and specificity compensatory to Figure 3.5.
Figure S7 Sensitivity and specificity as indications of biased detection of each classifier compensatory to the achieved accuracy in Figure 5. Sensitivity is the true positive rate, which is the ratio of the correctly classified positive cases among all the positive cases. Specificity is the true negative rate, which is the ratio of the correctly classified negative cases among all the negative cases. In the current study, positive cases are as “low demands”, “low vigilance” or “mind-wandering”, and negative cases are defined as “high demands”, “high vigilance” or “on-task”.
S8 Alpha power without baseline correction
Figure S8 Same features in Figure 3.9 plotted without baseline correction. This figure shows that in general, alpha power suppressed after stimulus-onset.
LIST OF PUBLICATIONS
J O U R N A L A R T I C L E S
Jin, C. Y., Borst, J. P., & van Vugt, M. K. (2020). Distinguishing Vigilance Decrement and Low Task Demands from Mind-wandering: A Machine Learning Analysis of EEG. European Journal of Neuroscience, 52(9), 4147–4164. doi: 10.1111/ejn.14863
Jin, C. Y., Borst, J. P., & van Vugt, M. K. (2019). Predicting task-general mind-wandering with EEG. Cognitive, Affective, & Behavioral Neuroscience, 19, 1059–1073. doi:10.3758/s13415-019-00707-1
P R E P R I N T
Jin, C. Y., Borst, J. P., & van Vugt, M. K. (2020). Decoding Study-Independent Mind-Wandering from EEG using Convolutional Neural Networks. bioRxiv preprint bioRxiv: 2020.12.08.416040. doi:10.1101/2020.12.08.416040
P O S T E R S
Jin, C. Y., van Vugt, M., & Borst, J. (2019). Distinguishing Vigilance Decrement and Low Task Demands from the Occurrence of Self-generated Thought: A Machine Learning Analysis of EEG Data. Poster presentation at the 2019 De Nederlandse Vereniging voor Psychonomie (NVP) Symposia Winter Conference, Egmond aan Zee, Netherlands.
Jin, C. Y., van Vugt, M., & Borst, J. (2018). EEG classifier can predict mind-wandering across different tasks. Poster presentation at the 16th Annual Meeting of the International Conference on Cognitive Modelling, Madison, WI, USA. Jin, C. Y., van Vugt, M., & Borst, J. (2017). Developing EEG biomarkers of mind-wandering. Poster presentation at the 2017 De Nederlandse Vereniging voor Psychonomie (NVP) Symposia Winter Conference, Egmond aan Zee, Netherlands.
ACKNOWLEDGEMENTS
This thesis is proof of my four-year PhD research. Travelling from a country 5,500 miles away to the Netherlands, being confused about my future and having many doubts about science and academia, I began my PhD life. It was a journey of learning, discovering, and even healing. In the end, I found joy and pride in being a researcher.
I would never have gone so far without the help of Dr. Marieke van Vugt, who not only supervises my PhD project but also helps me through many difficult moments in my life. Marieke’s knowledge, open-mindedness, enthusiasm, discipline, and patience greatly inspired and supported me. While Marieke provided me a safe environment to explore my research interest, Dr. Jelmer Borst, my daily supervisor, granted me access to many resources. He is a sharp reviewer who always pointed out angles that I missed and his solution-oriented mindset guided me. I am very grateful that I would have such an opportunity to learn from and work with both of you.
I am also grateful that my colleagues are always kind and supportive. Niels, Fokie, Marco, and Jacoline, I gained many insightful opinions from you. Oscar, Pallavi, and Stefan, it is nice to talk about our joint research interest. Mahya, Corné, Abby, Lionel, Hermine, Hang, Mark, Mega, Katja Mehlhorn, Katja Paul, Marlijn, Thomas, Vishal, Aniket, Ben, Yuri, Trudy, Harmen, Hagit, Pry, Emmanuel, and Sheng; I like having those interesting talks with you in a multi-cultural atmosphere. Your company makes me not feel lonely living in a foreign country. I also thank the secretaries and supporting staff for their assistance during my stay here. Elina, I appreciate your help and patience during my applications. Remco, you were always helpful whenever I encountered a computer problem.
And lastly, I thank my friends for still being part of my life even we are thousands-of-mile away with a six-hour time gap. I especially thank John, who not only helps me in my daily life as a responsible husband, but also respects my choice and always gives good advice on my research as a capable partner. PhD life is supposed to be stressful and difficult, but the relationship with you gives me the courage to face the challenge.
