Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-14T22:10:41.577Z Has data issue: false hasContentIssue false
  • English
  • Français

Embodiment and the Philosophy of Mind

Published online by Cambridge University Press:  08 January 2010

Andy Clark
Get access Rights & Permissions [Opens in a new window]

Extract

Cognitive science is in some sense the science of the mind. But an increasingly influential theme, in recent years, has been the role of the physical body, and of the local environment, in promoting adaptive success. No right-minded cognitive scientist, to be sure, ever claimed that body and world were completely irrelevant to the understanding of mind. But there was, nonetheless, an unmistakeable tendency to marginalize such factors: to dwell on inner complexity whilst simplifying or ignoring the complex inner-outer interplays that characterize the bulk of basic biological problem-solving. This tendency was expressed in, for example, the development of planning algorithms that treated real-world action as merely a way of implementing solutions arrived at by pure cognition (more recent work, by contrast, allows such actions to play important computational and problem-solving roles). It also surfaced in David Marr's depiction of the task of vision as the construction of a detailed threedimensional image of the visual scene. For possession of such a rich inner model effectively allows the system to ‘throw away’ the world and to focus subsequent computational activity on the inner model alone.

Type
Papers
Information
Royal Institute of Philosophy Supplements , Volume 43 , March 1998 , pp. 35 - 51
Copyright
Copyright © The Royal Institute of Philosophy and the contributors 1998

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1 Notable exceptions to this trend include work such as Gibson, J. J., The Ecological Approach to Visual Perception (Boston: Houghton-Mifflin, 1979)Google Scholar and, in a more philosophical key, Merleau-Ponty's, Maurice, La Structure du Comportment (Paris: Presses Universitaires de France, 1942)Google Scholar. Recent work in Animate Vision and ecological optics (see Section II below) is clearly influenced by Gibsonian ideas, while treatments such as Varela, F., Thompson, E. and Rosch, E., The Embodied Mind (Cambridge, MA: MIT Press, 1991)Google Scholar explicitly acknowledge Merleau-Ponty. There is a brief discussion of these historical roots in chapter 8 of my own Being There: Putting Brain, Body and World Together Again (Cambridge, MA: MIT Press, 1997).Google Scholar

2 See, e.g., Agre, P. and Rosenschein, S. (eds.) Computational Theories of Interaction and Agency (Cambridge, MA: MIT Press, 1996)Google Scholar; Kirsh, D. and Maglio, P. ‘On Distinguishing Epistemic from Pragmatic Action’, Cognitive Science 18 (1995), 513–49CrossRefGoogle Scholar; and Hutchins, E., Cognition in the Wild (Cambridge, MA: MIT Press, 1995).Google Scholar

3 SeeMarr, D.Vision (San Francisco, CA: W. H. Freeman, 1982).Google Scholar

4 This tradition is nicely critiqued in Churchland, P. S., Ramachandran, V. and Sejnowski, T., ‘A Critique of Pure Vision’ in koch, C. and Davies, J. (eds.), Large-Scale Neuronal Theories of the Brain (Cambridge, MA: MIT Press, 1994).Google Scholar

5 Roboticists refer (usually disparagingly) to this isolationist vision as the idea of a linear Sense-Think-Act Cycle, See, e.g., Malcolm, C., Smithers, T. and Hallam, J., ‘An Emerging Paradigm in Robot Architecture’, Edinburgh University Department of Artificial Intelligence Technical Report, 1989.Google Scholar

6 Major statements of this view include Haugeland, J., ‘Mind Embodied and Embedded’ in Houng, Y.-H. and Ho, J.-C. (eds.), Mind and Cognition (Tapei, Taiwan: Academia Sinica, 1995), pp. 3–38Google Scholar, and Van Gelder, T., ‘What Might Cognition Be, If Not Computation?’ Journal of Philosophy 92/7 (1995), 345–81CrossRefGoogle Scholar. Closely related claims and arguments appear in Van Gelder, T. and Port, R.. ‘It's About Time’ Introduction to Port, R. and Van Gelder, T. (eds.), Mind as Motion: Dynamics, Behavior, and Cognition (Cambridge, MA: MIT Press, 1995)Google Scholar. Thelen, E. and Smith, L., A Dynamic Systems Approach to the Development of Cognition and Action. (Cambridge, MA: MIT Press, 1994)Google Scholar, and Varela, Thompson and Rosch, The Embodied Mind

7 This vision is clearly contemplated in Haugeland ‘Mind Embodied’ and in Van Gelder, ‘What Might Cognition Be?’ Both authors, however, recognize the large space of intermediate possibilities. The term ‘post-Cartesian agent’ is from Van Gelder, p. 381. See also Thelen, and Smith, , A Dynamic Systems Approach, p. 338, Van Gelder and Port, It's About Time’, p. ix.Google Scholar

8 See Fodor, J. and Pylyshyn, Z., ‘Connectionism and Cognitive Architecture: A critical analysis’, Cognition 28 (1988), p. 13.CrossRefGoogle ScholarPubMed

9 See Rumelhart, D., McClelland, J. and the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vols. I and II (Cambridge, MA: MIT Press, 1986).Google Scholar

10 Churchland, P. S., Neurophilosophy (Cambridge, MA: MIT Press, 1986).Google Scholar

11 The details need not concern us here. But see e.g. my Microcognition: Philosophy, Cognitive Science and Parallel Distributed Processing (Cambridge, MA: MIT Press, 1989)Google Scholar, for discussion.

