Through neural stimulation to behavior manipulation: A novel method for analyzing dynamical cognitive models

Cognitive Science

Volumn 34, Issue 3, 2010, Pages 406-433

  Hope, Thomas ^a,b   Stoianov, Ivilin ^a   Zorzi, Marco ^a  

^a UNIVERSITY OF PADOVA   (Italy)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Analysis; Dynamical systems; Evolution; Genetic algorithms; Recurrent neural networks

Indexed keywords

EID: 77953178763     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1551-6709.2009.01079.x     Document Type: Article

References (50)

1. Aharonov, R., Segev, L., Meilijson, I., & Ruppin, E. (2003). Localization of function via lesion analysis. Neural Computation, 15 (4), 885-913.
2. Atick, J. (1992). Could information theory provide an ecological theory of sensory processing? Network, 3, 213-251.
3. Beer, R. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72 (1-2), 173-215.
4. Beer, R. D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In P. Maes, et al. (Eds.), Proceedings of the 4th international conference on the simulation of adaptive behaviour (pp. 421-429). Cambridge, MA: MIT Press.
5. Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4 (3), 91-99.
6. Beer, R. D. (2003). The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior, 11 (4), 209-243.
7. Biro, D., & Matsuzawa, T. (2001). Use of numerical symbols by the chimpanzee (Pan troglodytes): Cardinals, ordinals, and the introduction of zero. Animal Cognition, 4 (3), 193-199.
8. Brannon, E. M., Wusthoff, C. J., Gallistel, C. R., & Gibbon, J. (2001). Numerical subtraction in the pigeon: Evidence for a linear subjective number scale. Psychological Science: A Journal of the American Psychological Society / APS, 12 (3), 238-243.
9. Butterworth, B. (1999). The mathematical brain. London: Macmillan.
10. Dehaene, S. (2000). The number sense: How the mind creates mathematics? New York: Oxford University Press.
11. Dehaene, S., & Changeux, J. (1993). Development of elementary numerical abilities: A neuronal model. Journal of Cognitive Neuroscience, 5 (4), 390-407.
12. Durgin, F. H. (1995). Texture density adaptation and the perceived numerosity and distribution of texture. Journal of Experimental Psychology: Human Perception and Performance, 21 (1), 149-169.
13. Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7 (2-3), 195-225.
14. Feigenson, L., Carey, S., & Hauser, M. (2002). The representations underlying infants' choice of more: Object files versus analog magnitudes. Psychological Science: A Journal of the American Psychological Society / APS, 13 (2), 150-156.
15. Friston, K., Worsley, K., Frackowisk, R., Mazziotta, J., & Evans, A. (1994). Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, 1, 214-220.
16. Gallistel, C., & Gelman, R. (1992). Preverbal and verbal counting and computation. Cognition, 44 (1-2), 43-74.
17. Gallistel, C. R., & Gelman, R. (2000). Non-verbal numerical cognition: From reals to integers. Trends in Cognitive Sciences, 4(2), 59-65.
18. van Gelder, T. (1995). What might cognition be, if not computation? The Journal of Philosophy, 92 (7), 345-381.
19. van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21 (05), 615-628.
20. Gonzalez, R. C., & Richard, E. (1992). Woods, digital image processing. Reading, MA: Addison Wesley.
21. Harvey, I. (2001). Artificial evolution: A continuing SAGA. In T. Gomi (Ed.), Evolutionary robotics. From intelligent robotics to artificial life (pp. 94-109). Heidelberg, Germany: Springer.
22. Joanisse, M. F., & Seidenberg, M. S. (1999). Impairments in verb morphology after brain injury: A connectionist model. Proceedings of the National Academy of Sciences of the United States of America, 96 (13), 7592-7597.
23. Jordan, K., & Brannon, E. (2006a). Weber's Law influences numerical representations in rhesus macaques (Macaca mulatta). Animal Cognition, 9 (3), 159-172.
24. Jordan, K. E., & Brannon, E. M. (2006b). A common representational system governed by Weber's law: Nonverbal numerical similarity judgments in 6-year-olds and rhesus macaques. Journal of Experimental Child Psychology, 95 (3), 215-229.
