The AHA! experience: Creativity through emergent binding in neural networks

Cognitive Science

Volumn 35, Issue 1, 2011, Pages 1-33

  Thagard, Paul ^a   Stewart, Terrence C ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Binding; Conceptual combination; Convolution; Creativity; Emotion; Neural engineering framework; Neural networks; Neurocomputation; Representation

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; CREATIVITY; EMOTION; HUMAN; PSYCHOLOGICAL THEORY;

COMPUTER SIMULATION; CREATIVENESS; EMOTIONS; HUMANS; NEURAL NETWORKS (COMPUTER); PSYCHOLOGICAL THEORY;

EID: 79953168224     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1551-6709.2010.01142.x     Document Type: Article

References (87)

1. Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.
2. Asheim, B. T., & Gertler, M. S. (2005). The geography of innovation: Regional innovation systems. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), The Oxford handbook of innovation (pp. 291-317). Oxford, England: Oxford University Press.
3. Barsalou, L. W., Simmons, W. K., Barbey, A. K., & Wilson, C. D. (2003). Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences, 7, 84-91.
4. Bechtel, W. (2008). Mental mechanisms: Philosophical perspectives on cognitive neuroscience. New York: Routledge.
5. Blasdel, G. G., & Salama, G. (1986). Voltage sensitive dyes reveal a modular organization in monkey striate cortex. Nature, 321, 579-585.
6. Boden, M. (2004). The creative mind: Myths and mechanisms (2nd ed.). London: Routledge.
7. Bowden, E. M., & Jung-Beeman, M. (2003). Aha! Insight experience correlates with solution activation in the right hemisphere. Psychonomic Bulletin and Review, 2003 (10), 730-737.
8. Bridewell, W., Langley, P., Todorovski, L., & Dzeroski, S. (2008). Inductive process modeling. Machine learning, 71, 1-32.
9. Bunge, M. (2003). Emergence and convergence: Qualitative novelty and the unity of knowledge. Toronto: University of Toronto Press.
10. Chandrasekharan, S. (2009). Building to discover: A common coding model. Cognitive Science, 33, 1059-1086.
11. Churchland, P. M. (1989). A neurocomputational perspective. Cambridge, MA: MIT Press.
12. Churchland, P. S., & Sejnowski, T. (1992). The computational brain. Cambridge, MA: MIT Press.
13. Costello, F. J., & Keane, M. T. (2000). Efficient creativity: Constraint-guided conceptual combination. Cognitive Science, 24, 299-349.
14. Craver, C. F. (2007). Explaining the brain. Oxford, England: Oxford University Press.
15. Craver, C. F., & Bechtel, W. (2007). Top-down causation without top-down causes. Biology and Philosophy, 22, 547-663.
16. Crick, F. (1994). The astonishing hypothesis: The scientific search for the soul. London: Simon and Schuster.
17. Damasio, A. R. (1994). Descartes' error. New York: G. P. Putnam's Sons.
18. Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge, MA: MIT Press.
19. Eliasmith, C. (2004). Learning context sensitive logical inference in a neurobiological simulation. In S. Levy & R. Gayler (Eds.), Compositional connectionism in cognitive science (pp. 17-20). Menlo Park, CA: AAAI Press.
20. Eliasmith, C. (2005a). Cognition with neurons: A large-scale, biologically realistic model of the Wason task. In B. Bara, L. Barasalou, & M. Bucciarelli (Eds.), Proceedings of the XXVII Annual Conference of the Cognitive Science Society (pp. 624-629). Mahwah, NJ: Lawrence Erlbaum Associates.
21. Eliasmith, C. (2005b). Neurosemantics and categories. In H. Cohen & C. Lefebvre (Eds.), Handbook of categorization in cognitive science (pp. 1035-1054). Amsterdam: Elsevier.
22. Eliasmith, C. (forthcoming). How to build a brain. Oxford, England: Oxford University Press.
23. Eliasmith, C., & Anderson, C. H. (2003). Neural engineering: Computation, representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press.
24. Eliasmith, C., & Thagard, P. (2001). Integrating structure and meaning: A distributed model of analogical mapping. Cognitive Science, 25, 245-286.
25. Engel, A. K., Fries, P., König, P., Brecht, M., & Singer, W. (1999). Temporal binding, binocular rivalry, and consciousness. Consciousness and Cognition, 8, 128-151.
