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(a) (c) revealing glimpse of the short cuts and economies of the inner codes of vision. The nonveridicality of representation in art is so commonplace that we seldom question the reason why it works. (b) Pictorial Art and Vision 649 Figure 1. (a) An early example of outline drawing from France. (b) As you view this image from different angles, the changes in the distance from face to hand and in the shape of the head are subtle. A 3-D computer model of this scene would require large-scale relative motions and 3-D shape changes to maintain the 2-D view seen with changing viewpoints. (c) Impossible lighting, highlights, or shadows (note the overlapping cast shadows at the bottom) are difficult to spot in paintings, implying that human observers use a simplistic local model of light and shade. A line drawing of a building or an elephant can convey its 3-D structure very convincingly, but remember that there are no lines in the real world corresponding to the lines used in the drawings. The surface occlusions, folds, or
650 Pictorial Art and Vision creases that are represented by lines in drawings are revealed by changes in, say, brightness or texture in the real world, and these changes have one value extending on one side and a different value on the other. This is not a line. It is not obvious why lines should work at all. The effectiveness of line drawings is not based simply on learned convention, passed on through our culture. This point has been controversial (Kennedy 1975; see Deregowski 1989, and its following comments), but most recent evidence suggests that line drawings are universally interpreted in the same way—infants (Yonas and Arterberry 1994), stone-age tribesmen (Kennedy and Ross 1975), and even monkeys (Itakura 1994) appear to be capable of interpreting line drawings as we do. Nor is it the case that the lines in line drawings just trace the brightness discontinuities in the image, because this type of representation is rendered meaningless by the inclusion of cast shadow and pigment contours. By a quirk of design or an economy of encoding, lines may be directly activating the internal code for object structure, but only object contours can be present in the drawing for this shortcut to work. The shortcut, discovered and exploited by artists, hints at the simplicity of the internal code that underlies the vision of 3-D structures. This code is both simpler than the 2 ½-D sketch of David MARR and sparser than the compact, reversible codes (Olshausen and Field 1996) that may reflect the workings of early areas of VISUAL CORTEX. Both artists and brains have found out which are the key contours necessary to represent the essential structure of an object. By studying the nature of lines used in line drawings, scientists too may eventually join this group. Another aspect as commonplace and as informative as the effectiveness of lines is that pictures are flat and yet they provide consistent, apparently 3-D interpretations from a wide range of viewpoints. This is not only convenient for the artist, but also prime evidence that our impressions of a 3-D world are not supported by true, 3-D internal representations. If we had real 3-D vision, the scene depicted in a flat picture would have to distort grotesquely in 3-D space as we moved about the picture. To the contrary, however, objects in pictures seem reassuringly the same as we change our vantage point (with some interesting exceptions; see Gregory 1994). We don’t experience the distortions probably because the visual system does not generate a true 3-D representation of the object. It has some qualities of three dimensions but it is far from Euclidean. It may follow some other geometry, affine or nonmetric in nature (Todd and Reichel 1989; Busey, Brady, and Cutting 1990). The effectiveness of flat images is of course a boon to artists who do not have to worry about special vantage points and to film makers who can have theaters with more than one seat in them. It is also of great importance for understanding the internal representations of objects and space. Finally, consider the enormous range of discrepancies between light and shade in the world and their renditions in art. When light and shade were introduced into art about 2,200 years ago, it was through the use of local techniques such as lightening a surface fold to make it come forward (a Greek technique described by Pliny the Elder; see Gombrich 1976 for a beautiful reinterpretation of this ancient presentation of painting techniques). These local techniques of shading, shadows, and highlights were applied with little thought to making them all consistent with a given light source—and yet they all work very well. Even 500 years ago, when the geometry of perspective was well understood, the geometry of light was still ignored. The resulting errors in light and shadow would be caught immediately by any analysis based on physical optics, but pass unnoticed to human observers. Modern artists with a full understanding of the physics of light and shade available to them often still choose inconsistencies in lighting either because it never matters much, or perhaps because it looks better. Evidently, we as observers do not reconstruct a light source in order to recover the depth from shading and shadow, we do not act as optical geometers in the way that computer graphics programs can. We do not notice inconsistencies across different portions of a painting but recover depth cues locally. The message here is that in the real world, the information is rich and redundant, so we do not have to analyze the image much beyond a local region to resolve any ambiguities. When faced with the sparser cues of pictorial art, we