concept learning. overview what do we mean concept? why is concept learning tricky to understand?...
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Concept learning
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Overview
What do we mean concept?
Why is concept learning tricky to understand?
Connectionist nets as a simple model of concept learning
Some features of natural concept learning that make the picture less simple– Role of existing background knowledge– Qualitative shifts during conceptual development– Conceptual essence – an innate expectation?– Domain specificity – different types of learning for different kinds
of concept?– Social learning
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Explaining concept learning is hard because…
In principle, the number of possible concepts is infinite, or at least very large
- All the permutations of all possible features
So brute force, trial & error, would face a huge computational task
Almost all realistic models seek to constrain the space of candidate concepts that is considered
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“Classic” view of concepts
• Adult concepts have defining features
• Children's are "complexive"
• Concepts stand alone
• Concepts are static
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Alternatively
• Adults’ are not always classical either, … and sometime children's are classical
• Concepts are embedded in "theories"
• Concepts are radically context dependent
• Concepts may be convenient fictions
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Simple model of concept learning
Associationism
groups form among objects thru chance links
A
B
C
D
With further experience…some grow stronger, some weaken
A
B
C
D
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… it’s not so simple
Achdetermining tendency
Barsalouad hoc categories
Vygotskyqualitative shifts in concept learning tactics at different developmental stages:
contingent associationscomplexes“scientific concepts”
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Quinn & Johnson (1997)
Previous developmental research
Children at 3-4 months old … can form category representations
‘Basic’ category: CATShow a few cats and then switch to another animal – they
notice
‘Global’ [superordinate] category: MAMMALShow a few mammals and then switch to a fish, or a chair –
they notice
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Quinn & Johnson - model
10 outputs
3 hidden
13 inputsDimensions of facial features; no. of legs
One for each of 8 basic categories; mammal; furniture
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Quinn & Johnson - model
10 outputs
3 hidden
13 inputs
Dimensions of facial features; no. of legs
Target output for CAT
Input for CAT
Results
120 sweeps to get the mammal v. furniture bits
3600 sweeps to get the basic categories (mostly)
CHAIR was hardest – took another 3600 sweeps
Why?
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Quinn & Johnson - model
Example questions for critical evaluation:
Does the “‘global’ first, then later ‘basic’” order correspond to developmental data?
Is one test pattern for testing generalisation enough?
How does the network perform the categorisation ie what does it learn?
Do the results critically depend on values chosen for learning rate or momentum?
Answers to most of these questions can be gleaned from the original paper
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Asymmetric category learning
Previous developmental research
3 – 4 month old infants
Six pairs of cat photos
Then a novel cat & a dog
Infants prefer to look at the (novel) dog
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Asymmetric category learning
Then a novel cat & a dog
Infants do not show significant preference for (novel) CAT
Six pairs of DOG photos
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Mareschal, French, & Quinn (2000)
Why?
Possible explanation: the category CAT is “tighter”, has less variability than DOG
– the exemplars are more similar than the dogs
- the novel CAT is more likely to fall right inside the learned CAT category
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Mareschal et al. - model
10 outputs
8 hidden
10 inputsDimensions of facial features
Dimensions of facial features
Target values are the same as the inputs: the network is being trained to work out how to reproduce the input pattern
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Mareschal et al. - model
Train with 12 training patterns based on pictures of cats
Test with one novel CAT and one DOG
Use error as a measure:
error is greater for the DOG than the novel CAT
Train with 12 training patterns based on pictures of dogs
Test with one CAT and one novel DOG
Use error as a measure:
error is not much greater for the CAT than the novel DOG
Error is being used as an analogy for ‘looking time’
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Some features of natural concept learning that make the picture less simple
– Role of existing background knowledge– Qualitative shifts during conceptual
development– Conceptual essence – an innate expectation?– Domain specificity – different types of learning
for different kinds of concept?– Social learning
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Barrett et al. (1993) on category learning
Jasslers big brain, good memory, 2 chamber heart, round beakLoppets small brain, poor memory, 3 chamber heart, long beak
See lots of exemplars with three features [training]
Then:Test generalisation: Classify more exemplars with four features, one of which is
‘wrong’
Remains consistent with prior knowledge: big brain, good memory, 2 chamber heart, long beak
Becomes inconsistent with prior knowledge: small brain, good memory,2 chamber heart, round beak
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Gelman and Markman (1986)
[brontosaurus] [rhinoceros] [triceratops]
dinosaur rhinoceros dinosaur
cold blood warm blood ?
2 ½ years old: "cold blood"
Training Test generalisation
Conceptual knowledge trumps appearance
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Keil & Batterman (1984)Characteristic defining features
E1 Preschool definitionsIsland "a warm place“
E2 K, G2, G4 choose storiesCharacteristic v. Defining
Could this be news?Man on radio, wars, Children singfires etc, from book wars etc. to
rock musicShift across ages … but not at same rate across all concepts
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Could this be a lie?Telling tales on.. Said got bad
mark to geton with pals
Could this be a robber?Nasty man took Nice lady tooktv with permission toilet without..
…defining from startthese are moral terms - are they special?
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Artifacts v. natural kinds – two domains
Keil (1989)
Surgery
Racoon skunk "still racoon"
Coffee pot bird feeder "bird feeder"
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Summary
The terms concept/conceptual are used in different ways
Some uses are highly simplifiedConcept learning involves wide ranging
background knowledge, and may involve qualitative changes and re-organisations
Computationally, working out how to bring to bear just the relevant knowledge, quickly, is key
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Training and testing generalisation
Training
present a set of patterns
allow the ‘training algorithm’ to learn from these patterns
On each training sweep…- it gets to see what output the current weights would
produce for the input pattern
- it can compare that to the ‘correct answer’
- it can modify the weights towards being more correct
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Training and testing generalisation
Testing generalisationpresent a new set of patterns(the training process has not had any chance to try these out)
The network will perform wellonly if the weights learned during training abstracted a general rule that works for instances generally, not merely the specific items in the training set
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Modelling project
Guidelines
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Next week’s lecture
Next week’s lecture will look at learning word meanings
Quite similar issues are involved in learning concepts and learning word meanings