Animals versus animats: Or why not model the real iguana?

Adaptive Behavior

Volumn 17, Issue 4, 2009, Pages 269-286

  Webb, Barbara ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Minimal cognition; Models; Simple animals; Simulation; Synthetic

Indexed keywords

ANIMALIA; IGUANA; SQUAMATA;

EID: 70349229725     PISSN: 10597123     EISSN: 17412633     Source Type: Journal    
DOI: 10.1177/1059712309339867     Document Type: Article

References (80)

1. Andersson, S. B., Handzel, A. A., Shah, V., & Krishnaprasad, P. S. (2004). Robot phonotaxis with dynamic sound localization. In Proceedings of IEEE International Conference on Robotics and Automation (pp. 4833-4838). IEEE, New Orleans, USA.
2. Arbib, M. (1982). Rana computatrix: An evolving model of visuo-motor coordination in frog and toad. In J. E. Hayes, D. Michie, & Y. H. Pao (Eds.), Machine intelligence 10 (pp. 501-517). Chichester: Ellis Horwood Publishers.
3. Bechtel, W. (1998). Representations and cognitive explanations: Assessing the dynamicist's challenge in cognitive science. Cognitive Science, 22, 295-318.
4. Bedau, M. A. (1999). Can unrealistic computer models illuminate theoretical biology? In A. Wu (Ed.). Proceedings of the 1999 Genetic and Evolutionary Computation Conference (pp. 20-23). Orlando: GECCO.
5. 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.
6. Beer, R. D. (1997). The dynamics of adaptive behavior: A research program. Robotics and Autonomous Systems, 20, 257-289.
7. Beer, R. D. (2003a). The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior, 11, 209-243.
8. Beer, R. D. (2003b). Arches and stones in cognitive architecture. Adaptive Behavior, 11, 299-305.
9. Böhm, H., Schildberger, K., & Huber, F. (1991). Visual and acoustic course control in the cricket Gryllus bimaculatus. Journal of Experimental Biology, 159, 235-248.
10. Bongard, J., & Paul, C. (2000). Investigating morphological symmetry and locomotive efficiency using virtual embodied evolution. In J.-A. Meyer et al. (Eds.), From Animals to Animats: The Sixth International Conference on the Simulation of Adaptive Behavior.
11. Braitenberg, V. (1984). Vehicles: Experiments in synthetic psychology. Cambridge MA: MIT Press.
12. Brooks, R. A. (1989). How to build complete creatures rather than isolated cognitive simulators. In K. VanLehn (Ed.), Architectures for intelligence (pp. 225-239). Hillsdale, NJ: Erlbaum.
13. Brooks, R. A., (1990). Elephants don't play chess. Robotics and Autonomous Systems, 6, 3-15.
14. Brooks, R. A., & Stein, L. A. (1994). Building brains for bodies. Autonomous Robotics, 1, 7-25.
15. Calenbuhr, V., & Deneubourg, J. L. (1990). A model for trail following in ants: Individual and collective behaviour. In W. Alt & G. Hoffman (Eds.), Biological motion, Lecture notes in biomathematics, (pp. 453-469). Berlin: Springer-Verlag.
16. Callender, C., & Cohen, J. (2005). There is no special problem about scientific representation. http://philsci-archive.pitt.edu/archive/00002177/
17. Cartwright, N. (1983). How the laws of physics lie. Oxford: Clarendon Press.
18. Collins, S., Ruina, A., Tedrake, R., & Wisse, M. (2005). Efficient bipedal robots based on passive dynamic walkers. Science Magazine, 307, 1082-1085.
19. Clancy, W. J. (1997). Situated cognition. Cambridge: Cambridge University Press.
20. Clark, A. (1997). Being there: Putting brain, body, and world together. Cambridge MA: MIT Press.
21. Clark, A. (2001). Mindware: An introduction to the philosophy of cognitive science. Oxford: OUP.
22. Dennett, D. C. (1978). Why not the whole iguana? Behavioral and Brain Sciences, 1, 103-104.
23. Dennett, D. C. (1998). Brainchildren: Essays on designing minds. Cambridge, MA: MIT Press.
24. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.
25. Ehret, G. (1987). Categorical perception of speech signals: facts and hypotheses from animal studies. In S. Harnad (Ed.), Categorical perception: The groundwork of cognition. New York: Cambridge University Press.
26. Frigg, R. (2003). Self organised criticality-what it is and what it isn't. Studies in the History and Philosophy of Science, 34, 613-632.
27. Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