do not adopt a larger region of analysis— the local cues are meaningful, albeit inconsistent with cues in other areas of the painting. To the advantage of the artist, the inconsistencies go unnoticed. And again, like many aspects of art, this discrepancy between the art and the scene it depicts informs us about the brain within us as much as about the world around us. See also GESTALT PERCEPTION; ILLUSIONS; LIGHTNESS PERCEPTION; SHAPE PERCEPTION; STRUCTURE FROM VISUAL INFORMATION SOURCES; SURFACE PERCEPTION —Patrick Cavanagh References Busey, T. A., N. P. Brady, and J. E. Cutting. (1990). Compensation is unnecessary for the perception of faces in slanted pictures. Perception and Psychophysics 48: 1–11. Deregowski, J. B. (1989). Real space and represented space: Cross-cultural perspectives. Behavioral and Brain Sciences 12: 51–119. Gombrich, E. H. (1976). The Heritage of Apelles. Oxford: Phaidon Press. Gregory, R. (1994). Experiments for a desert island. Perception 23: 1389–1394. Itakura, S. (1994). Recognition of line-drawing representations by a chimpanzee (Pan troglodytes). Journal of General Psychology 121: 189–197. Kennedy, J. M. (1975). Drawings were discovered, not invented. New Scientist 67: 523–527. Kennedy, J. M., and A. S. Ross. (1975). Outline picture perception by the Songe of Papua. Perception 4: 391–406. Olshausen, B. A., and D. J. Field. (1996). Emergence of simplecell receptive field properties by learning a sparse code for natural images. Nature 381: 606–607. Todd, J. T., and F. D. Reichel. (1989). Ordinal structure in the visual perception and cognition of smoothly curved surfaces. Psychological Review 96: 643–657. Yonas, A., and M. E. Arterberry. (1994). Infants perceive spatial structure specified by line junctions. Perception 23: 1427–1435.
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COMPUTATIONAL INTELLIGENCE CULTURE,
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The MIT Encyclopedia of the Cogniti
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To the memory of Henry Bradford Sta
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Acquisition, Formal Theories of 1 A
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Language Impairment, Developmental
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Preface The MIT Encyclopedia of the
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Philosophy Robert A. Wilson The are
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Philosophy xvii tion are constant a
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Philosophy xix by HELMHOLTZ, Weber,
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Philosophy xxi behavioral effects.
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Philosophy xxiii Malcolm’s point
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Philosophy xxv chemical kinds—suc
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Philosophy xxvii been to create pro
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Philosophy xxix Decade of the Brain
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Philosophy xxxi also being, to some
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All A are B. No B are C. No A are C
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Philosophy xxxv minds of other anim
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Philosophy xxxvii Pinker, S. (1997)
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xl Psychology human CHESS champion
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xlii Psychology Helmholtz’s insig
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xliv Psychology the idea that “ob
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xlvi Psychology the tradition of Gi
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xlviii Psychology in LOGIC. Althoug
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1 Cognitive Neuroscience Neuroscien
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Neurosciences liii its more pragmat
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Neurosciences lv activity (see ELEC
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Neurosciences lvii immediate object
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Neurosciences lix ules. At the pres
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Neurosciences lxi the correlation b
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Neurosciences lxiii ple of this typ
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Neurosciences lxv such as those ass
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Neurosciences lxvii LTP is commonly
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Neurosciences lxix specific cogniti
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Neurosciences lxxi Marr, D. (1982).
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Computational Intelligence Michael
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Computational Intelligence lxxv Bay
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Computational Intelligence lxxvii a
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Computational Intelligence lxxix et
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Computational Intelligence lxxxi ri
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Computational Intelligence lxxxiii
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Computational Intelligence lxxxv SU
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Computational Intelligence lxxxvii
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Computational Intelligence lxxxix 1
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Linguistics and Language Gennaro Ch
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Linguistics and Language xciii (cf.
