palmer (after broadbent)
DESCRIPTION
Palmer (after Broadbent). Relevant size. 2. 8. cue 250 ms. interval 750 ms. test 100 ms. (Palmer, after Shaw). Model based on SDT. Processing before decision is assumed to be independent for each stimulus and may or may not be task-specific - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/1.jpg)
![Page 2: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/2.jpg)
![Page 3: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/3.jpg)
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
Palmer (after Broadbent)
![Page 4: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/4.jpg)
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
interval750 ms
test100 ms
cue250 ms
Relevant size
2 8
(Palmer, after Shaw)
![Page 5: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/5.jpg)
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
![Page 6: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/6.jpg)
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
![Page 7: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/7.jpg)
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).
![Page 8: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/8.jpg)
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
![Page 9: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/9.jpg)
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)
![Page 10: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/10.jpg)
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
![Page 11: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/11.jpg)
Easy search Hard search
![Page 12: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/12.jpg)
![Page 13: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/13.jpg)
Wolfe, J. M. (1998). What do 1,000,000 trials tell us about visual search? Psychological Science, 9(1), 33-39.
.
0
100
200
300
400
500
600
0 25 50 75 100 125 150
slope (msec/item)
Slope FrequencyAbout 2500 sessions x 400 trials/session
target-absent slopes
target-present slopes
![Page 14: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/14.jpg)
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
.
0%
10%
20%
30%
40%
50%
60%
0 5 10 15 20 25 30 35 40 45 50 55 60
slope (ms/item)
spatial configuration
feature
conjunction
![Page 15: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/15.jpg)
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
![Page 16: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/16.jpg)
There is a stimulusThere is a stimulus
![Page 17: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/17.jpg)
Local salience is computedLocal salience is computed
![Page 18: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/18.jpg)
locallocal differences differences create bottom-up create bottom-up
saliencesalience
![Page 19: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/19.jpg)
A limited set of coarse, categoricalA limited set of coarse, categoricalfeatures are computedfeatures are computed
““red”red”
““steep”steep”
![Page 20: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/20.jpg)
A weighted sum creates an A weighted sum creates an activation mapactivation map
Σωx
ωy
ωz
![Page 21: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/21.jpg)
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)
![Page 22: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/22.jpg)
Top-down guidance: Top-down guidance: Give weight to what you wantGive weight to what you want
Find theFind the green verticalsgreen verticals
![Page 23: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/23.jpg)
The activation map The activation map guidesguides re-entrant re-entrant attentional selection of objectsattentional selection of objects
Σωx
ωy
ωz
![Page 24: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/24.jpg)
but you do not “see” the output of but you do not “see” the output of the activation mapthe activation map
Σωx
ωy
ωz
![Page 25: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/25.jpg)
First StageFirst Stage BottleneckBottleneck
Guided Search is a two-stage modelGuided Search is a two-stage model
Second StageSecond Stage
???
??
?
?
?
Object Object RecognitionRecognition
![Page 26: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/26.jpg)
First StageFirst Stage BottleneckBottleneck
The core idea of Guided SearchThe core idea of Guided Search
Second StageSecond Stage
???
??
?
?
?
Σωx
ωy
ωz
First stage information First stage information guidesguides access to the access to the second stagesecond stage
![Page 27: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/27.jpg)
First StageFirst Stage BottleneckBottleneck Second StageSecond Stage
???
??
?
?
?
Σωx
ωy
ωz
![Page 28: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/28.jpg)
First StageFirst Stage BottleneckBottleneck
binding stagebinding stage
Second StageSecond Stage
???
??
?
?
?
Σωx
ωy
ωz
![Page 29: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/29.jpg)
A vexing problem
Find the 5Find the 5
![Page 30: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/30.jpg)
Umm…there is no 5
How do you know when to How do you know when to stop?stop?
![Page 31: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/31.jpg)
We know you are not marking every reject
How do you know when to How do you know when to stop?stop?
![Page 32: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/32.jpg)
The number marked as rejected is small (~4)
How do you know when to How do you know when to stop?stop?
![Page 33: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/33.jpg)
![Page 34: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/34.jpg)
Carrasco, Evert, Chang, & Katz ’95 (fig 1)
Orientation X color conjunction - free viewing
![Page 35: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/35.jpg)
Carrasco, Evert, Chang & Katz ’95 (fig 5)
Orientation X color conjunction - fixed viewing
![Page 36: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/36.jpg)
Carrasco, Evert, Chang, & Katz ’95 (fig 2)
![Page 37: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/37.jpg)
Carrasco, Evert, Chang, & Katz ’95 (fig 3)
Set size X Eccentricity
![Page 38: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/38.jpg)
Carrasco, Evert, Chang, & Katz ’95 (fig 4)
![Page 39: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/39.jpg)
Carrasco & Frieder ’97 (fig 1)
![Page 40: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/40.jpg)
RT
(m
sec)
% E
RR
OR
Carrasco & Frieder ’97 (fig 3)
![Page 41: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/41.jpg)
Carrasco & Frieder ’97 (fig 4)
![Page 42: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/42.jpg)
Carrasco & Frieder ’97 (fig 7)
![Page 43: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/43.jpg)
Carrasco & Frieder ’97 (fig 8)
![Page 44: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/44.jpg)
Carrasco & Yeshurun ’98 (fig 8)
![Page 45: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/45.jpg)
Carrasco & Yeshurun ‘98 (fig 9)
![Page 46: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/46.jpg)
Carrasco & Yeshurun ‘98 (fig 11)
![Page 47: Palmer (after Broadbent)](https://reader035.vdocuments.net/reader035/viewer/2022062301/56813521550346895d9c8829/html5/thumbnails/47.jpg)
Carrasco & Yeshurun ’98 (fig 12)