1. Cyclical vs. Stochastic Visual Search Models Andrew Elias 11 Aug 2011 NRL Code 7440 branch meeting How a Target’s Effect on the Eye Varies with Distance &

2. Eye Tracking Fixations: eye stops (avg. 400 ms) Saccades: jerky eye movements, no data intake

3. The Experiment 30 FA18 pilots 72 images 24 distinct maps With/without targets sorted: 3 clutter levels High/low local clutter near target Target:

4. Cyclical vs. Stochastic Visual Search Models Cyclical: avoid re- examining things Stochastic: don’t worry about re-examining things

6. Disclaimer: Examination Radius (parameter) chosen to make Stochastic model fit the best

7. …now choose parameters to make Cyclical model fit the best

9. Conclusion Stochastic model is correct. (at least for the majority of our trials)

10. Further Research Comparing clutter levels or pilot experience levels: do models/parameters change? Using model parameters as a new measure of global clutter (!!!) or pilot skill Analyze subjects/maps individually to see if some use/allow Cyclical model.

11. How a Target’s Effect on the Eye Varies With Distance Purpose: fixation “size” small?large?fuzzy?

12. How long are saccades?

13. At what distance does saccade length change? 120 px 55 px Map Edge

14. Successful Saccade (n.) a saccade which ultimately brings the subject closer to the target

15. Seeing or remembering P(Success) P(Success | Not Seeing) Distance to Target (px) P(Seeing)

16. “Bayesian” derivation of P(Seeing)

17. Successful Run-of-Saccades L : the number of saccades into the future that we observe

19. Conclusions 55 px is the threshold for glance-away saccades Attention-grabbing radius may be fuzzy (at least for a large group), but probably spans a subset of the the 55- 450 px zone. Maybe ~120 px These values may be target-dependent and map- dependent. –‘450 px’ value is especially map-dependent. Reaction “time” is less than two saccades.

20. a visualization: 44 (±3) px/deg

21. Further Research Remove all after-target fixations and see if 2 nd bump disappears, and if 1 st bump gets clearer Blend distributions to model the control-group function

22. Acknowledgments Maura Lohrenz, 7441 section head Melissa Beck (LSU) Todd, Stephanie, Mike, Jeremy fellow students! experiment participants

23. somebody ask a question!

25. The end

28. Some initial readings…