configural learning learning about holistic stimulus representations

Configural learning

Learning about holistic stimulus representations

no food

food

food no food

Structural discriminationsGeorge Ward-Robinson & Pearce, 2001

Structural discriminationsGeorge Ward-Robinson & Pearce, 2001

QuickTime™ and a decompressor

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food no food

Can this be solved in terms of simple associations?

Can it be solved with conditional learning?

food no food

If green: red-left + red-right - If blue: red-left - red-right +If green: blue-right + blue-left -

food no food

If green: red-left + red-right - If blue: red-left - red-right +If green: blue-right + blue-left -

relies on use of compound cues - red-left etc

food no food

so why not use fact these stimuli are all unique?

red-left&green-right+ red-right&green-left -

Some types of learning associative theory cannot explain.

Last week we saw how conditional learning can explain some of these

Today we consider an alternative approach - configural learning

Can associative theory adapt by changing the way in which the stimulus is represented?

So far have assumed that a compound stimulus is equivalent to the sum of its parts:

A --> food B--> food

A --> crB --> cr

AB --> CR

Predict SUMMATION

Feature negative discrimination

A --> food AB --> no food

CR cr

VA = ( - V )

Learning stops when ( = V )


VA = 1 VA + VB = 0

VA = ( - V )



VA = 1 VA + VB = 0

A becomes excitatory: V = +1B becomes inhibitory: V = -1

thus A alone predicts food, whereas A+B is neutral

Feature positive discrimination

A --> no food AB --> food

cr CR

VA = ( - V )



VA = 0 VA + VB = 1

VA = ( - V )



VA = 0 VA + VB = 1

B becomes excitatory: V = +1A eventually becomes neutral: V = 0

Thus A alone predicts nothing, but when B is present food is expected

Performance on feature negative and feature positive discriminations can be explained by the Rescorla-Wagner equation

If you condition to asymptote, it predicts perfect performance

But how about.......

Positive patterning discrimination:

A --> no food B --> no food AB --> food

cr cr CR

VA = ( - V )



VA = 0 VB = 0 VA + VB = 1


VA = 0 VB = 0 VA + VB = 1

This one is insoluble - you can never reach asymptote:

what is gained on AB trials is lost on A and B trials


But associative theory can explain accurate performanceBoth A and B acquire associative strength on compound trials, and lose some on element trials

Animals respond more on AB trials (when two signals for food are present) than on A or B trials (when there is only one)

But it doesn't predict perfect performance

Negative patterning discrimination

A --> food B --> food AB --> no food

CR CR cr

VA = ( - V )



VA = 1 VB = 1 VA + VB = 0

Simple associative theory can never predict accurate performance here


If A and B have enough associative strength to elicit responding, then the compound of A and B must elicit more responding, not less

-- violates summation principle

So can animals learn nonlinear discriminations of this type?

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click + tone --> no food

tone --> foodclick --> food

Redrawn from Rescorla, 1972

Blocks of 60 Trials

Percentage responses

Wagner (1971) and Rescorla (1972) suggested the unique stimulus account:

A stimulus compound should be treated as the combination of its elements...

A B+

A stimulus compound should be treated as the combination of its elements... PLUS a further stimulus that is generated only when those elements are presented together:

A B

ab

+

A stimulus compound should be treated as the combination of its elements... PLUS a further stimulus that is generated only when those elements are presented together:

A B

ab

+configural stimulus notvery salient; so only learned about when absolutely "forced"

Now the negative patterning discrimination looks like this:



A --> food B --> food AB ab --> no food



VA = 1 VB = 1 VA + VB+ Vab = 0


VA = 1 VB = 1 VA + VB+ Vab = 0

B becomes excitatory: V = +1A becomes excitatory: V = +1ab becomes inhibitory: V = -2

...and the discrimination is solved...

Rescorla tested this interpretation with the following experiment:

A + B + AB - AB + A ? B ?

A + B + C - AB + A ? B ?

Which group will respond more in the test?

Stage 1 Stage 2 Test

A + B + AB ab - AB ab + A ? B ?


A + B + AB ab - AB ab + A ? B ?

In Stage 1 A and B become excitatory and ab inhibitory; the combination of A, B and ab should therefore be neutral


A + B + AB ab - AB ab + A ? B ?


In Stage 2 the neutral AB ab is paired with food; the food is surprising, and A, B and ab all gain associative strength


A + B + AB ab - AB ab + A ? B ?


In Stage 2 the neutral AB ab is paired with food; the food is surprising, and A, B and ab all gain associative strength

In the Test A and B now have more associative strength than they started with


A + B + C - AB + A ? B ?


A + B + C - AB + A ? B ?

In Stage 1 A and B become excitatory


A + B + C - AB + A ? B ?

In Stage 1 A and B become excitatory

In Stage 2 the excitatory A and B both predict food -- thus two foods are predicted, but only one happens; this produces inhibitory learning, and the strength of A and B drops...


A + B + C - AB + A ? B ?

In Stage 1 A and B become excitatory.

In Stage 2 the excitatory A and B both predict food -- thus two foods are predicted, but only one happens; this produces inhibitory learning, and the strength of A and B drops...

