Skip to main content
Have a personal or library account? Click to login
Evaluating Artificial Models of Cognition Cover
Open Access
|Apr 2015

References

  1. Barandiaran, X. E., & Chemero, A. (2009). Animats in the modeling ecosystem. Adaptive Behavior, 17(4), 287-292.
  2. Beer, R. D., &Williams, P. L. (2009). Animals and animats:Why not both iguanas? Adaptive Behavior, 17(4), 296-302.
  3. Braitenberg, V. (1984). Vehicles, experiments in synthetic psychology. Cambridge, MA: MIT Press.
  4. Broadbent, D. E. (1958). Perception and communication. Oxford: Pergamon Press.
  5. Cleeremans, A., & French, R. M. (1996). From chicken squawking to cognition: Levels of description and the computational approach in psychology. Psychologica Belgica, 36, 1-28.
  6. Craver, C. F. (2007). Explaining the brain. Oxford: Oxford University Press.
  7. Cummins, R. (1983). The nature of psychological explanation. Cambridge, MA: MIT Press.
  8. Dawson, M. R. W. (2004). Minds and machines: Connectionism and psychological modeling. Malden, MA: Blackwell.
  9. Dennett, D. C. (1981). Reflections on D. R. Hofstadter’s “The Turing Test: A coffeehouse conversation”. In D. R. Hofstadter & D. C. Dennett (Eds.), The mind’s I (pp. 69-95). New York: Bantam Books.
  10. Dennett, D. C. (1991). Real patterns. Journal of Philosophy, 88(1), 27-51.
  11. Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, C., & Rasmussen, D. (2012). A large-scale model of the functioning brain. Science, 338(6111), 1202-1205. doi:10.1126/science.1225266.
  12. Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19(5), 329-335.
  13. Fodor, J. A. (1968). Psychological explanation: An introduction to the philosophy of psychology. New York: Random House.
  14. Frijda, N. H. (1967). Problems of computer simulation. Behavioral Science, 12(1), 59-67.
  15. Glennan, S. S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69(S3), S342-S353.
  16. Glymour, C. (1987). Android epistemology and the frame problem: Comments on Dennett’s “Cognitive wheels.” In Z. W. Pylyshyn (Ed.), The robot’s dilemma: Frame problem in Artificial Intelligence (pp. 65-75). Norwood: Ablex.
  17. Godfrey-Smith, P. (2008). Models and fictions in science. Philosophical Studies, 143(1), 101-116.
  18. Hinton, G. E., & Nowlan, S. J. (1987). How learning can guide evolution. Complex Systems, 1, 495-502.
  19. Koehn, P. (2010). Statistical machine translation. Cambridge: Cambridge University Press.
  20. Krohs, U. (2008). How digital computer simulations explain real-world processes. International Studies in the Philosophy of Science, 22(3), 277-292.
  21. Kuhn, T. S. (1970). The structure of scientific revolutions. Chicago: University of Chicago Press.
  22. Lewandowsky, S. (1993). The rewards and hazards of computer simulations. Psychological Science, 4(4) (July), 236-243.
  23. Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421-431.
  24. Levy, A. (2012).Models, fictions, and realism: Two packages. Philosophy of Science, 79(5) (November 19), 738-748.
  25. McClelland, J. L., Rumelhart, D. E. & PDP Research Group (Eds.) (1986). Parallel Distributed Processing: Explorations in the microstructures of cognition, Vol. 2: Psychological and biological models. Cambridge, MA: MIT Press.
  26. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
  27. Miłkowski, M. (2013). Explaining the computational mind. Cambridge, MA: MIT Press.
  28. Montebelli, A., Lowe, R., Ieropoulos, I., Melhuish, C., Greenman, J., & Ziemke, T. (2010). Microbial fuel cell driven behavioral dynamics in robot simulations. In H. Fellermann et al. (Eds.), Artificial Life XII: Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems (pp. 749-756). Cambridge, MA: MIT Press. Available at https://mitp-web2.mit.edu/sites/default/files/titles/alife/0262290758chap133.pdf [Accessed November 10, 2011].
