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
– Minimum time to initiate eye movement
• Perception in this time frame involves only
information available in a single glance
= 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
• 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
Detecting the Red Object
preattentively
Detecting the Circle
preattentively
Conjunctive Target composed of
multiple features not detectable
preattentively
Preattentive Features
orientation
Julesz & Bergen [1983]; Wolfe et al. [1992]
length
width
size
curvature
number
terminators
intersection
closure
Triesman & Gormican [1988]
Julesz [1985]
Triesman & Gelade [1980]
Triesman & Gormican [1988]
Julesz [1985]; Trick & Pylyshyn [1994]
Julesz & Bergen [1983]
Julesz & Bergen [1983]
Enns [1986]; Triesman & Souther [1985]
Nagy & Sanchez [1990]; D'Zmura [1991];
Kawai et al. [1995]; Bauer et al. [1996]
Beck [1983]; Triesman & Gormican [1988]
Julesz [1971]
Nakayama & Silverman [1986];
Driver & McLeod [1992]
Wolfe & Franzel [1988]
Nakayama & Silverman [1986]
Enns [1990]
Enns [1990]
colour (hue)
intensity
flicker
direction of motion
binocular lustre
stereoscopic depth
3-D depth cues
lighting direction
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 ???
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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?
• 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)