In the Test A and B now have less associative strength than they started with

Responding to A and B

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Group C-Group C-

Group AB-Group AB-

Redrawn from Rescorla 1973

Blocks of 10 trials

Mean percentage responses

So.. can Rescorla & Wagner explain everything?

Not quite: consider the following discriminations:

Discrimination 1: A+ AB-

Discrimination 2: AC+ ABC-

In the second case a common element C has been added on both reinforced and nonreinforced trials; this should make the discrimination harder...

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A +

AC+ABC-

AB-

Redrawn from Pearce 1994

Session

Responses per minute

BUT Rescorla & Wagner's theory predicts that the AC+ ABC- discrimination will be learned most easily

Because AC has more elements than A, it will acquire associative strength faster



on first trial VA = ( - V ) = ( - 0 )

Vc = ( - V ) = ( - 0 )

So AC will have twice as much strength as A after trial 1

Faster EXCITATORY learning



And the more AC predicts food, the greater the surprise on ABC- trials, and so the faster B will become inhibitory

Faster INHIBITORY learning



The faster the excitatory and inhibitory learning is acquired, the faster the discrimination is acquired

oops!

Nor can Rescorla & Wagner's theory explain any instance of generalization decrement

e.g. external inhibition (Pavlov, 1927)

control A+ test A CR=10

A+ test AB CR=5

the presence of B makes the animals respond less to A

yet if associative strengths summate, as Rescorla and Wagner predict, then if A = 1 and B = 0, then AB = A = 1

Pearce's theory of stimulus generalization (1987; 1994)

Limited capacity buffer representing overall pattern of stimulation that is present

Every stimulus is a

configure

and

unique

tone

context

context

Pearce's theory of stimulus generalization (1987; 1994)

a compound stimulus isNOT the sum of itselements

so you need a way of working out how much learning about one stimulus will affect responding to another

tone

context

context

Changing the stimulus in any way changes the contents of the buffer

you work out how similarthey are, and use that tocalculate how muchgeneralisation occurs

tone

context

context

clicker

context

context

Compound stimuli are unique -- NOT the sum of theirelements

or are they..?!

Despite claim that elementsnot represented, they areused to calculatesimilarity betweenconfigurations

tone

context

context

Generalization between two stimuli depends on :

(i) their similarity (number of common elements)(ii) the amount of associative strength tone and clicker have common and unique elements(ignore context for simplicity)

clickerunique

comm

n

comm

on

tone unique

comm

on

comm

on

TONE CLICKER

Suppose you condition a tone to asymptote (i.e. V=1) and then test the generalization to a click.

Let 50% of the buffer contents in each case be common elements

clickerunique

comm

n

comm

on

toneunique

comm

on

comm

on

Generalization = (V tone) x click/tone similarity

clickerunique

comm

n

comm

on

toneunique

comm

on

comm

on


Click/tone similarity = Pcom/Ptone total x Pcom/Pclick total

i.e. common/source x common/target

clickerunique

comm

n

comm

on

toneunique

comm

on

comm

on


Click/tone similarity = Pcom/Ptone total x Pcom/Pclick total

= 50% x 50%

= 25%

clickerunique

comm

n

comm

on

toneunique

comm

on

comm

on

Need to ask --

(i) associative strength of thing being generalised from?

clickerunique

comm

n

comm

on

toneunique

comm

on

comm

on

Need to ask --

(i) associative strength of thing being generalised from?(ii) what are the common elements mediating generalisation?

clickerunique

comm

n

comm

on

toneunique

comm

on

comm

on

Need to ask --

(i) associative strength of thing being generalised from?(ii) what are the common elements mediating generalisation?(iii) what % are common elements of stimulus generalised

from?

clickerunique

comm

n

comm

on

toneunique

comm

on

comm

on

Need to ask --

(i) associative strength of thing being generalised from?(ii) what are the common elements mediating generalisation?(iii) what % are common elements of stimulus generalised

from?(iv) what % are common elements of stimulus generalised to?

clickerunique

comm

n

comm

on

toneunique

comm

on

comm

on

Suppose you condition a tone+light compound to asymptote (V = +1) and then test generalization to the tone:

TL+ test T

tonetone

light

light

Suppose you condition a tone+light compound to asymptote (V = +1) and then test generalization to the tone:

TL+ test T

Let tone and light share no intrinsic common elementsSo the relevant common elements are those of the tone tone/light equally salient so tone 50% of total

tonetone

light

light

Generalization = (V tone+light) x (tone+light)/tone similarity

tonetone

light

light

Generalization = (V tone+light) x (tone+light)/tone similarity

= P tone/Ptone+light total x Ptone / Ptone total

= 50% x 100%

= 50%

tonetone

light

light

work out generalization in the following cases (V = +1):

(i) condition tone; test (tone+light)

i.e. A+ AB?

(ii) condition tone; test (tone+light+clicker)

i.e. A+ ABC?

(iii) condition (tone+light); test (clicker+light)

i.e. AB+ AC?



i.e. A+ AB? A/A x A/AB = 1/2


i.e. A+ ABC?

