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Concepts & CategorizationPowerPoint Presentation

Concepts & Categorization

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Geometric (Spatial) Approach

- Many prototype and exemplar models assume that similarity is inversely related to distance in some representational space

B

C

A

distance A,B small psychologically similar

distance B,C large psychologically dissimilar

Multidimensional Scaling

- Represent observed similarities by a multidimensional space – close neighbors should have high similarity
- Multidimensional Scaling (MDS): iterative procedure to place points in a (low) dimensional space to model observed similarities

MDS

- Suppose we have N stimuli
- Measure the (dis)similarity between every pair of stimuli (N x (N-1) / 2 pairs).
- Represent each stimulus as a point in a multidimensional space.
- Similarity is measured by geometric distance, e.g., Minkowski distance metric:

MDS procedure: move points in space to best model observed similarity relations

Example: 2D solution for bold faces similarity relations

2D solution for fruit words similarity relations

What’s wrong with spatial representations? similarity relations

- Tversky argued that similarity is more flexible than can be predicted by distance in some psychological space
- Distances should obey metric axioms
- Metric axioms are sometimes violated in the case of conceptual stimuli

Critical Assumptions of Geometric Approach similarity relations

- Psychological distance should obey three axioms
- Minimality
- Symmetry
- Triangle inequality

Similarities can be similarity relations asymmetric

“North-Korea” is more similar to “China” than vice versa

“Pomegranate” is more similar to “Apple” than vice versa

Violates symmetry

Violations of triangle similarity relations inequality

- Spatial representations predict that if A and B are similar, and B and C are similar, then A and C have to be somewhat similar as well (triangle inequality)
- However, you can find examples where A is similar to B, B is similar to C, but A is not similar to C at all violation of the triangle inequality
- Example:
- RIVER is similar to BANK
- MONEY is similar to BANK
- RIVER is not similar to MONEY

Feature Contrast Model similarity relations (Tversky, 1977)

- Model addresses problems of geometric models of similarity
- Represent stimuli with sets of discrete features
- Similarity is a flexible function of the number of common and distinctive features

# shared features

# features unique to X

#features unique to Y

Similarity(X,Y) = a( shared) – b(X but not Y) – c(Y but not X)

a,b, and c are weighting parameters

Example similarity relations

Similarity(X,Y) = a( shared) – b(X but not Y) – c(Y but not X)

` Lemon Orange

yellow orange

oval round

sour sweet

trees trees

citrus citrus

-ade -ade

\

Example similarity relations

Similarity(X,Y) = a( shared) – b(X but not Y) – c(Y but not X)

` Lemon Orange

yellow orange

oval round

soursweet

trees trees

citrus citrus

-ade -ade

Similarity( “Lemon”,”Orange” ) = a(3) - b(3) - c(3)

If a=10, b=6, and c=2 Similarity = 10*3-6*3-2*3=6

Contrast model predicts asymmetries similarity relations

Suppose weighting parameter b > c

Then, pomegranate is more similar to apple than vice versa because pomegranate has fewer distinctive features

Contrast model predicts violations of triangle inequality similarity relations

If weighting parameters are: a > b > c (common feature weighted more)

Then, model can predict that while Lemon is similar to Orange and Orange is similar to Apricot, the similarity between Lemon and Apricot is still low

Nearest neighbor problem similarity relations (Tversky & Hutchinson (1986)

- In similarity data, “Fruit” is nearest neighbor in 18 out of 20 items
- In 2D solution, “Fruit” can be nearest neighbor of at most 5 items
- High-dimensional solutions might solve this but these are less appealing

Typicality Effects similarity relations

- Typicality Demo
- will see X --- Y.
- need to judge if X is a member of Y.
- finger --- body part
- pansy --- animal

pants – furniture similarity relations

turtle – precious stone

robin – bird

dog – mammal

turquoise --- precious stone

ostrich -- bird

poem – reading materials

rose – mammal

whale – mammal

diamond – precious stone

book – reading material

opal – precious stone

Typicality Effects similarity relations

- typical
- robin-bird, dog-mammal, book-reading, diamond-precious stone

- atypical
- ostrich-bird, whale-mammal, poem-reading, turquoise-precious stone

Categorization Models similarity relations

- Similarity-based models: A new exemplar is classified based on its similarity to a stored category representation
- Types of representation
- prototype
- exemplar

Prototypes Representations similarity relations

- Central Tendency

P

Learning involves abstracting a set of prototypes

Graded Structure similarity relations

- Typical items are similar to a prototype
- Typicality effects are naturally predicted

atypical

P

typical

Classification of Prototype similarity relations

- If there is a prototype representation
- Prototype should be easy to classify
- Even if the prototype is never seen during learning
- Posner & Keele

Problem with Prototype Models similarity relations

- All information about individual exemplars is lost
- category size
- variability of the exemplars
- correlations among attributes

Exemplar model similarity relations

- category representation consists of storage of a number of category members
- New exemplars are compared to known exemplars – most similar item will influence classification the most

dog

??

cat

dog

dog

cat

dog

cat

Exemplars and prototypes similarity relations

- It is hard to distinguish between exemplar models and prototype models
- Both can predict many of the same patterns of data
- Graded typicality
- How many exemplars is new item similar to?

- Prototype classification effects
- Prototype is similar to most category members

Theory-based models similarity relations

- Sometimes similarity does not help to classify.
- Daredevil

Some Interesting Applications similarity relations

- 20 Questions:http://20q.net/
- Google Sets:http://labs.google.com/sets

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