toward fully automated person- independent detection of mind wandering robert bixler & sidney...
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Toward Fully Automated Person-Independent Detection of Mind Wandering
Robert Bixler & Sidney D’[email protected] of Notre DameJuly 10, 2013
mind wandering
indicates waning attention
occurs frequently 20-40% of the time
decreases performance comprehension memory
solutions
proactive mindfulness training
Mrazek (2013)
tailoring learning environment 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 Navalpakkam et al. (2012)
Multi-mode Saliency Dynamics Model for Analyzing Gaze and Attention 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 movementsmindless 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?
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 report
yes no total
end-of-page
209 651 860
within-page
1278 2839 4117
total 1487 3490 4977
tobii tx300
1. compute fixations OGAMA (Open Gaze and Mouse Analyzer)
(Voßkühler et al. 2008)
2. compute features
3. build supervised machine learning models
data analysis
global
local
context
features
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 since session start since text start since page start
previous page times 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?
End-of-page Within-page0
0.05
0.1
0.15
0.2
0.25
0.3
best model kappas
report type
kap
pa
1. can mind wandering be detected using eye gaze data?
End-
of-p
age
With
in-p
age
4045505560657075
AccuracyExpected Accuracy
accu
racy %
1. can mind wandering be detected using eye gaze data?
confusion matrices
end-of-page within-pageactual response
classified response
prior
yes noyes .54 .46 .23
no .23 .77 .77
actual response
classified response
prior
yes noyes .61 .39 .36
no .42 .58 .64
2. which features are most useful for detecting mind wandering?
End-of-page Within-page0
0.1
0.2
0.3
average kappa values across feature types
Global
Local
Context
Global + Local + Con-text
report type
kap
pa
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
End-
of-p
age
With
in-p
age
0.1
0.15
0.2
0.25
0.3
OriginalEnhanced
kap
pa
predictive validity
mw rate post 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
self-caught mind wandering
End-
of-p
age
With
in-p
age
Self-
Caug
ht0
0.10.20.3
self-caught vs. probe caught
report type
kap
pa
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
?