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Review of: High-Speed Visual Estimation Using Preattentive Processing (Healy, Booth and Enns 1996). Gene Chipman. Preattentive Processing =. Cognitive operations performed prior to focusing attention Tasks performed on multi-element data sets Tasks performed in 200 milliseconds or less

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review of high speed visual estimation using preattentive processing healy booth and enns 1996

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

Gene Chipman

preattentive processing
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
better visualization tools
= 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
prior study in psychology
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
preattentive features

orientation

Julesz & Bergen [1983]; Wolfe et al. [1992]

length

Triesman & Gormican [1988]

width

Julesz [1985]

size

Triesman & Gelade [1980]

curvature

Triesman & Gormican [1988]

number

Julesz [1985]; Trick & Pylyshyn [1994]

terminators

Julesz & Bergen [1983]

intersection

Julesz & Bergen [1983]

closure

Enns [1986]; Triesman & Souther [1985]

colour (hue)

Nagy & Sanchez [1990]; D'Zmura [1991];Kawai et al. [1995]; Bauer et al. [1996]

intensity

Beck [1983]; Triesman & Gormican [1988]

flicker

Julesz [1971]

direction of motion

Nakayama & Silverman [1986];Driver & McLeod [1992]

binocular lustre

Wolfe & Franzel [1988]

stereoscopic depth

Nakayama & Silverman [1986]

3-D depth cues

Enns [1990]

lighting direction

Enns [1990]

Preattentive Features
issues addressed by this paper
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
salmon migration
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)
fishy experiment
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 …
data presented to users
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
three different experiments
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 ?
numerical estimation
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
display duration
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)
feature difference
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
good things
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
bad things
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
where has it gone
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)