Toward Fully Automated PersonIndependent Detection of Mind Wandering Robert Bixler & Sidney D’Mello
[email protected] University of Notre Dame July 10, 2013
mind wandering
indicates waning attention
occurs frequently
20-40% of the time
decreases performance
comprehension memory
solutions
proactive
mindfulness training
tailoring learning environment
Mrazek (2013)
Kopp, Bixler, D’Mello (2014)
reactive
mind wandering detection
our goal is to detect mind wandering
related work – attention
Attention and Selection in Online Choice Tasks
Multi-mode Saliency Dynamics Model for Analyzing Gaze and Attention
Navalpakkam et al. (2012)
Yonetani, Kawashima, and Matsuyama (2012)
distinct from mind wandering
mind wandering detection
neural activity
physiology
acoustic/prosodic
eye movements
neural activity
Experience Sampling During fMRI Reveals Default Network and Executive System Contributions to Mind Wandering
Christoff et al. (2009)
physiology
Automated Physiological-Based Detection of Mind Wandering during Learning
Blanchard, Bixler, D’Mello (2014)
acoustic-prosodic
In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning
Drummond and Litman (2010)
eye movements mindless reading
mindful reading
research questions 1. can mind wandering be detected from eye gaze data? 2. which features are most useful for detecting mind wandering?
data collection
4 texts on research methods
self-paced page-by-page 30-40 minutes difficulty and value
auditory probes
tobii tx300
9 per text inserted psuedorandomly (4-12s)
type of report end-of-page within-page total
yes 209 1278 1487
no 651 2839 3490
total 860 4117 4977
data analysis 1. compute fixations
OGAMA (Open Gaze and Mouse Analyzer) (Voßkühler et al. 2008)
2. compute features 3. build supervised machine learning models
features
global
local
context
global features
eye movements
fixation duration saccade duration saccade length
fixation dispersion reading depth fixation/saccade ratio
local features
reading patterns
word length hypernym depth number of synonyms frequency
fixation type
regression first pass single gaze no word
context features
positional timing
previous page times
since session start since text start since page start
average previous page to average ratio
task
difficulty value
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)
1. can mind wandering be detected using eye gaze data? best model kappas 0.3
0.25 kappa
0.2 0.15 0.1 0.05 0 End-of-page
Within-page
report type
1. can mind wandering be detected using eye gaze data? 75
70
accuracy %
65 60
Accuracy
55
Expected Accuracy
50 45 40
End-of-page
Within-page
1. can mind wandering be detected using eye gaze data? confusion matrices end-of-page actual classified response response
within-page prior
actual classified response response
prior
yes no yes .54 .46 .23
yes no yes .61 .39 .36
no .23 .77 .77
no .42 .58 .64
2. which features are most useful for detecting mind wandering?
kappa
average kappa values across feature types 0.3
Global
0.2
Local
Context
0.1 0 End-of-page Within-page report type
Global + Local + Context
2. which features are most useful for detecting mind wandering? rank
end-of-page
within-page
1
previous value
saccade length max
2
previous difficulty
saccade length median
3
difficulty
fixation duration ratio
4
value
saccade length range
5
saccade length max
saccade length mean
6
saccade length range
saccade length skew
7
page number
fixation duration median
8
saccade length sd
fixation duration mean
9
saccade length mean
saccade duration mean
10
saccade length skew
saccade duration min
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
enhanced feature set
global
pupil diameter blink frequency saccade angle
local
cross-line saccades end-of-clause fixations
enhanced feature set 0.3
kappa
0.25 Original Enhanced
0.2 0.15 0.1
End-of-page
Within-page
predictive validity mw rate end-of-page predicted actual (model)
post transfer knowledge learning -.556 -.248
-.415 -.266
actual (all data)
-.239
-.207
within-page predicted actual (model) actual (all data)
-.496 -.095 -.255
-.431 -.090 -.207
self-caught mind wandering self-caught vs. probe caught 0.35 0.3
kappa
0.25 0.2 0.15
0.1 0.05 0 End-of-page
Within-page report type
Self-Caught
what does mind wandering look like?
saccades
slower shorter
more frequent blinks
larger pupil diameters
limitations
eye tracker cost
population validity
self-report
classification accuracy
future work
multiple modalities
different types of mind wandering
mind wandering intervention
acknowledgements
Blair Lehman Art Graesser Jennifer Neale Nigel Bosch Caitlin Mills
questions
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