Palmer (after Broadbent)
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Palmer (after Broadbent)
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interval750 ms
test100 ms
cue250 ms
Relevant size
2 8
(Palmer, after Shaw)
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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
Model based on SDT
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
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)
Max rule
Easy search: tilt among vertical Hard search: tilt (45) among tilted (22)
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
The maximum rule
Easy search Hard search
Wolfe, J. M. (1998). What do 1,000,000 trials tell us about visual search? Psychological Science, 9(1), 33-39.
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0 25 50 75 100 125 150
slope (msec/item)
Slope FrequencyAbout 2500 sessions x 400 trials/session
target-absent slopes
target-present slopes
Different tasks yield different Different tasks yield different slopesslopes
But slope is not a simple diagnostic for typeBut slope is not a simple diagnostic for type
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10%
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0 5 10 15 20 25 30 35 40 45 50 55 60
slope (ms/item)
spatial configuration
feature
conjunction
There is a continuum of There is a continuum of searchessearches
Set Size
slopes = ~0 msec/item
Set Size
40-60 msec/itemTarget absent
20-30 msec/itemTarget present
Set Size
10-20 msec/itemTarget absent
5-10 msec/itemTarget present
There is a stimulusThere is a stimulus
Local salience is computedLocal salience is computed
locallocal differences differences create bottom-up create bottom-up
saliencesalience
A limited set of coarse, categoricalA limited set of coarse, categoricalfeatures are computedfeatures are computed
““red”red”
““steep”steep”
A weighted sum creates an A weighted sum creates an activation mapactivation map
Σωx
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The activation map: local salience is weighted The activation map: local salience is weighted heavily and will attract attention (bottom-up)heavily and will attract attention (bottom-up)
Top-down guidance: Top-down guidance: Give weight to what you wantGive weight to what you want
Find theFind the green verticalsgreen verticals
The activation map The activation map guidesguides re-entrant re-entrant attentional selection of objectsattentional selection of objects
Σωx
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but you do not “see” the output of but you do not “see” the output of the activation mapthe activation map
Σωx
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First StageFirst Stage BottleneckBottleneck
Guided Search is a two-stage modelGuided Search is a two-stage model
Second StageSecond Stage
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Object Object RecognitionRecognition
First StageFirst Stage BottleneckBottleneck
The core idea of Guided SearchThe core idea of Guided Search
Second StageSecond Stage
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Σωx
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First stage information First stage information guidesguides access to the access to the second stagesecond stage
First StageFirst Stage BottleneckBottleneck Second StageSecond Stage
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Σωx
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First StageFirst Stage BottleneckBottleneck
binding stagebinding stage
Second StageSecond Stage
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Σωx
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A vexing problem
Find the 5Find the 5
Umm…there is no 5
How do you know when to How do you know when to stop?stop?
We know you are not marking every reject
How do you know when to How do you know when to stop?stop?
The number marked as rejected is small (~4)
How do you know when to How do you know when to stop?stop?
Carrasco, Evert, Chang, & Katz ’95 (fig 1)
Orientation X color conjunction - free viewing
Carrasco, Evert, Chang & Katz ’95 (fig 5)
Orientation X color conjunction - fixed viewing
Carrasco, Evert, Chang, & Katz ’95 (fig 2)
Carrasco, Evert, Chang, & Katz ’95 (fig 3)
Set size X Eccentricity
Carrasco, Evert, Chang, & Katz ’95 (fig 4)
Carrasco & Frieder ’97 (fig 1)
RT
(m
sec)
% E
RR
OR
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)