Hostname: page-component-7c8c6479df-27gpq Total loading time: 0 Render date: 2024-03-28T02:31:21.355Z Has data issue: false hasContentIssue false

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

Published online by Cambridge University Press:  25 August 2011

Matt Jones
Affiliation:
Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309mcj@colorado.eduhttp://matt.colorado.edu
Bradley C. Love
Affiliation:
Department of Psychology, University of Texas, Austin, TX 78712brad_love@mail.utexas.eduhttp://love.psy.utexas.edu

Abstract

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.

Type
Target Article
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. R. (1990) The adaptive character of thought. Erlbaum.Google Scholar
Anderson, J. R. (1991b) The adaptive nature of human categorization. Psychological Review 98:409–29.CrossRefGoogle Scholar
Anderson, J. R., Bothell, D., Lebiere, C. & Matessa, M. (1998) An integrated theory of list memory. Journal of Memory and Language 38:341–80.CrossRefGoogle Scholar
Anderson, J. R. & Schooler, L. J. (1991) Reflections of the environment in memory. Psychological Science 2:396408.CrossRefGoogle Scholar
Austerweil, J. & Griffiths, T. L. (2008) Analyzing human feature learning as nonparametric Bayesian inference. Advances in Neural Information Processing Systems 21:97104.Google Scholar
Baker, C. L., Saxe, R. & Tenenbaum, J. B. (2009) Action understanding as inverse planning. Cognition 113:329–49.CrossRefGoogle ScholarPubMed
Baldwin, J. D. & Baldwin, J. I. (1977) The role of learning phenomena in the ontogeny of exploration and play. In: Primate bio-social development: Biological, social and ecological determinants, ed. Chevalier-Skolnikoff, S. & Poirer, F. E., p. 343406. Garland.Google Scholar
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E. & Pouget, A. (2008) Probabilistic population codes for Bayesian decision making. Neuron 60:1142–52.CrossRefGoogle ScholarPubMed
Binmore, K. (2009) Rational decisions. Princeton University Press.Google Scholar
Bjorklund, D. F. & Pellegrini, A. D. (2000) Child development and evolutionary psychology. Child Development 71:1607–708.CrossRefGoogle ScholarPubMed
Boucher, L., Palmeri, T. J., Logan, G. D. & Schall, J. D. (2007) Inhibitory control in mind and brain: An interactive race model of countermanding saccades. Psychological Review 114:376–97.CrossRefGoogle Scholar
Bowlby, J. (1969) Attachment and loss, vol. 1: Attachment. Basic Books.Google Scholar
Brighton, H. & Gigerenzer, G. (2008) Bayesian brains and cognitive mechanisms: Harmony or dissonance? In: Bayesian rationality: The probabilistic approach to human reasoning, ed. Oaksford, M. & Chater, N., p. 189208. Oxford University Press.Google Scholar
Brown, S. D. & Steyvers, M. (2009) Detecting and predicting changes. Cognitive Psychology 58:4967.Google Scholar
Buller, D. J. (2005) Adapting minds: Evolutionary psychology and the persistent quest for human nature. MIT Press.Google Scholar
Burgess, N. & Hitch, G. J. (1999) Memory for serial order: A network model of the phonological loop and its timing. Psychological Review 106:551–81.Google Scholar
Busemeyer, J. R. & Johnson, J. G. (2008) Microprocess models of decision making. In: Cambridge handbook of computational psychology, ed. Sun, R., p. 302–21. Cambridge University Press.Google Scholar
Buss, D. M. (1994) The evolution of desire: Strategies of human mating. Basic Books.Google Scholar
Buss, D. M., Haselton, M. G., Shackelford, T. K., Bleske, A. L. & Wakefield, J. C. (1998) Adaptations, exaptations, and spandrels. American Psychologist 53:533–48.Google Scholar
Caramazza, A. & Shelton, J. R. (1998) Domain-specific knowledge systems in the brain: The animate-inanimate distinction. Journal of Cognitive Neuroscience 10:134.Google Scholar
Chater, N. & Manning, C. D. (2006) Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences 10:335–44.CrossRefGoogle ScholarPubMed
Chater, N. & Oaksford, M. (1999) The probability heuristics model of syllogistic reasoning. Cognitive Psychology 38:191258.Google Scholar
Chater, N. & Oaksford, M. (2008) The probabilistic mind: Prospects for a Bayesian cognitive science. In: The probabilistic mind: Prospects for rational models of cognition, ed. Oaksford, M. & Chater, N., p. 331. Oxford University Press.Google Scholar
