I identification assumption, 410–11 identified in the limit, 73–75 illusory conjunctions, 380–81 incommensurable, 14–15 index projection hypothesis, 395–97 individuation, 385, 388–89 informant learning, 72–74 information processing, 1, 4–10, 12–13, 15–17, 19, 41, 51–56, 69–73, 122–23, 125, 127–28, 199–202, 231–32, 259, 330–32 information processing devices, 51–54, 69–70 information processing hypothesis, 7, 15–16 information processing problem, 5, 19, 41, 51, 56, 71, 77, 113, 257, 259 input-output, 8, 31–32, 34, 39–46, 48, 50, 81–82, 86, 95, 97, 106, 120, 130–32, 160, 190 input-output function, 32, 34, 82, 95, 97, 106 input-output mapping, 32, 40–46, 48, 50, 130– 32, 151, 160, 162, 190 input-output relationship, 8, 97, 188 input unit, 129–30, 138–39, 144–47, 151, 158, 160–61, 164–67, 176, 180, 189–90, 194, 207, 333, 337, 343 intentionality, 83 intentional stance, 82–85, 251, 268, 323, 352 intermediate state evidence, 44–45, 98–99, 106, 257, 424 intertheoretic reduction, 159, 178, 183 isotropic processes, 114–16 J Jackend<strong>of</strong>f, Ray, 267, 276–79, 351 Jacquard loom, 76, 78–80, 328 James, William, 135–38 jittered density plot, 175–77 K Kuhn, Thomas, 14–15, 417, 424 L law <strong>of</strong> contiguity, 134–36, 190–91 law <strong>of</strong> habit, 134, 136–38 law <strong>of</strong> similarity, 134–35, 138 law <strong>of</strong> uphill analysis and downhill synthesis, 218 learning rule, 8, 131–32, 138, 141, 146, 149, 159–60, 190–91, 291, 333, 421 Leibniz, Gottfried, 50–51 and monads, 51 leverage, 206, 248, 262–63 limited order constraint, 365–66 linearly nonseparable problem, 143, 145–46 linearly separable problem, 143 linguistic competence, 351, 357 linguistic performance, 350 locality assumption, 342 local representations, 339, 341–43, 415 Locke, John, 126, 130, 135, 199, 235 logicism, 19, 21–23, 68, 81, 84–85, 133, 199, 203, 255, 266, 270, 272, 281, 334 M machine table, 42, 45, 50, 70, 330–32 machine head, 42–43, 45, 69–71, 80, 85, 152, 330–31, 347 machine state, 42–43, 330, 332 mark <strong>of</strong> the classical, 325–26, 334–35, 337–39, 343, 345–48, 352, 354–55 mark <strong>of</strong> the cognitive, 231, 325, 346 Marr, David, 5, 51, 53, 349–52, 355, 391, 415–16 and the tri-level hypothesis, 349–52, 355 massive modularity hypothesis, 116 matching law, 153 materialism, 59, 84, 113, 122, 255–56, 354 Merleau-Ponty, Maurice, 258, 361 methodological solipsism, 11, 206–8, 255, 261, 272–73, 317 metrical structure, 276, 278, 288 Mill, John Stuart, 135, 139 Miller, George, 4, 7, 406–8, 411 mind in action, 224, 227, 229–30, 234 <strong>Mind</strong> reading, 250 minimalism, 83–84, 296–97 minimalist music, 84, 296–97, 301 modularity, 113–19, 121, 194, 288, 342 packing problem, 113–14 modules, 56, 114–16, 194, 222–24, 288, 342, 379, 381–82, 390, 393 motion correspondence, 375–78, 384, 388, 416 Index 487
motion correspondence problem, 375–78, 388 multiple realization, 36–37, 86, 94, 123, 127, 199, 270 musical pitch, 288 N natural constraint, 375, 377, 414 navigation, 104, 229, 232, 234, 240, 242 nearest neighbour principle, 376–77 neurons, 8, 89, 114, 127, 136–37, 139–40, 152, 172– 73, 202, 246–47, 253–55, 287, 312, 339, 388–89 mirror neurons, 246–47, 253–54, 312 neuronally inspired, 8, 125, 128, 199, 282 neuron doctrine, 173 Newell, Alan, 78, 82, 86, 89, 94, 98–100, 110, 151, 226, 408, 420–21 nonlinearity, 139, 142, 217, 233 O object file, 381, 389–92 operators, 33, 75, 85, 222, 328, 336, 420 P parallel distributed processing (PDP), 128, 342 parallel processing, 127, 129, 334–38, 418 pattern completion, 286 pattern space, 143, 145–47, 150, 165–66, 172 perceptron, 141–42, 144–47, 150–51, 154–57, 160, 162, 165–66, 189–98, 240, 284, 337, 403 perceptron paradox, 192–93 phrase marker, 66–69, 79, 276 physical symbol system, 59, 61, 72, 75, 77–85, 88–89, 93–94, 110, 113, 119, 122, 151, 323, 354–55, 405 Piaget, Jean, 225–26 and concrete operations, 225 and formal operations, 225 piano-roll representation, 278 Pinker, Steven, 74, 288 pitch, 164–65, 167, 196, 277–78, 280, 286–88, 298 pitch class representation, 164–65, 167, 278 pop out, 101–3, 380–81 pop-out effect, 101, 380 posthumanism, 206, 250 poverty <strong>of</strong> the stimulus, 362–64, 368, 384 primary memory, 120–21 primitive, 15, 27–29, 33–34, 48–49, 51–53, 61, 77, 82, 102, 106, 108–10, 112, 256, 326–27, 385–86 primitive functions, 27–28, 152, 235 principle <strong>of</strong> modular design, 331–32 probability matching, 153–55, 348 problem behaviour graph, 99–100 problem <strong>of</strong> underdetermination, 74, 273, 364, 367, 390 problem solving, 7, 10, 98–100, 123, 201, 226–28, 370–71, 375, 397, 403–4, 407, 410, 412, 414, 421 production system, 89–92, 94, 100, 118, 177–78, 180–81, 183, 202, 320–22, 324, 332, 344, 403, 419, 421 prolongational reduction, 276–77 propositional attitudes, 83–85 propositional theory, 110, 394 protocol analysis, 98, 100, 201 protocols, 98–99, 420–21 punched card installation, 327–28 Pylyshyn, Zenon, 10, 48, 51, 94, 112, 268, 323, 353– 54, 358–60, 362, 366, 368, 371, 375, 382–98 on multiple object tracking, 386–87 on seeing and visualizing, 273, 354, 358, 360, 385, 397–98, 406, 417 on subitizing, 387 and the theory <strong>of</strong> visual cognition, 360, 368, 371, 375, 382–83, 385, 387–95, 397–98 R random access memory (RAM), 76 rationality, 84–85, 104, 122, 251, 255, 268, 270, 280, 354, 371, 398 readiness-to-hand, 319–20 recency effect, 121 recursion, 61, 63–65, 68, 133, 148, 201 Reich, Steve, 293, 296–98, 301 relative complexity evidence, 44–45, 88, 98, 100, 103, 106–8, 257, 394 relative velocity principle, 377–78 relaxation labelling, 372, 374, 413–14 relays, 30–33, 35–37, 140 reorientation, 104–6, 118, 189, 193–95, 239–43, 422 reorientation task, 104–5, 118, 189, 193–95, 488 Index
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MIND, BODY, WORLD
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MIND, FOUNDATIONS OF COGNITIVE SCIE
