1. Toward Fully Automated Person- Independent Detection of Mind Wandering Robert Bixler & Sidney D’Mello rbixler@nd.edu University of Notre Dame July 10, 2013

3. mind wandering  indicates waning attention  occurs frequently  20-40% of the time  decreases performance  comprehension  memory

4. solutions  proactive  mindfulness training  Mrazek (2013)  tailoring learning environment  Kopp, Bixler, D’Mello (2014)  reactive  mind wandering detection

5. our goal is to detect mind wandering

6. related work – attention  Attention and Selection in Online Choice Tasks  Navalpakkam et al. (2012)  Multi-mode Saliency Dynamics Model for Analyzing Gaze and Attention  Yonetani, Kawashima, and Matsuyama (2012)  distinct from mind wandering

7. mind wandering detection  neural activity  physiology  acoustic/prosodic  eye movements

8. neural activity Experience Sampling During fMRI Reveals Default Network and Executive System Contributions to Mind Wandering  Christoff et al. (2009)

9. physiology Automated Physiological-Based Detection of Mind Wandering during Learning  Blanchard, Bixler, D’Mello (2014)

10. acoustic-prosodic In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning  Drummond and Litman (2010)

11. eye movements mindless reading mindful reading

12. research questions 1.can mind wandering be detected from eye gaze data? 2.which features are most useful for detecting mind wandering?

13.  4 texts on research methods  self-paced page-by-page  30-40 minutes  difficulty and value  auditory probes  9 per text  inserted psuedorandomly (4-12s) data collection type of reportyesnototal end-of-page209651860 within-page127828394117 total148734904977 tobii tx300

14. 1.compute fixations  OGAMA (Open Gaze and Mouse Analyzer) (Voßkühler et al. 2008) 2.compute features 3.build supervised machine learning models data analysis

15.  global  local  context features

16. global features  eye movements  fixation duration  saccade duration  saccade length  fixation dispersion  reading depth  fixation/saccade ratio

17. local features  reading patterns  word length  hypernym depth  number of synonyms  frequency  fixation type  regression  first pass  single  gaze  no word

18. context features  positional timing  since session start  since text start  since page start  previous page times  average  previous page to average ratio  task  difficulty  value

19. supervised machine learning  parameters  window size (4, 8, or 12)  minimum number of fixations (5, 1/s, 2/s, or 3/s)  outlier treatment (trimmed, winsorized, none)  feature type (global, local, context, combined)  downsampling  feature selection  classifiers (20 standard from weka)  leave-several-subjects-out cross validation (66:34 split)

20. 1. can mind wandering be detected using eye gaze data?

22. confusion matrices end-of-page within-page actual response classified responseprior yesno yes.54.46.23 no.23.77 actual response classified responseprior yesno yes.61.39.36 no.42.58.64

23. 2. which features are most useful for detecting mind wandering?

24. rankend-of-pagewithin-page 1previous valuesaccade length max 2previous difficultysaccade length median 3difficultyfixation duration ratio 4valuesaccade length range 5saccade length maxsaccade length mean 6saccade length rangesaccade length skew 7page numberfixation duration median 8saccade length sdfixation duration mean 9saccade length meansaccade duration mean 10saccade length skewsaccade duration min

25. summary  mind wandering detection is possible  kappas of.28 to.17  end-of-page models performed better  global features were best  exception: context features highest ranked for end-of-page

26. enhanced feature set  global  pupil diameter  blink frequency  saccade angle  local  cross-line saccades  end-of-clause fixations

27. enhanced feature set

28. predictive validity mw ratepost knowledge transfer learning end-of-page predicted-.556-.415 actual (model)-.248-.266 actual (all data)-.239-.207 within-page predicted-.496-.431 actual (model)-.095-.090 actual (all data)-.255-.207

29. self-caught mind wandering

30. what does mind wandering look like?  saccades  slower  shorter  more frequent blinks  larger pupil diameters

31. limitations  eye tracker cost  population validity  self-report  classification accuracy

32. future work  multiple modalities  different types of mind wandering  mind wandering intervention

33. acknowledgements  Blair Lehman  Art Graesser  Jennifer Neale  Nigel Bosch  Caitlin Mills

34. questions ?