28. Goldenberg, E., Garcowski, J., & Beer, R. D. (2004). May we have your attention: analysis of a selective attention task. S. Schaal, A. J. Ijspeert, A. Billiard, S. Vijayakumar, J. Hallam, and J.-A. Meyer (Eds.) In From Animals to Animats 8: Proceedings of the Eighth International Conference on Simulation of Adaptive Behavior, July 13-17, 2004, Cambridge MA: MIT Press.
29. Guillot, A., & Meyer, J.-A. (1997). Synthetic animals in synthetic worlds. In T. L. Kunii & A. Luciani (Eds.), Synthetic worlds. John Wiley and Sons. Chichester: Langton, Boston MA, USA
30. Harvey, I., Di Paolo, E. A., Tuci, E., Wood, R., & Quinn, M. (2005). Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life, 11, 79-98.
31. Hedwig, B., & Poulet, J. F. P. (2004). Complex auditory behaviour emerges from simple reactive steering. Nature, 430, 781-785.
32. Hedwig, B., & Poulet, J. F. P. (2005). Mechanisms underlying phonotactic steering in the cricket Gryllus bimaculatus (de Geer) revealed with a fast trackball system. Journal of Experimental Biology, 208, 915-927.
33. Hennig, R. M., Franz A., & Stumpner A. (2004). Auditory processing in insects. Microscopy Research Techniques, 63, 351-374.
34. Horchler, A., Reeve, R., Webb, B., & Quinn, R. (2004). Robot phonotaxis in the wild: A biologically inspired approach to outdoor sound localization. Advanced Robotics, 18, 801-816.
35. Huber, F., & Thorson, J. (1985). Cricket auditory communication. Scientific American, 253, 47-54.
36. Husbands, P., Harvey, I., & Cliff, D. (1995). Circle in the round: State space attractors for evolved sighted robots. Robotics and Autonomous Systems, 15, 83-106.
37. Keeley, B. L. (2000). Shocking lessons from electric fish: The theory and practice of multiple realization. Philosophy of Science, 67, 444-465.
38. Kim, K.-J., & Cho, S.-B. (2006). A comprehensive overview of the applications of ALife. Artificial Life, 12, 153-182.
39. Koch, C. (1999). Biophysics of computation. Oxford: Oxford University Press.
40. Langton, C. (1989). Artificial life. In C. Langton (Ed.), Artificial life (pp. 1-47). Addison-Wesley.
41. Lund, H. H., Webb, B., & Hallam, J. (1997). A robot attracted to the cricket species. P. Husbands and I. Harvey (Eds.). Gryllus bimaculatus. In Fourth European Conference on Artificial Life. Cambridge MA: MIT Press.
42. Lund, H. H., Webb, B., & Hallam, J. (1998). Physical and temporal scaling considerations in a robot model of cricket calling song preference. Artificial Life, 4, 95-107.
43. MacLennan, B. (1992). Synthetic ethology: An approach to the study of communication. In C. G. Langton, C. Taylor, J. D. Farmer & S. Rasmussen (Eds.), Artificial Life II: The Second Workshop on the Synthesis and Simulation of Living Systems (pp. 631-658), Santa Fe Institute Studies in the Sciences of Complexity, Proceedings Vol. X. Red-wood City, CA: Addison-Wesley.
44. MacLennan, B. (2002). Synthetic ethology: A new tool for investigating animal cognition. In M. Bekoff, C. Allen, & G. M. Burghardt (Eds.), The cognitive animal: Empirical and theoretical perspectives on animal cognition (pp. 151-156). Cambridge MA: MIT Press.
45. MacLennan, B., & Burghardt, G. M. (1993). Synthetic ethology and the evolution of cooperative communication. Adaptive Behavior, 2, 161-188.
46. Mandik, P. (2002). Synthetic neuroethology. Metaphilosophy, 33(1-2), 11-29.
47. McMullin, E. (1985). Galilean idealization. Studies in History and Philosophy of Science, 16, 247-273.
48. Meyer, J. A., Guillot, A., Girard, B., Khamassi, M., Pirim, P., & Berthoz, A. (2005). The Psikharpax project: Towards building an artificial rat. Robotics and Autonomous Systems, 50(4), 211-223.
49. Michelsen, A., Popov, A. V., & Lewis, B. (1994). Physics of directional hearing in the cricket Gryllus bimaculatus. Journal of Comparative Physiology A, 175, 153-164.
50. Neumann, T. R., & Bülthoff, H. H. (2002). Behavior-oriented vision for biomimetic flight control. Proceedings of the EPSRC/BBSRC International Workshop on Biologically Inspired Robotics: The Legacy of W. Grey Walter (pp. 196-203), 14-16 August, HP Labs Bristol, UK.
51. Neumann, T. R., Huber, S., & Bülthoff, H. H. (2001). Artificial systems as models in biological cybernetics. Behavioural and Brain Sciences, 24(6), 1071-1072.
52. Pfeifer, R., & Scheier, R. (2000). Understanding intelligence. Cambridge MA: MIT Press.
