"Theoretical and Experimental Approaches to Auditory and Visual Attention" Cold Spring...

"Theoretical and Experimental Approaches to Auditory and Visual Attention"

Cold Spring Harbor April 20, 2008

Ervin Hafter and Anne-Marie Bonnel Department of Psychology

U.C.Berkeley

“A role for memory in Shared Attention”

Simplest definition of attention:

a process inferred when responses on one task are affected by responding simultaneously to another

S(+)

S(0)

S(+)

S( )

Detection Identification

Energy detection model: compare stimuli in the signal epochs

Two paradigms

S(0) is the level of the standard.

Are

a un

der

the

RO

C

1.00

Signal Levels in z

B

.50

.70

.60

.90

.80

1.5 2.01.00.50 2.5

A

% correct in 2A

FC

Ideal detection based on energy in the signal epochs.

Pro

babi

lity

den

sity

A

B

S(0)

S(+)

S(+)

S(0) λ

λz

Predicts d’B > d’A

But, Macmillan (w/tonal pedestal) and Bonnel et al. (w/visual pededestal) found

d’detection > d’identification

Stock Market National Debt University salaries

Audio demo comparing detection to identification.

Both attributed this to use of Transients

Thinking that, unlike the case with energy, responses to transients might be pre-attentive,

Bonnel et al. (1992) tested in a dual task with independent stimuli on side-by-side LEDs.

Thinking that, unlike the case with energy, responses to transients might be pre-attentive,

Bonnel et al. (1992) tested in a dual task with independent stimuli on side-by-side LEDs.

Detection was comparable to performance found with S instructed to attend to only one LED;

identification showed a tradeoff indicative of a shared attentional resource.

Thinking that, unlike the case with energy, responses to transients might be pre-attentive,

Bonnel et al. (1992) tested in a dual task with independent stimuli on side-by-side LEDs.

Detection was comparable to performance found with S instructed to attend to only one LED;

identification showed a tradeoff indicative of a shared attentional resource.

Caveat: “Perceptual grouping” in identification

S(0)

S(+)

S(-)

Visual Auditory

17 msCRT

500 Hz

Some changes in Berkeley

S(+)

S(0)

S(+)

S( )

Detection Identification

S( )

Ideal energy detection

Pro

babi

lity

den

sity

zλ λ

A

B

S(0)

S(+)

S(0)

S(+)

S(+)

S(0)

S(-)

0 +1-1 +2-2

λ

λ

λ

CC

1.00

Are

a un

der

the

RO

CSignal Levels in z

B

.50

.70

.60

.90

.80

1.5 2.01.00.50 2.5

A

% correct in 2A

FC

CC

Use the strictest possible criterion for asserting that there is no cost of shared attention:

compare performance in the dual-task to that found in the separate single tasks.

Support for transient hypotheses:

1) detection > identification

2) No cost in detection

3) Tradeoff in accord with instructions in identification

Attention-Operating Characteristic

d’au

diti

on

d’vision

3

2

1

3210

50%,50%

20%,80%

50%,50%

20%,80%

80%,20%

80%,20%

are single tasksand

Sharing-Index Attentional Operating Characteristic (SIAOC) normalizes data from a dual task in terms of the single-task controls.

d’dual

d’single

SI =

Sharing Index Vision)

Sha

ring

Ind

ex (

Aud

itio

n) 1.0

.8

.6

.4

.2

.2 .4 .6 .8 1.00

SIAOC

These data clearly imply that detection of transience put no demand on shared attention, unlike discrimination of energy.

SIAOC

Sha

ring

Ind

ex (

Aud

ition

)

Sharing Index (Vision)

.2 .4 .6 .8 1.0

1.0

.8

.6

.4

.2

A more direct test of the idea that detecting transience (change per se) doesn’t require shared attention simply removes transients as information.

A more direct test of the idea that detecting transience (change per se) doesn’t require shared attention simply removes transients as information.

Often called a reminder

Another classic ΔI/I

A classic ΔI/I

d’

Same signal levels

1.0

2.0

3.0

4.0

200 400 600 800 1000 1200 1400 1600 1800

Gap duration (ms)

nogap

Energy detection

Auditory single task

no gap

gap

Dashes suggest absolute identification, i.e. comparisons to long-term, context-coded memory, rather than to a sensory trace of the reminder.

1.0

2.0

3.0

4.0

200 400 600 800 1000 1200 1400 1600 1800

d’

Gap duration (ms)

Same signal levels

1.0

.8

.6

.4

.2

.2 .4 .6 .8 1.0Sharing Index (Vision)

SI-AOC

“larger” “smaller”

< 300 >

“different”

“different”“same”

represent 500-msec signals.

Test Duration {D}

Integration Time {IT}

Single taskD1 = {D}

{D} < {IT}

d’1

A or V

Impact of increased duration. e.g., , on apparent cost of attention.

