Searching the truth: Searching the truth: Visual search for abstract, well- Visual search for abstract, well- learned objects learned objects

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Searching the truth:

Visual search for abstract, well- Visual search for abstract, well-

learned objects learned objects

Denis Cousineau, Denis Cousineau,

Université de Montréal Université de Montréal

This talk will be available at This talk will be available at

www.mapageweb.umontreal.ca/cousined www.mapageweb.umontreal.ca/cousined

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How do we find a target?

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Visual search: a basic Visual search: a basic

proficiency…

very little understood…

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44

Two models of visual search…

 Serial search:Serial search:

 The famous 2 : 1 ratio of The famous 2 : 1 ratio of mean slopes;

mean slopes;

 Based on the MEAN Based on the MEAN response times;

response times;

Target absent

Target present Condition

1 2 3 4

Display size 320

360 400 440 480

Response time



Target absent

1 2 3 4

360 400 440 480

Response time

   



  

 Parallel searchParallel search

 Flat performance.Flat performance.

 Unlimited capacityUnlimited capacity

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55

Some problems with these Some problems with these

models…

 This dichotomy difficult This dichotomy difficult to conciliate with

to conciliate with

progressive transitions progressive transitions

 Mean performances are little diagnosticMean performances are little diagnostic

 Mimicking (Townsend, 1990)Mimicking (Townsend, 1990)

 Standard deviations can also be mimicked…Standard deviations can also be mimicked…

 2:1 ratio depends heavily on the stopping rule2:1 ratio depends heavily on the stopping rule

 How do we stop searching?How do we stop searching?

5 10 15 20

session 250

500 750 1000 1250

Response time



 

           

Target absent

1 2 3 4

360 400 440 480

Response time



Target absent

1 2 3 4

360 400 440 480

Response time

 

Target absent

1 2 3 4

360 400 440 480

Response time



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66

Standard model:

Serial Self-Terminating Search Serial Self-Terminating Search

(SSTS) (SSTS)

Get ready

Implicitly: a Random-Order visual search model

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Experiment 1 Experiment 1

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Methodology:

Visual search task Visual search task

 34 34 sessionssessions of training; 10 sessions of test, of training; 10 sessions of test, 4 subjects, consistent mapping:

4 subjects, consistent mapping:

 Targets:Targets: Distractors:Distractors:

 Targets had to be learned; Targets had to be learned;

*

Fixation point

Test display

Reaction time measured since stimulus presentation Circles indicating

where the stimuli will appear

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99

Mean results Mean results

 A seems to be perfectly serial; B is the least A seems to be perfectly serial; B is the least

“serial”

 Yet, we will see thatYet, we will see that

 B is nearly identical to AB is nearly identical to A

 None of them are random-order serialNone of them are random-order serial

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Results of Target-present RT Results of Target-present RT

distributions distributions

 A and B are the most similar!A and B are the most similar!

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1111

Modeling the modes of the Modeling the modes of the

distributions distributions

 The D =1 condition could be modeled with a The D =1 condition could be modeled with a normal distribution with parameters ; normal distribution with parameters ;

 The D = 2 condition should be the same as the The D = 2 condition should be the same as the D = 1 condition except shifted by and variance D = 1 condition except shifted by and variance

doubled;

 In general, the distributions have parametersIn general, the distributions have parameters

 The modes are pooled: a “mixture of distribution”The modes are pooled: a “mixture of distribution”

-With parameter

-With parameter according to SSTS according to SSTS -With free mixture parameter

-With free mixture parameter  unrestricted model unrestricted model

¹ ;¾

²

¹ ;¾

²

¿¿

¹ +(D ¡ 1)¿;D¾

²

¹ +(D ¡ 1)¿;D¾

²

p₁ = p₂ = ¢¢¢= p_D = 1=D p₁ = p₂ = ¢¢¢= p_D = 1=D

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Results of Target-present RT Results of Target-present RT

distributions distributions

 For all participants, the mixture parameters For all participants, the mixture parameters are not equal to 1/D.

are not equal to 1/D.

 The last mode is underrepresented. Errors?The last mode is underrepresented. Errors?

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Results of Target-

Results of Target- absent absent RT RT distributions

distributions

 B perform early terminationB perform early termination

 A does not, yet her ps are not equal!A does not, yet her ps are not equal!

