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8.PSY270 Knowledge.docx

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University of Toronto St. George
Kristie Dukewich

8. Knowledge 12/3/2012 2:14:00 PM Knowledge, ch.8 1) Why are categories important? Why don’t definitions work?  Categories > definitions 2) Comparison approach: How can we categorize by comparing to a standard?  Categories determined by similarity: Prototypes/Exemplars 3) The Network Approach. What is a network, and how is info stored?  “Privileged” levels of categories? 4) Representing relationships between categories: Semantic Networks 5) Representing concepts between networks: The Connectionist Approach 6) Categories represented in the brain 1. Categories > definitions  A concept = a mental representation that is used for a variety of cognitive functions (memory, reasoning, language)  Categorization = the process by which things are places into groups (categories)  Categories help us to understand objects in our environment = for us to take mental shortcuts and use knowledge from our experiences  Semantic memory = facts + knowledge  Categories are “pointers to knowledge”. Once you know something is in a category, you know a lot of general things about it and can focus your energy on specifying what is special about it.  The definitional approach to categorization doesn’t work because most categories contain members that do not conform to the definition. o Many natural objects & man-made objects are hard to identify with a strict definition due to their diversity.  The philosopher Wittgenstein proposed the idea of family resemblances to deal with the fact that definitions do not include all members of a category. His solution expressed how similar characteristics can be expressed and grouped in “family resemblances”.  = less exclusive than definitions, which did not include all members of a category.  FR = refer to the fact that things in a particular category resemble one another in a number of ways, which allows for some variation within a category 2. Categories determined by similarity: Prototypes/Exemplars  The idea behind the prototypical approach to categorization is that we decide whether an object belongs to a category by deciding whether it is similar to a standard representative of the category – the prototype. o A prototype is formed by averaging category members a person has encountered in the past. o Whereas the exemplar approach state that the standard is created by considering a number of typical members of a category. Prototypes: Is it SIMILAR to a standard of the category? – Similar but not an actual member of the category = it is an average representation of the category  Prototypicality is a term used to describe how well an object resembles the prototype of a particular category.  Category of BIRD = HP = Sparrow. LP = Penguin  Category of FURNITIRE. HP = Chair, LP = Mirror  The following IS TRUE of high-prototypical objects: Is it a TYPICAL member of the category? i. They have high family resemblances ii. Statements about them are verified rapidly. (how rapidly people can answer Qs about an objects category – “yes”/”no”, for Qs like “An apple/ pomegranate is a fruit” = faster response for HP objects like apple = typicality effect) iii. They are named first (BIRDS - sparrows before penguins) iv. They are affected by priming (“Green” prototype = matches the “good” green, but is a poor match for the light green = priming results in faster “same” judgments for prototypical colours than for nonprototypical colours) Exemplars: also involved determining whether an object is similar to a standard object. But the prototype is an “average” member of the category, and an exemplar standard involves many real examples, each one called an exemplar  The exemplar approach to categorization involves determining whether an object is similar to an exemplar. o An exemplar is an actual member of a category that a person has encountered in the past.  An advantage to the exemplar approach = it takes into account individual cases (like birds that don’t fly), thus it doesn’t discard info about atypical cases within a category, such as penguin in the “bird” category. o The exemplar approach can also deal more easily with categories that contain widely varying members, like games. Researchers have concluded that people use both approaches to categorization.  Prototypes may be more important as people initially learn about categories, (when it’s harder to learn about exceptions like ostriches/penguins)  and then later exemplar info may become more important.  Exemplars may work best for small categories (e.g. US presidents), and prototypes best for larger categories (e.g. birds).  Protoypes = learnt first, for BIG categories (Birds, Cars)  Exemplars = learnt after prototypes (more info), for SMALL categories (Presidents) 3. “Privileged” levels of categories?  The kind of organization in which larger, more general categories are divided into smaller, more specific categories is called hierarchical categorization. o Furniture  Chairs  (Kitchen chairs, Sofas, Stools etc)  Experiments by Rosch indicate that a “basic level” of categories (such as guitar, as opposed to musical instrument/rock guitar) is a „privileged‟ level of categorization that reflects peoples everyday experience.  Basic level is most important, as it is the level above which info is lost, and below which little info is gained. o Superordinate (Furniture)  BASIC (Chair)  Subordinate (Kitchen chair)  Experiments in which experts were tested show that the basic level of categorization can depend on a persons degree of expertise. 4. Representing relationships between categories: Semantic Networks  How hierarchies of information are organized in the mind – explained with the semantic network approach  The semantic network approach proposes that concepts are arranged in networks that represent the way concepts are organized in the mind. Collins and Quillian’s model on semantic networks: is a network that consists of nodes that are connected by links.  Concepts and properties of concepts are located at the nodes. o Skeleton nodes are connected by links o Links between nodes are based on automatic associations = e.g. canary & bird, bird & animal  Properties that hold for most members of a concept are stored at higher-level nodes. E.g. Bird – “Can fly”, rather than having the “can fly” by each exemplar birds node like by the word canary. o Storing shared properties just once at a higher-level node is called cognitive economy.  We start by entering the network at the concept node (e.g. for canary  “can sing”, “is yellow”)  Concepts at nodes are most specific at the bottom, and more general on top  Going up: Canary  Bird  Animal  To deal with exceptions exemplars of birds which can’t fly like ostriches/penguins had “can’t fly” next to their specific bird type.  SUPPORTED - Collins and Quillian’s model o by the results of experiments using the sentence verification technique (SVT) based on travelling time of links between nodes  E.g. SVT - It will take longer to answer “yes” to a canary is an animal, than a canary is a bird. As it takes more time to travel along two links to get from canary to animals, but only one to get from canarybird o The spreading activation (SA) feature of the model is supported by priming experiments.  SA – is activity that spreads out along any link that is connected to an activated node (dashed lines alo
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