User:ElanJM/sandbox: Difference between revisions
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==Heading 1== |
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===Early Studies=== |
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==Intro== |
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[I am having trouble separating this and Possible Explanations] |
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[Another issue is staying on track. Most of the literature is about how we categorize information |
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The major dispute is against the Classical view and other views like the Prototype view] |
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[I still need to sort out a few things but there are excellent contradictory findings] |
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The typicality effect generally effects to the phenomenon whereby individuals respond faster to more typical examples of a category across many different types of tasks. <ref name=ER>{{cite journal|last=Rosch|first=Eleanor|coauthors=Simpson, Carol, Miller, R. Scott|title=Structural bases of typicality effects.|journal=Journal of Experimental Psychology: Human Perception and Performance|date=Jan 1 1976|volume=2|issue=4|pages=491–502|doi=10.1037/0096-1523.2.4.491}}</ref> |
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Rosch et al. 1973, 1975 used family resemblance to explain the categorization. They address the central tendency as a potential explanation. Also argue against frequency as a possible explanation |
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If a person is asked to think of an example of a bird, it is more likely that they picture a robin than a penguin or bat. <ref name=Rosch>{{cite journal|last=Rosch|first=Eleanor|journal=Journal of Experimental Psychology: General|year=1975|volume=104|issue=3|pages=192-233}}</ref> These findings were presented in the work of Eleanor Rosch, one of the early researchers to identify the effects of typicality who sparked an extensive topic in categorization literature. <ref name=BBC>{{cite book|last=Murphy|first=Gregory L.|title=The big book of concepts|year=2004|publisher=MIT Press|location=Cambridge, Mass.|isbn=0262632993|pages=22|edition=1st MIT Press paperback ed.}}</ref> <ref name=Rein>{{cite journal|last=Rein|first=J. R.|coauthors=Goldwater, M. B., Markman, A. B.|title=What is typical about the typicality effect in category-based induction?|journal=Memory & Cognition|date=16 March 2010|volume=38|issue=3|pages=377–388|doi=10.3758/MC.38.3.377}}</ref> |
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Rosch argues that many natural categories are continuous and are structured according to the degree to which they are judged to be good examples of that category. |
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Typicality in itself is a gradation in which items of a category can be either very typical to the prototype, moderately typical, or atypical. <ref name= BBCT>{{cite book|last=Murphy|first=Gregory L.|title=The big book of concepts|year=2004|publisher=MIT Press|location=Cambridge, Mass.|isbn=0262632993|pages=31|edition=1st MIT Press paperback ed.}}</ref> |
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Using this gradation researchers have conducted robust experiments on participants to tap into the way in which we store and represent categories in our mind. |
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There are conflicting findings and explanations for this phenomenon, all of which demonstrate the influence that typicality gradient has on the way we categorize objects. <ref name=Barsalou>{{cite journal|last=Barsalou|first=Lawrence W.|title=Ideals, central tendency, and frequency of instantiation as determinants of graded structure in categories.|journal=Journal of Experimental Psychology: Learning, Memory, and Cognition|date=1 January 1985|volume=11|issue=4|pages=629–654|doi=10.1037/0278-7393.11.1-4.629}}</ref> |
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==Early Theories and Disputes== |
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Rosch identifies five types of typicality effects |
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====Rosch ==== |
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1. Subject ratings of the typicality of items. |
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2. Order in which category items are learned |
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3. Verification times for category membership |
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4. Probability of item output |
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5. Expectations generated by category name |
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<ref>{{cite journal|last=Rosch|first=Eleanor|coauthors=Simpson, Carol, Miller, R. Scott|title=Structural bases of typicality effects.|journal=Journal of Experimental Psychology: Human Perception and Performance|date=Jan 1 1976|volume=2|issue=4|pages=491–502|doi=10.1037/0096-1523.2.4.491}}</ref> |
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<ref>{{cite book|last=Murphy|first=Gregory L.|title=The big book of concepts|year=2004|publisher=MIT Press|location=Cambridge, Mass.|isbn=0262632993|pages=23|edition=1st MIT Press paperback ed.}}</ref> |