 C does this too often compared to his error rateC does this too often compared to his error rate

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In sum In sum

1.1. Regarding the exhaustivity prediction:Regarding the exhaustivity prediction:

 The participants sometimes stop earlier than predicted The participants sometimes stop earlier than predicted by an exhaustive search

by an exhaustive search

 This predicts errors, but too many errors are predicted.This predicts errors, but too many errors are predicted.

 Regarding the random-order prediction:Regarding the random-order prediction:

 The participants are serial…The participants are serial…

 ……but they are not randombut they are not random

 Seriality is one process going on, but there must be a Seriality is one process going on, but there must be a second process which aims at biasing the search

second process which aims at biasing the search

itinerary so that targets will be visited earlier than by itinerary so that targets will be visited earlier than by chance.

chance.

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A new model of visual search:

m-Sr-STS m-Sr-STS

 The The MostlyMostly Serial, Serial, RoughlyRoughly Self-Terminating Search Self-Terminating Search

Fixate

Is it a target

?

LTM or STM

« Yes »

Another location &

won’t give up

?

Memory for location

« No »

Pre attentive module

?

no

yes

no

¼

yes

 Essentially a two-stage model (Chun & Wolfe, 1996, Essentially a two-stage model (Chun & Wolfe, 1996, Wolfe, 1994, Cousineau & Larochelle, 2004).

Wolfe, 1994, Cousineau & Larochelle, 2004).

The pre-attentive The pre-attentive module outputs module outputs probabilities probabilities

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Yet, there is still some magic Yet, there is still some magic

left…

 Unbeknownst to the participantsUnbeknownst to the participants

 was diagnostic:was diagnostic: was irrelevant:was irrelevant:

 The pre-attentive module could drive attention The pre-attentive module could drive attention on the stimuli having those conjunctions of

on the stimuli having those conjunctions of features

features

 A parallel search for conjunctionsA parallel search for conjunctions

 It should be an impossible feat according to It should be an impossible feat according to

Treisman (1980), Wolfe (1994) and many others.

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Let’s concentrate on the Let’s concentrate on the

decision mechanism decision mechanism

 The The MostlyMostly Serial, Serial, RoughlyRoughly Self-Terminating Search Self-Terminating Search

Fixate

?

LTM or STM

« Yes »

Another location &

won’t give up

?

Memory for location

« No »

?

no

yes

no

¼

yes

The pre-attentive The pre-attentive module outputs module outputs probabilities probabilities

 What is “Recognizing a target”?What is “Recognizing a target”?

 How does cycling occurs?How does cycling occurs?

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Experiment 2 Experiment 2

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Methodology:

Same-different task Same-different task

 Well-trained participants (10 hours to reach Well-trained participants (10 hours to reach asymptote then 5 hours of testing).

asymptote then 5 hours of testing).

 The display size D is fixed at 1;The display size D is fixed at 1;

 The stimuli are varying in complexity C, e.g. The stimuli are varying in complexity C, e.g.

* Fixation point

Test display Target to be

memorized

*

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2020

Mean “Same” response times Mean “Same” response times

 Saying “Same”Saying “Same”

 is very fast is very fast

 affected by C (20 ms/spike)affected by C (20 ms/spike)

 Linearity is not found using characters Linearity is not found using characters instead of complex stimuli

instead of complex stimuli

 Parallel, limited-capacity models complies Parallel, limited-capacity models complies with such results

with such results

 e.g. a template matching process?e.g. a template matching process?

Same

Condition

1 2 3 4

Complexity C 280

300 320 340 360 380 400

Response times



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Mean “Different” response Mean “Different” response

times times

 A main effect of the A main effect of the number of differences number of differences

but no effect of complexity!

 Suggests that responding “Different” Suggests that responding “Different”

requires the localization of at least one requires the localization of at least one

difference.

 Parallel search for a difference benefits from the Parallel search for a difference benefits from the presence of many differences

presence of many differences

1

2

3

4

Nber of differences

1 2 3 4

Complexity C 280

300 320 340 360 380 400

Response times



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The Revised possible The Revised possible

explanation explanation

 There might be two distinct processes:There might be two distinct processes:

 one for confirming the sameness, one for confirming the sameness,

 one for establishing the “differenceness”one for establishing the “differenceness”

 How do they relate to one another? In succession?How do they relate to one another? In succession?

