Notions of embodiment, situatedness, and dynamics are increasingly being debated in cognitive sci ence. However, these debates are often carried out in the absence of concrete examples. In order to build intuition, this paper explores a model agent to illustrate how the perspective and tools of dynam ical systems theory can be applied to the analysis of situated, embodied agents capable of minimally cognitive behavior. Specifically, we study a model agent whose “nervous system” was evolved using a genetic algorithm to catch circular objects and to avoid diamond-shaped ones. After characterizing the performance, behavioral strategy and psychophysics of the best-evolved agent, its dynamics are analyzed in some detail at three different levels: (1) the entire coupled brain/body/environment sys tem; (2) the interaction between agent and environment that generates the observed coupled dynam ics; (3) the underlying neuronal properties responsible for the agent dynamics. This analysis offers both explanatory insight and testable predictions. The paper concludes with discussions of the overall picture that emerges from this analysis, the challenges this picture poses to traditional notions of rep resentation, and the utility of a research methodology involving the analysis of simpler idealized mod els of complete brain/body/environment systems.
Get full access to this article
View all access options for this article.
References
1.
Abraham, R. H., & Shaw, C. D. (1992). Dynamics: The geometry of behavior. Addison–Wesley .
2.
Abarbanel, H. D. I., & Rabinovich, M. I. (2001). Neurodynamics: Nonlinear dynamics and neurobiology . Current Opinion in Neurobiology,11, 423–430 .
3.
Agre, P. E. (1997). Computation and human experience. Cambridge, UK: Cambridge University Press .
4.
Arieli, A., Sterkin, A., Grinvald, A., & Aertsen, A. (1996). Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses . Science,273, 1868–1871 .
5.
Arkin, R. C. (1998). Behavior-based robotics. Cambridge, MA: MIT Press .
6.
Ashby, W. R. (1952). Design for a brain. Wiley .
7.
Bäck, T. (1996). Evolutionary algorithms in theory and practice. Oxford: Oxford University Press .
8.
Ballard, D. (1991). Animate vision . Artificial Intelligence,48, 57–86 .
9.
Bechtel, W. (1998). Representations and cognitive explanations: Assessing the dynamicist’s challenge in cognitive science .Cognitive Science,22(3), 295–318 .
10.
Beer, R. D. (1990). Intelligence as adaptive behavior: an experiment in computational neuroethology. Academic Press .
11.
Beer, R. D. (1995a). A dynamical systems perspective on agent–environment interaction . Artificial Intelligence,72, 173–215 .
12.
Beer, R. D. (1995b). Computational and dynamical languages for autonomous agents. In R. Port, & T. van Gelder (Eds.), Mind as motion, explorations in the dynamics of cognition(pp. 121–147). Cambridge, MA: MIT Press .
13.
Beer, R. D. (1995c). On the dynamics of small continuous-time recurrent neural networks . Adaptive Behavior,3(4), 471–511 .
14.
Beer, R. D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In P. Maes, M. Mataric, J. A. Meyer, J. Pollack, & S. Wilson (Eds.), From animals to animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (pp. 421–429). Cambridge, MA: MIT Press .
15.
Beer, R. D. (1997). The dynamics of adaptive behavior: A research program . Robotics and Autonomous Systems,20, 257–289 .
16.
Beer, R. D. (1998). Framing the debate between computational and dynamical approaches to cognitive science . Behavioral and Brain Sciences,21(5), 630-630 .
17.
Beer, R. D. (2000). Dynamical approaches to cognitive science . Trends in Cognitive Sciences,4(3), 91–99 .
18.
Beer, R. D., & Gallagher, J. C. (1992). Evolving dynamical neural networks for adaptive behavior . Adaptive Behavior, 1, 91–122 .
19.
Beer, R. D., Chiel, H. J., & Gallagher, J. C. (1999). Evolution and analysis of model CPGs for walking II. General principles and individual variability . J. Computational Neuroscience,7(2), 119–147 .
20.
Bickhard, M. H., & Terveen, L. (1995). Foundational issues in artificial intelligence and cognitive science: Impasse and solution. Elsevier .
21.
Boyton, R. M. (1975). Color, hue and wavelength. In E. C. Carterette, & M. P. Freidman (Eds.), Handbook of perception, volume 5 (pp. 301–347). Academic Press .
22.
Brooks, R. A. (1991a). New approaches to robotics . Science, 253, 1227–1232 .
23.
Brooks, R. A. (1991b). Intelligence without representation . Artificial Intelligence,47, 139–159 .
