Podcast
Questions and Answers
Which view emphasizes total abstraction in category learning?
Which view emphasizes total abstraction in category learning?
- Mixed view
- Exemplar view
- Prototype view (correct)
- Varying Abstraction Model (VAM)
What does the exemplar view emphasize in category learning?
What does the exemplar view emphasize in category learning?
- High memory load
- Partial abstraction
- Zero abstraction (correct)
- Total abstraction
Which model performs better on category learning tasks, suggesting the storage of concepts using an exemplar representation?
Which model performs better on category learning tasks, suggesting the storage of concepts using an exemplar representation?
- Varying Abstraction Model (VAM)
- Generalized Context Model (GCM) (correct)
- MDS-Based Prototype Model (MPM)
- Mixed Model
What is the primary concern regarding the storage of every example of categories in memory?
What is the primary concern regarding the storage of every example of categories in memory?
What do computational models for concepts and categories require assumptions about?
What do computational models for concepts and categories require assumptions about?
Which model suggests that some form of partial abstraction can describe empirical categorization decisions?
Which model suggests that some form of partial abstraction can describe empirical categorization decisions?
What is the impact of level of abstraction on the quality of model predictions in some cases?
What is the impact of level of abstraction on the quality of model predictions in some cases?
What do different models predict typicality based on?
What do different models predict typicality based on?
What do category learning experiments involve in terms of stimuli?
What do category learning experiments involve in terms of stimuli?
What is involved in the third stage of category learning experiments?
What is involved in the third stage of category learning experiments?
Which model simulates human performance on category learning tasks better, suggesting a mixed model may be more appropriate?
Which model simulates human performance on category learning tasks better, suggesting a mixed model may be more appropriate?
What does the Prototype view emphasize at the cost of losing specific information?
What does the Prototype view emphasize at the cost of losing specific information?
The prototype view suggests that people abstract out the central tendency of a category based on:
The prototype view suggests that people abstract out the central tendency of a category based on:
The exemplar view suggests that we store in memory every example of a given category that we encounter, emphasizing:
The exemplar view suggests that we store in memory every example of a given category that we encounter, emphasizing:
Conceptual representations are potentially useful because they provide a means of reducing the amount of data needed to be stored in memory. This statement is linked to the concept of:
Conceptual representations are potentially useful because they provide a means of reducing the amount of data needed to be stored in memory. This statement is linked to the concept of:
What is the major assumption of the prototype view?
What is the major assumption of the prototype view?
The exemplar view suggests that experience with a category doesn’t lead to the formation of an abstracted prototype, but rather:
The exemplar view suggests that experience with a category doesn’t lead to the formation of an abstracted prototype, but rather:
The prototype view is based on the idea that, on the basis of experience with the category examples, people:
The prototype view is based on the idea that, on the basis of experience with the category examples, people:
The exemplar view suggests that we store in memory every example of a given category that we encounter, rather than:
The exemplar view suggests that we store in memory every example of a given category that we encounter, rather than:
The prototype view emphasizes the abstraction of the central tendency of a category at the cost of:
The prototype view emphasizes the abstraction of the central tendency of a category at the cost of:
The exemplar view emphasizes the storage of all the individual members of a category, at the potential cost of:
The exemplar view emphasizes the storage of all the individual members of a category, at the potential cost of:
The primary concern regarding the storage of every example of categories in memory is:
The primary concern regarding the storage of every example of categories in memory is:
The idea that conceptual knowledge is stored in the form of an abstraction is a major assumption of:
The idea that conceptual knowledge is stored in the form of an abstraction is a major assumption of:
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Study Notes
Prototype vs. Exemplar Views in Category Learning
- Prototype view emphasizes total abstraction, while exemplar view emphasizes zero abstraction
- Prototype view reduces memory load but loses specific information, while exemplar view retains specific information at the cost of memory load
- Plausibility issues raised regarding the storage of every example of categories in memory
- Computational models for concepts and categories require assumptions about data representation
- Different models predict typicality based on featural overlap and level of abstraction (prototype or exemplar)
- Level of abstraction has little impact on the quality of model predictions in some cases
- Category learning experiments use stimuli that are easily manipulated, with learning and transfer phases
- Third stage of experiments involves computationally modeling categorization decisions and response latencies
- Generalized Context Model (GCM) and MDS-Based Prototype Model (MPM) simulate human performance on category learning tasks
- GCM performs better than MPM, suggesting storage of concepts using an exemplar representation
- Question raised about using either exemplar or prototype representation, suggesting a mixed model may be more appropriate
- Varying Abstraction Model (VAM) suggests some form of partial abstraction can describe empirical categorization decisions
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