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8) Concepts & Categories (Concepts = Categories (Useful (Category: A…

- - - - ( Wittgenstein, 1953) "Game" no attributes that are shared by ALL members of the category
        
        Boards? (No, ball games)
        
        Winners and losers (No, solitaire?)
        
        Amusement? (No, serious games?)
      - No single attribute shared by ALL members of a category
        But each member has at LEAST 1 attribute in common with other members
    - - Typicality
        
        (Rosch & Mervis, 1975) Eg. Different types of fruit rated for typicality on a (1-7) scale: typicality is not uniform
        
        Orange (1.07), coconut (4.50), tomato (5.57)
      - Typicality + Family resemblance = high correlation
        
        (Rosch & Mervis, 1978) Ss listed attributes possessed by each exemplar of a category (eg. bird)
        
        Derived the family resemblence score: sum of the weighted scores of attributes
        
        Greater weight given to attributes shared with other exemplars
        
        Robin = 13, Swallow = 13, Penguin = 3
        
        High correlation between rated typicality and family resemblance score
        
        Eg. Robin (high) VS penguin (low scores): typical exemplars share more attributes in common with other members of the category
        
        Prototype: member of a category having the highest family resemblance scores (eg. apple prototype of fruits)
      - Internal structure
        
        Family resemblance: better exemplars share more attributes in common with other exemplars
        
        A category is centred around a prototype
        
        Typicality gradient: some members of a category are better exemplar (more typical) than others (eg. apple more typical than coconut)
    - - (McCloskey & Glucksberg): Asked Ss to decide category membership for each exemplar-category pair twice a month apart
      - Some exemplars (eg. bookend-furniture) low between-subject agreement and within subject consistency
      - Artificial categories: boundaries are clearly defined
  - - - Subordinate (furniture) --> Basic-level (chair, desk) ---> Subordinate (arm chair, office chair)
      - Basic level category: best balance between informativeness & distinctiveness
      - Many attributes are common to members within the category (subordinates)
      - Few attributes in common with members of other categories
      - Most general level: people use similar motor movements when interacting with category members (eg. chairs but not tables sitting)
      - Informative
        
        Communication = effective (Chair broke, not a piece of furniture broke)
        
        Learned first by children: car not vehicle
        
        Expressed economically: short name (chair > arm chair)- simple sign language gestures
    - - Experts prefer subordinate names
        
        Faces: matched faster to subordinate level than basic level (Benedict Cumberbatch > a human face)
        
        Dog experts: Beagle, Terrier, Labrador
      - Is expertise necessary for "subordinate shift" (Anaki & Bentin, 2009)
        
        Down-shift from basic to subordinate level: familiar objects as well as faces/objects of expertise
        
        Eg. towers, bridges that are familiar
        
        Superordinate: Living/non-living, Basic: monkey/human (face), tower/bridge, Subordinate (exemplar) familiar faces & towers (eg. Benedict Cumberbatch, Tower of Pisa)
  - - - Feature dimensions are not independent, they are correlated
      - If category member "Has feathers' it is also likely to "has a beak" but not "has petals"
      - Artificial categories: feature dimensions are independent & can be combined arbitrarily "black" with "circle" or "square" or "cross" equally
    - - Artificial category learning study (Bruner 1956)
        
        Shape (3), Colour (3), No of shapes (3), No of borders (3)
        
        S Task: work out concept experimenter has in mind
        (Eg. Black (colour) cards with circles (shape)
        
        Concept universe: 4 feature dimensions (3 x 3 x 3 x 3 = 81 possible cards)
        
        Features may be combined arbitrarily