26. Fauconnier, G., & Turner, M. (2002). The way we think. New York: Basic Books.
27. Feynman, R. (1999). The pleasure of finding things out. Cambridge, MA: Perseus Books.
28. Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986). Neuronal population coding of movement direction. Science, 233 (4771), 1416-1419.
29. Grandjean, D., Sander, D., & Scherer, K. R. (2008). Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization. Consciousness and Cognition, 17, 484-495.
30. Hebb, D. O. (1949). The organization of behavior. New York: Wiley.
31. Hebb, D. O. (1980). Essay on mind. Hillsdale, NJ: Lawrence Erlbaum.
32. Hofstadter, D. (1995). Fluid concepts and creative analogies: Computer models of the fundamental mechanisms of thought. New York: Basic Books.
33. Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press/Bradford Books.
34. Holyoak, K. J., & Thagard, P. (1995). Mental leaps: Analogy in creative thought. Cambridge, MA: MIT Press/Bradford Books.
35. Hopfield, J. (1999). Odor space and olfactory processing: Collective algorithms and neural implementation. Proceedings of the National Academy of Sciences of the United States of America, 96, 12506-12511.
36. Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: A theory of analogical access and mapping. Psychological Review, 104, 427-466.
37. Hummel, J. E., & Holyoak, K. J. (2003). A symbolic-connectionist theory of relational inference and generalization. Psychological Review, 110, 220-264.
38. James, W. (1884). What is an emotion? Mind, 9, 188-205.
39. Johnson, J. S., Spencer, J. P., & Schöner, G. (2008). Moving to higher ground: The dynamic field theory and the dynamics of visual cognition. New Ideas in Psychology, 26, 227-251.
40. Koestler, A. (1967). The act of creation. New York: Dell.
41. Kounios, J., & Beeman, M. (2009). The Aha! moment: The cognitive neuroscience of insight. Current Directions in Psychological Science, 18, 210-216.
42. Koza, J. R. (1992). Genetic programming. Cambridge, MA: MIT Press.
43. Kunda, Z., Miller, D., & Claire, T. (1990). Combining social concepts: The role of causal reasoning. Cognitive Science, 14, 551-577.
44. Laird, J., Rosenbloom, P., & Newell, A. (1986). Chunking in Soar: The anatomy of a general learning mechanism. Machine Learning, 1, 11-46.
45. Langley, P., Simon, H., Bradshaw, G., & Zytkow, J. (1987). Scientific discovery. Cambridge, MA: MIT Press/Bradford Books.
46. Lenat, D., & Brown, J. S. (1984). Why AM and Eurisko appear to work. Artificial Intelligence, 23, 269-294.
47. McCormick, D. A., Connors, B. W., Lighthall, J. W., & Prince, D. A. (1985). Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. Journal of Neurophysiology, 54, 782-806.
48. Medin, D., & Shoben, E. (1988). Context and structure in conceptual combination. Cognitive Psychology, 20, 158-190.
49. Mednick, S. A. (1962). The associate basis of the creative process. Psychological Review, 69, 220-232.
50. Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The psychology of computer vision (pp. 211-277). New York: McGraw-Hill.
51. Mumford, M. D. (2002). Social innovation: Ten cases from Benjamin Franklin. Creativity research journal, 14, 253-266.
52. Nersessian, N. (2008). Creating scientific concepts. Cambridge, MA: MIT Press.
53. Parisien, C., & Thagard, P. (2008). Robosemantics: How Stanley the Volkswagen represents the world. Minds and Machines, 18, 169-178.
54. Pereira, F. C. (2007). Creativity and artificial intelligence. Berlin: Mouton de Gruyter.
55. Plate, T. (2003). Holographic reduced representations. Stanford: CSLI.
56. Prinz, J. (2004). Gut reactions: A perceptual theory of emotion. Oxford, England: Oxford University Press.
57. Roskies, A. L. (1999). The binding problem. Neuron, 24, 7-9.
58. Rumelhart, D. E., & McClelland, J. L. (Eds.). (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge, MA: MIT Press/Bradford Books.
59. Sahdra, B., & Thagard, P. (2003). Procedural knowledge in molecular biology. Philosophical Psychology, 16, 477-498.
60. Salinas, E., & Abbott, L. F. (1994). Vector reconstruction from firing rates. Journal of Computational Neuroscience, 1, 89-107.