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Eleanor Rosch proposed that natural categories are continuous with items that are structurally organized based on the degree to which they are judged to be good examples of the category. <ref name=ER></ref> |
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This led her and the literature to conclude significant effects of typicality across five domains: <ref name=ER></ref> |
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Barsalou provides a good overview, criticizes early tests by Rossa, & Mervis et al. |
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* Subject ratings of the typicality of items |
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Another suggestion is ideals, which are similar in principle, but are not necessarily the central tendency of their category. Rather, they are at the periphery of their categories and tend to be extreme values. One example is zero calories as an ideal for the category of diet. Obviously this value is not central, but is certainly typical of the category of ‘diet’.<ref>{{cite journal|last=Barsalou|first=Lawrence W|title=Ideals, Central Tendency, and Frequency of Instantiation as Determinants of Graded Structure in Categories|journal=Journal of Experimental Psychology: Learning, Memory and Cognition|date=Oct 1985|volume=11|issue=4|pages=629-654|doi=10.1037/0278-7393.11.4.629|accessdate=14 March 2012}}</ref> |
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* Order in which category items are learned |
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* Verification times for category membership |
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* Probability of an item output |
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* Expectations generated by the category name |
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Across several tasks and categories people agree upon typical and atypical examples of a category. <ref name=Rosch></ref>. Participants rate “chair” as a highly typical example of furniture, and agree that “stove” and “rug” are atypical examples of furniture. <ref name=Rosch></ref>. Likewise, participants will rate cars and buses as more typical examples of vehicles than tractors and wagons. <ref name=Rosch></ref>. |
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====Categorization==== |
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Empirical evidence of these findings extends across perceptual domains. <ref name= ER></ref> |
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Findings by Rips, Schoben, and Smith in 1973 were inconsistent with models on memory that posited a hierarchical structure (pg 207). |
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There are certain colours and forms that are better examples of colours and forms than others. <ref name=Eleanor>{{cite journal|last=Rosch|first=Eleanor H.|title=Natural categories|journal=Cognitive Psychology|date=1973|volume=4|issue=3|pages=328–350|doi=10.1016/0010-0285(73)90017-0}}</ref> |
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This in turn can affect learning, as Rosch suggested while teaching Dani people the names of colours and forms. Typicality is evident in the results as certain colours and forms are more attractive than the atypical ones, and are more easily remembered than the less salient instances of colour and shape. <ref name= ER></ref> |
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Other studies have replicated these findings. <ref name=Armstrong>{{cite journal|last=Armstrong|first=Sharon Lee|coauthors=Gleitman, Lila R., Gleitman, Henry|title=What some concepts might not be|journal=Cognition|date=May 1983|volume=13|issue=3|pages=263–308|doi=10.1016/0010-0277(83)90012-4}}</ref> Typicality effects have also been shown for categories thought to be well-defined categories; for example “odd” numbers, “even” numbers, and “female”. <ref name=Armstrong></ref> Even the instances of these categories could be classified by participants in terms of their typicality; “3” is judged to be a better odd number than “501” and “mother” is deemed a better example of a female than “comedienne”. <ref name=Armstrong></ref> |
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Hierarchical models include ‘Set inclusion’, or the “IS-A” relation. They suggest a structural hierarchy whereby properties at the subordinate level are present in superordinate levels. As one moves across each level, this is supposed to require more time. For example, it should take longer to verify that ‘a pine is a plant’ than ‘a pine is an evergreen’ because one has to move up more levels to reach the superodinate level of ‘plant’. <ref>{{cite book|last=Murphy|first=Gregory L.|title=The big book of concepts|year=2004|publisher=MIT Press|location=Cambridge, Mass.|isbn=0262632993|pages=23|edition=1st MIT Press paperback ed.}}</ref> |
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<ref>{{cite journal|last=Collins|first=A|coauthors=Quillian, M|title=Retrieval time from semantic memory|journal=Journal of Verbal Learning and Verbal Behavior|date=1 April 1969|volume=8|issue=2|pages=240–247|doi=10.1016/S0022-5371(69)80069-1|accessdate=16 March 2012}}</ref> |