Fixate « Yes »

« No »

Pre attentive module no

yes

¼

Has located a difference

? STM

?

STM

not yet

yes

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Slow “Same” vs. fast “Different”

in the in the

C = 4 condition C = 4 condition

 The two conditions are very close (mean The two conditions are very close (mean

difference of 13 ms). Do they follow in time?

 Again, let’s look at distributionsAgain, let’s look at distributions

0

1

2

3

4

Nber of differences

1 2 3 4

Complexity C

280 300 320 340 360 380 400

Response times



Same Different

1 2 3 4

Complexity C



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Distributions of RT in Same and Distributions of RT in Same and

(very) Different responses at C = (very) Different responses at C = 4 4

 The slow “Different” responses are faster The slow “Different” responses are faster (by 4 ms) than the slow “Same” responses.

(by 4 ms) than the slow “Same” responses.

  One process cannot operate *after* the One process cannot operate *after* the other.

other.

300 400 500 600 Response times 0

25 50 75 100

Effectif

300 330 360 390 420 diff = 4

270 300 330 360 390 420

diff = 0



diff = 0 = -104.65 + 1.25 * diff4 R-Deux = 1.00

Same

300 400 500 600 Response times 0

50 100 150

Effectif

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Revised revised-architecture Revised revised-architecture

 ““No” may not be an option for a neural No” may not be an option for a neural decision mechanism…

decision mechanism…

Fixate « Yes »

« No »

yes

¼

Has located a difference

? STM

?

STM

not yet not

sure yes

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In conclusion…

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Visual search is a proficiency Visual search is a proficiency

(1/2) (1/2)

Proficiencies are an amalgam of Proficiencies are an amalgam of

processes

^ Parallel pre attention process outputs Parallel pre attention process outputs probabilities

probabilities

 Serial deployment of central attentionSerial deployment of central attention

 Stopping rule which can end prematurelyStopping rule which can end prematurely

 Unitary (template matching?) recognition Unitary (template matching?) recognition process

process

 Unitary (find-a-difference) rejection processUnitary (find-a-difference) rejection process

 In sum, the SSTS architecture was all wrong.In sum, the SSTS architecture was all wrong.

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Visual search is a proficiency Visual search is a proficiency

(1/2) (1/2)

Processes are

Processes are univoque univoque

(from french: One and only one meaning, one and only one semantic content, but also one and only (from french: One and only one meaning, one and only one semantic content, but also one and only one voice)

one voice) As an exampleAs an example

 If a “not-face” is presented to a face recognition If a “not-face” is presented to a face recognition

module, does it “knows” that it is not a face, or does it module, does it “knows” that it is not a face, or does it remains “silent” by omitting to respond…

remains “silent” by omitting to respond…

 What would be a brain which detects objects (of many What would be a brain which detects objects (of many kind) and their negation? what would be the EEG of kind) and their negation? what would be the EEG of such a system?

such a system?

 Negation is not part of the neural process toolboxNegation is not part of the neural process toolbox

 it is not “To be or not to be” but “To be and to un-be”it is not “To be or not to be” but “To be and to un-be”

 ““NO” branches should be forbidden in psychology.NO” branches should be forbidden in psychology.

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Methodological consideration Methodological consideration

 Distribution analyses rocks!Distribution analyses rocks!

 Mean results can be interpreted in so many ways Mean results can be interpreted in so many ways that they cannot reject any model at all.

that they cannot reject any model at all.

 We have been stuck with a fruitless dichotomy We have been stuck with a fruitless dichotomy for over 40 years because we were unable to for over 40 years because we were unable to

make the data speak.

 Anyone with a serious model should implement it Anyone with a serious model should implement it using distributions or remain quiet

using distributions or remain quiet

 Distribution modeling and testing is not difficult Distribution modeling and testing is not difficult (it can be learned in 3 hours).

(it can be learned in 3 hours).

 as long as you know matlab or mathematica…as long as you know matlab or mathematica…

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Thank you.

This talk will be available at This talk will be available at www.mapageweb.umontreal.ca/cousined www.mapageweb.umontreal.ca/cousined