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34 Cards in this Set
- Front
- Back
What are necessary and sufficient features? |
Necessary: if any of these features are missing, it is not a member of this category Sufficient: if it has all the features, it is definitely a member of this category |
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What is the first claim of the Classical View of Concept Formation? |
1. Concepts are mentally represented as definitions. A definition provides the necessary and sufficient features for membership to that category. |
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What is the second claim of the Classical View of Concept Formation? |
2. Every object is either in or not in a category. There are no in between cases. |
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What is the third claim of the Classical View of Concept Formation? |
3. Every member of category is thought to be as equally representative of a category as every other member. |
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What is Family Resemblance? |
A weighted sum of the featural overlap amongst category members. |
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What is the relationship between typicality and family resemblance? |
The graded typicality structure that we see inthe categories is well described by the featural overlap of the categorymembers |
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What is the Polymorphous Concept Model? |
The representativeness of anexemplar is a function of the degree of overlap between the features associatedwith the exemplar and the features associated with the category. |
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What is graded structure caused by? |
Thereappears to be shared variance between typicality and familiarity –butfamiliarity did not contribute to graded category structure |
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What are some other variables that are related to graded structure? |
- Ageof Acquisition (the estimated age at which a word is learned) - Wordfrequency (the frequency with which a word is found in text corpora) - Imageability (the ease with which an object canbe pictured in your mind) |
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What is the Contrast Model? |
According to the contrast model the similaritybetween the exemplars i and j can be calculated from the featurescommon to the two exemplars, the features of i that are not present in j,and the features of j that are notpresent in i |
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What are other ways apart from MDS to represent similarity relations? |
- Additive trees - Additive clustering |
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What are some criticisms of similarity? |
- Our representations must be based on data far richer than similarity? - By explaining everything, it explains nothing |
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What is the prototype view? |
A category representation consists of asummary of all of the examples of the category |
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What is the exemplar view? |
A conceptual representation consists of all the individual members of a category |
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What are the costs of the prototype and exemplar view? |
- The prototype view is useful because it reducesmemory load; BUT–this reductioncomes at the cost of specific information - The exemplar view is useful because it retainsspecific information; BUT it comesat a cost of memory load |
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Is the Family Resemblance model exemplar or prototype? |
The familyresemblance model predicts typicality as a function of featural overlapbetween category members (exemplar) |
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Is the Polymorphous Concept model exemplar or prototype? |
The familyresemblance model predicts typicality as a function of featural overlapbetween category members (exemplar) |
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What is the prototype measure of typicality? |
Typicalitycan be predicted as a function of the distance between each category member ina multidimensional space and the central tendency of that category |
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What is the exemplar measure of typicality? |
Typicalitycan also be predicted as a function of the mean distance between each categorymember in a multidimensional space and each other category member in that space |
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What are the two phases of category learning experiments? |
- A learningphase – how long does it take/how well can you learn a category structure - A transfer phase – presented with unseen stimuli from the categoryand make inferences about their category |
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What is the process of category learning experiments? |
- In the Learning Phase participants are shownstimuli drawn from two categories - They are asked if the stimulus is from categoryA or B - They are given feedback - They learn the categories over time - Once the category structure is learntparticipants are shown a mixture of old and new stimuli, and asked tocategorize them. - This gives us insight intoour ability to generalize from a stored category representation to novelstimuli |
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What is the Generalised Context Model? |
The probability of a stimulus being categorizedas a member of a given category is a weighted function of the distance betweenthe target stimulus and the members of the two categories in the space(exemplar). |
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What is the MDS-based Prototype Model? |
The probability of a stimulus being categorizedas a member of a given category is a weighted function of the distance betweenthe target stimulus and the prototypes (central tendencies) of the twocategories in the space |
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What is the Varying Abstraction Model? |
Some form of partialabstraction could be used to describe the empirical categorizationdecisions |
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What are the three levels of concept hierarchy? |
Super-ordinate, basic, and sub-ordinate |
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What are the super-ordinate, basic, sub-ordinate levels? |
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What are the implications of the levels of the concept hierarchy? |
- Because of the high level of abstraction super-ordinate categories lack informativeness - Sub-ordinate categories are highly informative, but lack distinctiveness - Basic level categories appear to contain the best balance between informativeness and distinctiveness |
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What are contrast categories? |
Categories that exist within the same domain, and at the same level of abstraction |
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What is Tversky's Contrast Model? |
Accordingto the contrast model the similarity between the exemplars i and j can be calculatedfrom the features common to the two exemplars, the features of i that are not present in j, and the features of i that are not present in i |
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What did Dry & Storm find regarding contrast categories? |
- Typical exemplars within one category wouldappear to have more features that are distinctive to their respective category, and fewerfeatures that are distinctive to the contrast category - Common features tend to pull categoriestogether; distinctive features tend to help delineate the borders ofcategories. |
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What is deductive reasoning? |
Involves using given true premises to reach aconclusion that is also true. - Allmen are mortal. - Socratesis a man. - Therefore, Socrates is mortal. |
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What is inductive reasoning? |
Isprobabilistic -it only states that, given the premises, the conclusion isprobable. - 7-10%of males are red-green colour-blind. - Joeis a male. - Therefore, the probability that Joe is red-greencolour-blind is 7-10% |
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What is premise monotonicity? |
- Whenmore categories are added to the premise the argument is stronger. - Whennegative evidence is added, thelikelihood of accepting the conclusion should decrease |
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What is premise diversity? |
Themore diverse the premise categories are, the stronger the argumen |