QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Palmer (after Broadbent)

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

interval750 ms

test100 ms

cue250 ms

Relevant size

2 8

(Palmer, after Shaw)

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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.

.

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

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

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

ωy

ωz

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

ωy

ωz

but you do not “see” the output of but you do not “see” the output of the activation mapthe activation map

Σωx

ωy

ωz

First StageFirst Stage BottleneckBottleneck

Guided Search is a two-stage modelGuided Search is a two-stage model

Second StageSecond Stage

???

??

?

?

?

Object Object RecognitionRecognition

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

First StageFirst Stage BottleneckBottleneck Second StageSecond Stage

???

??

?

?

?

Σωx

ωy

ωz

First StageFirst Stage BottleneckBottleneck

binding stagebinding stage

Second StageSecond Stage

???

??

?

?

?

Σωx

ωy

ωz

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)