53. Poulet, J. F. A., & Hedwig, B. (2005). Auditory orientation in crickets: Pattern recognition controls reactive steering. Proceedings of the National Academy of Science USA, 102, 15665-15669.
54. Ray, T. S. (1994). An evolutionary approach to synthetic biology: Zen and art of creating life Artificial Life, 1, 179- 209.
55. Reeve, R., & Webb, B. (2003). New neural circuits for robot phonotaxis. Philosophical Transactions of the Royal Society A, 361, 2245-2266.
56. Reeve, R., Webb, B., Indiveri, G., Horchler, A., & Quinn, R. (2005). New technologies for testing a model of cricket phonotaxis on an outdoor robot. Robotics and Autonomous Systems, 51(1), 41-54.
57. Schildberger, K. (1984). Temporal selectivity of identified auditory interneurons in the cricket brain. Journal of Comparative Physiology A, 155, 171-185.
58. Schildberger, K., & Horner, M. (1988). The function of auditory neurons in cricket phonotaxis. 1. Influence of hyper-polarization of identified neurons on sound localization. Journal of Comparative Physiology A, 163, 621-631.
59. Scholes, S., Wilson, M., Sendova-Franks, A. B., & Melhuish, C. (2004). Comparisons in evolution and engineering: The collective intelligence of sorting. Adaptive Behavior, 12, 147-159.
60. 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 6: Proceedings of the Sixth International Conference on the Simulation of Adaptive Behavior (pp. 430-439). Cambridge MA: MIT Press.
61. Srinivasan, M. V., & Venkatesh, S. (1997). From living eyes to seeing machines. Oxford: Oxford University Press.
62. Steels, L. (2001). The methodology of the artificial. Behavioral and Brain Sciences, 24, 1077-1078.
63. 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.
64. Taylor, P. (1989). Revising models and generating theory. Oikos, 54, 121-126.
65. Verschure, P. F. M. J., Voegtlin, T., & Douglas, R. J. (2003). Environmentally mediated synergy between perception and behaviour in mobile robots. Nature, 425, 620-624.
66. Voegtlin, T., & Verschure, P. F. M. J. (1999). What can robots tell us about brains? A synthetic approach towards the study of learning and problem solving. Reviews in the Neurosciences, 10(3-4), 291-310.
67. Walter, W. G. (1950). An imitation of life. Scientific American, 182(5), 42-45.
68. Walter, W. G. (1951). A machine that learns. Scientific American, 185(2), 60-63.
69. Webb, B. (1991). Do computer simulations really cognize? Journal of Experimental and Theoretical Artificial Intelligence, 3, 247-254.
70. Webb, B. (1995). Using robots to model animals: a cricket test. Robotics and Autonomous Systems, 16, 117-134.
71. Webb, B. (2000). What does robotics offer animal behaviour? Animal Behaviour, 60, 545-558.
72. Webb, B. (2001). Can robots make good models of biological behaviour? Behavioural and Brain Sciences, 24(6), 1033- 1050.
73. Webb, B. (2006). Validating biorobotic models. Journal of Neural Engineering, 3, R25-R35.
74. Webb, B., & Harrison, R. R. (2000). Integrating sensorimotor systems in a robot model of cricket behavior. Gerard T. McKee and Paul S. Schenker (Eds.). Sensor Fusion and Decentralised Control in Robotic Systems III. Boston Nov. 6-8: Proceedings of SPIE - The International Society for Optical Engineering. v. 4196.
75. Webb, B., & Scutt, T. (2000). A simple latency dependent spiking neuron model of cricket phonotaxis. Biological Cybernetics, 82(3), 247-269.
76. Weisberg, M. (2007). Three kinds of idealization. Journal of Philosophy, CIV(12), 639-659.
77. Wilson, S. W. (1985). Knowledge growth in an artificial animal. Proceedings of the First International Conference on Genetic Algorithms and Their Applications. John J. Grefenstette (ed.) (pp. 16-23). Hillsdale, New Jersey: Lawrence Erlbaum Associates.
78. Wilson, S. W. (1991). The animat path to AI. In J. A. Meyer & S. W. Wilson (Eds.), From Animals to Animats: Proceedings of the First International Conference on the Simulation of Adaptive Behavior. Cambridge, Massachusetts: MIT Press/Bradford Books.
79. Wimsatt, W. C. (1987). False models as means to truer theories. In M. Nitecki & A. Hoffman, (Eds.), Neutral models in biology. London: Oxford University Press.
80. Wohlers, D. W., & Huber, F. (1982). Processing of sound signals by six types of neurons in the prothoracic ganglion of the cricket, Gryllus campestris L. Journal of Comparative Physiology A, 146, 161-173.