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Chapter 9

Chapter 9.doc

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Judith Shedden

Chapter 9: Concepts and Generic Knowledge - Ordinary concepts are the building blocks of which all our knowledge is created Definitions: What do we know when we know what a dog is? - One possibility is that we may have a dictionary definition (looking for common elements) o th Wittgenstein (20 century philosopher) noted that philosophers had been trying to define terms like knowledge… but these terms are still without accepted, full definitions o Example: the word “game” Family Resemblance - What we need is a way of identifying concepts that highlights what the members of a category have in common, while allowing exceptions - We can keep the content of our definitions but be more flexible in how we use the definitions - We can say “with these conditions, it is probably a dog, and without these features, it is unlikely to be a dog” o These phrasings allow some degree of uncertainty, some number of exceptions to the rule - Wittgenstein’s proposal: members of a category have family resemblance to each other o Family resemblance: features that are common in the family o Imagine the “ideal” for each family – and then each member of the family will have atleast some features in common with this ideal o The more the features an object has, the more likely that we are to believe it is in the category o Family resemblance is a matter of degree, not all-or-none Prototypes and Typicality Effects - Boundaries set the “boundaries” for a category - Prototype theory: perhaps the best way to identify a category is to specify the “center” of the category rather than the boundaries - All judgments about dogs are made with reference to this ideal (comparison between test case and the prototype in your memory) - Prototype = ideal for the category (average of the various category members you have encountered) Fuzzy Boundaries and Graded Membership - Since the category is characterized by its center (the prototype), and not by its boundaries, there’s no way that we can say whether something is inside of the category or outside o Each category will have what’s called a “fuzzy boundary” – with no clear specification of category membership and nonmembership o Objects closer to the prototype are “better” members of the category than objects farther from the prototype – thus categories that depend on a prototype have graded membership Testing the Prototype Motion - Sentence Verification task: participants are presented with a succession of sentences; their job is to indicate whether each sentence is true or false - Participants respond more quickly for true sentences than for false, and also more quickly for familiar categories (“a penguin is a bird” vs “a robin is a bird”) o Participants compare object with prototype - Production task: We ask people to name as many birds or dogs as they can o They will start at the center of the category and work their way outwards o First birds mentioned should be the birds that yielded the fast response times in verification (this is what happened) o Members of a category that are “privileged” on one task turn out to be privileged in other tasks The Converging Evidence of Prototypes - Sentence Verification: Items close to the prototype are more quickly identified as category members; category membership is determined by assessing the similarity to the prototype - Production: Items close to the prototype are earliest and most likely to be mentioned in a production task (begins with the prototype and works outwards) - Picture identification: Pictures of dogs for ex, similar to the prototype are more quickly identified - Explicit judgments of category membership: explicitly asked participants to judge how typical various category members were for the category (“typicality” studies) o Closer to the prototype; more “birdy” or “doggy” - Tasks asking people to think about categories: o First, ask participants to make up sentences about a category (e.g. “I saw two birds in a tree”) o The experimenters rewrite these sentences, substituting the category name either the name of a prototypical member of the category or not-so-prototypical member o Different participants rate how silly or implausible the sentences seem o Hypothesis: Thinking about a category, they are in fact thinking about the prototype for that category Basic-Level Categories - Rosch’s other theme: Rosch argued that there is a “natural” level of categorization, neither too specific nor too general, that we tend to use in our conversations and our reasoning - This is the “basic-level categorization” - Basic-level categories are identified as a single word, while more specific categories are identified only via a phrase: “chair” is basic but “kitchen chair” is not at basic-level - When asked to describe an object, we are likely to use the basic-level term - We have an easy time with basic-level categories, but some difficulty with more-encompassing categories - Memory errors experiment: Participants read a story and their story memory was tested o Some specific terms, participants falsely recalled that they heard something more general (e.g. “jeans