A statistical mechanics definition of insight

Computational Intelligence

Volumn , Issue , 2012, Pages 139-162

  Clark, Kevin B ^a  

^a NONE   (United States)

Author keywords

Artificial intelligence; Classical and quantum algorithms; Emergent computation; Heuristics; Information processing; Learning and memory; Natural computing; Problem solving; Quantum biology; Soft matter physics

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMOBILE MANUFACTURE; AUTONOMOUS AGENTS; BIOREMEDIATION; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; CONTROLLED DRUG DELIVERY; DATA PROCESSING; DECISION THEORY; MACHINERY; MAMMALS; PARALLEL PROCESSING SYSTEMS; PROBLEM SOLVING; QUANTUM COMPUTERS; QUANTUM THEORY; STATISTICAL MECHANICS; TURING MACHINES;

EMERGENT COMPUTATION; HEURISTICS; LEARNING AND MEMORY; NATURAL COMPUTING; QUANTUM ALGORITHMS; SOFT-MATTER PHYSICS;

COMPLEX NETWORKS;

EID: 84878778237     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (129)

1. Leadbeater, E. and Chittka, L. (2007). Social learning in insects-from miniature brains to consensus. Curr. Biol., 17, R703-R713.
2. Watanabe, S. and Huber, L. (2006). Animal logics: Decisions in the absence of human language. Anim. Cogn., 9, 235-245.
3. Applewhite, P. B. Plant and animal behavior: An introductory comparison. In:Eisenstein, E. M., editor. Aneural organisms in neurobiology. London: Plenum Press; 1975.
4. Trewavas, A. (2003). Aspects of plant intelligence. Ann. Bot. (Lond), 92, 1-20.
5. Crespi, B. J. (2001). The evolution of social behavior in microorganisms. Trends. Ecol. Evol., 16, 178-183.
6. Corning, W. C. and Von Burg, R. Protozoa. In: Corning, W. C., Dyal, J. A. and Willows, A. O. D., editors. Invertebrate learning. New York: Plenum; 1973.
7. www.ibm.com/research/deepblue/.
8. www.ibm.com/innovation/us/watson/.
9. Hélie, S. and Sun, R. (2010). Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychol. Rev., 117, 994-1024.
10. Hellingwerf, K. J. (2005). Bacterial observations: A rudimentary form of intelligence? Trends. Microbiol., 13, 152-158.
11. Fogel, D. B. and Fogel, L. J. (1995). Evolution and computational intelligence. IEEE Trans. Neural Netw., 4, 1938-1941.
12. Mayer, R. E. The search for insight: Grappling with Gestalt psychology's unanswered questions. In: Sternberg, R. J. and Davidson, J. E., editors. The nature of insight. Cambridge: MIT Press; 1996.
13. Simon, H. A. (1986). The information processing explanation of Gestalt phenomena. Comp. Hum. Behav., 2, 241-255.
14. Weisberg, R. W. Prolegomena to theories of insight in problem solving: A taxonomy of problems. In: Sternberg, R. J. and Davidson, J. E., editors. The nature of insight. Cambridge: MIT Press; 1996.
15. Newell, A. and Simon, H.A. Human problem solving. Englewood Cliffs: Prentice Hall; 1972.
16. Broom, D. M. (2007). Cognitive ability and sentience: Which aquatic animals should be protected? Diseases of Aquatic Organisms, 75, 99-108.
17. Goldsmith, E. (2000). Intelligence is universal in life. Rivista di Biologia, 93, 399-411.
18. Margulis, L. (2001). The conscious cell. Ann. NY. Acad. Sci., 929, 55-70.
19. Mather, J. A. (2008). Cephalopod consciousness: Behavioral evidence. Consci. Cogn., 17, 37-48.
20. Premack, D. (2007). Human and animal cognition: Continuity and discontinuity. Proc. Natl. Acad. Sci. U. S. A., 104, 13861-13867.
21. Skorupski, P. and Chittka, L. (2006). Animal cognition: An insect's sense of time. Curr. Biol., 16, R851-R853.
22. Parker, S.T. and McKinney, M.L. Origins of intelligence: The evolution of cognitive development in monkeys, apes, and humans. Baltimore: The Johns Hopkins University Press; 1999.
23. Thorndike, E.L. Animal intelligence: Experimental studies. New York: Macmillan; 1911.