61. Sander, D., Grandjean, D., & Scherer, K. R. (2005). A systems approach to appraisal mechanisms in emotion. Neural Networks, 18, 317-352.
62. Saunders, D., & Thagard, P. (2005). Creativity in computer science. In J. C. Kaufman & J. Baer (Eds.), Creativity across domains: Faces of the muse (pp. 153-167). Mahwah, NJ: Lawrence Erlbaum Associates.
63. Shastri, L., & Ajjanagadde, V. (1993). From simple associations to systematic reasoning: A connectionist representation of rules, variables, and dynamic bindings. Behavioral and Brain Sciences, 16, 417-494.
64. Smith, E. E., & Kosslyn, S. M. (2007). Cognitive psychology: Mind and brain. Upper Saddle River, NJ: Pearson Prentice Hall.
65. Smith, E., & Osherson, D. (1984). Conceptual combination with prototype concepts. Cognitive Science, 8, 337-361.
66. Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46, 159-217.
67. Sternberg, R. J., & Davidson, J. E. (Eds.). (1995). The nature of insight. Cambridge, MA: MIT Press.
68. Stewart, T. C., Choo, X., & Eliasmith, C. (2010a). Dynamic behaviour of a spiking model of action selection in the basal ganglia. In D. Salvucci & G. Gunzelmann (Eds.), Proceedings of the 10th International Conference on Cognitive Modeling (pp. 235-240). Philadelphia, PA: Drexel University.
69. Stewart, T. C., Choo, X., & Eliasmith, C. (2010b). Symbolic reasoning in spiking neurons: A model of the cortex/basal ganglia/thalamus loop. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 1100-1105). Portland, OR: Cognitive Science Society.
70. Stewart, T. C., & Eliasmith, C. (2009). Compositionality and biologically plausible models. In W. Hinzen, E. Machery, & M. Werning (Eds.), Oxford handbook of compositionality. Oxford, England: Oxford University Press.
71. Subranamiam, K., Kounios, J., Parrish, T. B., & Jung-Beeman, M. (2009). A brain mechanism for facilitation of insight by positive affect. Journal of Cognitive Neuroscience, 21, 415-432.
72. Sweeny, A. (2009). BlackBerry planet. Missisaugua, ON: Wiley.
73. Thagard, P. (1988). Computational philosophy of science. Cambridge, MA: MIT Press.
74. Thagard, P. (1999). How scientists explain disease. Princeton: Princeton University Press.
75. Thagard, P. (2000). Coherence in thought and action. Cambridge, MA: MIT Press.
76. Thagard, P. (2006a). Hot thought: Mechanisms and applications of emotional cognition. Cambridge, MA: MIT Press.
77. Thagard, P. (2006b). How to collaborate: Procedural knowledge in the cooperative development of science. Southern Journal of Philosophy, 44, 177-196.
78. Thagard, P. (2009). Why cognitive science needs philosophy and vice versa. Topics in Cognitive Science, 1, 237-254.
79. Thagard, P. (2010). The brain and the meaning of life. Princeton, NJ: Princeton University Press.
80. Thagard, P., & Aubie, B. (2008). Emotional consciousness: A neural model of how cognitive appraisal and somatic perception interact to produce qualitative experience. Consciousness and Cognition, 17, 811-834.
81. Treisman, A. (1996). The bindng problem. Current Opinion in Neurobiology, 6, 171-178.
82. Tripp, B. P., & Eliasmith, C. (2010). Population models of temporal differentiation. Neural Computation, 22, 621-659.
83. Werning, M., & Maye, A. (2007). The cortical implementation of complex attribute and substance concepts: Synchrony, frames, and hierarchical binding. Chaos and Complexity Letters, 2, 435-452.
84. Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings. Cambridge, MA: Harvard University Press.
85. Wisniewski, E. J. (1997). Conceptual combination: Possibilities and esthetics. In T. B. Ward, S. M. Smith, & J. Viad (Eds.), Conceptual structures and processes: Emergence, discovery, and change (pp. 51-81). Washington, DC: American Psychological Association.
86. Wolpert, D. M., & Ghahramani, Z. (2000). Computational principles of movement neuroscience. Nature Neuroscience, 3, 1212-1217.
87. Wu, L. L., & Barsalou, L. W. (2009). Perceptual simulation in conceptual combination: Evidence from property generation. Acta Psychological, 132, 173-189.