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Typicality effects are also seen in the time it takes participants to verify semantic statements of category membership. <ref name=Rips>{{cite journal|last=Rips|first=L|coauthors=Shoben, E, Smith, E|title=Semantic distance and the verification of semantic relations|journal=Journal of Verbal Learning and Verbal Behavior|date=1|year=1973|month=February|volume=12|issue=1|pages=1–20|doi=10.1016/S0022-5371(73)80056-8|accessdate=11 March 2012}}</ref> |
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[This could be a good place for a diagram] |
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The reaction time of participants to respond if a sentence is true is related to the type of noun used for the task priming. <ref name=Rips></ref> That is, if “bird” is flashed before “hawk” as opposed to “animal” before “hawk” participants will respond faster. <ref name=Rips></ref> However, if “mammal” is used for priming instead of “animal” there is an inverse effect; it takes longer to verify that “a dog is a mammal” than “a dog is an animal”. <ref name=Rips></ref> |
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====Spreading Activation and Semantic Retrieval==== |
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Obviously typicality effects pose a challenge to this theory. |
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=====Collins and Quillian===== |
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Rips, Shoben, and Smith (1973) demonstrated that the ease with which people judge category membership depends on the typicality of category members. <ref>{{cite journal|last=Rips|first=L|coauthors=Shoben, E, Smith, E|title=Semantic distance and the verification of semantic relations|journal=Journal of Verbal Learning and Verbal Behavior|date=1|year=1973|month=February|volume=12|issue=1|pages=1–20|doi=10.1016/S0022-5371(73)80056-8|accessdate=11 March 2012}}</ref> |
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Under the model proposed by Allan Collins and Ross Quillian, a concept is seen as a node in a network, with relational links between nodes and labelled properties associated at each level of node. |
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<ref name=Collins>{{cite journal|last=Collins|first=Allan M.|coauthors=Quillian, M. Ross|title=Retrieval time from semantic memory|journal=Journal of Verbal Learning and Verbal Behavior|date=1969|volume=8|issue=2|pages=240–247|doi=10.1016/S0022-5371(69)80069-1}}</ref> |
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The links between the nodes are of different types, such as, superordinate (“isa”) links and subordinate links. <ref name=Loftus>{{cite journal|last=Collins|first=Allan M.|coauthors=Loftus, Elizabeth F.|title=A spreading-activation theory of semantic processing.|journal=Psychological Review|date=1 January 1975|volume=82|issue=6|pages=407–428|doi=10.1037/0033-295X.82.6.407}}</ref> |
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This forms a hierarchy involving property relations at the subordinate categories and superset relations at the superordinate categories. <ref name=Collins></ref> |
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This hierarchy suggested by Allan Collins and M. Ross Quillian (1969) suggests that to verify the statement “a canary can sing” requires a person to start at the node “canary” and then at that specific level examine the properties of canary to verify that “can sing” is one of them. For the statement “a canary can fly” one would have to move up in the hierarchy to “birds” to retrieve the property of flying. <ref name=Collins></ref> Moving up a level in the hierarchy requires more processing time during retrieval; hence, it takes longer for a person to verify that a canary can fly than it does to verify that a canary can sing. <ref name=Collins></ref> |
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====Learning==== |
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This leads into the notion of cognitive economy, which posits that properties are stored in memory only once, at more superordinate levels, such that “flying” will be stored with “birds” and not again with “robin”. <ref name=Loftus></ref> To test whether categories are structured in such a manner, Collins and Quillian (1969) measured the reaction time of participants to verifying statements of different levels. Their results verified their hypotheses but were later challenged by the findings of typicality effects. <ref name=Loftus></ref> |
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====Word Meaning==== |
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==== |
=====Collins and Loftus===== |
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A revision of their spreading activation model was created in order to account for typicality effects in semantic verification tasks. <ref name=Loftus></ref> |