  29. Neisser, U. (1963). The imitation of man by machine: The view that machines will think as man does reveals misunderstanding of the nature of human thought. Science, 139(3551): 193-197.
  30. Newell, A., Shaw, J. C., & Simon, H. A. (1960). A variety of intelligent learning in a general problem solver. In M. C. Yovits & S. Cameron (Eds.), Selforganizing systems: Proceedings of an interdisciplinary conference (pp. 153-189). Oxford: Pergamon Press.
  31. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
  32. Oaksford, M., Chater, N., & Larkin, J. (2000). Probabilities and polarity biases in conditional inference. Journal of Experimental Psychology. Learning, Memory, and Cognition, 26(4) (July), 883-99.
  33. Di Paolo, E., Noble, J., & Bullock, S. (2000). Simulation models as opaque thought experiments. In M. Bedau, J. McCaskill, N. Packard & S. Rasmussen (Eds.), The Seventh International Conference on Artificial Life (pp. 497-506). Cambridge, MA: MIT Press.
  34. Piccinini, G., & Craver, C. F. (2011). Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese, 183(3) (March 11), 283-311. doi:10.1007/s11229-011-9898-4.
  35. Pinker, S., & Prince, A. (1988). On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition, 23, 73-193.
  36. Santos, D., Sangbae, K., Spenko, M., Parness, A., & Cutkosky, M. (2007). Directional adhesive structures for controlled climbing on smooth vertical surfaces. In Proceedings 2007 IEEE International Conference on Robotics and Automation, 1262-1267. IEEE.
  37. Sanz, R., & Hern´andez, C. (2010). Autonomy, intelligence and animat mesmerization. In C. Hern´andez, J. Gómez & R. Sanz (Eds.), From brains to systems: Preprints of the BICS 2010 conference on brain-inspired cognitive systems (pp. 256-270).Madrid. Preprint available at http://tierra.aslab.upm.es/events/BIC2010/documents/BICS-2010-Preprints-complete.pdf.
  38. Simon, H. A. (1996). The sciences of the artificial. Cambridge, MA: MIT Press.
  39. Sperling, G. (1960). The information available in brief visual presentations. Psychological Monographs: General and Applied, 74 (11), 1-29.
  40. Súarez, M. (2003). Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science, 17(3) (October 1), 225-244. doi:10.1080/0269859032000169442.
  41. Súarez, M., (Ed.) (2009). Fictions in science: Philosophical essays on modeling and idealization. Vol. 4. New York: Routledge.
  42. Sun, R. (2009). Theoretical status of computational cognitive modeling. Cognitive Systems Research, 10(2), 124-140.
  43. Tolman, E. C. (1939). Prediction of vicarious trial and error by means of the schematic sowbug. Psychological Review, 46(4), 318-336.
  44. Webb, B. (2009). Animals versus animats: Or why not model the real iguana? Adaptive Behavior, 17(4) (July 28), 269-286.
  45. Weber, B. H., & Depew, D. J. (Eds.) (2003). Evolution and learning: The Baldwin effect reconsidered. Cambridge, MA: MIT Press.
  46. Weisberg, M. (2013). Simulation and similarity: Using models to understand the world. New York: Oxford University Press.
  47. Zeigler, B. (1976). Theory of modelling and simulation. New York: Wiley.
DOI: https://doi.org/10.1515/slgr-2015-0003 | Journal eISSN: 2199-6059 (formerly 0860-150X) | Journal ISSN: 0860-150X
Language: English
Page range: 43 - 62
Published on: Apr 10, 2015
Published by: University of Białystok
In partnership with: Paradigm Publishing Services
Related subjects:

© 2015 Marcin Miłkowski, published by University of Białystok
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.