12 Elman, J., ‘Representation and Structure in Connectionist Models’, in Altman, G. (ed.), Cognitive Models of Speech Processing (Cambridge, MA: MIT Press, 1991).Google Scholar

13 For some hints of such content-sensitive complexity, see Knierim, J. and Van Essen, D., ‘Visual Cortex: Cartography, Connectivity and Concurrent Processing’, Current Opinion in Neurobiology 2 (1992), 150–5.CrossRefGoogle ScholarPubMed

14 Ballard, D., ‘Animate Vision’, Artificial Intelligence 48 (1991), 57–86.CrossRefGoogle Scholar

15 Marr, D., Vision (San Francisco, CA: W. H. Freeman, 1982).Google Scholar

16 See, e.g., Pylyshyn, Z. (ed.), The Robot's Dilemma: The Frame Problem in Artificial Intelligence (Norwood: Ablex, 1987).Google Scholar

17 Ballard, D., Hayhoe, M., Pook, P. and Rao, R., ‘Deictic Codes for the Embodiment of Cognition’, Behavioral and Brain Sciences, forthcoming.Google Scholar

18 Matraric, M., ‘Navigating with a Rat Brain: A Neurobiologically Inspired Model for Robot Spatial Representation’, in Meyer, J.-A. and Wilson, S. (eds.), From Animals to Animals 1 (Cambridge, MA: MIT Press, 1991)Google Scholar. This work is further discussed in Clark, Being There, chapter 2.

19 For more on this theme, see Millikan, R. ‘Pushmi-pullyu Representations’, in May, L., Friedman, M. and Clark, A. (eds.), Mind and Morals (Cambridge, MA: MIT Press, 1996).Google Scholar

20 This argument is the centrepiece of Van Gelder, ‘What Might Cognition Be?’ where we read, for example, that: ‘The core dynamical hypothesis … goes hand in hand with a conception of cognitive systems … as complexes of continuous, simultaneous and mutually-determining change. [] In this vision, the cognitive system is not just the encapsulated brain; rather, since the nervous system, body, and environment are all constantly changing and simultaneously influencing each other, the true cognitive system is a single unified system embracing all three’ (p. 373). The argument is also visible in Van Gelder, and Port, ‘It's About Time’, pp. 23–5Google Scholar, in Thelen, and Smith, , A Dynamic Systems Approach, p. 27Google Scholar, and in Varela, , Thompson, and Rosch, , The Embodied Mind, pp. 172–5.Google Scholar

21 Here Van Gelder ‘What Might Cognition Be?’ (p. 353) notes that: ‘arm angle and engine speed are at all times both determined by, and determining, each other's behavior … there is nothing mysterious about this relationship … Yet it is much more subtle and complex than the standard concept of representation can handle.’ This example is treated in detail in Clark, A. and Toribio, J., ‘Doing Without Representing?’ Synthese 101 (1995), 401–31.CrossRefGoogle Scholar

22 For example, in Ashby's, W. RossIntroduction to Cybernetics (New York: Wiley, 1956).Google Scholar

23 Merleau-Ponty, Maurice, The Structure of Behavior (New York: Beacon, 1963)Google Scholar. Originally La Structure du Comportment (Paris: Presses Universitaire de France, 1942).Google Scholar

24 For an accessible introduction to these dynamical approaches, see Kelso, S.Dynamic Patterns (Cambridge, MA: MIT Press, 1995)Google Scholar. A classic text is Abraham, R. and Shaw, C.Dynamics – The Geometry of Behavior (Redwood, MA: Addison-Wesley, 1992).Google Scholar

25 For a fuller discussion, see Clark, Being There, chapters 5, 6 and 8.

26 See A. Clark and R. Grush, ‘Towards a Cognitive Robotics’ (submitted).

27 For example, the posterior parietal neuronal population in the rat which encodes information about which way the rat's head is facing and which is exploited in radial maze running – see Naughton, B. and Nadel, L., ‘Hebb-Marr Networks and the Neurobiological Representation of Action in Space’, in Gluck, M. and Rumelhart, D. (eds.), Neuroscience and Connectionist Theory (Erlbaum, 1990)Google Scholar.