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When subjects mention two category members together in a sentence the more typical one is likely to be mentioned first.<ref>{{cite journal|last=Kelly|first=Michael H|coauthors=Bock, J.Kathryn, Keil, Frank C|title=Prototypicality in a linguistic context: Effects on sentence structure|journal=Journal of Memory and Language|date=Feb 1986|volume=25|issue=1|pages=59–74|doi=10.1016/0749-596X(86)90021-5|accessdate=14 March 2012}}</ref> For example, one is more likely to say 'apples and limes' over 'limes and apples'. |
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The revised model by Allan Collins and Elizabeth Loftus (1975) accounts for why atypical instances can take longer to verify or categorize, despite being at a closer level in the hierarchy. <ref name=Loftus></ref> Because of experiences, the strength of the links between nodes differ. <ref name=Loftus></ref> Frequency of exposure, for example, can strengthen the link between nodes, such that if a person often uses the link that a robin is a bird but rarely uses the link that a penguin is a bird, the former link will be stronger and more quickly verified than the latter. <ref name=Loftus></ref> |
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==Possible Explanations== |
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Further emphasized in the revised model was the clarification of the cognitive economy. Collins and Loftus (1975) emphasized that properties did not necessarily have to be stored at the most superordinate category, in fact, they may be stored at several levels in the hierarchy, making them more easily accessible. <ref name=Loftus></ref> |
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[This part is challenging without overlapping with the early studies and going off track] |
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====Prototype Theory==== |
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Classical View |
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Prototype view |
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Eleanor Rosch’s findings led to her posit the significant of prototypes in categorization. |
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==Examples== |
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A category in which the prototype is central to the variations of items is easier to learn than a category in which the prototype is fuzzy and a peripheral member. <ref name =Eleanor></ref> |
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[Again, need to ensure this covers ideas not mentioned in early studies] |
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Consequently, when learning a category, the prototype tends to be learned first, regardless of its centrality to the category. <ref name =Eleanor></ref> Furthermore, when defining the category, the prototype is operationally vital to the definition and is generally the best example of that category. <ref name =Eleanor></ref> |
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[I was thinking of putting bullets of examples of common typicality effects] |
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[This might also be a good place for an image] |
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Earlier research by Posner and Keele in 1968 exemplified the effects of prototypes using dot pattern arrangements. <ref>{{cite book|last=Murphy|first=Gregory L.|title=The big book of concepts|year=2004|publisher=MIT Press|location=Cambridge, Mass.|isbn=0262632993|pages=28|edition=1st MIT Press paperback ed.}}</ref> After generating a prototype of a random arrangement of dots, they created random distortions of that pattern; some minor and some major. <ref name=BBC>{{cite book|last=Murphy|first=Gregory L.|title=The big book of concepts|year=2004|publisher=MIT Press|location=Cambridge, Mass.|isbn=0262632993|pages=22|edition=1st MIT Press paperback ed.}}</ref> When they tested participants using these different dot patterns, they found that participants more readily identified the prototype as a member of the category of dot patterns, even if it was not shown during test trials. <ref name=BBC></ref> Furthermore, the patterns that were minor distortions were learned better than the patterns made by major distortions from the prototype. <ref name=BBC></ref> |
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<ref>{{cite journal|last=Goldin|first=Sarah E.|title=Memory for the ordinary: Typicality effects in chess memory.|journal=Journal of Experimental Psychology: Human Learning & Memory|date=1|year=1978|month=January|volume=4|issue=6|pages=605–616|doi=10.1037/0278-7393.4.6.605|accessdate=11 March 2012}}</ref> |
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====Voice==== |
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Mullennix et al 2009 |
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Typicality effects have been shown to be present in voice recognition. This can be applied to real-life as it plays a role in earwitness testimony. <ref>{{cite journal|last=Mullennix|first=J. W.|coauthors=Ross, A., Smith, C., Kuykendall, K., Conard, J., Barb, S.|title=Typicality effects on memory for voice: Implications for earwitness testimony|journal=Applied Cognitive Psychology|date=7|year=2009|month=October|volume=25|issue=1|pages=29–34|doi=10.1002/acp.1635|accessdate=9 March 2012}}</ref>- Highly typical target voices and highly typical voice foils were confused the most. <ref>{{cite journal|last=Mullennix|first=J. W.|coauthors=Ross, A., Smith, C., Kuykendall, K., Conard, J., Barb, S.|title=Typicality effects on memory for voice: Implications for earwitness testimony|journal=Applied Cognitive Psychology|date=7|year=2009|month=October|volume=25|issue=1|pages=29–34|doi=10.1002/acp.1635|accessdate=9 March 2012}}</ref> |