Chater, N., Oaksford, M., Nakisa, R. & Redington, M. (2003) Fast, frugal, and rational: How rational norms explain behavior. Organizational Behavior and Human Decision Processes 90:6386.CrossRefGoogle Scholar
Chater, N., Reali, F. & Christiansen, M. H. (2009) Restrictions on biological adaptation in language evolution. Proceedings of the National Academy of Sciences USA 106:1015–20.Google Scholar
Chater, N., Tenenbaum, J. & Yuille, A. (2006) Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Sciences 10(7):287–91.CrossRefGoogle ScholarPubMed
Chomsky, N. (1959) A review of B. F. Skinner's Verbal Behavior . Language 35:2658.Google Scholar
Clark, D. D. & Sokoloff, L. (1999) Circulation and energy metabolism in the brain. In: Basic neurochemistry: Molecular, cellular and medical aspects, ed. Siegel, G. J., Agranoff, B. W., Albers, R. W., Fisher, S. K. & Uhler, M. D., p. 637–70. Lippincott-Raven.Google Scholar
Clearfield, M. W., Dineva, E., Smith, L. B., Diedrich, F. J. & Thelen, E. (2009) Cue salience and infant perseverative reaching: Tests of the dynamic field theory. Developmental Science 12:2640.Google Scholar
Colunga, E. & Smith, L. (2005) From the lexicon to expectations about kinds: A role for associative learning. Psychological Review 112(2):347–82.CrossRefGoogle ScholarPubMed
Conati, C., Gertner, A., VanLehn, K. & Druzdzel, M. (1997) On-line student modeling for coached problem solving using Bayesian networks. In: User modeling: Proceedings of the Sixth International Conference, UM97, Berlin, 1997, pp. 231–42, ed. Jameson, A., Paris, C. & Tasso, C.. Springer.Google Scholar
Cosmides, L. & Tooby, J. (1992) Cognitive adaptations for social exchange. In: The adapted mind: Evolutionary psychology and the generation of culture, ed. Barkow, J., Cosmides, L. & Tooby, J.. p. 163228. Oxford University Press.Google Scholar
Cree, G. S. & McRae, K. (2003) Analyzing the factors underlying the structure and computation of the meaning of chipmunk, cherry, chisel, cheese, and cello (and many other such concrete nouns). Journal of Experimental Psychology: General 132:163201.Google Scholar
Crick, F. (1989) The recent excitement about neural networks. Nature 337:129–32.CrossRefGoogle ScholarPubMed
Czerlinski, J., Gigerenzer, G. & Goldstein, D. G. (1999) How good are simple heuristics? In: Simple heuristics that make us smart, ed. Gigerenzer, G. & Todd, P. M., p. 97118. Oxford University Press.Google Scholar
Danks, D. (2008) Rational analyses, instrumentalism, and implementations. In: The probabilistic mind: Prospects for Bayesian cognitive science, ed. Oaksford, M. & Chater, N., p. 5975. Oxford University Press.CrossRefGoogle Scholar
Daugman, J. G. (2001) Brain metaphor and brain theory. In: Philosophy and the neurosciences: A reader, ed. Bechtel, W., Mandik, P., Mundale, J. & Stufflebeam, R. S., p. 2336. Blackwell.Google Scholar
Davis, T. & Love, B. C. (2010) Memory for category information is idealized through contrast with competing options. Psychological Science 21:234–42.Google Scholar
Daw, N. & Courville, A. (2007) The pigeon as particle filter. Advances in Neural Information Processing Systems 20:1528–35.Google Scholar
Daw, N. D., Courville, A. C. & Dayan, P. (2008) Semi-rational models: The case of trial order. In: The probabilistic mind: Prospects for rational models of cognition, ed. Oaksford, M. & Chater, N., p. 431–52. Oxford University Press.Google Scholar
Daw, N. D., Niv, Y. & Dayan, P. (2005) Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience 8:1704–11.CrossRefGoogle ScholarPubMed
Dawes, R. M. & Corrigan, B. (1974) Linear models in decision making. Psychological Bulletin 81:95106.CrossRefGoogle Scholar
Dawkins, R. (1987) The blind watchmaker. W. W. Norton.Google Scholar
Denève, S. (2008) Bayesian spiking neurons. I: Inference. Neural Computation 20:91117.CrossRefGoogle ScholarPubMed
Denève, S., Latham, P. E. & Pouget, A. (1999) Reading population codes: A neural implementation of ideal observers. Nature Neuroscience 2:740–45.CrossRefGoogle ScholarPubMed