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Linguistics and Language xcv follow
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Linguistics and Language xcvii Ther
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Linguistics and Language xcix wheth
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Linguistics and Language ci THOUGHT
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Linguistics and Language ciii for e
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Linguistics and Language cv How do
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Linguistics and Language cvii strea
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Linguistics and Language cix Chung,
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cxii Culture, Cognition, and Evolut
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cxiv Culture, Cognition, and Evolut
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cxvi Culture, Cognition, and Evolut
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cxviii Culture, Cognition, and Evol
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cxx Culture, Cognition, and Evoluti
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cxxii Culture, Cognition, and Evolu
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cxxiv Culture, Cognition, and Evolu
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cxxvi Culture, Cognition, and Evolu
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cxxviii Culture, Cognition, and Evo
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cxxx Culture, Cognition, and Evolut
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cxxxii Culture, Cognition, and Evol
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2 Acquisition, Formal Theories of l
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4 Affordances distinctive feature o
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6 Agency characteristic of any part
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8 Aging, Memory, and the Brain meas
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10 AI and Education mostly just pre
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12 A-Life idea that a homunculus of
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14 Ambiguity Seeley, T. D. (1989).
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16 Amygdala, Primate nuclei, each w
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18 Analogy issue of analogical retr
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20 Anaphora Winston, P. H. (1982).
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22 Animal Cognition Papers from the
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24 Animal Navigation inappropriate?
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26 Animal Navigation, Neural Networ
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28 Animism Further Readings Blair,
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30 Anomalous Monism Inagaki, K. (19
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32 Archaeology Similarly, a modalit
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34 Articulation diverse because no
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36 Artifacts and Civilization still
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38 Artificial Life hunting, or evas
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40 Attention ignore the other provi
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42 Attention in the Animal Brain th
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44 Attention in the Human Brain Fig
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46 Attribution Theory Stuss, D. T.,
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48 Audition Storms, M. D. (1973). V
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50 Auditory Attention Hartmann, W.
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52 Auditory Physiology brain measur
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54 Auditory Physiology Figure 1. Sc
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56 Auditory Plasticity thus be invo
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58 Autism See also AUDITION; AUDITO
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60 Autocatalysis Asperger, H. (1944
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62 Automata details and definitions
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64 Autonomy of Psychology been an i
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66 Autopoiesis Owens, D. (1989). Le
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68 Basal Ganglia for GPe projection
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70 Bayesian Learning DeVito, J. L.,
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72 Bayesian Networks which represen
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74 Behavior-Based Robotics dynamics
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76 Behavior-Based Robotics Referenc
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78 Behaviorism are dealing with act
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80 Belief Networks Nisbett, R. E.,
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82 Binding by Neural Synchrony and
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84 Binding by Neural Synchrony Gray
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86 Binding Theory References Fodor,
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88 Blindsight Everaert, M. (1991).
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90 Bloomfield, Leonard Holmes, G. (
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92 Boltzmann Machines Boas’s thin
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94 Brain Mapping Dean, T. (1991). D
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96 Broadbent, Donald E. Figure 1. B
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98 Cajal, Santiago Ramón y cogniti
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100 Case-Based Reasoning and Analog
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102 Categorial Grammar into α, whe
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104 Categorization Joshi, A., K. Vi
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106 Causal Reasoning Rips, L. J. (1
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108 Causation Mackie, J. L. (1974).
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110 Cellular Automata Strawson, G.
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112 Cerebral Cortex (ii) its arrang
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114 Chess, Psychology of have the s
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116 Chunking does the whole room. T
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118 Civilization References Church,
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120 Cognition and Aging Chomsky, N.
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122 Cognitive Archaeology Fillmore,
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124 Cognitive Architecture Gowlett,
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126 Cognitive Artifacts References
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128 Cognitive Development Group, Ed
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130 Cognitive Dissonance Further Re
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132 Cognitive Ethology Hollnagel, E
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134 Cognitive Linguistics Boden, M.
