Comparative psychology Concept learning Number Time Conditional learning

Comparative psychology

Concept learningNumber TimeConditional learning

Just more stuff that animals can do? NO!

Associations are for all of us – and they are clever! Don’t just glue things that occur together – sensitive to correlations – can track causal relationships

...and in man they can do clever stuff: connectionist networks – language, pattern recognition

so can animals do clever stuff too?

Comparative psychology

Concept learningNumber TimeConditional learning

Asks -- How do animals do these things?

Is it the same way as humans do them?

Need to analyse tasks to ask these questions

Learn more about our abilities too

Categorization and concept formation

Charlotte Bonardi

C82NAB Neuroscience and Behaviour

Concept: The mental representation of something. Many concepts are based on the sharing of common properties by items in a class.

Concept formation: The induction of concepts that divide items into classes according to their shared properties (categorization).

Note that concepts are not always defined by specific features... sometimes do not have NECESSARY or SUFFICIENT features to define them...

"polymorphous"

e.g. What is the defining feature of a game?

even simple thingslike tables...

even simple thingslike tables...

Types of concept:

(i) Basic level concept -- based on similarity of perceptual qualities (e.g., bird, flower).

(ii) Superordinate concept -- groups of basic level concepts; not based on perceptual similarity (e.g. politician, tools).

(iii) Abstract concept -- does not refer to individual entity, but to some property, relation or state (e.g., sameness, truth).

Questions..

Can animals form basic level concepts? superordinate concepts?abstract concepts?

If so, how do they do it?

Do animals form concepts in the same way as humans?

Basic level concept formation in animals

Bhatt, Wasserman, Reynolds & Knauss, 1988Pigeons in a chamber with choice of four response keys.Shown pictures of flowers, cars, people and chairs

Birds learned to peck different keys for exemplars of each of thefour categories of picture.

chair

flower car

people


Bhatt, Wasserman, Reynolds & Knauss, 1988Pigeons in a chamber with choice of four response keys.Shown pictures of flowers, cars, people and chairs

Birds learned to peck different keys for exemplars of each of thefour categories of picture.

Then they tested them with some new exemplars, that they had never seen before......


Bhatt, Wasserman, Reynolds & Knauss, 1988

They also were able to respond correctly to the new exemplars, that they had never seen before.

This suggests that the birds had formed a “concept” of flowers, cars, people and chairs.

However, performance was more accurate with the training stimuli (80%) than with the novel, test stimuli (60%).

Theories of basic level concept formation -- how do they do it?:

(i) Exemplar theory: Learn about every instance independently. Canclassify novel exemplars on the basis of similarity to learned instances.

(ii) Prototype theory: Abstract a prototype that corresponds to the central tendency of training exemplars. Classify novel exemplars on basisof similarity to protype.

Animals are clearly storing information about the training exemplars --which is why they were more accurate with them than with the noveltest stimuli. This implies their performance can be explained byexemplar theory


Animals are clearly storing information about the training exemplars --which is why they were more accurate with them than with the noveltest stimuli. This implies their performance can be explained byexemplar theory

However, it is reported that humans show the prototype effect (e.g., Homa et al., 1981) -- they categorize the prototype more accurately than the training stimuli, even if it has never been seen before

This is more consistent with prototype theory

... and doesn’t seem to fit exemplar theory


So do animals store examplars and humans a prototype?

Are humans and animals forming basic-level concepts in different ways?

Red Rag to a Bull!

so someone tries to show a prototype effect in animals...


A B C D E F

Aydin & Pearce, 1994. The prototype effect in pigeons:

ABC +

DEF -

The positive and negative prototypes are defined as ABC and DEF...

A B C D E F

Aydin & Pearce, 1994. The prototype effect in pigeons:

+++

ABC +

DEF -

The birds trained on three-element displays, created by distorting the prototypes (swapping one prototype element for one from the other category):

+++

ABC +

DEF -


------

ABC +

DEF -


------

ABC +

DEF -


The animals were taught that the three positive patterns were always paired with food, whereas the three negative patterns were not. Birds pecked a response key more at positive than at negative patterns.

The animals were taught that the three positive patterns were always paired with food, whereas the three negative patterns were not. Birds pecked a response key more at positive than at negative patterns.

Then the birds were tested with the training patterns and the prototypes....

ABC ABF AEC DBC105

110

115

120

125

130

135

Series1

DEF DEC DBF AEF0

5

10

15

20

25

30

35

40

Series1

The birds responded more to the positive prototype ABC than toany of the positive patterns, and less to the negative prototype, DEF, than to any of the negative patterns.