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====Vision==== |
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This effect is also seen with visual stimuli, such that it takes less time to identify a picture of a robin as a bird than a picture of a chicken as a bird. |
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<ref>{{cite book|last=Murphy|first=Gregory L.|title=The big book of concepts|year=2004|publisher=MIT Press|location=Cambridge, Mass.|isbn=0262632993|pages=23|edition=1st MIT Press paperback ed.}}</ref> |
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<ref>{{cite journal|last=Busey|first=TA|coauthors=Tunnicliff, JL|title=Accounts of blending, distinctiveness, and typicality in the false recognition of faces|journal=Journal of experimental psychology. Learning, memory, and cognition|year=1999|month=September|volume=25|issue=5|pages=1210-35|pmid=10505343}}</ref> |
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<ref>{{cite journal|last=Tanaka|first=JW|coauthors=Corneille, O|title=Typicality effects in face and object perception: further evidence for the attractor field model.|journal=Perception & psychophysics|year=2007|month=May|volume=69|issue=4|pages=619-27|pmid=17727115|accessdate=10 March 2012}}</ref> |
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==Effects in Specific Populations== |
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====Children==== |
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==Potential Explanations== |
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The literature shows that by 12 months, children begin to associate object words with prototypical objects. It is not until 18 months that children start to associate words with atypical exemplars, such as, a bird with an ostrich. <ref>{{cite journal|last=Meints|first=Kerstin|coauthors=Plunkett, Kim, Harris, Paul L., Dimmock, Debbie|title=What is ‘on’ and ‘under’ for 15-, 18- and 24- month-olds? Typicality effects in early comprehension of spatial prepositions|journal=British Journal of Developmental Psychology|year=2002|month=March|volume=20|issue=1|pages=113–130|doi=10.1348/026151002166352|accessdate=9 March 2012}}</ref> |
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Studies with 15, 18, and 24 month year olds have shown typicality effects in their preferential looking. By measuring the longest look and total amount of looking, researchers aim to measure the infant’s perception of the typicality of the situation. |
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====Central Tendency==== |
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<ref>{{cite journal|last=Meints|first=Kerstin|coauthors=Plunkett, Kim, Harris, Paul L., Dimmock, Debbie|title=What is ‘on’ and ‘under’ for 15-, 18- and 24- month-olds? Typicality effects in early comprehension of spatial prepositions|journal=British Journal of Developmental Psychology|year=2002|month=March|volume=20|issue=1|pages=113–130|doi=10.1348/026151002166352|accessdate=9 March 2012}}</ref> |
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<ref>{{cite journal|last=Jerger|first=S|coauthors=Damian, MF|title=What's in a name? Typicality and relatedness effects in children.|journal=Journal of experimental child psychology|year=2005|month=September|volume=92|issue=1|pages=46-75|pmid=15904928|accessdate=11 March 2012}}</ref> |
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Some researchers posit that when participants were asked to classify items in artificial categories they appeared to inductively create a central tendency of the distribution which they use to classify the items. <ref name=Eleanor></ref> This was suggested by Eleanor Rosch, who posited that the central tendency plays a significant role in categorization. For learning colour and forms, it was found that subjects could learn those concepts in which the central members were natural prototype faster than categories organized in different ways. <ref name =Eleanor></ref> |
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One potential reason the central tendency is most likely to produce a prototype is because it is presumed that the central tendency minimizes the average distance of category members to the category standards. <ref name=Barsalou></ref> Hence it is likely to yield a best exemplar, one that is closest to all other items in the category. |
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There are limitations to the central tendency approach. Central tendency may vary across contexts, based on perspectives. <ref name=Barsalou></ref> A forest ranger and a pet store owner will have different central tendencies for animal size and ferocity based on their experiences. <ref name=Barsalou></ref> |
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Furthermore, another limitation is the inherent induction strength of central tendency which in itself can yield a typicality effect. <ref name=Rein></ref> |