28 Israel, David ‘Bogdan on Information’, Mind & Language 3/2 (1988), 123–40CrossRefGoogle Scholar makes essentially the same point. See also CantwellSmith, Brian, The Origin of Objects (Cambridge, MA: MIT Press, 1996).Google Scholar

29 The phrase is from Clark and Toribio, ‘Doing Without Representing?’.

30 This move is explicitly made in Haugeland, ‘Mind Embodied’ and is also clearly in evidence in van Gelder and Port, ‘It's About Time’.

31 See Thelen and Smith, A Dynamic Systems Approach.

32 I borrow this case from Grush, R., ‘Emulation and Cognition’ (Ph.D. Dissertation, University of California at San Diego, 1995)Google Scholar. A further treatment is available in Clark and Grush, ‘Towards a Cognitive Robotics’.

33 This figure is established by, for example, using artificial vibrators strapped to the tendons to disrupt proprioceptive signals arriving from the muscle spindles, and timing the gap between such disruptive input and alterations to the arm motion itself (see Redon, C., Hay, L. and Velay, J. L., ‘Proprioceptive Control of Goal Directed Movements in Man, Studied by Means of Vibratory Muscle Tendon Stimulation’, Journal of Motor Control 23/2 (1991), 101–8).Google Scholar

34 See van der Meulen, J., Gooskens, R., van der Gon, J. J. Dennier, Gielen, C. C. A. M. and Wilhelm, K., ‘Mechanisms Underlying Accuracy in Fast Goal-directed Arm Movements in Man’, Journal of Motor Behavior 22/1 (1990), 67–84.CrossRefGoogle ScholarPubMed

35 See Grush, , ‘Emulation and Cognition’ for a review.Google Scholar

36 See Ito, M., The Cerebellum and Neural Control (New York: Raven Press, 1984)Google Scholar, Kawato, M., Furukawa, K. and Suzuki, R., ‘A Hierarchical Neural Network Model for the Control and Learning of Voluntary Movement’, Biological Cybernetics 57 (1987), 169–85CrossRefGoogle ScholarPubMed, and Wolpert, D., Ghahramani, Z. and Jordan, M., ‘An Internal Model for Sensorimotor Integration’, Science 269 (1995), 1880–2.CrossRefGoogle ScholarPubMed

37 E.g.Kawato, M., ‘Computational Schemes and Neural Network Models for Formation and Control of Multijoint Arm Trajectory’, in Miller, W. T. III, Sutton, R.; and Werbos, P. (eds.), Neural Networks for Control (Cambridge, MA: MIT Press, 1990), Wolpertet al., ‘An Internal Model’.Google Scholar

38 Grush, ‘Emulation and Cognition’.

39 See e.g. Fetz, D. and Landers, D., ‘The effects of Mental Practice on Motor Skill Learning and Performance: A Meta-Analysis’, Journal of Sport Psychology 5 (1983), 25–57.CrossRefGoogle Scholar

40 Decety, J., Sjoholm, H., Ryding, E., Stenberg, G. and Ingvar, D., ‘The Cerebellum Participates in Cognitive Activity‘, Brain Research 535 (1990), 313–17.CrossRefGoogle Scholar

41 Rumelhart, D., McClelland, J., Smolensky, P. and Hinton, G., ‘Schemata and Sequential Thought Processes in PDP Models’, in Rumelhart, D., McClelland, J. and the PDP research Group (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. II (Cambridge, MA: MIT Press, 1986), pp. 7–58.Google Scholar

42 Clark, A., ‘Magic Words: How Language Augments Human Cognition’, in Carruthers, P. and Boucher, J. (eds.) Thought and Language: Interdisciplinary Themes (Cambridge: Cambridge University Press, forthcoming).Google Scholar

43 Dennett, D., ‘Labelling and Learning’, Mind and Language 8 (1994), 540–8.CrossRefGoogle Scholar See also Chapter 13 of his Darwin's Dangerous Idea (New York: Simon & Schuster, 1995).Google Scholar

44 Jackendoff, R., ‘How Language Helps Us Think’, Pragmatics and Cognition 4/1 (1996), pp. 1–34.Google Scholar

45 Ibid., pp. 19–22.

46 Hutchins, E., Cognition in the Wild (Cambridge, MA: MIT Press, 1995).Google Scholar

47 For example, see the comments on pp. 277–8 of his ‘Replies to Critics’, in Loewer, B. and Rey, G. (eds.), Meaning in Mind: Fodor and his critics (Oxford: Blackwell, 1991), pp. 255–319.Google Scholar

48 Syntactic properties are any non-semantic properties that can be directly exploited by a physical system. Temporally extended processes, as described in section II, are in this sense syntactic too.

49 Knierim and Van Essen, ‘Visual Cortex’, 150–5.

50 Van Gelder, ‘What Might Cognition Be?’.

51 See papers in Port and Van Gelder (eds.), Mind as Motion.