Aave Hannus B

Frans W. Cornelissen B

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Simultaneous colour search renders other object
features less salient
Ronald van den Berga,c, Frans W. Cornelissenb,c, Aave Hannusb,c, Jos B.T.M. Roerdinka,c
aInstitute for Mathematics and Computing Science
bLaboratory for Experimental Ophthalmology
cSchool of Behavioral and Cognitive Neurosciences
University of Groningen, The Netherlands
r.van.den.berg@rug.nl
INTRODUCTION
BACKGROUND
.Common approach to visualise multidi-
mensional data sets:
–map data dimensions to separate vi-
sual features (e.g., [5, 6, 8]).
–assumption: features can be judged
independently of each other.
.However, dependencies found in
processing of visual features [3, 7]
.Colour tends to dominate in visual con-
junction search, as found in two of our
previous studies [4, 9]:
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COLOUR ORIENTATION
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Single feature search
Conjunction search
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COLOUR SIZE
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Figure 1: Colour in conjunction search reduces -
performance for orientation (left) and size (right)
-task: conjunction search with perceptually
matched contrasts (BOX 1)
-result: simultaneous colour search results in
declined performance for orientation and size
.Existence of a feature hierarchy?
.If so, is it a ﬁxed hierarchy?
BOX 1. CONTRAST MATCHING
Previous studies
Subjects searched for a target among 12 distractors
(stimulus time: 200ms). This task was carried out
with 10 different target-distractor contrasts for each
feature, providing a sigmoidal contrast-performance
curve. Using function ﬁts, we determined perceptually
matched colour, orientation, and size contrasts.
Current study
Perceptually similar colour and size contrasts for
the current experiment were determined by taking
the difference between the 70% and 50% correct
perfomance contrasts from the previous studies:
∆c = colour contrast70% − colour contrast50%
∆s = size contrast70% − size contrast50%
0 1 2 3 4 5 6 7 8 9 10
10%
30%
50%
70%
90%
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log of contrast
D
Search performance as function of contrast −− example
PRESENT STUDY
.Assess existence of colour/size
processing asymmetry in a simpliﬁed
visualization environment
.Assess relationship between feature
contrast and performance
.Approach: track subjects’ eye move-
ments during complex conjunction
search tasks with varying feature con-
trasts
METHODS & MATERIALS
Subjects Five volunteers with normal
or corrected-to-normal vision
Apparatus P4 3.2Ghz PC, LaCie 22”,
Matlab 6.5, Psychophysics Toolbox [1],
Eyelink Toolbox [2], Eyelink II
Figure 4: Experiment stimulus
Stimuli Cue followed by node-link di-
agram. All items were randomly as-
signed one of 10 possible colours and
sizes.
Task Find the cued item
Colours & sizes The distance be-
tween every two consecutive colours
was ∆c × C and the distance between
every two consecutive sizes was ∆s×S,
where:
∆c, ∆s = default colour and size step
C = colour step multiplication factor
S = size step multiplication factor
Procedure
1. Select perceptually similar ∆c and ∆s
(see BOX 1)
Track eye movements during:
2. Single feature search (SFS), with
C = S = 1
3. Conjunction search (CS) tasks, with
C = 1,S = 1
C = 1,S = 1.5
C = 1,S = 2
C = 1
2,S = 1
RESULTS & DISCUSSION
(Errors are computed as the number of colour/size steps
between the colour/size of the target and that of the ﬁxated
node)
S=1 1.5 2 1 1.5 2 C=1 0.5 1 0.5
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Condition
size contrast
manipulation (C=1)
color contrast
manipulation (S=1)
Effects of contrast manipulation on error
Color
Size
Figure 5: Mean error for different size (left) and different
color (right) contrasts.
.Size contrast affects size error, but not
color error
.Color contrast affects color error, but
not size error
0 500 1000 1500 2000 2500 3000
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Linear fits of error time courses (N=4)
Single Feature Search
Color
Size
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Conjunction Search
time (ms)
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Figure 6: Time courses of colour and size error in single
feature (top) and conjunction search (bottom), with C=S=1.
While error is nearly the same in SFS, there is a tendency
towards colour in CS.
.Earlier found tendency towards colour
dominance in conjunction search also
exists in a more complex search task
.It can be compensated for by manipu-
lating feature contrasts
.Hence, feature hierarchies are not ﬁxed
and may be adapted to the require-
ments of a particular visualisation
REFERENCES
[1] D .H. Brainard. The Psychophysics Toolbox. Spatial Vision, 10:433–436, 1997.
[2] F .W. Cornelissen, E. Peters, J. Palmer. The eyelink toolbox: Eye tracking with MATLAB and the Psyschophysics
toolbox. Behavior Research Methods, Instruments & Computers, 34:613–617, 2000.
[3] K. R. Gegenfurtner. Cortical mechanisms of colour vision. Nat Rev Neurosci, 4(7):562–72, 2003.
[4] A. Hannus, H. Bekkering, E. Drost, R. Bontjer, and F. W. Cornelissen. Feature processing asymmetry in a colour and
orientation conjunction-search task. Perception (suppl), 33:13, 2004.
[5] C .G. Healey, R .St. Amant, and J. Chang. Assisted Visualization of E-Commerce Auction Agents. Proceedings
Graphics Interface 2001, 201–208, 2001.
[6] C .G. Healey, J .T. Enns, L .G. Tateosian, and M. Remple. Perceptually-Based Brush Strokes for Nonphotorealistic
Visualization. ACM Transactions on Graphics, 23(1):64–96
[7] Z. Li. A saliency map in primary visual cortex. TRENDS in Cognitive Sciences, 6(1):9–16, 2002
[8] R .C. Tam, C .G. Healey, B. Flak, and P. Cahoon. Volume Rendering of Abdominal Aortic Aneurysms. Proceedings
IEEE Visualization ’97, 43–50, 1997.
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APGV 2005, Second Symposium on Applied Perception in Graphics and Visualization, 26-28 August 2005, A Coru˜ na, Spain

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