24. Mackintosh, N.J. Conditioning and associative learning. Oxford: Oxford University Press; 1983.
25. Pepperberg, I. M. Cognitive and communicative abilities of Grey parrots. In: Bekoff, M., Allen, C. and Burghardt, G. M., editors. The cognitive animal: Empirical and theoretical perspectives on animal cognition. Cambridge: The MIT Press; 2002.
26. Emery, N. J. (2006). Cognitive ornithology: The evolution of avian intelligence. Phil. Trans. R.. Soc. B, 361, 21-41.
27. Frye, M. A. (2010). Multisensory systems integration for high-performance motor control in flies. Curr. Opin. Neurobiol., 20, 347-352.
28. Clark, K. B. (2010). Origins of learned reciprocity in solitary ciliates searching grouped 'courting' assurances at quantum efficiencies. BioSystems, 99, 27-41.
29. Clark, K. B. (2010). Bose-Einstein condensates form in heuristics learned by ciliates deciding to signal 'social' commitments. BioSystems 99, 167-178.
30. Dussutour, A., Lattyb, T., Beekman, M. and Simpson, S. J. (2010). Amoeboid organism solves complex nutritional challenges. Proc. Natl. Acad. Sci. U. S. A., 107, 4607-4611.
31. Nakagaki, T., Yamada, H. and Troth, A. (2000). Maze-solving by an amoeboid organism. Nature, 407, 470.
32. Peak, D., West, J. D., Messinger, S. M. and Mott, K. A. (2004). Evidence for complex, collective dynamics and emergent, distributed computation in plants. Proc. Natl. Acad. Sci. U.S.A., 101, 918-922.
33. Bowden, E. M., Jung-Beeman. J., Fleck, J. and Kounios, J. (2005). New approaches to demystifying insight. Trends Cogn. Sci., 9, 322-328.
34. Kaplan, C. A. and Simon, H. A. (1990). In search of insight. Cogn. Psychol., 22, 373-419.
35. Seifert, C. M., Meyer, D. E., Davidson, N., Patalano, A. L. and Yaniv, I. Demystification of cognitive insight: Opportunistic assimilation and the prepared-mind perspective. In: Sternberg, R. J. and Davidson, J. E., editors. The nature of insight. Cambridge: MIT Press; 1996.
36. Weisberg, R. Creativity: Genius and other myths. New York: Freeman; 1986.
37. Horvitz, L.A. Eureka!: Scientific breakthroughs that changed the world. New York:John Wiley and Sons, Inc.; 2002.
38. Sternberg, R.J. and Davidson, J.E., editors. The nature of insight. Cambridge: MIT Press; 1996.
39. Anderson, B. (2000). The g factor in non-human animals. Novartis Found. Symp., 233, 79-90.
40. Epstein, R., Kirshnit, C. E., Lanza, R. P. and Rubin, L. C. (1984). 'Insight' in the pigeon: Antecedents and determinants of an intelligent performance. Nature, 308, 61-62.
41. Köhler, W. The mentality of apes. New York: Harcourt, Brace and Company, Inc.; 1924.
42. Shettleworth, S. J. (2009). Animal cognition: Deconstructing avian insight. Curr. Biol., 19, R1039-R1040.
43. Schooler, J. W., Ohlsson, S. and Brooks, K. (1993). Thoughts beyond words: When language overshadows insight. J. Exp. Psychol. Gen., 122, 166-183.
44. Perkins, D. (1998). In the country of the blind: An appreciation of Donald Campbell's vision of creative thought. J. Creat. Behav., 32, 177-191.
45. Weisberg, R. and Alba, J. (1981). An examination of the alleged role of fixation in the solution of several 'insight' problems. J. Exp. Psychol. Gen., 110, 169-192.
46. Chronicle, E. P., Ormerod, T. C. and MacGregor, J. N. (2004). What makes an insight problem? The roles of heuristics, goal, conception, and solution recoding in knowledgelean problems. J. Exp. Psychol. Learn. Mem. Cogn., 30, 14-27.
47. Luo, J. and Knoblich, G. (2007). Studying insight problem solving with neuroscientific methods. Methods, 42, 77-86.
48. Sandkühler, S. and Bhattacharya, J. (2008). Deconstructing insight: EEG correlates of insightful problem solving. PLoS One, 3, e1459. doi: 10.1371/journal.pone.0001459.
49. Zeman, A. (2001). Consciousness. Brain, 124, 1263-1289.