This is evidence of a kind of prototype effect

(though not everyone thinks this evidence is good enough - pigeons fail to learn more complex prototypes)

Narrowing the gap... humans and animals more similar than we thought...

so let’s ask the converse question

– do humans store exemplars as well?

Whittlesea, 1987But do humans store information about the training items...?

Prototype FURIG

1 FEKIG FUTEG PURYG FYRIP KURIT

2 FYKIG FUTYG PUREG FERIP PURIT

3 FUKIP PUTIG FURYT FYREG KERIG

But do humans store information about the training items...?Whittlesea, 1987

Prototype FURIG

1 FEKIG FUTEG PURYG FYRIP KURIT STUDY



Pretest with all stimuli (30ms presentation followed by a mask; then had to write down as many letters as they could).

Studied list 1 Tested with lists 1, 2 and 3.

If they have abstracted the prototype, then they should be equallygood at categorising 1, 2 and 3, as they all differ from the prototype by two letters...

Whittlesea, 1987

Prototype FURIG




But if they are remembering the exemplars, they should be bestwith 1 (studied) and better at 2 than at 3 – list 2 more similar to list 1 than list 3.

So do we abstract a prototype? maybe not...Whittlesea, 1987

Prototype FURIG




Prototype theory says subjects should be equally good at lists 1, 2 and 3 -- all equally similar to the prototype.

Exemplar theory says list 1 should be easiest (studied),then list 2 (differs a little from studied list) and

then list 3 (differs a lot from studied list)

And that is what they found...

1 1.072 0.803 0.51

(improvement from pretest)

Conclusion: Both humans and animals retain information about thetraining items/exemplars (consistent with exemplar theory)

So what about prototype theory?

It turns out that exemplar theory can even explain the prototype effect!

The two theories do not make very different predictions after all.

How? Let’s go back to Pearce’s experiment:

we need to explain why birds peck more at positive prototype than to other members of the positive category

A B C +

D E F -

Training stimuli:

+++

Prototypes

------

Training stimuli :

+++

------

Positive Trained stimulus

A B C +

Positive Prototype

Training stimuli:

+++

------


Training stimuli:

+++

------


Training stimuli:

+++

------

five positive, four negative


Training stimuli:

+++

------

Positive Prototype Positive Trained stimulus


Training stimuli:

+++

------



Training stimuli:

+++

------

six positive, three negative



Conclusion: If exemplar theory assumes that each stimulus is composedof a set of elements, that are more or less associated with categorymembership, then it can explain the prototype effect

This explanation is actually viewed as a new theory

"feature theory"

Feature theory versus Exemplar theory

The difference between feature theory and exemplar theory is subtle.

They both say you store something about the stimuli on each trial

Exemplar theory implies that each stimulus is a configuration

-- new stimuli classified on basis of similarity to stored configures

Feature theory says that each stimulus is composed of a set of elements,

-- new stimuli classified on basis of sharing stored elements

They can probably both explain the prototype effect (but easier to show with feature theory).

There is still controversy about which of the three theories is best

Some people argue that actually categories are formed by means of associative learning- for example, about stimulus features

The features of the category become ASSOCIATED with the categorylabel:

Features --> Category

To do this they showed that these associations are subject to

BLOCKING

because this is a key characteristic of associative learning:

Feature-->Category X+ Feature-->Category X? small cr

X+ Feature-->Category X? BIG CR

Strength of association not determined by number of pairings, butby SURPRISINGNESS OF OUTCOME

An experiment by Shanks (1990), based on Gluck and Bower (1988).

Subjects given series of trials in which sets of medical symptoms and a diagnosis are presented. The subjects must estimate the extent to which a particular symptom is associated with a particular disease.

runny nose

headache

flu

TRAINING PHASE

If I have a runny

nose, what have I

caught?

TEST PHASE

An experiment by Shanks (1990), based on Gluck and Bower (1988).

Subjects given series of trials in which sets of medical symptoms and a diagnosis are presented. The subjects must estimate the extent to which a particular symptom is associated with a particular disease.

There were two diseases, one common (e.g. flu), which occurred alot, and one rare (e.g. inflamed hair or IH), which didn’t occur often.

Three possible symptoms:

one target symptom: (a -- e.g, a headache)

and two nontarget symptoms: b, -- e.g., a runny nose, and

c -- e.g., split ends

The following is a simplification of Shanks’s experiment:

IHac

IHac

IHac

IHac

IHac

IHac

IHc

IHc

IHc

IHc

fluab

fluab

fluab

fluab

fluab

flu b

flu b

flub

flu b

flu b

flub

flu b

flu b

flub

flu b

fluab

flub

flu b

flu b

flub

flu b

flub

flu b

flu b

flub

flub

flu b

flub

flu b

flu b

Test a?

