QuickTi me™ and a
TIFF (U ncompressed) decompressor
are needed to see this pi cture.
Palmer (after Broadbent)
Relevant size
2
8
cue
250 ms
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
interval
750 ms
test
100 ms
(Palmer, after Shaw)
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Model based on SDT
Processing before decision is assumed to be independent for each
stimulus and may or may not be task-specific
Set size effect can be calculated using the decision integration model
based on SDT (Shaw)
1)
The internal representation of each stimulus is independent of set size
2)
The stimulus representation is noisy; both target and distracters -->
the more distracters in a display, the greater the chance that the
brightness of one will fall in the target range
Set size effect can be calculated using the
decision integration model based on SDT (Shaw)
3) The decision is determined by the stimulus representation that
yields the maximum likelihood (max rule) -- stimulus with the
maximum value on any given trial
4) Mean value of distracter’s representation is zero, and its variability
is 1

The effect of increasing set size is to shift the distribution of the
maximum stimulus representation generated by the set of
distracters (determined by whichever distracter happens to
generate the highest value).
SDT assumes that the vertical distracters generate a smaller
response from the filters selective to the tilted target
Discriminating target from distractor:

both the mean separation between target and distractors and
the intrinsic variablity of these representations determine how
discriminable the target is from the distractors
 for a given orientation difference between target and distractor,
as distributions variance increases, discriminability decreases
Max rule
Easy search: tilt among vertical
Hard search: tilt (45) among tilted (22)
Response strength
 p (c) depends on the overlap of both distributions
 response to the 45 target is in the same location (~9); response to the tilted
distractor is shifted rightward (~4 to ~7)
The maximum rule
Set Size >1
 for finding a single target, a decision based on choosing the largest response
across the units is close to the best use of the available information, provided that
the responses for each of the units is independent
• noise interval (distracters only)
• signal interval (n-1 distracters & target)
• the observer looks for the largest value of the samples in each presentation and
then chooses the presentation interval that has the larger of the two maximum
values
 the greater the set size, the higher the probability that the maximum
emerges from the noise interval
Easy search
Hard search
.
Slope Frequency
About 2500 sessions x 400 trials/session
600
500
target-present slopes
target-absent slopes
400
300
#
o
f
in
st
a
n
ce
s
200
100
0
0
25
50
75
100
125
150
slope (msec/item)
Wolfe, J. M. (1998). What do 1,000,000 trials tell us about visual search?
Psychological Science, 9(1), 33-39.
Different tasks yield different slopes
60%
conjunction
feature
spatial configuration
50%
40%
30%
p
ro
p
or
ti
on
20%
10%
0%
0
5
10
15
20
25
30
35
40
45
50
55
60
slope (ms/item)
But slope is not a simple diagnostic for type
There is a continuum of searches
There is a stimulus
Local salience is computed
local differences
create bottom-up
salience
“red”
“steep”
A limited set of coarse, categorical
features are computed
A weighted sum creates an
activation map
The activation map: local salience is weighted
heavily and will attract attention (bottom-up)
Top-down guidance:
Give weight to what you want
Find the green verticals
The activation map guides re-entrant
attentional selection of objects
but you do not “see” the output of
the activation map
Guided Search is a two-stage model
Object
Recognition
?
?
?
?
?
?
First Stage
Bottleneck
?
?
Second Stage
FirstThe
stage
information
guidesSearch
access to the
core
idea of Guided
second stage
?
?
?
?
?
?
First Stage
Bottleneck
?
?
Second Stage
?
?
?
?
?
?
First Stage
Bottleneck
?
?
Second Stage
binding stage
?
?
?
?
?
?
First Stage
Bottleneck
?
?
Second Stage
A vexing problem
Find the 5
Umm…there is no 5
How do you know when to stop?
We know you are not
marking every reject
How do you know when to stop?
The number marked as
rejected is small (~4)
How do you know when to stop?
Orientation X color conjunction - free viewing
Carrasco, Evert, Chang, & Katz ’95 (fig 1)
Orientation X color conjunction - fixed viewing
Carrasco, Evert, Chang & Katz ’95 (fig 5)
Carrasco, Evert, Chang, & Katz ’95 (fig 2)
Set size X Eccentricity
Carrasco, Evert, Chang, & Katz ’95 (fig 3)
Carrasco, Evert, Chang, & Katz ’95 (fig 4)
Carrasco & Frieder ’97 (fig 1)
RT (msec)
% ERROR
Carrasco & Frieder ’97 (fig 3)
Carrasco & Frieder ’97 (fig 4)
Carrasco & Frieder ’97 (fig 7)
Carrasco & Frieder ’97 (fig 8)
Carrasco & Yeshurun ’98 (fig 8)
Carrasco & Yeshurun ‘98 (fig 9)
Carrasco & Yeshurun ‘98 (fig 11)
Carrasco & Yeshurun ’98 (fig 12)