Mind, Body, World- Foundations of Cognitive Science, 2013a

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primitive form of spatial information is imagery, or whether images are constructed from more primitive propositional codes (Anderson, 1978; Block, 1981; Kosslyn, Thompson, & Ganis, 2006; Pylyshyn, 1973, 1981a, 2003b). Even though classical cognitive science is functionalist in nature and (in the eyes of its critics) shies away from biology, it also appeals to implementational evidence in its study of memory. The memory deficits revealed in patient Henry Molaison after his hippocampus was surgically removed to treat his epilepsy (Scoville & Milner, 1957) provided pioneering biological support for the functional separations of short-term from long-term memory and of declarative memory from nondeclarative memory. Modern advances in cognitive neuroscience have provided firm biological foundations for elaborate functional decompositions of memory (Cabeza & Nyberg, 2000; Poldrack et al., 2001; Squire, 1987, 2004). Similar evidence has been brought to bear on the imagery debate as well (Kosslyn, 1994; Kosslyn et al., 1995; Kosslyn et al., 1999; Kosslyn, Thompson, & Alpert, 1997). In the paragraphs above I have taken one tradition in cognitive science (the classical) and shown that its study of one phenomenon (human memory) reflects the use of all of the levels of investigation that have been the topic of the current chapter. However, the position that cognitive explanations require multiple levels of analysis (e.g., Marr, 1982) has not gone unchallenged. Some researchers have suggested that this process is not completely appropriate for explaining cognition or intelligence in biological agents (Churchland, Koch, & Sejnowski 1990; Churchland & Sejnowski, 1992). For instance, Churchland, Koch, & Sejnowski (1990, p. 52) observed that “when we measure Marr’s three levels of analysis against levels of organization in the nervous system, the fit is poor and confusing.” This observation is based on the fact that there appear to be a great many different spatial levels of organization in the brain, which suggests to Churchland, Koch, & Sejnowski that there must be many different implementational levels, which implies in turn that there must be many different algorithmic levels. The problem with this argument is that it confuses ontology with epistemology. That is, Churchland, Koch, & Sejnowski (1990) seemed to be arguing that Marr’s levels are accounts of the way nature is—that information processing devices are literally organized into the three different levels. Thus when a system appears to exhibit, say, multiple levels of physical organization, this brings Marr-as-ontology into question. However, Marr’s levels do not attempt to explain the nature of devices, but instead provide an epistemology—a way to inquire about the nature of the world. From this perspective, a system that has multiple levels of physical organization would not challenge Marr, because Marr and his followers would be comfortable applying their approach to the system at each of its levels of physical organization. Multiple Levels of Investigation 53
Other developments in cognitive science provide deeper challenges to the multiple-levels approach. As has been outlined in this chapter, the notion of multiple levels of explanation in cognitive science is directly linked to two key ideas: 1) that information processing devices invite and require this type of explanation, and 2) that cognition is a prototypical example of information processing. Recent developments in cognitive science represent challenges to these key ideas. For instance, embodied cognitive science takes the position that cognition is not information processing of the sort that involves the rule-governed manipulation of mentally represented worlds; it is instead the control of action on the world (Chemero, 2009; Clark, 1997, 1999; Noë, 2004, 2009; Robbins & Aydede, 2009). Does the multiplelevels approach apply if the role of cognition is radically reconstrued? Churchland, Koch, & Sejnowski. (1990, p. 52) suggested that “[‘]which really are the levels relevant to explanation in the nervous system[’] is an empirical, not an a priori, question.” One of the themes of the current book is to take this suggestion to heart by seeing how well the same multiple levels of investigation can be applied to the three major perspectives in modern cognitive science: classical, connectionist, and embodied. In the next three chapters, I begin this pursuit by using the multiple levels introduced in Chapter 2 to investigate the nature of classical cognitive science (Chapter 3), connectionist cognitive science (Chapter 4), and embodied cognitive science (Chapter 5). Can the multiple levels of investigation be used to reveal principles that unify these three different and frequently mutually antagonistic approaches? Or is modern cognitive science beginning to fracture in a fashion similar to what has been observed in experimental psychology? 54 Chapter 2
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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
Page 20 and 21: The fragmentation of psychology is
Page 22 and 23: qualitatively different kinds of qu
Page 24 and 25: should we examine “cognitive scie
Page 26 and 27: discrete symbols stored in a separa
Page 28 and 29: Some researchers have attempted to
Page 30 and 31: tool, or by its designer. The perso
Page 32 and 33: experiments fall into new relations
Page 34: With the foundations of the three d
Page 37 and 38: level, one asks what physical mecha
Page 39 and 40: thinking was logic, then thinking m
Page 41 and 42: equation of thought is of the secon
Page 43 and 44: ewritten as “A is B,” which in
Page 45 and 46: combinations of the binary inputs p
Page 47 and 48: Vannevar Bush. This machine was a p
Page 49 and 50: In Lovecraft’s (1933) story, the
Page 51 and 52: given in Table 2-2 (Hillis, 1998).