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That is, these central tendencies have a lot of overlap with the features of the category and more specifically, the characteristic features of the category; this in it of itself is enough to make them appear to be the most central to the category, even if they are not. <ref name=Rein></ref> |
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Ultimately, the factors determining graded structure in one context are different in another context. At least two other determinants play a major role in categorization; ideals and frequency of instantiation. <ref name=Barsalou></ref> |
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====Ideals==== |
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Ideal characteristics are those that an exemplar must have if it is to be the best instance of a category and serve the goal of the category. <ref name=Barsalou></ref> Ideals are not always the central tendency of a category; For example, zero calories would the ideal amount one would consume on a diet, but zero is not the central tendency for number of calories in food. <ref name=Barsalou></ref> |
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Barsalou (1985) specified that ideals were highly correlated with graded structure for what he called goal-derived categories. He distinguished these categories from common taxonomic categories that Rosch and others were using in their studies. |
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====Frequency of Instantiation==== |
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Frequency may be the reason that it takes longer for people to verify statements involving animals<ref name=Rips></ref> People are exposed to the label “mammal” much less frequently than “animal”. <ref name=Rips></ref> This may explain why participants are quicker to verify that “a dog is an animal” than “a dog is a mammal” despite the fact that “animal” is superordinate or a superset and further away from “dog” in the semantic hierarchy. <ref name=Rips></ref> <ref name=Collins></ref> |
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Furthermore frequency can explain why penguins are judged to be less typical examples of birds. <ref name=BBCT></ref> Given that these studies are conducted in North America, it is presumed that participants have been exposed to robins and sparrows significantly more frequently than penguins. <ref name=BBCT></ref> |
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There is a strong debate over the influence that the frequency one experiences the instances in the category plays in typicality effects. <ref name=Barsalou></ref> |
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While several researchers belittle the effects of frequency; <ref name=BBCT></ref> Rosch et al. go so far as to suggest that frequency is a product of typicality and is not in it of itself influential on typicality effects. <ref name=ER></ref> Subjects have been shown to overestimate the frequency of typical category members versus atypical category members. <ref name=ER></ref> |
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Criticisms against the effects of frequency |
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====[Maybe a section with Mental Disorders or Brain Lesions]==== |
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Revision as of 03:24, 10 April 2012
Intro
The typicality effect generally effects to the phenomenon whereby individuals respond faster to more typical examples of a category across many different types of tasks. [1] If a person is asked to think of an example of a bird, it is more likely that they picture a robin than a penguin or bat. [2] These findings were presented in the work of Eleanor Rosch, one of the early researchers to identify the effects of typicality who sparked an extensive topic in categorization literature. [3] [4] Typicality in itself is a gradation in which items of a category can be either very typical to the prototype, moderately typical, or atypical. [5] Using this gradation researchers have conducted robust experiments on participants to tap into the way in which we store and represent categories in our mind. There are conflicting findings and explanations for this phenomenon, all of which demonstrate the influence that typicality gradient has on the way we categorize objects. [6]
Early Theories and Disputes
Rosch
Eleanor Rosch proposed that natural categories are continuous with items that are structurally organized based on the degree to which they are judged to be good examples of the category. [1]
This led her and the literature to conclude significant effects of typicality across five domains: [1]
- Subject ratings of the typicality of items
- Order in which category items are learned
- Verification times for category membership
- Probability of an item output
- Expectations generated by the category name
Across several tasks and categories people agree upon typical and atypical examples of a category. [2]. Participants rate “chair” as a highly typical example of furniture, and agree that “stove” and “rug” are atypical examples of furniture. [2]. Likewise, participants will rate cars and buses as more typical examples of vehicles than tractors and wagons. [2].
