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Concepts & Categorization. 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.

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Geometric spatial approach
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
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:





2d solution for fruit words
2D solution for fruit words similarity relations


What s wrong with spatial representations
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
Critical Assumptions of Geometric Approach similarity relations

  • Psychological distance should obey three axioms

    • Minimality

    • Symmetry

    • Triangle inequality


Similarities can be asymmetric
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 inequality
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 tversky 1977
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
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

\


Example1
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
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
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 tversky hutchinson 1986
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
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 effects1
Typicality Effects similarity relations

  • typical

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

  • atypical

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


Is this a chair

Is this a “cat”? similarity relations

Is this a “chair”?

Is this a “dog”?


Categorization models
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
Prototypes Representations similarity relations

  • Central Tendency

P

Learning involves abstracting a set of prototypes


Graded structure
Graded Structure similarity relations

  • Typical items are similar to a prototype

  • Typicality effects are naturally predicted

atypical

P

typical


Classification of prototype
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
Problem with Prototype Models similarity relations

  • All information about individual exemplars is lost

    • category size

    • variability of the exemplars

    • correlations among attributes


Exemplar model
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
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
Theory-based models similarity relations

  • Sometimes similarity does not help to classify.

    • Daredevil


Some interesting applications
Some Interesting Applications similarity relations

  • 20 Questions:http://20q.net/

  • Google Sets:http://labs.google.com/sets


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