24.
Carello, C., Turvey, M. T., Kugler, P. N., & Shaw, R. (1984). Inadequacies of the computer metaphor. In M. S. Gassaniga (Ed.), Handbook of cognitive neuroscience (pp. 229– 248). Plenum Press .
25.
Cangelosi, A., Greco, A., & Harnad, S. (2000). From robotic toil to symbolic theft: Grounding transfer from entry-level to higher-level categories . Connection Science, 12(2), 143–162 .
26.
Chiel, H. J., Beer, R. D., & Gallagher, J. C. (1999). Evolution and analysis of model CPGs for walking I. Dynamical modules .Journal of Computational Neuroscience,7(2), 99–118 .
27.
Churchland, P. S., Ramachandran, V. S., & Sejnsowski, T. J. (1994). A critique of pure vision. In C. Koch and J. Davis (Eds.),Large-scale neuronal theories of the brain (pp. 23– 60). Cambridge, MA: MIT Press .
28.
Clancey, W. J. (1997). Situated cognition: On human knowledge and computer representations. Cambridge, MA: Cambridge University Press .
29.
Clark, A. (1997). Being there: Putting brain, body and world together again. Cambridge, MA: MIT Press .
30.
Clark, A., & Grush, R. (1999). Towards a cognitive robotics . Adaptive Behavior,7(1), 5–16 .
31.
Clark, A., & Toribio, J. (1995). Doing without representing? Synthese,101, 401–431 .
32.
Cliff, D. (1991). Computational neuroethology: A provisional manifesto. In J. A. Meyer, & S. W. Wilson (Eds.), From animals to animats, Proceedings of the First International Conference on Simulation of Adaptive Behavior (pp. 29–39). Cambridge, MA: MIT Press .
33.
Cliff, D., Harvey, I., & Husband P. (1993). Explorations in evolutionary robotics . Adaptive Behavior,2, 73–110 .
34.
Cummins, R. (1989). Meaning and Mental Representation. Cambridge, MA: MIT Press .
35.
Damper, R. I., & Harnad, S. R. (2000). Neural networks models of categorical perception . Perception, and Psychophysics62(4), 843–867 .
36.
Dean, J. (1998). Animats, & what they can tell us . Trends in Cognitive Sciences,2(2), 60–67 .
37.
Dennett, D. C. (1980). The milk of human intentionality . Behavioral and Brain Sciences,3, 4128–4430 .
38.
Ehret, G. (1987). Categorical perception of sound signals: Facts and hypotheses from animal studies. In S. Harnad (Ed.), Categorical perception: The groundwork of cognition (pp. 301–331). Cambridge, UK: Cambridge University Press .
39.
Elman, J. L. (1995). Language as a dynamical system. In R. F. Port, & T. van Gelder (Eds.), Mind as motion (pp. 195– 225). Cambridge, MA: MIT Press .
40.
Funahashi, K., & Nakamura, Y. (1993). Approximation of dynamical systems by continuous time recurrent neural networks .Neural Networks,6, 801–806 .
41.
Gardner, H. (1985). The mind’s new science. Basic Books .
42.
Gibson, J. J. (1979). The ecological approach to visual perception. Lawrence Erlbaum .
43.
Glenberg, A. M. (1997). What is memory for? Behavioral and Brain Sciences,20, 1–55 .
44.
Harnad, S. (Ed.) (1987). Categorical perception: The groundwork of cognition. Cambridge, UK: Cambridge University Press .
45.
Harnad, S., Hanson, S. J., & Lubin, J. (1995). Learned categorical perception in neural nets: Implications for symbol grounding. In V. Honovar, & L. Uhr (Eds.), Symbol processors and connectionist network models in artificial intelligence and cognitive modeling: Steps toward principled integration (pp. 191–206). Academic Press .
46.
Harvey, I. (1996). Untimed and misrepresented . AISB Quarterly, Winter 1996, No. 96, 20–27 .
47.
Haugel, J. (1991). Representational genera. In W. Ramsey, S. P. Stich, & D. E. Rumelhart (Eds.), Philosophy and connectionist theory (pp. 61–89). Lawrence Erlbaum .
48.
Hendriks-Jansen, H. (1996). Catching ourselves in the act. Cambridge, MA: MIT Press .
49.
Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press .
50.
Johnson, S. D. (1994). Simple hybrid systems . International Journal of Bifurcation and Chaos,4(6), 1655–1665 .
51.
Keijzer, F. (2001). Representation and behavior. Cambridge, MA: MIT Press .