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136 Cognitive Maps oneself in a dif
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138 Cognitive Modeling, Connectioni
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140 Cognitive Modeling, Connectioni
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142 Cognitive Modeling, Symbolic th
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144 Color Categorization (Kay and K
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146 Color, Neurophysiology of reaso
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148 Columns and Modules (about 2% o
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150 Communication or repeated patch
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152 Competence/Performance Distinct
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154 Computation for example, one en
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156 Computation and the Brain Figur
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158 Computational Complexity Koch,
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160 Computational Lexicons will per
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162 Computational Linguistics appli
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164 Computational Neuroanatomy (pre
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166 Computational Neuroscience Schw
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168 Computational Psycholinguistics
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170 Computational Theory of Mind Lu
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172 Computational Vision Pylyshyn,
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174 Computer-Human Interaction Broo
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176 Concepts in the dendritic cable
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178 Concepts Five broad classes of
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180 Conceptual Change concept, perh
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182 Conceptual Role Semantics Furth
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184 Conditioning and the Brain Skin
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186 Connectionism, Philosophical Is
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188 Connectionist Approaches to Lan
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190 Consciousness items from memory
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192 Consciousness voluntary action,
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194 Consciousness, Neurobiology of
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196 Constraint Satisfaction exponen
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198 Context and Point of View Conte
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200 Control Theory There are import
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202 Cortex punishing those who chea
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204 Cortical Localization, History
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206 Creoles The Creative Person Thr
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208 Cross-Cultural Variation Refere
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210 Cultural Evolution through soci
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212 Cultural Psychology (Goddard 19
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214 Cultural Relativism such divers
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216 Cultural Symbolism Cultural Sym
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218 Culture labor: different patter
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220 Data Mining References Darwin,
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222 Decision Making The study of de
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224 Decision Trees known as preprun
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226 Deficits odological issues (see
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228 Depth Perception Figure 2.
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230 Detectors behavior of nonhuman
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232 Discourse behave in accordance
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234 Distinctive Features ory (Bem 1
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236 Distributed AI Gnanadesikan, A.
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238 Domain Specificity Földiák, P
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240 Dominance in Animal Social Grou
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242 Dreaming de Waal, F. (1989). Do
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244 Dynamic Approaches to Cognition
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246 Dynamic Programming that a dyna
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248 Dynamic Semantics A simple but
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250 Dyslexia The most common form o
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252 Ebbinghaus, Hermann The English
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254 Echolocation of fluttering inse
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256 Ecological Psychology the contr
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258 Ecological Validity determine,
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260 Economics and Cognitive Science
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262 Electric Fields computer-based
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264 Electrophysiology, Electric and
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266 Eliminative Materialism and tha
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268 Emergentism much less unity in
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270 Emotion and the Animal Brain (i
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272 Emotion and the Human Brain exp
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274 Emotions background of emotions
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276 Epiphenomenalism One critical r
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278 Episodic vs. Semantic Memory Ja
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280 Epistemology and Cognition Pern
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282 Essentialism FOLK PSYCHOLOGY, o
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284 Ethics Domain Specificity in Co
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286 Ethnopsychology beliefs; this s
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288 Ethology Quinn, N., and D. Holl
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290 Evoked Fields Craig, W. (1918).
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292 Evolution and Ethics themselves
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294 Evolutionary Computation and me
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296 Evolutionary Psychology about h
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298 Expert Systems Hirschfeld, L.,
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300 Explanation Djakow, I. N., N. W
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302 Explanation-Based Learning PROC
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304 Explanatory Gap Explanatory Gap
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306 Externalism of any component wi
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308 Eye Movements and Visual Attent
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310 Face Recognition respond to vis
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312 Feature Detectors ally selectiv
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314 Features Suga, N. (1994). Multi
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316 Focus for dinner, but JOHN is a
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318 Folk Biology (poodle, white oak
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320 Form/Content (von Eckardt 1994,
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322 Formal Systems, Properties of t
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324 Formal Theories Gödel, K. (193
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326 Frame Problem Schmidt-Schauss,
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328 Freud, Sigmund Dummett, M. (197
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330 Functional Decomposition tune,
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332 Functionalism when and where th
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334 Functionalism brains. However,
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336 Game-Playing Systems similarity
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338 Game Theory is no clear way at
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340 Gender equilibrium. Refinements
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342 Generative Grammar The second s
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344 Gestalt Perception (Geschwind a
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346 Gestalt Psychology was achieved
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348 Gestalt Psychology Figure 2. A
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350 Gibson, James Jerome observing
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352 Golgi, Camillo sentence; σ exp
