1. iZoom: A Better Way to View Large Data Sets? Mike Ashmore CPSC 462

2. Problem: Eye Trackers Suck. Interactive applications reveal just how noisy and error-prone eye tracker data is ~1˚ tracking accuracy on a good calibration Up to 30-40 pixels error on a high- resolution display High jitter of data just makes reliable selection that much harder

3. Problem: Application Designers Suck. Teeny-tiny portions of the screen need to be accurately selected.  Everything in WinAmp / XMMS: 6-8 pixels  Microsoft Word ruler bar (tabs, etc.): 2 pixels  Image editing applications / WYSIWYG print layout tools: 1 pixel But they’ve been offering a magnifier tool for ages now! Perhaps there’s a lesson to be learned …

4. Solution #1: Make everything bigger so it’s easier to select (the DUPLO approach) Since it works so well for image editors, we’ll just magnify the whole screen. Big wins: Faster, less fatiguing selection of UI elements (c.f. Fitts’ Law) Lossage: Amount of information available decreases in proportion to the square of the magnification (2x magnification = 1/4 as much information)

5. Solution #2: Let the display slide around on a virtual desktop Big wins: You get unlimited desktop real estate. Icons can be as chunky as you want for fast selection of targets within display. Lossage: Very little context available for data. Again, 2x magnification of the virtual desktop leaves 3/4 of the desktop non- visible at all times.

6. iZoom Solution: Gaze- Contingent Fisheye Displays Big win: A portion of the screen is magnified, but context is still available in the periphery. Best of both worlds! Possible Problem: Can people use it without getting motion sickness? Other Possible Problem: Curiously, nobody else seems to have published much on this idea. Maybe it hasn’t worked for anybody else, either.

8. The Experiment Simple selection task: look at the window with the “X” Three conditions:  Non-fisheye (the control condition)  Naïve fisheye Always-on version of lens  “Smart” fisheye Lens only appears after detecting a certain “intentness” of fixation

10. Challenges Bad Calibration = Frustrated test subject  A Bug! Tobii never reports more than 200 calibration data points. Are calibrations being truncated?  Solution: More sophisticated calibration routine  Don’t take calibration samples until we’re fairly certain subject is fixating in the right spot  Redo calibration points that deviate too far from actual screen coordinates. Be fascist about it until every single point is near perfect

11. Challenges The enemy of fisheye performance: the Gutwin effect (aka the oscillating lens of doom). Cause: Non-intuitive mapping from control to display  Look one inch to the left, expect to see the lens move exactly one inch to the left in “data space”  Particularly troublesome because display and control are so confounded together

12. The Gutwin Effect Techniques to address this issue:  Remap control space to match distorted visual field  Don’t distort visual field at all (Miniotas / 2004: expanding target zones)  Grab and hold fisheye at position of initial fixation

13. Data Chart

14. Data Analysis Use of postgreSQL for outlier analysis Outlier criterion:    Future work (this evening, probably): Integrate SQL and R mathematical analysis package for instant build of results

15. Future Work More sophisticated data analysis. This is clearly a non-normal distribution. What is it, then? Gamma? Beta? Select-and-hold fisheye Center fixation at start of trials Counting task - when selection is not an issue, does fisheye improve performance (accuracy / speed) on detailed inspection tasks?