52.
Kelso, J. A. S. (1995). Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press .
53.
Kimura, M., & Nakano, R. (1998). Learning dynamical systems by recurrent neural networks from orbits . Neural Networks,11, 1589–1599 .
54.
Lakoff, G., & Johnson, M. (1998). Philosophy in the flesh. Basic Books .
55.
Lewin, R. (1992). Complexity: Life at the edge of chaos. Macmillan .
56.
Liberman, A. M., Cooper, F. S., Shankweiler, D. P., & Studdert-Kennedy, M.(1967). Perception of the speech code . Psychological Review,74, 431–461 .
57.
Markman, A. B., & Dietrich, E. (2000). In defense of representation .Cognitive Psychology,40, 138–171 .
58.
Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition. D. Reidel .
59.
Meyer, J. A. (1996). Artificial life and the animat approach to artificial intelligence. In M. Boden (Ed.), Artificial intelligence (pp. 325–354). Academic Press .
60.
Mitchell, M. (1996). An introduction to genetic algorithms. Cambridge, MA: MIT Press .
61.
Nolfi, S., & Floreano, D. (2000). Evolutionary Robotics. Cambridge, MA: MIT Press .
62.
Parisi, D. (1997). Artificial life and higher level cognition . Brain and Cognition,34, 160–184 .
63.
Pfeifer, R., & Scheier, C. (1999). Understanding intelligence. Cambridge, MA: MIT Press .
64.
Platt, M. L. (2002). Neural correlates of decisions . Current Opinion in Neurobiology,12, 141–148 .
65.
Port, R. F., & van Gelder, T. (Eds.) (1995). Mind as motion: Explorations in the dynamics of cognition. Cambridge, MA: MIT Press .
66.
Ramsey, W. (1997). Do connectionist representations earn their explanatory keep? Mind and Language,12(1), 34–66 .
67.
Skarda, C., & Freeman, W. J. (1987). How brains make chaos in order to make sense of the world . Behavioral and Brain Sciences,10, 161–195 .
68.
Slocum, A. C., Downey, D. C., & Beer, R. D. (2000). Further experiments in the evolution of minimally cognitive behavior: From perceiving affordances to selective attention. In J. Meyer, A. Berthoz, D. Floreano, H. Roitblat, & S. Wilson (Eds.), From animals to animats: Proceedings of the Sixth International Conference on the Simulation of Adaptive Behavior (pp. 430–439). Cambridge, MA: MIT Press .
69.
Strogatz, S. H. (1994). Nonlinear dynamics and chaos. Addison-Wesley .
70.
Studdert-Kennedy, M., Liberman, A. M., Harris, K. S., & Cooper, F. S. (1970). Motor theory of speech perception: A reply to Lane’s critical review . Psychological Review, 77, 234–249 .
71.
Suchman, L. A. (1987). Plans and situated actions. Cambridge, UK: Cambridge University Press .
72.
Thelen, E., & Smith, L. B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press .
73.
Thelen, E., Schöner, G., Scheier, C., & Smith, L. B. (2001). The dynamics of embodiment: A field theory of infant perseverative reaching . Behavioral and Brain Sciences, 24, 1–86 .
74.
Tijsseling, A., & Harnad, S. (1997). Warping similarity space in category learning by brackprop nets. In M. Ramscar, U. Hahn, E. Cambouropolos, & H. Pain (Eds.) Proceedings of SimCat 1997: Interdisciplinary Workshop on Similarity and Categorization (pp. 263–269). Edinburgh, UK: Department of Artificial Intelligence, Edinburgh University .
75.
Townsend, J. T., & Busemeyer, J. (1995). Dynamic representation of decision-making. In R. Port, & T. van Gelder (Eds.),Mind as motion: Explorations in the dynamics of cognition (pp. 101–120). Cambridge, MA: MIT Press .
76.
van Gelder, T. (1998). The dynamical hypothesis in cognitive science .Behavioral and Brain Sciences,21(5), 615–665 .
77.
Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind. Cambridge, MA: MIT Press .
78.
Webb, B. (2000). What does robotics offer animal behaviour? Animal Behaviour,60, 545–558 .
79.
Wheeler, M., & Clark, A. (1999). Genic representation: Reconciling content and causal complexity . British Journal for the Philosophy of Science,50, 103–135 .
80.
Wyttenbach, R. A., May, M. L., & Hoy, R. R. (1996). Categorical perception of sound frequency by crickets . Science, 273, 1542–1544 .