25. Keinan, A., Hilgetag, C. C., Meilijson, I., & Ruppin, E. (2004). Causal localization of neural function: The Shapley value method. Neurocomputing, 58-60, 215-222.
26. Keinan, A., Meilijson, C. C. H. I., Ruppin, E., Hilgetag, C. C., & Meilijson, I. (2003). Fair attribution of functional contribution in artificial and biological networks. Neural Computation, 16, 1887-1915.
27. McCloskey, M. (1991). Networks and theories: The place of connectionism in cognitive science. Psychological Science, 2, 387-395.
28. Meck, W. H., & Church, R. M. (1983). A mode control model of counting and timing processes. Journal of Experimental Psychology. Animal Behavior Processes, 9 (3), 320-334.
29. Miller, A. L., & Baker, R. A. (1968). The effects of shape, size, heterogeneity, and instructional set on the judgment of visual number. The American Journal of Psychology, 81 (1), 83-91.
30. Moyer, R. S., & Landauer, T. K. (1967). Time required for Judgements of numerical inequality. Nature, 215 (5109), 1519-1520.
31. Nieder, A., Freedman, D. J., & Miller, E. K. (2002). Representation of the quantity of visual items in the primate prefrontal cortex. Science, 297 (5587), 1708-1711.
32. Nowak, A. (2004). Dynamical minimalism: Why less is more in psychology. Personality and Social Psychology Review, 8 (2), 183-192.
33. Oja, E. (1989). Neural networks, principal components, and subspaces. International Journal of Neural Systems, 1 (1), 61-68.
34. Perry, C., Ziegler, J. C., & Zorzi, M. (2007). Nested incremental modeling in the development of computational theories: The CDP + model of reading aloud. Psychological Review, 114 (2), 273-315.
35. Plaut, D. C., & Shallice, T. (1991). Deep dyslexia: A case study of connectionist neuropsychology. Cognitive Neuropsychology, 10, 377-500.
36. Port, R. F., & Gelder, T. V. (1998). Mind as motion: Explorations in the dynamics of cognition. Cambridge, MA: MIT Press.
37. Rao, C. R. (1964). The use and interpretation of principal component analysis in applied research. Sankhya: The Indian Journal of Statistics, Series A, 26 (4), 329-358.
38. Roitman, J. D., Brannon, E. M., & Platt, M. L. (2007). Monotonic coding of numerosity in macaque lateral intraparietal area. PLoS Biology, 5 (8), e208.
39. Romo, R., & Salinas, E. (2003). Flutter discrimination: Neural codes, perception, memory and decision making. Nature Reviews: Neuroscience, 4 (3), 203-214.
40. Seth, A. (2008). Causal networks in simulated neural systems. Cognitive Neurodynamics, 2 (1), 49-64.
41. Shallice, T. (1988). From neuropsychology to mental structure. Cambridge, UK: Cambridge University Press.
42. Shapley, L. (1953). A value for n-person games. In H. W. Kuhn & A. W. Tucker (Eds.), Contributions to the theory of games (pp. 307-317). Princeton, NJ: Princeton University Press.
43. Spieler, D., & Balota, D. (1997). Bringing computational models of word naming down to the item level. Psychological Science, 8, 411-416.
44. Thelen, E., & Smith, L. B. (1996). A dynamic systems approach to the development of cognition and action (p. 408). Cambridge, MA: MIT Press.
45. Tomko, G. J., & Crapper, D. R. (1974). Neuronal variability: Non-stationary responses to identical visual stimuli. Brain Research, 79 (3), 405-418.
46. Uller, C., Jaeger, R., Guidry, G., & Martin, C. (2003). Salamanders (Plethodon cinereus) go for more: Rudiments of number in an amphibian. Animal Cognition, 6, 105-112.
47. Verguts, T., & Fias, W. (2004). Representation of number in animals and humans: A neural model. Journal of Cognitive Neuroscience, 16 (9), 1493-1504.
48. Watson, R. A., & Pollack, J. B. (2001). Coevolutionary dynamics in a minimal substrate. I, GECCO-01 (pp. 702-709). San Francisco, CA: Morgan Kaufmann.
49. Zorzi, M., & Butterworth, B. (1999). A Computational model of number comparison. In Proceedings of the 21st Annual Conference of the Cognitive Science Society (pp. 1-4). Vancouver, British Columbia: Lawrence Erlbaum Associates.
50. Zorzi, M., Stoianov, I., & Umiltà, C. (2005). Computational modeling of numerical cognition. Handbook of Mathematical Cognition, 5, 67-83.