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354 Good Old Fashioned AI (GOFAI) S
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356 Grammatical Relations object (i
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358 Greedy Local Search solution is
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360 Gustation use of or, not the wo
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362 Head-Driven Phrase Structure Gr
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364 Head Movement because they are
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366 Hearing See also BINDING THEORY
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368 Helmholtz, Hermann Ludwig Ferdi
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370 Hemispheric Specialization grou
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372 Heuristic Search Puce, A., T. A
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374 High-Level Vision Figure 1. A g
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376 High-Level Vision the world ser
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378 Historical Linguistics accounts
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380 Human Nature cognition to highl
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382 Human Universals advantage of s
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384 Hume, David Jankowiak, W. (1995
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386 Illusions Figure 2. Top: Illusi
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388 Imagery Shepard (1984) suggests
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390 Imitation made by another (Rizz
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392 Implicature Grice observes that
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394 Implicit vs. Explicit Memory Sa
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396 Indexicals and Demonstratives p
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398 Individualism beyond-the-head e
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400 Inductive Logic Programming of
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402 Infant Cognition structural ale
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404 Inference learning systems whos
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406 Informational Semantics symmetr
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408 Inheritance Fodor, J. (1984). S
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410 Intelligence A change in the fi
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412 Intentional Stance appropriate
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414 Intentionality my desire for wo
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416 Intersubjectivity (especially o
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418 Intersubjectivity Ecological an
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420 Introspection significant perio
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422 James, William individual langu
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424 Judgment Heuristics small sampl
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426 Justification fied beliefs are
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428 Kinds 4. Faculties: Kant develo
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430 Knowledge-Based Systems Clancey
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432 Knowledge Compilation MYCIN and
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434 Language Acquisition References
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436 Language Acquisition regulariza
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438 Language and Cognition Bloom, P
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440 Language and Communication pros
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442 Language and Gender social inte
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444 Language and Modularity Coates,
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446 Language Change Leonard, L. (19
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448 Language, Innateness of Curtiss
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450 Language, Neural Basis of Broca
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452 Language of Thought defenders t
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454 Language Production disfluencie
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456 Language Universals Communicati
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458 Lashley, Karl Spencer (1890-195
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460 Laws References Beach, F. A. (1
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462 Learning Systems matters are co
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464 Lexical Access two types of soc
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466 Lexical Functional Grammar Furt
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468 Lexicon type is allowed in each
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470 Lexicon, Neural Basis of patien
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472 Limbic System Figure 2. Lightne
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474 Limbic System basolateral limbi
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476 Linguistic Stress reference to
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478 Linguistics, Philosophical Issu
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480 Linguistics, Philosophical Issu
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482 Local Representation assumed th
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484 Logic Programming implicit in T
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486 Logical Form in Linguistics App
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488 Logical Form, Origins of Hornst
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490 Logical Omniscience, Problem of
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492 Long-Term Potentiation can deri
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494 LOT Otto, T., H. Eichenbaum, S.
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496 Machiavellian Intelligence Hypo
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498 Machine Translation models (Has
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500 Machine Translation documents,
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502 Machine Vision infer from image
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504 Magic and Superstition emotion
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506 Magnetic Resonance Imaging sinc
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508 Manipulation and Grasping Firth
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510 Manipulation and Grasping See a
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512 Materialism understanding brain
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514 Memory 4. What is the relations
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516 Memory This problem formed the
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518 Memory, Animal Studies the hipp
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520 Memory, Human Neuropsychology M
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522 Memory Storage, Modulation of i
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524 Mental Causation McGaugh, J. L.
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526 Mental Models process of compre
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528 Mental Representation distingui
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530 Mental Retardation accompanied
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532 Mental Rotation Figure 1. Illus
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534 Metacognition is usually superi
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536 Metaphor disaster. Sometimes a
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538 Metaphor and Culture the extens
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540 Metareasoning Figure 1. The con
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542 Metarepresentation has been des
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544 Meter and Poetry This causes a
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546 Mind-Body Problem Figure 3. Fig
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548 Mind Design How can the complex
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550 Minimum Description Length Econ
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552 Mobile Robots mining, waste rem
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554 Modal Logic Musliner D., E. Dur
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556 Modeling Neuropsychological Def
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558 Modularity of Mind that perhaps
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560 Modularity of Mind modularity m
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562 Morphology formal and universal
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564 Motion, Perception of aspects o
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566 Motivation Motion and Its Use i
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568 Motivation and Culture abstract
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570 Motor Control Paul, R. (1990).