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Contents List of Figures and Tables
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5.5 Umwelten, Affordances, and Enac
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List of Figures and Tables Figure 2
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Table 2-1. Examples of the truth va
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happened in psychology. “One acco
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1 The Cognitive Sciences: One or Ma
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The fragmentation of psychology is
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qualitatively different kinds of qu
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should we examine “cognitive scie
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discrete symbols stored in a separa
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Some researchers have attempted to
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tool, or by its designer. The perso
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experiments fall into new relations
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With the foundations of the three d
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level, one asks what physical mecha
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thinking was logic, then thinking m
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equation of thought is of the secon
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ewritten as “A is B,” which in
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combinations of the binary inputs p
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Vannevar Bush. This machine was a p
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In Lovecraft’s (1933) story, the
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given in Table 2-2 (Hillis, 1998).
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switch was closed matched the durat
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2.6 Multiple Levels of Investigatio
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mentioned in the current section ar
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How does a Turing machine generate
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The problem with reverse engineerin
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2.10 Architectures against Homuncul
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out at any given time (Newell, 1980
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experiment, people write questions
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hypothesis (Dawson, 1998), is used
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Other developments in cognitive sci
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and strong equivalence are reviewed
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After establishing his own existenc
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For it is a very remarkable fact th
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used to solve a complex problem by
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In the code given above, recursion
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The complex, clausal structure of a
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within a phrase marker. The recursi
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new physical state, the direction i
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Bever, Fodor, and Garrett (1968) us
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found that informant learning permi
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derived, in the same way as new kno
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physical symbol system hypothesis:
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semantic lives, in which they have
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A device that can compute every pos
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contents of mental states and behav
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as being tokens of a particular typ
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-1.5 Grande Prairie Slave Lake Fort
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A production system is a general-pu
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in thinking about cognition without
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ased on informed judgment, and how
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paranoids. Forty practising psychia
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if they are weakly equivalent), acc
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Newell and Simon (1972) created a c
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is a property that is not local, bu
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A third approach to validating a mo
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growing variety of models of reorie
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are instructed to scan across the i
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paper (Pylyshyn, 1973), which propo
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location to the first. In this cond
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The packing problem is concerned wi
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I should like to propose a generali
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attempt to localize function. For i
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solution to the problem? For instan
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links between the two at the differ
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4 Elements of Connectionist Cogniti
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twentieth centuries (Warren, 1921).
