Post on 23-Feb-2016
description
transcript
Toward Fully Automated Person-Independent Detection of Mind Wandering
Robert Bixler & Sidney D’Mellorbixler@nd.eduUniversity 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 questions1. 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
0.050.1
0.150.2
0.250.3
best model kappas
report type
kapp
a
1. can mind wandering be detected using eye gaze data?
End-
of-pa
ge
Within-
page
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
GlobalLocalContextGlobal + Local + Con-text
report type
kapp
a
2. which features are most useful for detecting mind wandering?
rank
end-of-page within-page
1 previous value saccade length max2 previous difficulty saccade length
median3 difficulty fixation duration
ratio4 value saccade length
range5 saccade length
maxsaccade 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-pa
ge
Within-
page
0.1
0.15
0.2
0.25
0.3
OriginalEnhancedka
ppa
predictive validitymw 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-pa
ge
Within-
page
Self-
Caug
ht0
0.10.20.3
self-caught vs. probe caught
report type
kapp
a
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
?