Empirical evidence of these findings extends across perceptual domains. [1] There are certain colours and forms that are better examples of colours and forms than others. [7] This in turn can affect learning, as Rosch suggested while teaching Dani people the names of colours and forms. Typicality is evident in the results as certain colours and forms are more attractive than the atypical ones, and are more easily remembered than the less salient instances of colour and shape. [1]
Other studies have replicated these findings. [8] Typicality effects have also been shown for categories thought to be well-defined categories; for example “odd” numbers, “even” numbers, and “female”. [8] Even the instances of these categories could be classified by participants in terms of their typicality; “3” is judged to be a better odd number than “501” and “mother” is deemed a better example of a female than “comedienne”. [8]
Typicality effects are also seen in the time it takes participants to verify semantic statements of category membership. [9] The reaction time of participants to respond if a sentence is true is related to the type of noun used for the task priming. [9] That is, if “bird” is flashed before “hawk” as opposed to “animal” before “hawk” participants will respond faster. [9] However, if “mammal” is used for priming instead of “animal” there is an inverse effect; it takes longer to verify that “a dog is a mammal” than “a dog is an animal”. [9]
Spreading Activation and Semantic Retrieval
Collins and Quillian
Under the model proposed by Allan Collins and Ross Quillian, a concept is seen as a node in a network, with relational links between nodes and labelled properties associated at each level of node. [10] The links between the nodes are of different types, such as, superordinate (“isa”) links and subordinate links. [11] This forms a hierarchy involving property relations at the subordinate categories and superset relations at the superordinate categories. [10]
This hierarchy suggested by Allan Collins and M. Ross Quillian (1969) suggests that to verify the statement “a canary can sing” requires a person to start at the node “canary” and then at that specific level examine the properties of canary to verify that “can sing” is one of them. For the statement “a canary can fly” one would have to move up in the hierarchy to “birds” to retrieve the property of flying. [10] Moving up a level in the hierarchy requires more processing time during retrieval; hence, it takes longer for a person to verify that a canary can fly than it does to verify that a canary can sing. [10]
This leads into the notion of cognitive economy, which posits that properties are stored in memory only once, at more superordinate levels, such that “flying” will be stored with “birds” and not again with “robin”. [11] To test whether categories are structured in such a manner, Collins and Quillian (1969) measured the reaction time of participants to verifying statements of different levels. Their results verified their hypotheses but were later challenged by the findings of typicality effects. [11]
Collins and Loftus
A revision of their spreading activation model was created in order to account for typicality effects in semantic verification tasks. [11] The revised model by Allan Collins and Elizabeth Loftus (1975) accounts for why atypical instances can take longer to verify or categorize, despite being at a closer level in the hierarchy. [11] Because of experiences, the strength of the links between nodes differ. [11] Frequency of exposure, for example, can strengthen the link between nodes, such that if a person often uses the link that a robin is a bird but rarely uses the link that a penguin is a bird, the former link will be stronger and more quickly verified than the latter. [11] Further emphasized in the revised model was the clarification of the cognitive economy. Collins and Loftus (1975) emphasized that properties did not necessarily have to be stored at the most superordinate category, in fact, they may be stored at several levels in the hierarchy, making them more easily accessible. [11]
Prototype Theory
Eleanor Rosch’s findings led to her posit the significant of prototypes in categorization. A category in which the prototype is central to the variations of items is easier to learn than a category in which the prototype is fuzzy and a peripheral member. [7] Consequently, when learning a category, the prototype tends to be learned first, regardless of its centrality to the category. [7] Furthermore, when defining the category, the prototype is operationally vital to the definition and is generally the best example of that category. [7]
Earlier research by Posner and Keele in 1968 exemplified the effects of prototypes using dot pattern arrangements. [12] After generating a prototype of a random arrangement of dots, they created random distortions of that pattern; some minor and some major. [3] When they tested participants using these different dot patterns, they found that participants more readily identified the prototype as a member of the category of dot patterns, even if it was not shown during test trials. [3] Furthermore, the patterns that were minor distortions were learned better than the patterns made by major distortions from the prototype. [3]
Potential Explanations
Central Tendency
Some researchers posit that when participants were asked to classify items in artificial categories they appeared to inductively create a central tendency of the distribution which they use to classify the items. [7] This was suggested by Eleanor Rosch, who posited that the central tendency plays a significant role in categorization. For learning colour and forms, it was found that subjects could learn those concepts in which the central members were natural prototype faster than categories organized in different ways. [7]
One potential reason the central tendency is most likely to produce a prototype is because it is presumed that the central tendency minimizes the average distance of category members to the category standards. [6] Hence it is likely to yield a best exemplar, one that is closest to all other items in the category.
There are limitations to the central tendency approach. Central tendency may vary across contexts, based on perspectives. [6] A forest ranger and a pet store owner will have different central tendencies for animal size and ferocity based on their experiences. [6]
Furthermore, another limitation is the inherent induction strength of central tendency which in itself can yield a typicality effect. [4] That is, these central tendencies have a lot of overlap with the features of the category and more specifically, the characteristic features of the category; this in it of itself is enough to make them appear to be the most central to the category, even if they are not. [4]
Ultimately, the factors determining graded structure in one context are different in another context. At least two other determinants play a major role in categorization; ideals and frequency of instantiation. [6]
Ideals
Ideal characteristics are those that an exemplar must have if it is to be the best instance of a category and serve the goal of the category. [6] Ideals are not always the central tendency of a category; For example, zero calories would the ideal amount one would consume on a diet, but zero is not the central tendency for number of calories in food. [6] Barsalou (1985) specified that ideals were highly correlated with graded structure for what he called goal-derived categories. He distinguished these categories from common taxonomic categories that Rosch and others were using in their studies.