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572 Motor Learning which muscular f
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574 Multisensory Integration Clearw
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576 Naive Mathematics people will l
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578 Naive Physics will follow a str
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580 Naive Sociology particularly in
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582 Narrow Content 1980; see also F
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584 Nativism innate, to particular
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586 Nativism, History of Spelke, E.
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588 Natural Kinds Scott, D. (1996).
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590 Natural Language Generation Fig
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592 Natural Language Processing Hov
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594 Navigation There are many appli
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596 Neural Development illustrate.
Page 731 and 732: 598 Neural Plasticity the problem i
Page 733 and 734: 600 Neural Plasticity cortical stag
Page 735 and 736: 602 Neuroendocrinology produces a l
Page 737 and 738: 604 Neuron the patterns of synaptic
Page 739 and 740: 606 Neurotransmitters The second ne
Page 741 and 742: 608 Nonmonotonic Logics In addition
Page 743 and 744: 610 Nonmonotonic Logics a nonclassi
Page 745 and 746: 612 Numeracy and Culture school (Br
Page 747 and 748: 614 Object Recognition, Animal Stud
Page 749 and 750: 616 Object Recognition, Human Neuro
Page 751 and 752: 618 Oculomotor Control Whether this
Page 753 and 754: 620 Olfaction Schiller, P. H., S. D
Page 755 and 756: 622 Origins of Intelligence RELATIO
Page 757 and 758: 624 Parallelism in AI of the face.
Page 759 and 760: 626 Parameter-Setting Approaches to
Page 761 and 762: 628 Parsimony and Simplicity Normal
Page 763 and 764: 630 Pattern Recognition and Feedfor
Page 765 and 766: 632 Perception neuropsychological s
Page 767 and 768: 634 Perceptual Development (Bahrick
Page 769 and 770: 636 Philosophical Issues in Linguis
Page 771 and 772: 638 Phonological Rules and Processe
Page 773 and 774: 640 Phonology tone distinguishes si
Page 775 and 776: 642 Phonology, Acquisition of that,
Page 777 and 778: 644 Phonology, Neural Basis of feat
Page 779 and 780: 646 Physicalism these emergent prop
Page 781: 648 Pictorial Art and Vision novel
Page 785 and 786: 652 Planning References McCulloch,
Page 787 and 788: 654 Plasticity References Allen, J.
Page 789 and 790: 656 Population-Level Cognitive Phen
Page 791 and 792: 658 Positron Emission Tomography Fi
Page 793 and 794: 660 Poverty of the Stimulus Argumen
Page 795 and 796: 662 Pragmatics or token-reflexives
Page 797 and 798: 664 Presupposition Discourse Descri
Page 799 and 800: 666 Primate Amygdala The consequent
Page 801 and 802: 668 Primate Cognition References Bo
Page 803 and 804: 670 Primate Language Both Kanzi and
Page 805 and 806: 672 Probabilistic Reasoning As a re
Page 807 and 808: 674 Problem Solving of the new rock
Page 809 and 810: 676 Procedural Semantics knowledge-
Page 811 and 812: 678 Propositional Attitudes Newell,
Page 813 and 814: 680 Prosody and Intonation Phonolog
Page 815 and 816: 682 Prosody and Intonation, Process
Page 817 and 818: 684 Psychoanalysis, Contemporary Vi
Page 819 and 820: 686 Psychoanalysis, History of and
Page 821 and 822: 688 Psycholinguistics Glymour, C. (
Page 823 and 824: 690 Psychological Laws Tanenhaus, M
Page 825 and 826: 692 Psychophysics light is preselec