(i) lots more b-->flu pairings than c-->IH pairings -- 30 versus 10

Test a?

(ii) a always presented with b on flu trials, and with c on IH trials

IHac

IHac

IHac

IHac

IHac

IHac

IHc

IHc

IHc

IHc

fluab

fluab

fluab

fluab

fluab

flu b

flu b

flub

flu b

flu b

flub

flu b

flu b

flub

flu b

fluab

flub

flu b

flu b

flub

flu b

flub

flu b

flu b

flub

flub

flu b

flub

flu b

flu b

Test a?

(iii) same number of ab-->flu and ac-->pig pairings -- 6 and 6

IHac

IHac

IHac

IHac

IHac

IHac

IHc

IHc

IHc

IHc

fluab

fluab

fluab

fluab

fluab

flu b

flu b

flub

flu b

flu b

flub

flu b

flu b

flub

flu b

fluab

flub

flu b

flu b

flub

flu b

flub

flu b

flu b

flub

flub

flu b

flub

flu b

flu b

If number of pairings are critical subjects would, given symptom a be equally likely to diagnose flu as Inflamed Hair

- same number of pairings....

Test a?

IHac

IHac

IHac

IHac

IHac

IHac

IHc

IHc

IHc

IHc

fluab

fluab

fluab

fluab

fluab

flu b

flu b

flub

flu b

flu b

flub

flu b

flu b

flub

flu b

fluab

flub

flu b

flu b

flub

flu b

flub

flu b

flu b

flub

flub

flu b

flub

flu b

flu b

Test a?

Associative learning makes a different prediction:

IHac

IHac

IHac

IHac

IHac

IHac

IHc

IHc

IHc

IHc

fluab

fluab

fluab

fluab

fluab

flu b

flu b

flub

flu b

flu b

flub

flu b

flu b

flub

flu b

fluab

flub

flu b

flu b

flub

flu b

flub

flu b

flu b

flub

flub

flu b

flub

flu b

flu b

Associative analysis of task

Subjects are given symptom a, and asked to say which disease itpredicts, flu or inflamed hair. Thus the critical factor is the strength of the

a--->flu and a-->IH associations -- which is stronger?

Symptom a is paired with flu the same number of times as it ispaired with inflamed hair, so all other things being equal, a should be associated equally strongly with both diseases.

Test a?

But all other things are not equal: a is not paired alone....

ab-->flu and ac-->inflamed hair

IHac

IHac

IHac

IHac

IHac

IHac

IHc

IHc

IHc

IHc

fluab

fluab

fluab

fluab

fluab

flu b

flu b

flub

flu b

flu b

flub

flu b

flu b

flub

flu b

fluab

flub

flu b

flu b

flub

flu b

flub

flu b

flu b

flub

flub

flu b

flub

flu b

flu b

Test a?

.

.. and b predicts flu better than c predicts IH – more pairings of bflu

IHac

IHac

IHac

IHac

IHac

IHac

IHc

IHc

IHc

IHc

fluab

fluab

fluab

fluab

fluab

flu b

flu b

flub

flu b

flu b

flub

flu b

flu b

flub

flu b

fluab

flub

flu b

flu b

flub

flu b

flub

flu b

flu b

flub

flub

flu b

flub

flu b

flu b

Test a?

b c inflamed hairflu

IHac

IHac

IHac

IHac

IHac

IHac

IHc

IHc

IHc

IHc

fluab

fluab

fluab

fluab

fluab

flu b

flu b

flub

flu b

flu b

flub

flu b

flu b

flub

flu b

fluab

flub

flu b

flu b

flub

flu b

flub

flu b

flu b

flub

flub

flu b

flub

flu b

flu b

The Rescorla-Wagner model predicts that an association will onlyform between two events if the second event is surprising. Thatis why you get blocking. Thus in normal conditioning:

b flu ab fluI know...

a flu

a flu

get good learning about a because flu is surprising. But in blocking:

get poor learning about a because flu is NOT surprising -- it ispredicted by b. This is blocking.

In the diagnosis task b-->flu association is strong.

Because b is a good predictor of flu, the a-->flu association will be blocked:

b flu ab fluI know...

a flu

c IH ac IH ?!!really?!

a IH

But c-->IH association weak

As c is a poor predictor of inflamed hair, less blocking occurs:

Exemplar theory predicts that, given symptom a, subjects will be equally likely to predict flu as inflamed hair.

Associative theory predicts that, given symptom a, subjects will befar more likely to choose the rare disease, inflamed hair.

Exemplar theory predicts that, given symptom a, subjects will be equally likely to predict flu as inflamed hair.

Associative theory predicts that, given symptom a, subjects will befar more likely to choose the rare disease, inflamed hair.