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connectionist cognitive science dev
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via unsupervised learning (Carpente
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symbols. However, as connectionism
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Empiricist philosopher John Locke c
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The ability of A’s later activity
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In spite of the popularity and succ
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then become a different kind of net
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space, an entire row of a truth tab
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the only time that output unit acti
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The multilayer network illustrated
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input pattern in an all-or-none fas
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function such as the logistic. “N
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esponse is interpreted as represent
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define more complex probabilistic c
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A second condition of the perceptro
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esponses to determine the trigger f
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hidden unit computes its error by s
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The musical notation for the C majo
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and then determine the relationship
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In the pitch class representation u
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input signals coming from all of th
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chords. For instance, none of the h
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ecording or wiretapping (Calvin & O
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anding when a hidden unit’s activ
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classical form. This result suggest
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technique, the ID3 algorithm (Quinl
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Decision Point From Table 4-4 Equiv
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For example, mushrooms that were as
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color; that red, for instance, even
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When New Connectionism arose in the
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idea is to give the network as many
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in contiguity with the UR, the CS b
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stronger, or more intense, or faste
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simulations also revealed new pheno
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the correlation between the connect
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Given the breadth of connectionist
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were provided earlier in this chapt
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On the other hand, the functional n
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5 Elements of Embodied Cognitive Sc
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can do away with the body” (Hayle
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of the environment in which he find
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salesman’s tour increases linearl
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of the superorganism’s components
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next. Theraulaz and Bonabeau (1999)
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physical robot that acts like a Bra
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seriously as a contributor to the c
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to perception. Gibson (1966, p. 49)
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modules provide basic, general-purp
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several hours to complete a task (M
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Scribner’s own work (Scribner & T
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a virtual interface that was increa
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The embodied approach’s emphasis
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eorganizing the entire system. The
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understanding. The extended mind hy
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old clocks and gas meters” (Grey
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the British Association (Hayward, 2
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without feature cues. Their goal wa
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obot has stopped moving, and two ar
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cognitive science ventures to study
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the human brain (Gallese et al., 19
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y changing its facial expression, d
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others’ actions mind reading or m
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that others “are like me in being
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is illusory (Clark, 2003; Dennett,
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Even though an agent’s body can b
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interaction with the world are not
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The lofty goals of artificial intel
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This system is then placed in an in
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Reviewing the three approaches with
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For instance, the exposition uses i
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this interface, internal thinking,
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The conductor is not the only contr
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grouped into distinct auditory stre
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and stored in memory in a form diff
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theorize about mental processing be
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Krumhansl’s (1990) internalizatio
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mighty Alps, whose white and shinin
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The isolated genius is a recurring
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6.4 The Connectionist Approach to M
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will not occur (Gasser, Eck, & Port
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preferences (Bugatti, Flammini, & M
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The key feature from above is tonal
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The vein for which three hundred ye
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complexities of sound were produced
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Reich’s idea of a musical process
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orchestra brings the score to life
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meaning and that this meaning is so
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evaluation, interpretation, and des
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software objects can now evolve and
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instruments can serve as behavioura
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the case that Austro-German music i
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Interactions between perception of
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7 Marks of the Classical? 7.0 Chapt
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in this section. One context for th
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Critical to Vera and Simon’s (199
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Vera and Simon (1993) pointed out t
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physical scene that is being viewed
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the three schools of thought in cog
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was employed to work on the 1880 Un
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predefined value was reached (Willi
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Indeed, the interactions between a
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determine which production will act
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classical models they inspire are d
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a word in memory to be accessed in
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7.5 Local versus Distributed Repres
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Finally, different degrees of super
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to produce networks that are capabl
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e essentially embodied and embedded
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to question explicit rules as a mar
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the electronic circuits which perfo
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which make use of the knowledge att
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instance, meaningful, complex token
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constraints on symbol manipulations
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linguistic structures. That is, suc
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8 Seeing and Visualizing 8.0 Chapte
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an enormously high-quality visual w
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for the discovery of subjective sen
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The primary visual data caused by t
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A related phenomenon is inattention
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Spontaneously and by our own power,
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depends upon noticing and reacting
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this location. The affected locatio
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For our purposes, though, the above
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The nearest neighbour principle is
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8.5 Vision, Cognition, and Visual C
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Feature integration theory arose to
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(Pylyshyn, 2007, p. 32). Part of Py
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independent of those for processing
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(Pylyshyn & Storm, 1988). However,
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& Stanton, 1978; Sakata et al., 198
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that compose early vision, and whic
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with embodied cognitive science. Py
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esults by demonstrating that they a
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According to the index projection h
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9 Towards a Cognitive Dialectic 9.0
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summarizes that text visually by us
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Another way to illustrate the diffe
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tension? One approach to achieving
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interested in reformulating cogniti
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On the other side of the antagonism
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For instance, Simon’s (1969) earl
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was a low-level interpretation that
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computation approach appeals to ele
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etween scientific paradigms (Kuhn,
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each of the three schools of though
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It is pleasurable and easy to creat
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exploring how this problem might be
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References Abu-Mostafa, Y. S. (1990
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Baddeley, A. D. (1990). Human memor
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Bernstein, L. (1976). The unanswere
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Brooks, R. A., & Flynn, A. M. (1989
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Chase, V. M., Hertwig, R., & Gigere
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Comrie, L. J. (1933). The Hollerith
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