Page 53 and 54: switch was closed matched the durat
Page 55 and 56: 2.6 Multiple Levels of Investigatio
Page 57 and 58: mentioned in the current section ar
Page 59 and 60: How does a Turing machine generate
Page 61 and 62: The problem with reverse engineerin
Page 63 and 64: 2.10 Architectures against Homuncul
Page 65 and 66: out at any given time (Newell, 1980
Page 67 and 68: experiment, people write questions
Page 69: hypothesis (Dawson, 1998), is used
Page 73 and 74: and strong equivalence are reviewed
Page 75 and 76: After establishing his own existenc
Page 77 and 78: For it is a very remarkable fact th
Page 79 and 80: used to solve a complex problem by
Page 81 and 82: In the code given above, recursion
Page 83 and 84: The complex, clausal structure of a
Page 85 and 86: within a phrase marker. The recursi
Page 87 and 88: new physical state, the direction i
Page 89 and 90: Bever, Fodor, and Garrett (1968) us
Page 91 and 92: found that informant learning permi
Page 93 and 94: derived, in the same way as new kno
Page 95 and 96: physical symbol system hypothesis:
Page 97 and 98: semantic lives, in which they have
Page 99 and 100: A device that can compute every pos
Page 101 and 102: contents of mental states and behav
Page 103 and 104: as being tokens of a particular typ
Page 105 and 106: -1.5 Grande Prairie Slave Lake Fort
Page 107 and 108: A production system is a general-pu
Page 109 and 110: in thinking about cognition without
Page 111 and 112: ased on informed judgment, and how
Page 113 and 114: paranoids. Forty practising psychia
Page 115 and 116: if they are weakly equivalent), acc
Page 117 and 118: Newell and Simon (1972) created a c
Page 119 and 120: 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
Page 446 and 447:
Bernstein, L. (1976). The unanswere
Page 448 and 449:
Brooks, R. A., & Flynn, A. M. (1989
Page 450 and 451:
Chase, V. M., Hertwig, R., & Gigere
Page 452 and 453:
Comrie, L. J. (1933). The Hollerith
Page 454 and 455:
Dawson, M. R. W., Kelly, D. M., Spe
Page 456 and 457:
Downing, H. A., & Jeanne, R. L. (19
Page 458 and 459:
Fletcher, G. J. O. (1995). The scie
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Gjerdingen, R. O. (1989). Using con
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Greeno, J. G., & Moore, J. L. (1993
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Hauser, M. D., Chomsky, N., & Fitch
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Hopcroft, J. E., & Ullman, J. D. (1
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Josephson, M. (1961). Edison. New Y
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Kolers, P. (1972). Aspects of motio
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Leahey, T. H. (1987). A history of
Page 474 and 475:
Luria, A., Tsvetkova, L., & Futer,
Page 476 and 477:
McNaughton, B., Barnes, C. A., Gerr
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Monelle, R. (2000). The sense of mu
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Oaksford, M., & Chater, N. (1991).
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Peretz, I., Kolinsky, R., Tramo, M.
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Pylyshyn, Z. W. (1980). Computation
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Révész, G. E. (1983). Introductio
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Samuels, R. (1998). Evolutionary ps
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Shepard, R. N. (1984b). Ecological
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Steedman, M. J. (1984). A generativ
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Tolman, E. C. (1932). Purposive beh
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Verfaille, V., Depalle, P., & Wande
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Wexler, K., & Culicover, P. W. (198
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Zittoun, T., Gillespie, A., & Corni
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conduit metaphor, 299-303, 308 cons
Page 505 and 506:
motion correspondence problem, 375-
Page 507:
Turing equivalence, 95, 152 Turing
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