Memory, thinking and language.pdf

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Semantic hierarchies It has proved a very persuasive idea that knowledge representations are organized in memory as a semantic network of interconnected concepts which represent semantic relations between categories of concepts. One influential model was Collins and Quillian’s (1969) hierarchical semantic network in which categories like animals and plants are defined as concepts which are related to each other in various ways. As shown in Figure 4, the links between the concepts represent relations between categories. Thus the concept ‘canary’ is a member of the ‘bird’ category. In addition concepts are defined in terms of defining features, for example a canary ‘can sing’ and ‘is yellow’. Birds have features like ‘has wings’, ‘can fly’, ‘has feathers’. Quillian’s theory was designed to be implemented as a computer program which could comprehend sentences on the basis of the information about concepts in its database. This program, known as the Teachable Language Comprehender (TLC), was an early attempt to model human conceptual knowledge, a prototype of later knowledge-based computer systems. Let us suppose the model is faced with a sentence like A canary is a bird. In order to understand this sentence, it is necessary to search through the semantic hierarchy in the database to retrieve information about the concepts ‘canary’ and ‘bird’. Given a sentence like A canary is a bird, a search is activated throughout the hierarchy starting from both the ‘canary’ and ‘bird’ concepts until the two searches intersect to provide a link between the two concepts. The longer it takes for a path to be found to where the two concepts interact, the longer the response. Thus in Figure 4 there is a direct link between ‘canary’ and ‘bird’. However, the sentence A canary is an animal would require a search further up the hierarchy to connect the concepts ‘canary’ and ‘animal’. The same principle applies to sentences about features. A canary can sing refers to a direct link between ‘canary’ and ‘can sing’. But to understand the sentence A canary breathes a path would need to be activated up the hierarchy to the ‘animal’ concept in order to retrieve the facts that a canary is a bird, a bird is an animal, animals breathe and therefore a canary can breathe. You may well be wondering what all this has got to do with human memory. In the introductory chapter I referred to the computer analogy of human functioning. The basic idea is that the 19
20 Figure 4 Part of Collins and Quillian’s (1969) semantic hierarchy for animals. knowledge stored in human memory is like the information stored in the database of a computer. So the implication of the Collins and Quillian model is that people’s knowledge of objects in the environment is organized as a semantic hierarchy of concepts and categories. Of course, the network in Figure 4 displays only a tiny fraction of all human knowledge. It is meant to reflect biological knowledge that cats and dogs and birds are animals, that pigs can’t fly, that elephants have trunks and that whales are mammals. To represent the vast amount of knowledge in human memory there would have to be thousands, perhaps millions, of other concept hierarchies, for example about man-made objects, abstract ideas, and many others. In addition there are many links between hierarchies, for example canaries are often kept in man-made cages. One reason why Quillian’s biological hierarchy was taken up by psychologists was that his model was experimentally tested by Collins and Quillian (1969) using a sentence verification task. Subjects were asked to judge (verify) whether sentences are true or false, for example Acanary is a bird, A shark is a bird, A canary can sing, A canary canbreathe. The prediction was that people would take longer to decide about sentences which involve longer searches through the network. The results of the experiment confirmed this prediction. Sentences which require a
Page 2 and 3: General editor Peter Herriot New Es
Page 4 and 5: Judith Greene Memory, Thinking and
Page 6 and 7: To the 3 Js, Janet, John and Jonath
Page 8 and 9: My grateful thanks to Pat Vasiliou
Page 10 and 11: 1 Introduction There is a special d
Page 12 and 13: 3 Figure 1 Information processing
Page 14: epresentative selection of experime
Page 17 and 18: 8 bridge. Yet, if we are totally ho
Page 19 and 20: 10 reformulation of the overall str
Page 21 and 22: 12 accommodation of old strategies
Page 23 and 24: 14 experience’ which enables the
Page 25 and 26: 16 Figure 3 A solution to the nine
Page 27: 18 psychology. But my ability to dr
Page 31 and 32: 22 Another point to notice about th
Page 33 and 34: 24 A typical example of a feature c
Page 35 and 36: 26 links between ‘ostrich’ and
Page 37 and 38: 28 Figure 5 Basic levels. Source: R
Page 39 and 40: 30 Evaluation of conceptual models
Page 41 and 42: 32 Semantic memory and episodic mem
Page 43 and 44: 34 Further support for the notion o
Page 46 and 47: 4 Active memory The models of memor
Page 48 and 49: need them. In other words, it refer
Page 50 and 51: future plans. Memory is rarely a pa
Page 52 and 53: their contents are variable, althou
Page 54 and 55: to describe an ostrich, then the
Page 56 and 57: isa animal is mentioned in the dog
Page 58 and 59: Perhaps the best way to think about
Page 60 and 61: events fits a restaurant script or
Page 62 and 63: situations becomes even more genera
Page 64 and 65: 55 Figure 9 The perceptual cycle. S
Page 66: personal experiences of idiosyncrat
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Page 73 and 74: 64 Figure 10 Simplified version of
Page 75 and 76: 66 Figure 13 Chomsky’s (1965) the
Page 77 and 78: 68 The main experimental methodolog
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70 Chomsky’s explanation is that
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72 Clark and Clark (1977) pointed o
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74 be formulated as program instruc
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76 although in normal discourse the
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78 systems, one a mental lexicon fo
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80 so were less likely to confuse s
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82 sounds would be totally meaningl
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84 meanings be distinguished from g
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86 linguistic contexts. The main qu
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88 idea, though, is that there is a
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90 direct reference in the first se
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92 ensure that speaker and hearer s
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94 was allowed to call in informati
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96 not familiar with. Although the
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98 1 A very influential notion has
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100 organized in such a way that re
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102 brushed off for use again. How
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104 haven’t got enough change to
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106 Figure 16 Tower of Hanoi proble
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108 Figure 17 Means ends analysis i
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110 Figure 18 Missionaries and cann
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112 laboratory, a fire bell went of
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114 One of the main attractions of
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116 These conflict resolution rules
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118 Simon (1975) used productions t
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120 even though some thought might
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122 and pegs problem. Unlike previo
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124 like finishing this sentence, o
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126
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128 learning. In reaction to the no
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130 facts (for example Arthur works
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132 information in declarative memo
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134 Adding a new superficially attr
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136 when needed. Sometimes, too, at
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138 turn will trigger new procedure
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140 Anderson admits that the perfor
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142 generating too many incorrect s
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144
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146 ability to recognize and exploi
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148 If people learn rules for solvi
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150 assessments made of them, playi
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152 In a rather different context W
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154 proposition that people learn f
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156 and writers to articulate exper
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158 and Quillian, 1969), typical in
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160 performance on memory tasks and
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162 Figure 23 The regulatory system
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164
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166 6 Language and communication El
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168 Bever, T.G. (1970) ‘The cogni
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170 Greene, J. (1970) ‘The semant
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172 Nisbett, R.E. and Wilson, T.D.
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174 Smith, E.E., Shoben, E.J. and R
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176 feature comparison models, 22-5
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