50. Beck, F., Eccles, J.C., 1992. Quantum aspects of brain activity and the role of consciousness. Proc. Natl. Acad. Sci. U.S.A., 89, 11356-11362.
51. Craddock, T. J. A., Beauchemin, C. and Tuszynnski, J. A. (2009). Information processing mechanisms in microtubules at physiological temperature: model predictions from experimental tests. BioSystems, 97, 28-34.
52. Hameroff, S., Nip, A., Porter, M. and Tuszynski, J. (2002). Conduction pathways in microtubules, biological quantum computation, and consciousness. BioSystems, 64, 149-168.
53. Penrose, R. The emperor's new mind. Oxford: Oxford University Press; 1989.
54. Pribham, K.H. Brain and perception. Hillsdale: Lawrence Earlbaum; 1991.
55. Bekenstein, J. D. (2004). Black holes and information theory. Contemp. Phys., 45, 31-43.
56. Armus, H. L., Montgomery, A. R., and Gurney, R. L. (2006). Discrimination learning and extinction in Paramecia (P. caudatum). Psychol. Rep., 98, 705-711.
57. Ben-Jacob, E., Becker, I., Shapira, Y. and Levine, H. (2004). Bacterial linguistic communication and social intelligence. Trends Microbiol., 12, 366-372.
58. Clark, K. B. The mating judgments of microbes. In: Clark, K. B., editor. Social learning theory. Hauppauge: Nova Science Publishers, Inc.; in press.
59. Perkins, T. J. and Swain, P. S. (2009). Strategies for cellular decision-making. Mol. Sys. Biol., 5, 326. doi: 10.1038/msb.2009.83.
60. Shapiro, J. A. (1998). Thinking about bacterial populations as multicellular organisms. Annu. Rev. Microbiol., 52, 81-104.
61. Strassmann, J. E., Zhu, Y. and Queller, D. C. (2000). Altruism and social cheating in the social amoeba Dictyostellium discoideum. Nature, 408, 965-967.
62. Urushihara, H. (1996). Choice of partners: Sexual cell interactions in Dictyostelium discoideum. Cell Struct. Funct., 21, 231-236.
63. Albus, J. S. and Barbera, A. J. (2005). RCS: A cognitive architecture for intelligent multi-agent systems. Annu. Rev. Control, 29, 87-99.
64. Krichmar, J. L. and Edelman, G. M. (2002). Machine psychology: Autonomous behavior, perceptual categorization, and conditioning in a brain-based device. Cereb. Cortex, 12, 818-830.
65. Rzevski, G. (2003). On conceptual design of intelligent mechatronic systems. Mechatron., 13, 1029-1045.
66. Chao, Z. C., Bakkum, D. D. and Potter, S. M. (2008). Shaping embodied neural networks for adaptive goal-directed behaviors. PLoS Comput. Biol., 4, e1000042. doi:10.1371/journal.pcbi.1000042.
67. DeMarse, T. B., Wagenaar, D. A., Blau, A. W. and Potter, S.M. (2001). The neurally controlled animat: Biological brains acting with simulated bodies. Auton. Robots, 11, 305-310.
68. Reger, B. D., Fleming, K. M., Sanguineti, V., Alford, S. and Mussa-Ivaldi, F. A. (2000). Connecting brains to robots: An artificial body for studying the computational properties of neural tissue. Artif. Life, 6, 307-324.
69. Anderson, J.R. Rules of the mind. New York: Erlbaum; 1993.
70. Knoblich, G., Ohlsson, S. and Raney, G. E. (2001). An eye movement study of insight problem solving. Mem. Cogn., 29, 1000-1009.
71. Wertheimer, M. (1985). A Gestalt perspective on computer simulation of cognitive processes. Comp. Hum. Behav., 1, 19-33.
72. Berry, D.C., editor. How implicit is implicit learning?. New York: Oxford University Press; 1997.
73. Bowers, J.S. and Marsolek, C.J., editors. Rethinking implicit memory. New York:Oxford University Press; 2003.
74. Hall, G. Perceptual and associative learning. Oxford: Clarendon Press; 1991.
75. Jiménez, L., editor. Attention and implicit learning. Amsterdam: John Benjamins Publishing Company; 2003.
76. Milner, D.A.and Rugg, M.D., editors. The neuropsychology of consciousness. San Diego: Academic Press; 1992.