Test Duration {D}

Integration Time {IT}

Single taskD1 = {D}

{D} < {IT}

d’1

Dual taskD2 = ½ {D}A

V

d’2=.707d’1

d’2=.707d’1

{D} < {IT}

A or V

. d’1

Single task D1 = {IT}

{D} > {IT}

Test Duration {D}

Integration Time {IT}

A or V

. d’1

Single task D1 = {IT}

{D} > {IT}

Test Duration {D}

Integration Time {IT}

.707d’1< d’2 ≤ d’1

.707d’1< d’2 ≤ d’1 Dual task ½{D} < {D2} < {IT}

{D} > {IT}

A or V

. d’1

Single task D1 = {IT}

{D} > {2IT}

Test Duration {D}

Integration Time {IT}

A or V

. d’1

Single task D1 = {IT}

{D} > {2IT}

Test Duration {D}

Integration Time {IT}

d’2

Dual task {D2} = {IT}

{D} > {IT}

d’2

*Here, time sharing produces the same result as no sharing.

A or V

A still stronger test forces the subject to use context-coded memory by simply removing the reminder.

“other”

“other”“standard”

“large” “small”

It seems clear that responses based on context-coded memory were limited by sharing of an attentional resource.

.2 .4 .6 .8 1.0

1.0

.8

.6

.4

.2

Sharing Index (Vision)

Sha

ring

Ind

ex (

Aud

ition

)

Perhaps the reason that use of change per se did not provoke a cost of sharing is that it was done in sensory trace (?rehearsal?) memory?

Subjects can be forced to use sensory-trace memory by roving the standard from trial to trial.

Standard Test

‘louder’

‘louder’

‘softer’

‘softer’

Rove

1

3

2

4

TrialCorrectresponse

da

Unlike the case with context-coding, performance fell as the ephemeral sensory-trace faded over time.

Roved levelsFixed levels

Auditory

Identification

0.5

1.0

1.5

2.0

2.5

3.0

GAP Duration (sec)

1 2 3 4 5 6 7 8

50-ms reminders and signals

*Need higher signals in roving

Most intriguing is that despite very poor performance, especially with long delays, there was no cost of sharing.

1.0

.8

.6

.4

.2

.2 .4 .6 .8 1.0Sharing Index (Vision)

Sha

ring

Ind

ex (

Aud

ition

)

8350

Gap (ms)

1022510310

<Gap (ms)>

rove

loud, dim, green, hot, salty, etc.

CompareCompare

Context-coded, long-term memoryExperience

In typical, everyday life, we label sensory stimuli on the basis of comparisons to long-term memory.

TestSensory/ Neural

CompareCompare

Context-coded, long-term memoryExperience

Sensory/ Neural

T1 T2SOA

Reminder

When presented with an adjacent standard, the response may be to

TestSensory/ Neural

loud, dim, green, hot, salty, etc.

CompareCompare

Context-coded, long-term memoryExperience

TestSensory/ Neural

ReminderSensory/ Neural

When presented with an adjacent standard, the response may be to simply ignore it, labeling the test in accord with long term memory.

T1 T2SOA loud,

dim, green, hot, salty, etc.

CompareCompare

Context-coded, long-term memoryExperience

TestSensory/ Neural

Reminder

Compare

Sensory/ Neural

Rehearsal Memory

Without a reliable context-coded memory, S must compare the test to the reminder the in sensory trace memory.

T1 T2SOA loud,

dim, green, hot, salty, etc.

louder, dimmer, greener, hotter, saltier, etc..

CompareCompare

Context-coded, long-term memoryExperience

TestSensory/ Neural

“loud, dim, green, hot, salty, etc.”

T1 T2SOA

Reminder

Compare

Our audio/video dual-task shows these comparisons to be independent, i.e., no cost of sharing.

Conversely, these comparisons were limited by a shared attentional resource.

Sensory/ Neural

Rehearsal Memory

louder, dimmer, greener, hotter, saltier, etc..

Okay, so comparisons to the sensory trace memory produced no cost of shared attention.

What in the world is trace memory?

Okay, so comparisons to the sensory trace memory produced no cost of shared attention.

What in the world is trace memory?

Recently, we’ve approached this in terms of Weber’s Law.

Bringing the lab up to 1834.

I = k I

In signal detection terms, k can be treated as a multiplicative noise

Weber’s Law

3

1

-1

-3

-5

-7Thr

esho

ld 1

0 lo

g (Δ

I/I)

Pedestal (dB)

55 60 65 70 75

IdentificationPed = Sig = 50 ms Gap = 1s

Without Roving: performance based on based on long-term, labeled memory produce a constant Weber fraction

What happens when comparisons are to a roved standard?

Thresholds go up. But in what way?

To answer this, we parse the data in terms of the individual standards, analyzing performance separately for each pedestal level.

3

1

-1

-3

-5

-7Thr

esho

ld 1

0 lo

g (Δ

I/I

)

Pedestal

55 60 65 70 75

No RoveLabeled memoryRoveTrace memory

3

1

-1

-3

-5

-7

Thr

esho

ld (

dB)

Pedestal (dB)

55 60 65 70 75

multiplicative noise

multiplicative + additive noise

I = k I + c

The change in slope implies a second, additive noise, c.

6

0

-4

-6

-2

4

2

55 60 65 70 75

ΔI = kI + c

What is c?

Perhaps it is a decision noise associated with responding to the stimulus in trace memory?

Maybe it is simply the result of decay in the trace that makes that adds noise to the remembered amplitude code.

Our next plan is to go into fMRI in search of sensory rehearsal. Wish us luck.