Associative theory wins!

Proportion common disease (flu) diagnoses: .37Proportion rare disease (IH) diagnoses: .63

Suggests that associative learning is the best explanation of performance on this categorization task in human subjects.

Superordinate level concept formation

Superordinate level concept formation in animals

Superordinate categories have members that are not necessarilyphysically similar to each other, but share a common associate.

Can animals can form categories of this kind?

Wasserman, De Volder & Coppage, 1992

Pigeons trained with slides of people, chairs, cars and flowers

The birds reinforced for making R1 (pecking one specific key) to either people or chairs, and for making R2 (pecking a different key) to either cars and flowers.

people and chairs in one category, and cars and flowers in another.

Then trained to make R3 to people, and R4 to cars.Tested with chairs and flowers, and given a choice of R3 and R4.

R3 to chairs and R4 to flowers counted as correct responses.

Superordinate level concept formation in animals

Animals seem to have formed a superordinate category; treating people and chairs as equivalent because both paired with the same response in the first phase of training

This is a more complex type of categorization because the categorymembers are not physically similar.

Is this the same as how people do it?

Some people (e.g. Pearce, 1997) argue that this is not true categorization like that seen in humans, but just simple “associative learning” -- and that what we do is somehow more complicated.

But need to specify exactly how what we do is more complicated, so we can test...

Abstract concept formation in animals

Relatively little work has been done on abstract concepts in animals. The one that has been studied most is same/different.

Usually studied using a Match-to-sample technique (MTS).

Birds shown a sample key e.g. red; then they were given a choice of two comparison keys, red and green.

Must peck the same colour as the sample -- i.e. red. On other trials the bird gets a green sample; then task is to peck the green comparison.

correct

correct

correct correct

Birds could master this, but were typically poor at transferring toskill to different colours (e.g., blue and yellow). This suggests they had not really learned the concept of same.

correct correct

However, more complex training techniques seemed to producebetter results:

Wasserman, Hugart & Kirkpatrick-Steger, 1995.

Pigeons were shown complex stimulus displays, and given a choice of a red and a green key.

Abstract concept formation in animals

They were reinforced for pecking the red key on same trials, and the green key on different trials.

They were trained in this way for arrays involving one set of specific icons (the top half of the figure)

Finally they were tested with a further set of arrays involving a second set of specific icons (the bottom half of the figure).

References

General:

Bouton, M.E. (2007) Learning and Behavior. Sinauer Associates.

Lea, S.E.G. ((1984). In what sense do pigeons learn concepts? In Animal Cognition (Roitblat, H.L., Bever, T.G., & Terrace. H.S. (Eds.) (pp.263-276). Lawrence Erlbaum Associates.

Pearce, J.M. (1997). Animal Learning and Cognition. Lawrence Erlbaum Associates.

Wynne, C.D.L. (2001) Animal Cognition. Palgrave: Macmillan

Shanks, D.R. (1995) The Psychology of Associative Learning. Cambridge University Press.

http://www.pigeon.psy.tufts.edu/avc/huber/

Aydin, A., & Pearce, J.M. (1994). Prototype effects in categorization by pigeons. Journal of Experimental Psychology: Animal Behaviour Processes, 20, 264-277. Bhatt, R.S., Wasserman,E.A., Reynolds, W.F., & Knauss, K.S. (1988) Conceptual behaviour in pigeons. Journal of Experimental Psychology: Animal Behaviour Processes, 14, 219-234.

Gluck, M.A., & Bower, G.H. (1988). From conditioning to category learning: an adaptive networkmodel. Journal of Experimental Psychology: General, 117, 224-247.

Homa, D., Sterling, S., & Trepel, L. (1981). Limitations of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Human Learning and Memory, 7, 418-439.

Shanks, DR. (1990). Connectionism and the learning of probabilistic concepts. Quarterly Journalof Experimental Psychology, 42A, 209-237.

Wasserman, E.A., Hugart, J.A., & Kirkpatrick-Steger, K. (1995). Pigeons show same–different conceptualization after training with complex visual stimuli. Journal of Experimental Psychology: Animal Behaviour Processes, 21, 248-252.

Wasserman, E.A., De Volder, C.L., & Coppage, D.J. (1992). Non-similarity-based conceptualisation via secondary or mediated generalization. Psychological Science, 3, 374-379.

Whittlesea, B.W.A. (1987). Preservation of specific experiences in the representation of general knowledge. Journal of Experimental Psychology: Learning, Memory & Cognition, 13, 3-17.

Lectures and handouts on my webpage

webquiz for multiple choice practice:

http://www.sinauer.com/bouton/

[email protected]

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Comparative psychology Concept learning Number Time Conditional learning