Page 827 and 828: 694 Quantifiers together at a given
Page 829 and 830: 696 Radical Interpretation Gärdenf
Page 831 and 832: 698 Rational Agency Hookway, C. (19
Page 833 and 834:
700 Rational Choice Theory that eve
Page 835 and 836:
702 Rational Decision Making The ra
Page 837 and 838:
704 Rationalism vs. Empiricism term
Page 839 and 840:
706 Reading Garrod 1981). Alongside
Page 841 and 842:
708 Realism and Antirealism that un
Page 843 and 844:
710 Recurrent Networks Figure 1. A
Page 845 and 846:
712 Reductionism Giles, C. L., G. M
Page 847 and 848:
714 Reference, Theories of McCauley
Page 849 and 850:
716 Reinforcement Learning LEARNING
Page 851 and 852:
718 Relational Grammar Figure 1. Ac
Page 853 and 854:
720 Religious Ideas and Practices a
Page 855 and 856:
722 Representation Lawson, E. T., a
Page 857 and 858:
724 Rules and Representations corre
Page 859 and 860:
726 Running Machines plausible that
Page 861 and 862:
728 Sapir-Whorf Hypothesis Darnell,
Page 863 and 864:
730 Science, Philosophy of Schema t
Page 865 and 866:
732 Scientific Thinking and Its Dev
Page 867 and 868:
734 Self As both Sartre and Gibson
Page 869 and 870:
736 Self-Knowledge twist, the psych
Page 871 and 872:
738 Self-Organizing Systems issue i
Page 873 and 874:
740 Semantics words or morphemes (o
Page 875 and 876:
742 Semantics, Acquisition of Chier
Page 877 and 878:
744 Semantics-Syntax Interface Sema
Page 879 and 880:
746 Sense and Reference understood
Page 881 and 882:
748 Sentence Processing the way he
Page 883 and 884:
750 Sentence Processing (1990), Alt
Page 885 and 886:
752 Sexual Attraction, Evolutionary
Page 887 and 888:
754 Shape Perception they probably
Page 889 and 890:
756 Sight Rock, I. (1983). The Logi
Page 891 and 892:
758 Sign Languages Garrett, Eds., L
Page 893 and 894:
760 Signal Detection Theory the uni
Page 895 and 896:
762 Signal Detection Theory subclas
Page 897 and 898:
764 Similarity people’s categoriz
Page 899 and 900:
766 Single-Neuron Recording suggest
Page 901 and 902:
768 Situated Cognition and Learning
Page 903 and 904:
770 Situatedness/Embeddedness smoos
Page 905 and 906:
772 Sleep verification may use simp
Page 907 and 908:
774 Sleep motor systems signaled by
Page 909 and 910:
776 Smell organization of carbon at
Page 911 and 912:
778 Social Cognition in Animals all
Page 913 and 914:
780 Social Dominance from experimen
Page 915 and 916:
782 Social Play Behavior Bekoff, M.
Page 917 and 918:
784 Sociolinguistics issues, inform
Page 919 and 920:
786 Spatial Perception Figure 1. Re
Page 921 and 922:
788 Speech Perception featural leve
Page 923 and 924:
790 Speech Recognition in Machines
Page 925 and 926:
792 Speech Synthesis Speech recogni
Page 927 and 928:
794 Sperry, Roger Wolcott O’Shaug
Page 929 and 930:
796 Spoken-Word Recognition paradig
Page 931 and 932:
798 Statistical Learning Theory See
Page 933 and 934:
800 Statistical Learning Theory Fig
Page 935 and 936:
802 Stereo and Motion Perception on
Page 937 and 938:
804 Stereotyping References Faugera
Page 939 and 940:
806 Stress References Ashmore, R. D
Page 941 and 942:
808 Stress, Linguistic Magarinos, A
Page 943 and 944:
810 Structure from Visual Informati
Page 945 and 946:
812 Superstition striate to MT/V5 t
Page 947 and 948:
814 Supervised Learning in Multilay
Page 949 and 950:
816 Surface Perception to use a “
Page 951 and 952:
818 Synapse Synapse See COMPUTATION
Page 953 and 954:
820 Syntax, Acquisition of there is
Page 955 and 956:
822 Syntax, Acquisition of morpholo
Page 957 and 958:
824 Syntax-Semantics Interface Jaku
Page 959 and 960:
826 Systematicity Chomsky, N. (1986
Page 961 and 962:
828 Technology and Human Evolution
Page 963 and 964:
830 Temporal Reasoning all of the a
