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Quantum probability and comparative cognition

Published online by Cambridge University Press:  14 May 2013

Randolph C. Grace
Affiliation:
Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. randolph.grace@canterbury.ac.nzsimon.kemp@canterbury.ac.nz
Simon Kemp
Affiliation:
Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. randolph.grace@canterbury.ac.nzsimon.kemp@canterbury.ac.nz

Abstract

Evolution would favor organisms that can make recurrent decisions in accordance with classical probability (CP) theory, because such choices would be optimal in the long run. This is illustrated by the base-rate fallacy and probability matching, where nonhumans choose optimally but humans do not. Quantum probability (QP) theory may be able to account for these species differences in terms of orthogonal versus nonorthogonal representations.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2013 

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References

Ashby, F. G. & Gott, R. E. (1988) Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, & Cognition 14:3353.Google Scholar
Ashby, F. G. & Maddox, W. T. (2005) Human category learning. Annual Reviews of Psychology 56:149–78.Google Scholar
Charnov, E. L. (1976) Optimal foraging, the marginal value theorem. Theoretical Population Biology 9:129–36.Google Scholar
De Fraja, G. (2009) The origin of utility: Sexual selection and conspicuous consumption. Journal of Economic Behavior & Organisation 72:5169.Google Scholar
Fantino, E. & Esfandiari, A. (2002) Probability matching: Encouraging optimal responding in humans. Canadian Journal of Experimental Psychology, 56:5863.CrossRefGoogle ScholarPubMed
Hartl, J. A. & Fantino, E. (1996) Choice as a function of reinforcement ratios in delayed matching to sample. Journal of the Experimental Analysis of Behavior 66:1127.CrossRefGoogle ScholarPubMed
Hursh, S. R. (1984) Behavioral economics. Journal of the Experimental Analysis of Behavior 42:435–52.CrossRefGoogle ScholarPubMed
Kenrick, D. T., Griskevicius, V., Sundie, J., Li, N. P., Li, Y. J. & Neuberg, S. L. (2009) Deep rationality: The evolutionary economics of decision making. Social Cognition 27:764–85.Google Scholar
Koselj, K., Schnitzler, H.-U. & Siemers, B. M. (2011) Horseshoe bats make adaptive pre-selection decisions, informed by echo cues. Proceedings of the Royal Society B: Biological Sciences 278:3034–41.CrossRefGoogle Scholar
Krivan, V., Cressman, R. & Schneider, C. (2008) The ideal free distribution: A review and synthesis of the game-theoretic perspective. Theoretical Population Biology 73:403–25.Google Scholar
Macphail, E. M. (1987) The comparative psychology of intelligence. Behavioral and Brain Sciences 10:645–56.Google Scholar
Maddox, W. T., Ashby, F. G. & Bohil, C. J. (2003) Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory & Cognition 29:650–62.Google ScholarPubMed
Rachlin, H., Green, L., Kagel, J. H. & Battalio, R. (1976) Economic demand theory and psychological studies of choice. In: The psychology of learning and motivation (Vol. 10), ed. Bower, G. H., pp. 129–54. Academic Press.Google Scholar
Robson, A. & Samuelson, L. (2011) The evolution of decision and experienced utilities. Theoretical Economics 6:311–39.Google Scholar
Roth, G. & Dicke, U. (2005) Evolution of the brain and intelligence. Trends in Cognitive Sciences 9:250–57.Google Scholar
Sebastian-Gonzalez, E., Botella, F., Sempere, R. A. & Sanchez-Zapata, J. A. (2010) An empirical demonstration of the ideal free distribution: Little Grebes Tachybaptus ruficollis breeding in intensive agricultural landscapes. Ibis 152:643–50.Google Scholar
Shepard, R. N. (1994) Perceptual-cognitive universals as reflections of the world. Psychonomic Bulletin & Review 1:228.Google Scholar
Smith, J. D., Ashby, F. G., Berg, M. E., Murphy, M. S., Spiering, B., Cook, R. G. & Grace, R. C. (2011) Pigeons' categorization may be exclusively nonanalytic. Psychonomic Bulletin & Review 18:422–28.Google Scholar
Smith, J. D., Beran, M. J., Crossley, M. J., Boomer, J. & Ashby, F. G. (2010) Implicit and explicit category learning by macaques (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes 36:5465.Google Scholar
Smith, J. D., Berg, M. E., Cook, R. G., Murphy, M. S., Crossley, M. J., Boomer, J., Spiering, B., Beran, M. J., Church, B. A., Ashby, F. G. & Grace, R. C. (2012) Implicit and explicit categorization: A tale of four species. Neuroscience and Biobehavioral Reviews 36:2355–69.Google Scholar
Stephens, D. W. & Krebs, J. R. (1986) Foraging theory. Princeton University Press.Google Scholar
Tversky, A. & Kahneman, D. (1980) Causal schemata in judgments under uncertainty. In: Progress in social psychology, Vol. 1, ed. Fishbein, M., pp. 4972. Erlbaum.Google Scholar