77. Stadler, M.A.and Frensch, P.A., editors. Handbook of implicit learning. Thousands Oaks: Sage Publications, Inc.; 1998.
78. Squire, L.R.and Butters, N., editors. Neuropsychology of memory. New York:Guilford Press; 1992.
79. Umiltà, C.and Moscovitch, M., editors. Attention and performance 15: Conscious and nonconscious information processing. Cambridge: The MIT Press; 1994.
80. Nielsen, M.A. and Chuang, I.L.. Quantum computation and quantum information. Cambridge: Cambridge University Press; 2000.
81. Clark, K.B. (2010). The question of revisiting or revising the Fluctuon model. Foundations of Information Science (FIS) Digest, 541(14). http://fis.cts.sbg.ac.at and http://www.mdpi.org.FIS.
82. Jumarie, G. (1975). Further advances on the general thermodynamics of open systems via information theory: Effective entropy, negative information. Int. J. Systems Sci., 6, 249-268.
83. Jumarie, G. (1975). A relativistic information theory model for general systems Lorentz transformation of organizability and structural entropy. Int. J. Systems Sci., 6, 865-886.
84. Clark, K.B.and Krahl, S.E. Primitive microbial intelligences expressed as extended computational objects in modern Kaluza-Klein metr. White Paper submitted to Defense Advanced Research Projects Agency (DARPA-BAA 07-68, Math Challenge 5); 2008.
85. Bak, P. How nature works: The science of self-organized criticality. New York:Springer; 1996.
86. Haken, H. Information and self-organization: A macroscopic approach to complex systems. New York: Springer; 2000.
87. Jensen, H.J. Self-organized criticality: Emergent complex behavior in physical and biological systems. Cambridge: Cambridge University Press; 1998.
88. Kelso, J.A.S. Dynamic patterns: The self-organization of brain and behavior. Cambridge: The MIT Press; 1995.
89. Wertheimer, M. Laws of organization in perceptual forms. London: Harcourt Brace Jovanovich; 1938.
90. Bickhard, M.H. The dynamic emergence of representation. In: Clapin, H., Staines, P. and Slezak, P., editors. Representation in mind: New approaches to mental representation. New York: Elsevier; 2004.
91. Thelen, E., and Smith, L. A dynamic systems approach to the development of cognition and action. Cambridge: The MIT Press; 1994.
92. Bickhard, M.H., and Terveen, L. Foundational issues in artificial intelligence and cognitive science: Impasse and solution. Amsterdam: Elsevier Science; 1995.
93. Langton, C.G. (1990). Computation at the edge of chaos: Phase transitions and emergent computation. Physica D, 42, 12-37.
94. Wolfram, S. (1984). Universality and complexity in cellular automata. Physica D, 10, 1-35.
95. Dixon, J. A. and Bangert, A. S. (2002). The prehistory of discovery: Precursors of representational change in solving gear-system problems. Develop. Psychol., 38, 918-933.
96. Dixon, J. A. and Bangert, A. S. (2004). On the spontaneous discovery of a mathematical relation during problem solving. Cogn. Sci., 28, 433-449.
97. Dixon, J. A. and Dohn, M. C. (2003). Redescription disembeds relations: Evidence from relational transfer and use in problem solving. Mem. Cogn., 31, 1082-1093.
98. Stephen, D. G., Boncoddo, R. A., Magnuson, J. S. and Dixon, J. A. (2009). The dynamics of insight: Matematical discovery as a phase transition. Mem. Cogn., 37, 1132-1149.
99. Stephen, D. G. and Dixon, J. A. (2009). The self-organization of insight: Entropy and power laws in problem solving. J. Prob. Solv., 2, 72-101.
100. Siegler, R. S. (2005). Children's learning. Am. Psychol., 60, 769-778.
101. Siegler, R. S. Microgenetic analyses of learning. In: Kuhn, D. and Siegler, R. S., editors. Handbook of child psychology, vol. 2: Cognition, perception, and language. Hoboken: Wiley; 2006.
102. Wolfram, S. A new kind of science. Champaign: Wolfram Media, Inc.; 2002.
