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Concepts and Categorization.docx

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University of Guelph
PSYC 2650
Dan Meegan

Concepts and Categorization 10/26/2012 9:45:00 PM Concepts and Categorization Think back to your childhood. Your mother was showing you a picture book, and labeling all the objects depicted, "chicken, bluejay, dog, donkey, goldfish, salmon, whale, chair, desk, clock...." After a few repetitions of the book, your mother (or father) would ask questions about the pictures in the book; "What is this?" or "Which color is that?". Not only did you learn to know the specific labels for the objects, but you also could identify new birds or fish or furniture that you hadn't seen before. Furthermore, you seemed to have developed an implicit knowledge of the features that were important for identifying each category - the large piece of six-foot high furniture in a neighbor's home was still a clock because it had a round face with numbers. How did you, as a child (and now as an adult with even more complex category knowledge) store all this information? How were you able to retrieve it when a little brother or sister came along, and asked you, "What is a clock?" or "Is this a bird?" How are concepts represented? Classical view: concepts as definitions  Dog is a creature that: o Is mammalian o Has 4 legs o Barks o Wags its tail, etc.  When asked whether a candidate creature is a dog, we use our definition as a checklist o Can‟t check off everything  not dog  Problem: difficult to come up with a complete definition of most concepts, especially abstract concepts (Wittgenstein) o Dog is a creature that:  Has 4 legs  what about a dog amputee?  Barks  what about the Egyptian basenji? Terminology  Probabilistic definitions: keeping the content of our definitions bet being more flexible in our use of our definitions o A dog is a creature that probably has four legs and barks and a creature without these features is unlikely to be a dog  Family resemblance: some categorizes are „fuzzier‟ than others, in reality, very rare to find family that has all same features o If enough features overlap in family will see resemblance o Matter of degree not all or none  Necessity: is there something individual member of category needs to have in order to be in category  Sufficiency: is there one thing that member needs to be sufficient in category Lab #6:  How is categorical knowledge represented in semantic memory networks  A hypothesis… Collins and Quillian (1969): 2 characteristics  Two important assumptions o Principle of inheritance applies  Features are only stored once and as high as possible in hierarchy (reduces redundancy)  If canary is below bird in hierarchy, then it would inherit all traits that birds and above in hierarchy have o Network has hierarchical structure  One item is found under one concept, which itself is nested within another higher concept  The original item belongs to both higher-level concepts  Response times of subjects on each sentence are measured and used as evidence for the existence of this network  One type of sentence expresses semantic (factual) relations o "A canary has skin"  The semantic distance between two words will be determined by the connections in the network and is expressed by the shortest interval between these two words o For instance, the semantic distance between "canary" and "bird" is 1, while it is 2 between "canary" and "animal". Thus, the semantic distance in the sentence "A canary has skin" is 2 o With correct sentence, people take longer to make their decision as the semantic distance increases  When sentence is incorrect the reverse applies (response time will be longer when the semantic distance between two words is smaller) o Need to have incorrect sentences to force participants to verify the truth and see RTs Typicality Effect  Some members of a category are more typical of that category than others  Participants react more quickly to sentences like “A robin is a bird” than to sentences like “A chicken is a bird”  Picture identification o See pictures of dogs etc. o Press “yes” if a dog, press “no” if not, RT is measured o German Shepherds and Collies identified more quickly than Chihuahuas and Dachshunds o Former are more similar to prototypical dog  Thinking about categories o Shown sentences “I saw two birds in a tree” o Sentences were then substituted by members of category  E.g. “I saw two penguins in a tree” or “ I saw two robins in a tree” o Participants were asked to rate sentences based on how implausible they were o When people hear sentences they immediately visualize the prototypical member and thus when a member that is close to prototype is substituted in the mental image only varies slightly o When a member far from prototype is substituted the image looks absurd and therefore is rated higher on ridiculousness Prototype Theory  Prototype represents the central tendency for a given category - no need for defining features, no boundaries on definition of category o „morph‟ all dogs together o Not like any specific dog you have seen before, it would have all of the common features that dogs have and would be used as basis for „mold‟ for dogs  See furry creature, the more features it has in common with prototype, the more likely it is a dog  Since prototypes are center of category, no way of knowing whether you are in or outside of category o Fuzzy boundary  no clear specification of category membership or non-membership o Graded membership  not all category members are equal (some dogs are doggier than others)  Typicality effects are determined by similarity to the prototype o More similar to prototype, faster RTs o Rate 1 to 7 how typical of a category different items are  Production tas
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