a Joerg Bauer 17 éve
419
Még több ilyen
Function and appearane are imp as well
Boat-Ship: Call it whatever
Trout-Bass: Ask an expert
Concepts dirven by context and content
50% both true, fals
50 "Yes, cat
Categorizatio also driven by function, location socio-historical context
Pond is water although judged to contain only 63% H20
Tears are not water although rated to contain 87% H20
Its rather knowledge than theory
Underspciefied: How are complex categories/ theories combined?: pet fish
Good because it hints to problems with "similarity"
Developemental Evi: Kiel: characterisic to defining shift
Murpy: They dont know about biological categories
Racoon disguised as a skunk
Looks and Behaves like Zebra: 4 yrs-zebra 7 yrs-horse
Kroska and Goldstone: 2 emo Szenario categorzed as fear but more similar to joy
Rips Pizza Dissotiation: More similar to quater but more likely (=categorized) a pizza
Unclear Definition: Are prototypes lists or typical members?
Family resemblence often predicts typicality scores
Explains many categories that lack clear definitions (game, furniture)
Semanitc Transfer?: Complex Concepts Lead to wrong interpretaions in real life: pet-fish = dog trout
Barsalou: goal derived categories have no family resemlance: presents that john likes
Medin & Shoeben: typicality context dependent: kaffeelöffel paradox: large wooden>small wooden
Implies we only use lists of attributes but we also use reason: blue bird=probably "warrum," but fat man is prob not a "klatau"
Hampton: Some astract concepts (belief) has no prototype
Roesch and Mervis: Robins share more properties with other category member than penguins
Armstrong: defined cat seem to have typiclity: female: mother vs. policewoman
Dual Proces: conept core to judge generel membership, prototype to evlauate instances
Mervis & Roesch: < RT faster and less errors for typical examplars
Calculate family resemblecne: one shared by 16, one by 14=30
high typicality instances match on high weighted values
Statistacl distribution determines weight
More similar features - quciker match
comparing of features with stored rep
Weighting=Cue Validity= Some features more important than others e.g. bird=feathers
In some natural objects, attributes cluster together
Essentialism: Consistent with (current) expertise
Theory: If thing need explanations: This guy is intoxicated: Thats why he jumps the pool
Classical: Exact definitions needed: law
Prototype: Fast and Superficial: Fast decisions under uncertainty
Borderline Cases
McCloskey & Glucksberg: yes/no
Where does red turn into orange
Possilbly: Lack of knowledge
Typical items: chair= furniture: high over time and people
bookends =furniture?: changes across people and time
Intransitivity: big ben = clock = furniture
Bad RTs: RTs not a good predictor
is a dog an animal < Is a dog a mamal
Varying Knowlege: Experts vs. Normal people
Does not explain Typicality effets
Some things dont have definitions - more holistic
Cats would still be cats even if turs out they are robots from mars
Some Categories have no def. features
Example: Furniture / chair
Wittgenstein: Games are similar
Typicality more important than defining features:
Defined by functional /not defined by pyhsical properties
No neccessary and sufficient properties for all chairs
A canary is an animal > A canary is a bird
1. Some propertes in beginning / Most at end
Everybody represents in this way
All concepts are represented this way
are equally good
share a) neccessary and b) sufficient features
All or none are members
abstract, by relations
by Prototype
by Rule / defintion
Aquired by autobiographical experience
Procedural Memory
Schemas
Rule based "What to do in a Restaurant"
Semantic Memory
Propositions: "Dogs" bark, Dogs have fur, Dogs are pets