103. Gray, L. (2003). A mathematician looks at Wolfram's new kind of science. Notices of AMS, 50, 200-211.
104. Clark, K. B. Live soft-matter quantum computing. In: Salander E. C., editor. Computer search algorithms. Hauppauge: Nova Science Publishers, Inc; 2011.
105. Deutsch, D. (1985). Quantum theory, the Church-Turing Principle and the universal quantum computer. Proc. R.. Soc. Lond. A, 400, 97.
106. Pascual-Leone, J. (1970). A mathematical model for the transition rule in Piaget's developmental stages. Acta Psychol., 32, 301-345.
107. Pothos, E. M. and Busemeyer, J. R. (2009). A quantum probability explanation for violations of 'rational' decision theory. Proc. R.. Soc. B, doi: 10.1098/rspb.2009.0121.
108. Zabaleta, O. G. and Arizmendi, C. M. (2010). Quantum dating market. doi:arXiv:1003.1153v1.
109. Clark, K. B. (2010). On classical and quantum error-correction in ciliate mate selection. Commun. Integr. Biol., 3, 374-378.
110. Clark, K. B. (2010). Arrhenius-kinetics evidence for quantum tunneling in microbial "social" decision rates. Commun. Integr. Biol., 3, 540-544.
111. Clark, K. B. (2010). Quantum 'social' intelligences expressed by microbes. Sci. Topics, http://www.scitopics.com/Quantum_Social_Intelligences_Expressed_by_Microbes.html.
112. Bianconi, G. (2002). Quantum statistics in complex networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 66, 056123.
113. Bianconi, G. (2003). Size of quantum networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 67, 056119.
114. Bianconi, G. (2007). A statistical mechanics approach for scale-free networks and finite-scale networks. Chaos, 17, 026114.
115. Bianconi, G. and Barabási, A.-L. (2001). Bose-Einstein condensation in complex networks. Phys. Rev. Lett., 86, 5632-5635.
116. Grover, L. K. A fast quantum mechanical algorithm for database search. In:Proceedings of the 28th annual ACM symposium on the theory of computing. New York: AMC Press; 1996.
117. Ponce-Dawson, S., Keizer, J. and Pearson, J. E. (1999). Fire-diffuse-fire model of dynamics of intracellular calcium waves. Proc. Natl. Acad. Sci. U.S.A., 96, 6060-6063.
118. Jaffe, L. (1993). Classes and mechanisms of calcium waves. Cell Calcium, 14, 736-745.
119. Kulish, V. V. and Lage, J. L. (2002). On the relationship between fluid velocity and de Broglie's wave function and the implications to the Navier-Stokes equation. Int. J. Fluid Mech. Res., 29, 40-52.
120. Tsekov, R. (2009). Nonlinear theory of quantum Brownian motion. Int. J. Theor. Phys., 48, 85-94.
121. De Pitta, M., Volman, V., Levine, H., and Ben-Jacob, E. (2009). Multimodal encoding in a simplified model of intracellular calcium signaling. Cogn. Process., 10, S55-S70.
122. De Pitta, M., Volman, V., Levine, H., Pioggia, G., De Rossi, D. and Ben-Jacob, E. (2008). Coexistence of amplitude and frequency modulations in intracellular calcium dynamics. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 77, 060903.
123. Patel, A. J. (2001). Why genetic information processing could have a quantum basis. J. Biosci., 26, 145-51.
124. Dominguez, D. C. (2004). Calcium signalling in bacteria. Mol. Microbiol., 54, 291-297.
125. Nowycky, M. C. and Thomas, A. P. (2002). Intracellular calcium signaling. J. Cell. Sci., 115, 3715-3716.
126. Plattner, H. and Klauke, N. (2001). Calcium in ciliated protozoa: Sources, regulation, and calcium-regulated cell functions. Int. Rev. Cytol., 201, 115-208.
127. Plieth, C. (2005). Calcium: Just another regulator in the machinery of life? Ann. Bot., 96, 1-8.
128. Martinac, B., Saimi, Y. and Kung, C. (2008). Ion channels in microbes. Physiol. Rev., 88, 1449-1490.
129. Wheeler, G. L. and Brownlee, C. (2008). Ca(2+) signaling in plants and green algae-changing channels. Trends Plant Sci., 13, 506-514.