Page 965 and 966:
832 Teuber, Hans-Lukas In actual gr
Page 967 and 968:
834 Texture Interest in the percept
Page 969 and 970:
836 Thalamus Figure 1. A simplified
Page 971 and 972:
838 Theorem Proving The subject pos
Page 973 and 974:
840 Theory of Mind argued for a “
Page 975 and 976:
842 Time in the Mind implemented by
Page 977 and 978:
844 Top-Down Processing in Vision b
Page 979 and 980:
846 Transparency Figure 1. of a sin
Page 981 and 982:
848 Turing, Alan Mathison contribut
Page 983 and 984:
850 Twin Earth cards if the differe
Page 985 and 986:
852 Typology Wilson, R. (1995). Car
Page 987 and 988:
854 Uncertainty how large are the c
Page 989 and 990:
856 Understanding Understanding See
Page 991 and 992:
858 Unsupervised Learning LEARNING,
Page 993 and 994:
860 Utility Theory neoclassical the
Page 995 and 996:
862 Vagueness “tall” will remai
Page 997 and 998:
864 Visual Anatomy and Physiology E
Page 999 and 1000:
866 Visual Anatomy and Physiology F
Page 1001 and 1002:
868 Visual Cortex, Cell Types and C
Page 1003 and 1004:
870 Visual Neglect Frames of refere
Page 1005 and 1006:
872 Visual Object Recognition, AI i
Page 1007 and 1008:
874 Visual Processing Streams repre
Page 1009 and 1010:
876 von Neumann, John about petting
Page 1011 and 1012:
878 Vygotsky, Lev Semenovich x y Vo
Page 1013 and 1014:
880 Walking and Running Machines be
Page 1015 and 1016:
882 Wh-Movement on Intelligent Robo
Page 1017 and 1018:
884 Whorfianism what it’s like th
Page 1019 and 1020:
886 Word Meaning, Acquisition of Wi
Page 1021 and 1022:
888 Word Recognition Gleitman, L. R
Page 1023 and 1024:
890 Working Memory, Neural Basis of
Page 1025 and 1026:
892 Working Memory, Neural Basis of
Page 1027 and 1028:
894 Writing Systems Buchsbaum, M. S
Page 1029 and 1030:
896 Wundt, Wilhelm consonant (as in
Page 1031 and 1032:
898 X-Bar Theory Meischner, W., and
Page 1033 and 1034:
900 X-Bar Theory stands for XP, the
Page 1035 and 1036:
902 Contributors James R. Bergen Sa
Page 1037 and 1038:
904 Contributors Olivier D. Faugera
Page 1039 and 1040:
906 Contributors Paul Kay Universit
Page 1041 and 1042:
908 Contributors John K. Niederer U
Page 1043 and 1044:
910 Contributors Wolf Singer Max Pl
Page 1046 and 1047:
Name Index A Aarsleff, H., 475, 476
Page 1048 and 1049:
Bender, M. B., 40, 833 Benedict, R.
Page 1050 and 1051:
Campos, J. J., 839, 840 Candland, D
Page 1052 and 1053:
Davis, M., 117, 118, 184, 185, 269,
Page 1054 and 1055:
Fenson, L., 886, 887 Fernald, A., 4
Page 1056 and 1057:
Goldin, S. E., 115 Goldinger, S. D.
Page 1058 and 1059:
Herbrand, 323 Hering, O., 146, 385
Page 1060 and 1061:
Kalish, M., 350, 351 Kalman, R. E.,
Page 1062 and 1063:
Lauterbur, P. C., 505, 506 Lave, J.
Page 1064 and 1065:
Marx, K., 216 Marzi, C. A., 88, 89
Page 1066 and 1067:
Murdock, B. B., 237, 238 Murdock, G
Page 1068 and 1069:
Piaget, J., cxxvi, 28, 29, 123, 128
Page 1070 and 1071:
Rosen, C., 717, 718 Rosen, G. D., 2
Page 1072 and 1073:
Shoemaker, S., 334, 335, 420, 428,
Page 1074 and 1075:
Thagard, P. R., 18, 181 Thal, D. J.
Page 1076 and 1077:
Webb, B., 75, 76 Webb, J. C., 117,
Page 1078 and 1079:
Subject Index A Aboutness, 1 Absolu
Page 1080 and 1081:
Chomsky and the innateness of langu
Page 1082 and 1083:
Cultural symbolism, cxv, cxxii, 29,
Page 1084 and 1085:
Fixed action patterns, 288 Flight-o
Page 1086 and 1087:
folk psychology, 398 internalism, 3
Page 1088 and 1089:
Life, 471 Lightness perception, xlv
Page 1090 and 1091:
MUMBLE, 590, 591 Myth of Jones, xxi
Page 1092 and 1093:
Potential theory, 885 Poverty, 660
Page 1094 and 1095:
Semantical indices, 659 Semantics,
Page 1096 and 1097:
children’s theories of, 839 false
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