Frequency of Instantiation
Frequency may be the reason that it takes longer for people to verify statements involving animals[9] People are exposed to the label “mammal” much less frequently than “animal”. [9] This may explain why participants are quicker to verify that “a dog is an animal” than “a dog is a mammal” despite the fact that “animal” is superordinate or a superset and further away from “dog” in the semantic hierarchy. [9] [10]
Furthermore frequency can explain why penguins are judged to be less typical examples of birds. [5] Given that these studies are conducted in North America, it is presumed that participants have been exposed to robins and sparrows significantly more frequently than penguins. [5] There is a strong debate over the influence that the frequency one experiences the instances in the category plays in typicality effects. [6] While several researchers belittle the effects of frequency; [5] Rosch et al. go so far as to suggest that frequency is a product of typicality and is not in it of itself influential on typicality effects. [1] Subjects have been shown to overestimate the frequency of typical category members versus atypical category members. [1]
Criticisms against the effects of frequency
References
- ^ a b c d e f g Rosch, Eleanor (Jan 1 1976). "Structural bases of typicality effects". Journal of Experimental Psychology: Human Perception and Performance. 2 (4): 491–502. doi:10.1037/0096-1523.2.4.491.
{{cite journal}}: Check date values in:|date=(help); Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ a b c d Rosch, Eleanor (1975). Journal of Experimental Psychology: General. 104 (3): 192–233.
{{cite journal}}: Missing or empty|title=(help) - ^ a b c d Murphy, Gregory L. (2004). The big book of concepts (1st MIT Press paperback ed. ed.). Cambridge, Mass.: MIT Press. p. 22. ISBN 0262632993.
{{cite book}}:|edition=has extra text (help) - ^ a b c Rein, J. R. (16 March 2010). "What is typical about the typicality effect in category-based induction?". Memory & Cognition. 38 (3): 377–388. doi:10.3758/MC.38.3.377.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ a b c d Murphy, Gregory L. (2004). The big book of concepts (1st MIT Press paperback ed. ed.). Cambridge, Mass.: MIT Press. p. 31. ISBN 0262632993.
{{cite book}}:|edition=has extra text (help) - ^ a b c d e f g h Barsalou, Lawrence W. (1 January 1985). "Ideals, central tendency, and frequency of instantiation as determinants of graded structure in categories". Journal of Experimental Psychology: Learning, Memory, and Cognition. 11 (4): 629–654. doi:10.1037/0278-7393.11.1-4.629.
- ^ a b c d e f Rosch, Eleanor H. (1973). "Natural categories". Cognitive Psychology. 4 (3): 328–350. doi:10.1016/0010-0285(73)90017-0.
- ^ a b c Armstrong, Sharon Lee (May 1983). "What some concepts might not be". Cognition. 13 (3): 263–308. doi:10.1016/0010-0277(83)90012-4.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ a b c d e f g Rips, L (1). "Semantic distance and the verification of semantic relations". Journal of Verbal Learning and Verbal Behavior. 12 (1): 1–20. doi:10.1016/S0022-5371(73)80056-8.
{{cite journal}}:|access-date=requires|url=(help); Check date values in:|date=and|year=/|date=mismatch (help); Unknown parameter|coauthors=ignored (|author=suggested) (help); Unknown parameter|month=ignored (help) - ^ a b c d e Collins, Allan M. (1969). "Retrieval time from semantic memory". Journal of Verbal Learning and Verbal Behavior. 8 (2): 240–247. doi:10.1016/S0022-5371(69)80069-1.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ a b c d e f g h Collins, Allan M. (1 January 1975). "A spreading-activation theory of semantic processing". Psychological Review. 82 (6): 407–428. doi:10.1037/0033-295X.82.6.407.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Murphy, Gregory L. (2004). The big book of concepts (1st MIT Press paperback ed. ed.). Cambridge, Mass.: MIT Press. p. 28. ISBN 0262632993.
{{cite book}}:|edition=has extra text (help)