1. Review of: High-Speed Visual Estimation Using Preattentive Processing (Healy, Booth and Enns 1996) Gene Chipman

2. Preattentive Processing = Cognitive operations performed prior to focusing attention Tasks performed on multi-element data sets Tasks performed in 200 milliseconds or less –Minimum time to initiate eye movement Perception in this time frame involves only information available in a single glance

3. = Better Visualization Tools Geared toward general issue of formulating guidelines for designing visual presentation techniques Poor assignment of features to data dimensions can interfere with viewer’s ability to extract information Properly designed tools allow users to perform visual analysis rapidly and accurately

4. Prior Study in Psychology Gibson, LaBerge, Schneider and Shiffrin, and Logan formally define automacity Treisman et al. (1992) notes differences in preattentive processing –Governed by innate mechanisms (not trained) –Did experiments in target and boundary detection Other research by Julesz, Duncan and Humphreys, and Wolfe

5. Detecting the Red Object preattentively

6. Detecting the Circle preattentively

7. Conjunctive Target composed of multiple features not detectable preattentively

8. Preattentive Features orientationJulesz & Bergen [1983]; Wolfe et al. [1992] lengthTriesman & Gormican [1988] widthJulesz [1985] sizeTriesman & Gelade [1980] curvatureTriesman & Gormican [1988] numberJulesz [1985]; Trick & Pylyshyn [1994] terminatorsJulesz & Bergen [1983] intersectionJulesz & Bergen [1983] closure Enns [1986]; Triesman & Souther [1985 ] colour (hue) Nagy & Sanchez [1990]; D'Zmura [1991]; Kawai et al. [1995]; Bauer et al. [1996] intensityBeck [1983]; Triesman & Gormican [1988] flickerJulesz [1971] direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992] binocular lustreWolfe & Franzel [1988] stereoscopic depthNakayama & Silverman [1986] 3-D depth cuesEnns [1990] lighting directionEnns [1990]

9. Issues Addressed by this paper Can Preattentive processing be extended to rapid and accurate numerical estimation How do changes in display duration and degree of feature difference influence preattentive processing Can preattenvie processing be applied to real world tasks

10. Salmon Migration ??? A sentence I never expected to read in HCI –“Salmon are a well-known fish that are found, among other areas, on the western coast of Canada.” Gave a real world task for this study, the migration return of salmon for ocean to their birth river. Added a complication to investigating the real issue –Required data manipulation –Added factors that are not clear (variation in spatial distribution)

11. Fishy Experiment Rectangles placed in space based on fish starting location Features changed are color and orientation –Color was red or blue –Orientation was vertical or 60 degrees –Feature differences are relatively equal perceptually Two data aspects were migration direction (north or south) and stream function (high or low) –Data aspects had different spatial distributions A feature change is mapped to each data aspect Users were NOT informed this was fish data –A real world application but …

12. Data Presented to Users Users asked to estimate the percentage of rectangles with a given feature, to the nearest 10% Constant trials had relevant data mapped to one feature (color or orientation) Variable trials also had irrelevant data mapped to the other feature to investigate interference

13. Three different experiments Numerical Estimation –Can users do numerical estimation preattentively ? Display Duration –At what duration can users no longer do numerical estiamtion ? Feature Difference –How much feature difference is necessary ?

14. Numerical Estimation Mean Error was affected by interval being estimated –Middle values (around 50%) were less accurate Visual feature did not matter –Color and Orientation were the same –Constant and Variable trials were the same Spatial distribution affected accuracy –Users more accurate for stream function which was more distributed spatially

15. Display Duration Trials were displayed with random durations –15, 45, 105, 195 and 450 milliseconds Estimation accuracy was stable for 105 milliseconds and higher Feature interference (Constant vs. Variable) not dependent on duration Interesting to note knee in curve at 100 mSec –Psychological Moment defined as about 0.1 sec (Blumenthal, 1977; Card, Moran, and Newell, 1983)

16. Feature Difference Three different data mapping conditions –Small: 0 and 5 degrees and two shades of red –Medium: 0 and 15 degrees, red and purple –Large: 0 and 60 degrees, red and blue Mapping condition for other two experiments Subjects were accurate (avg. error < 10%) for Large difference at 45 and 195 mSec and for Medium difference at 195 mSec No evidence of feature interference

17. Good things Shows that preattentive processing can be used for numerical estimation –Extends previous work beyond detection and boundaries Shows that mapping a second irrelevant feature does not affect accuracy Shows that color and orientation equally useful features regardless of duration and degree of difference Shows that spatial difference may have an impact

18. Bad things Uses ‘real world’ data to show laboratory results applied, but does not establish this in any formal manner Use of fish data adds complications such as issues with spatial distribution and correlations between features (data was edited to remove and suspected correlation) Random data would have been just as good

19. Where has it gone? Oriented Texture Slivers: A Technique for Local Value Estimation of Multiple Scalar Fields –Weigle, Emigh, Liu, Taylor, Enns, Healey; GI 2000 Improved Histograms for Selectivity Estimation of Range Predicates –Poosala; 1996 3D (Healy)