her stained” to “pants were stained”) o Conversely, if the story had general terms, they were misremembered as being more specific (“animals” to “dogs”) o Each time, we revised the story in the direction of basic level categorization - Basic level categories seem to reflect a natural way to categorize the objects in our world Exemplars Analogies from Remembered Exemplars - Categorization can draw on knowledge about specific category members rather than on more general information about the overall category - Exemplar-based reasoning: when categorization is supported by memories of a specific item, rather than remembered knowledge about the item in general - Very similar to the prototype view: o Categorize objects by comparing them to a mentally represented “standard” - Different than prototype view: o What the standard is: standard in prototype is an average representing the entire category; standard in exemplar is provided by whatever example of the category comes to mind - Process is the same: resemblance is great, you judge the candidate as being relevant… if minimal, you seek some alternative categorization Explaining Typicality Data with an Exemplar Model - Exemplar-based approach can also explain the graded-membership pattern - People make their judgments in a task by comparing the pictures to specific memories of fruits – that is, to specific, mentally represented examples - If you see a picture of an apple, your memory search will be fast because apples are common in your experience so you’ve had many opportunities to establish apple memories (well primed) - Production task: you’ll quickly name apples and oranges because these fruits are represented many times in your memory (compared to fig) o Production will favour the typical fruits – not because of prototyping but because of the pattern of what’s available in memory Exemplars Preserve Information about Variability - Exemplar-based views can easily explain typicality effects - Items that are frequent in the world will be frequently represented (and well primed) in memory, and so will readily come to mind - The graded-membership pattern favours neither the prototype nor the exemplar theory – both are compatible with the evidence The pliability of mental categories - Our concepts may be more pliable/flexible – and this flexibility is easily accommodated by the exemplar view - Participants were asked to judge how typical various birds were from an “American point of view” and then a “Chinese point of view”… Chinese point of view, participants rated swan and peacock as more typical than robin/eagle - Barsalou notes that there are “goal-derived categories” and completely “ad hoc categories” o Goal-derived categories: “Things to eat on a diet”, “Things to carry out of your house in case of a fire” o Ad-hoc categories: “Things that could fall on your head” or “things that you might see in paris” - We must include more than prototypes – people must also have some knowledge that allows them to “adjust” their prototypes, and to create new prototypes whenever they are needed - Exemplar-based view: Category judgments depend on specific examples that come to mind and we have already noted that different exemplars may come to mind on different occasions – the search for remembered exemplars can be influenced by cueing and priming o No problem with ad hoc categories o There is no reliance here on prototypes, there is no issue of creating ad hoc prototypes on the spot o There is no difference between how people judge familiar categories and how they judge ad hoc categories: In both cases, they draw on a remembered instance and base their judgment on that instance Exemplar Use and Concept Use - Advantages of prototypes: represent in an efficient and economical manner what’s typical/common - Perhaps our mental representation of a category includes both prototype and exemplar information - Hybrid between the two models: eg. Child sees a camel for the first time; then for that moment, this remembered exemplar represents the full breadth of the child’s camel knowledge… after seeing a few more, the child may “average” them together to create a camel prototype o Novice: has to use exemplars o Experienced category users: will have many examples to create a prototype - Experiment: use novel (fictional) categories to ensure that participants are unfamiliar with the category at the start (e.g. this animal is a “lepton”) o Lepton picture: has a long neck, a few with shorter necks o If you use prototype: then if you see a lepton with a short neck… you’d say no (because it doesn’t resemble the average long-necked lepton) o If you use exemplars: you’d compare it to a bunch you’ve seen, and then you may say yes o Your response will tell what sort of representation was guiding your judgment - Early in category learning, participants say yes to test pictures that resemble specific leptons that they’ve already seen, even if the test pictures show creatures that aren’t typical for the category o This suggest a reliance to exemplars - As training proceeds… the pattern reverses o Participants say yes to the picture if it resembles the category average - Both theories may captur
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