Cyclical vs. Stochastic Visual Search Models

Andrew Elias11 Aug 2011NRL Code 7440 branch meeting

How a Target’s Effect on the Eye Varies with Distance

&

Eye TrackingFixations: eye stops (avg. 400 ms)

Saccades: jerky eye movements, no data intake

The Experiment

30 FA18 pilots

72 images24 distinct mapsWith/without targetssorted:

3 clutter levelsHigh/low local clutter near target

Target:

Cyclical vs. Stochastic Visual Search Models

Cyclical: avoid re-examining things

Stochastic: don’t worry about re-examining things

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

…now choose parameters to make Cyclical model fit the best

Conclusion

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

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.

How a Target’s Effect on the Eye Varies With

Distance

Purpose: fixation “size”

small? large? fuzzy?

How long are saccades?

At what distance does saccade length change?

120 px

55 px

Map Edge

Successful Saccade (n.)

a saccade which ultimately brings

the subject closer to the target

Seeing or remembering

P(S

ucc

ess

)

P(S

ucc

ess

| N

ot

Seein

g)

Distance to Target (px) Distance to Target (px)

Distance to Target (px)

P(S

eein

g)

“Bayesian” derivation of P(Seeing)

Successful Run-of-Saccades

L: the number of saccades into the future that we observe

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.

a visualization:

44 (±3) px/deg

Further Research

• Remove all after-target fixations and see if 2nd bump disappears, and if 1st bump gets clearer

• Blend distributions to model the control-group function

Acknowledgments

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

somebody ask a question!

The end

Some initial readings…