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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




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
  • 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:
what s wrong with spatial representations
What’s wrong with spatial representations?
  • 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
  • Psychological distance should obey three axioms
    • Minimality
    • Symmetry
    • Triangle inequality
similarities can be asymmetric
Similarities can be 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 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 (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


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



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

` Lemon Orange

yellow orange

oval round


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

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

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

pants – furniture

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
  • typical
    • robin-bird, dog-mammal, book-reading, diamond-precious stone
  • atypical
    • ostrich-bird, whale-mammal, poem-reading, turquoise-precious stone
categorization models
Categorization Models
  • 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
  • Central Tendency


Learning involves abstracting a set of prototypes

graded structure
Graded Structure
  • Typical items are similar to a prototype
  • Typicality effects are naturally predicted




classification of prototype
Classification of Prototype
  • 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
  • All information about individual exemplars is lost
    • category size
    • variability of the exemplars
    • correlations among attributes
exemplar model
Exemplar model
  • 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









exemplars and prototypes
Exemplars and prototypes
  • 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
  • Sometimes similarity does not help to classify.
    • Daredevil
some interesting applications
Some Interesting Applications
  • 20 Questions:
  • Google Sets: