Lecture 8 – Categories 1 great detail is needed for planning & executing current responses some detail about context of new learning can be emcoded with that learning, and be a cue. less detailed representation is more useful for learning general, widely-applicable lessons for future reference Review: more detail vs. less detail

Lecture 8 – Categories 2 My dog sleeping. My dog. All golden retrievers. All dogs. All canines. All mammals… Each of these is a category. Categorization is the process of deciding which details matter, and which don’t, for some purpose. Categorization

Lecture 8 – Categories 3 Bruner, Goodnow, & Austin (1956): reduce complexity of environment generalize lessons guide choice of response make hierarchical knowledge available Advantage we gain by categorizing things:

Lecture 8 – Categories 4 1. What is the structure of natural categories like? That is a question about the world. 2. How is information about natural categories represented in memory? That is a question about your mind. Two questions about categories:

Lecture 8 – Categories 5 Is this a question about the world, or about us? Which two are most similar: sheep, goats, cows? To some extent, structure of natural categories is given by the world. To some extent, it is impressed upon the world by human cognition. 1. The structure of natural categories

Lecture 8 – Categories 6 Most important work was done by Eleanor Rosch. A.Hierarchy. Rosch argued that our categorical knowledge is organized in a hierarchy: superordinate, basic level, subordinate. This is a relation of containing. 1. The structure of natural categories

Lecture 8 – Categories 7 Mammal DogsCatsHorses Collie AiredalePersian SiameseArabian There are three levels in this hierarchy. They are not all equal.

Lecture 8 – Categories 8 The basic level is the most important one Between superordinate and subordinate It’s the one we use when we name an object. It’s the one children learn first. Basic level

Lecture 8 – Categories 9 Superordinate – items in a category can be very different from each other. E.g., table, chair, lamp. Subordinate – items in different categories can be very similar to each other. E.g., dining room chair, patio chair. Basic level – items similar to others in category, different from those in other categories. Shared characteristics

Lecture 8 – Categories 10 Things in the world present themselves in a hierarchy of levels of categorization At basic level, items in a category look like each other but not like members of other categories. Basic level is one used spontaneously in naming objects. Review - Hierarchy

Lecture 8 – Categories 11 Rosch argued that some members of a category are “better” than others – that is, more typical. such members have ‘family resemblance.’ typical members are similar to other members, unlike non-members of category Rosch’s second contribution - Typicality

Lecture 8 – Categories 12 Some categories are natural – e.g., mammals. Some categories are artificial – e.g., all animals that weigh more than 100 lbs. Some are functional: things to bring out of the house in the event of fire. 2. The representation of natural categories

Lecture 8 – Categories 13 Four models: Prototype Feature frequency Nearest Neighbour Average distance 2. The representation of natural categories

Lecture 8 – Categories 14 A prototype is a typical member of a category Prototype theories say that, through experience, we create a central example of each category. Prototype may exist only in your mind (e.g., not as an actual object in the world). Prototype models

Lecture 8 – Categories 15 Categorization is based on how many features the to-be-classified object shares with each of the available categories. E.g., a whale shares ‘breathes air’ and ‘gives birth to live young’ with mammals. It shares ‘lives in the ocean’ and ‘moves by tail and flipper action’ with fish. So we should expect confusion about whales Feature frequency

Lecture 8 – Categories 16 New object is compared with each exemplar of each stored category. Compute difference between object and each exemplar in each category. New object is classified in category containing object it is most similar to (smallest difference). Nearest Neighbour

Lecture 8 – Categories 17 Same comparison of new object to all stored exemplars of categories as in nearest neighbour. Decision based on which category has smallest average distance from the new object. Compare with Nearest Neighbour model – here, it is average distance for the category, not just which exemplar is closest, that counts. Average Distance

Lecture 8 – Categories 18 Two fundamentally different views of how we store category information: Prototype model ‘what is generally true’ about something is stored and available when needed this view emphasizes abstract representations Review – models of representation

Lecture 8 – Categories 19 Review – models of representation N.N. and A.D. models ‘what is generally true’ is not stored, but computed when needed. these views emphasize storage of individual experiences with objects