(iii) condition (tone+light); test (clicker+light)

i.e. AB+ AC?



i.e. A+ AB? A/A x A/AB = 1/2


i.e. A+ ABC? A/A x A/ABC = 1/3

• condition (tone+light); test (clicker+light)

i.e. AB+ AC?



i.e. A+ AB? A/A x A/AB = 1/2



• condition (tone+light); test (clicker+light)

i.e. AB+ AC? A/AB x A/AC = 1/4

There is also a little complication with V....

Compare with Rescorla Wagner equation for one stimulus:

V = ( - V )

Pearce uses this equation for acquisition of V:

V = ( - (V + g))

Adds together acquired strength (V) and generalised strength (g)

generalized associative strength acts like normal associative strength during acquisition

V = ( - (V + g))

generalized associative strength acts like normal associative strength during acquisition

V = ( - (V + g))

but it doesn't generalise!!

To see why this is important, let's look at overshadowing and blocking:

light + light? CR = 10

tone+light + light? CR = 5

tone+ tone+light + light? CR = 2

Control light + light ?

light acquires strength in training V = +1

in test responding determined by generalization

P light/Plight x Plight / Plight = 1

lightlight

overshadowing tone&light + light ?

tone&light configure acquires strength in training V = +1

in test responding determined by generalization

P light/Ptone+light x Plight / Plight = 1/2

tonelight

light light

blockingtone + tone&light + light ?

in Stage 1 tone acquires strength in training V = +1

tone

blocking tone + tone&light + light ?

in Stage 2 learning about tone generalises to tone/light :

P tone/Ptone x Ptone / Ptone+light = 1/2

tone tone

light

light


So tone/light starts halfway to asymptote because of generalisation

Vtone+light = ( - (Vtone+light + gtone+light))

= ( - (0 + 1/2))

tone tone

light

light



Half of its total associative strength will be generalised, and only half will be acquired

tone tone

light

light



Half of its total associative strength will be generalised, and only half will be acquired

Only the acquired half can generalise to other stimuli

tone tone

light

light


test responding determined by generalization to tone of 1/2 of what is acquired by tone/light: ( P light/Ptone+light x Plight / Plight = 1/2 ) x 1/2 = 1/4

lighttone

light

light

Pearce's model can explain things that the unique cue (Rescorla & Wagner) cannot

But it's a paradox: it rejects the idea of stimulus elements, and yet it uses them all the time

Brandon Vogel and Wagner (2000) analysed Pearce's model in terms of stimulus elements

They argued that the best way of thinking about Pearce's model is in terms of removed elements

Imagine you have two stimuli, A and B:

If you present them in compound, which elements are active?

A B

Simple model

AB compound

A B

Rescorla and Wagner's account:added elements

AB compound

ab

A B

Pearce's account:removed elements (remember buffer is limited capacity)

AB compound

A B

So can these models explain external inhibition?

Simple model

A+ test AB

A

B

A


Rescorla Wagner added elements model

A+ test AB

A

B

A

ab


Pearce's removed elements model

A+ test AB

A

B

A

Can removed elements explain other Pearce predictions?

condition tone; test tone+light+clicker


A

B

A

C

Can removed elements explain other Pearce predictions?

condition tone+light; test clicker+light

i.e. AB+ AC? A/AB x A/AC = 1/4

A

B C

A

A connectionist version of the unique cue view?

foodfood no food

A connectionist version of the unique cue view?

food no food

left right

configural units

Finally - configural cues versus conditional learning

Many of the tasks we have considered today could be solved in terms of conditional learning

e.g. A --> food B --> food AB --> no food

A signals that B is nonreinforced (or vice versa)

but others not so easily:

So which is right?

Configural learning very probably does occur

the question is whether it is enough to explain all data - or do we need a theory of conditional learning too...

the experiments I presented at the end of my last lecture were designed to examine this question...

quite possible that some tasks better solved by a conditional learning mechanism

References

Brandon, S.E., Vogel, A.H., & Wagner, A.R. (2000). A componential view of configural cues in generalization and discrimination in Pavlovian conditioning. Behavioral Processes, 110, 67-72. *

George, D., Ward-Robinson, J., & Pearce, J.M. (2001). Discrimination of structure I: Implications for connectionist theories of discrimination learning. Journal of Experimental Psychology: Animal Behavior Processes, 27, 206-218.

Pearce, J.M. (1987). A model for stimulus generalization in Pavlovian conditioning. Psychological Review, 94, 61-73. *

Pearce, J.M. (1994). Similarity and discrimination: A selective review and a connectionist model. Psychological Review, 101, 587-607.

Rescorla, R.A. (1973). "Configural" conditioning in discrete-trial bar pressing. Journal of Comparative and Physiological Psychology, 79, 301-317. *

Rescorla, R.A. (1972). Evidence for "Unique stimulus" account of configural conditioning. Journal of Comparative and Physiological Psychology, 85, 331-338. *

Wagner, A.R. (1971). Elementary associations. In H.H. Kendler & J.T. Spence (Eds.) Essays in neobehaviorism: A memorial volume to Kenneth W. Spence. New York: Appleton-Century-Crofts.

configural learning learning about holistic stimulus representations

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