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U-M PSYCH 240 - Semantic Memory and Categorization
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PSYCH 240 1ST Edition Lecture 11 Outline of Last Lecture : AmnesiaFinishing up Working Memory LectureIV. Tripartite Theory of Working Memory (continued from last week)I. Explicit and Implicit MemoryII. AmnesiaIII. PET Study of Implicit/Explicit MemoryIV. Behavioral Study of Implicit/Explicit MemoryOutline of Current Lecture: Semantic Memory and CategorizationI. Semantic Memory CategorizationII. What defines a mental category?III. Theories of Similarity JudgmentsCurrent LectureSemantic Memory and CategorizationI. Semantic Memory and Categorizationa. Memory taxonomy: semantic, episodic, impliciti. Distinctions w/in Explicit Memory1. Explicit/declarative: conscious, verbalizable, knowing thata. Episodic: memory of personal episodesi. Tied to a specific time and placeii. Personal perspectiveiii. First kiss, walking to class this morning, game vs. WisconsinThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.2. Semantic: general knowledge memory of factsa. Not tied to specific time or placeb. Pittsburgh is in PA, 2+2 = 4, birds have wingsb. Categorization as a fundamental process1. Typical category: birdsa. Big Bird, Roadrunner, finch, blue jay, penguinii. Why do we classify things?1. The world is “clumpy”2. We treat similar things the samea. Food b. Enemies iii. How do we know that something is in the same category? How do we know that a new breed of dog is a dog?1. Categorization allows inferences about members of a classa. Categorization by Pigeons (Wasserman et al. 1987)i. Experiment 1. Pigeons Peck one of four keys depending on stimulus2. Trained for 30 daysii. Results: 1. 81% accurate with old exemplars2. 64% accurate with new exemplarsiii. PIGEON WAS ABLE TO ABSTRACT CATEGORY FROM EXAMPLESiv. Categorization by conceptual knowledge1. Categories are not always similarity-baseda. Hawk is a bird (like flamingo) but visually similar to batb. Gelman and Markman (1983): children as young as 4 will say the black bird feeds mashed up food to young (not milk)II. What defines a mental category?a. Classical view: defining featuresi. Defining properties: necessary and sufficient1. Bachelor = unmarried adult male2. Dog = mammal, four legs, barks, wags tailii. Many scientific classification systems are based on defining properties BUT… our brains may not always categorize things by such rulesiii. Problems with Classical View1. Some things do not have necessary and sufficient conditionsa. Game? Basketball chessi. What makes something a “game”?2. Problems with exceptions: a. In a monk a bachelor?b. Is a game-show “reality TV”?b. Modern view: characteristic and probabilistic featuresi. Probabilistic Categories1. Psychologically, properties/features are characteristic not defininga. Something that belongs to a category if it is similar to members of that categoryb. Some members have more characteristic properties than othersc. Category boundaries are fuzzyi. Fuzzy setsii. Probabilistic: properties of concept probably, but do not always, belong to the conceptc. More examples of Typicality: i. Sentence verification1. Judge each of the following as “true” or “false”a. A robin is a birdb. A chicken is a birdc. Tennis is a sportd. Curling is a sport2. Results: people are faster to verify more typical exemplars than less typical exemplars (quicker to say robin or tennis than chicken or curling)ii. Hedges1. Which of the following are you more likely to hear?a. A whale is technically a mammalb. A cow is technically a mammal2. Such linguistic “hedges” only necessary when the classical categorization system (mammal: animal that produces milk) does not co-occur with a psychologically important characteristic property (lives on land)III. Theories of similarity of judgmentsa. Typesi. Exemplar Theories1. Exemplar: example of a category2. Multiple exemplars are stored in memory3. Categorize new things based on similarity to stores exemplars (e.g. closest exemplar)ii. Prototype Theories1. Prototype: a best, ideal, or average example2. Only a “prototype” is stored in memory3. Categorize based on similarity to prototypeb. Concepts may lie in a geometric space OR similarity may be based on concordance of multiple features (see Smith article)c. Geometric Theory and Problemsi. Geometric similarity: start with subjective ratings1. On a scale of 1-6, how similar isa. Apple to a plum?b. Plum to a lemon?c. Apple to a lemon?d. Olive to a plum?2. These ratings can be used to place the concepts in a geometric space (Using “multi-dimensional scaling”)ii. Geometric Representation1. Similarities may lie in a geometric space2. “watermelon” and “honeydew” are closer than “honey dew” and “tomato”iii. Metric Axioms1. If concepts are really represented in a geometric space similarities should satisfy certain properties (axioms) of (geo)metric space: a. Minimality: the dissimilarity between concept and itself must always be the smallest possiblei. But, a highly familiar concept is rated as more similar to itself than one that is less familiar1. Apple-apple more similar than pomegranate-pomegranate2. New York-New York more similar Butte-Butteb. Symmetry: the similarity between two concepts must be the same regardless of the orderi. How similar is an apple to a plum?ii. How similar is a plum to an apple?1. People will say that an apple is more similar to a mango than a mango is to an apple. a. This is violation of symmetryiii. Experiments on people’s ratings of similarities have found that: 1. An unfamiliar category is judged more similar to a familiar category than vice versaa. “pomegranate” judged more similar to “apple” than “apple” to “pomegranate”c. Triangle Inequality: if one concept is similar to a second concept, and the second concept is similar to the third concept, then the first and the third must be reasonably similari. Jamaica is similar to Cubaii. Cuba is similar to Chinaiii. But Jamaica and China are not similard.i. Lemon is similar to orangeii. Orange is similar to apricotiii. Lemon is not similar to apricotd. Tversky’s Contrast Modeli. People give similarity ratings inconsistent with geometric spaceii. Feature-based similarity approaches do not require these metric axiomsiii. Feature-based approaches look at features in common and different features1. Similarity of L and O = (features that L and O have in common)Minus


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