Towards a stochastic theory of saccadic eye movement GURU Oct 1, 2001 Jonathan Nelson.
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Transcript of Towards a stochastic theory of saccadic eye movement GURU Oct 1, 2001 Jonathan Nelson.
Towards a stochastic theory of saccadic eye movement
GURU Oct 1, 2001
Jonathan Nelson
Thanks especially to Wendy Ark Gary Cottrel Carrie Joyce Javier Movellan Marty Sereno Ruth Williams
Outline Function of eye movement in vision Traditional analyses of eye
movement Three papers using stochastic
processes to model eye movement Research towards a better
generative model
Why model eye movement? for cognitive science and economics: how do a person’s
knowledge and goals influence their information-gathering in perception? (applications also to entertainment and interface design)
for medical diagnosis: how does a physician/technician read an x-ray? Can we automate or assist the physician?
for understanding and treating pathological conditions: autism, williams’ syndrome. Can training appropriate eye movement help autistics in social situations?
for artificial perception: are the visual data that humans acquire also informative for active, sequential artificial vision systems?
Saccadic eye movement Eccentricity and image resolution Saccades and fixations Relationship to task, applications
Traditional analyses Large number of papers Inspired by behavioral statistics:
lose dynamic character, or dichotomize/polychotomize time
Yet unexplained qualitative results: “highly individual scanpaths” “high individual consistency from trial
to trial”
Towards a stochastic theory Regions of interest in an image Timescale
Regions of interest:
Regions of interest:
Regions of interest:
Regions of interest:
Timescale Discrete or continuous? Every point sampled by the eye
tracker? Every nth millisecond? Every fixation?
Timescale Nearby fixations grouped together Saccade points rejected
What kind of SP? Discrete state, usually with regions
of interest that contain current point of gaze as state
Discrete time, with points of meaningful fixations forming the time step
Time-homogenous Markov chain
White et al.: mammograms How do experts read
mammograms? Can experts’ patterns in reading
mammograms suggest means of computer aided diagnosis?
from http://sprojects.mmi.mcgill.ca/mammography/imageanalysis.htm
White et al.’s modeling One model per subject, per image A sequential clustering algorithm
to determine regions of interest Exclude transitions to same state,
and fixations outside of regions of interest
“Not enough data”
Joyce: face perception Goal: augment or corroborate
eyewitness testimony, using eye movement EEG GSR
Task: view a face recognize it as not novel, after a
delay
Task: “Study this face”
Task:
“Have you seenthis face before?”
Joyce’s regions of interest 10 ROI:
hair right eye nose etc.
Normalized faces Nearest neighbor
Joyce’s model Markov States: each of 10 regions of
interest Time: from entering region till
leaving region is one discrete step
Joyce’s results More entropy in saccade
sequences when viewing a novel face
Matches qualitative findings
Eye-typing: Salvucci Goal: have users type a pre-
selected word, by looking at an on-screen keyboard
Models used: saccade or fixation HMM letter HMM grammar
Saccade-fixation HMM If previous time is fixation, current time is most likely
fixation If low velocity, current time most likely fixation “Standard HMM parameter estimation” (Rabiner, 1989) Viterbi to optimize
Fixations and centroids
“Centroid submodel HMM” States:
peaked bivariate distribution for (x, y)
diffuse bivariate distribution for (x, y)
Grammar for words Which word gives the highest
likelihood of the centroid data?
Results 92% accuracy with 1000 word vocabulary too computationally intensive for realistic-size
(e.g. 50,000 words) vocabulary, for next few years
… a nice proof of concept (previous systems required >750ms between
fixations, and 4 degrees between targets) (for simpler multiple-letter models, use a
string-edit distance algorithm to find the nearest vocabulary word)
Future work More temporal dependence: try
3rd+ order model Can a sophisticated string-edit
distance algorithm correct for bias in limited sampling?
Future work Systematic exploration of time index Improvement of saccade-fixation HMM Distribution of fixations within regions What is the nature of individual
differences? Relationship of knowledge and goals to
task
Sources Cited White, KP; Hutson, TL; Hutchinson, TE (1997). Modeling
human eye behavior during mammographic scanning: preliminary results. IEEE Transactions on Systems, Man and Cybernetics, A, 27, 494-505.
Salvucci, DD (1999). Inferring intent in eye-based interfaces: tracing eye movements with process models. Proceedings of the 1999 Computer Human Interaction Conference.
Joyce, CA (2000). Saving Faces: Using Eye Movement, ERP, and SCR Measures of Face Processing and Recognition to Investigate Eyewitness Identification. Ph.D. dissertation, University of California, San Diego.