Dennis, S. & Humphreys, M. S. (1998) Cuing for context: An alternative to global matching models of recognition memory. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., p. 109–27. Oxford University Press.Google Scholar
Devlin, J. T., Gonnerman, L. M., Andersen, E. S. & Seidenberg, M. S. (1998) Category-specific semantic deficits in focal and widespread brain damage: A computational account. Journal of Cognitive Neuroscience 10:7794.Google Scholar
Dickinson, A. M. (2000) The historical roots of organizational behavior management in the private sector: The 1950s–1980s. Journal of Organizational Behavior Management 20(3/4): 958.CrossRefGoogle Scholar
Doll, B. B., Jacobs, W. J., Sanfey, A. G. & Frank, M. J. (2009) Instructional control of reinforcement learning: A behavioral and neurocomputational investigation. Brain Research 1299:7494.CrossRefGoogle ScholarPubMed
Doucet, A., Godsill, S. & Andrieu, C. (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10:197208.CrossRefGoogle Scholar
Dunbar, K. (1995) How scientists really reason: Scientific reasoning in real-world laboratories. In: Mechanisms of insight, ed. Sternberg, R. J. & Davidson, J., p. 365–95. MIT Press.Google Scholar
Dyson, F. W., Eddington, A. S. & Davidson, C. (1920) A determination of the deflection of light by the sun's gravitational field, from observations made at the total eclipse of May 29, 1919. Philosophical Transactions of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences 220:291333.Google Scholar
Ein-Dor, T., Mikulincer, M., Doron, G. & Shaver, P. R. (2010) The attachment paradox: How can so many of us (the insecure ones) have no adaptive advantages? Perspectives on Psychological Science 5(2):123–41.CrossRefGoogle ScholarPubMed
Einstein, A. (1916) Die Grundlage der allgemeinen Relativitätstheorie [The foundation of the generalized theory of relativity]. Annalen der Physik 354(7):769822.Google Scholar
Elman, J. L. (1990) Finding structure in time. Cognitive Science 14:179211.Google Scholar
Elman, J. L. (1993) Learning and development in neural networks: The importance of starting small. Cognition 48:7199.Google Scholar
Engelfriet, J. & Rozenberg, G. (1997) Node replacement graph grammars. In: Handbook of graph grammars and computing by graph transformation, vol. 1, ed. Rozenberg, G., p. 194. World Scientific.Google Scholar
Estes, W. K. (1957) Theory of learning with constant, variable, or contingent probabilities of reinforcement. Psychometrika 22:113–32.CrossRefGoogle Scholar
Fitelson, B. (1999) The plurality of Bayesian measures of confirmation and the problem of measure sensitivity. Philosophy of Science 66:362–78.Google Scholar
Fodor, J. A. & Pylyshyn, Z. (1988) Connectionism and cognitive architecture: A critical analysis. Cognition 28: 371.CrossRefGoogle ScholarPubMed
Frank, M. J., Seeberger, L. & O'Reilly, R. C. (2004) By carrot or by stick: Cognitive reinforcement learning in Parkinsonism. Science 306:1940–43.CrossRefGoogle ScholarPubMed
Gabbay, D., Hogger, C. & Robinson, J., eds. (1994) Handbook of logic in artificial intelligence and logic programming, vol. 3: Nonmonotonic reasoning and uncertain reasoning. Oxford University Press.Google Scholar
Geisler, W. S. & Diehl, R. L. (2003) A Bayesian approach to the evolution of perceptual and cognitive systems. Cognitive Science 27:379402.Google Scholar
Geisler, W. S., Perry, J. S., Super, B. J. & Gallogly, D. P. (2001) Edge co-occurrence in natural images predicts contour grouping performance. Vision Research 41:711–24.CrossRefGoogle ScholarPubMed
Geman, S., Bienenstock, E. & Doursat, R. (1992) Neural networks and the bias/variance dilemma. Neural Computation 4:158.CrossRefGoogle Scholar
Geman, S. & Geman, D. (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6:721–41.Google Scholar
Gentner, D. (1983) Structure-mapping: A theoretical framework for analogy. Cognitive Science 7:155–70.Google Scholar
Gentner, D., Brem, S., Ferguson, R. W., Markman, A. B., Levidow, B. B., Wolff, P. & Forbus, K. D. (1997) Analogical reasoning and conceptual change: A case study of Johannes Kepler. Journal of the Learning Sciences 6(1):340.Google Scholar
Gibson, J. J. (1979) The ecological approach to visual perception. Houghton Mifflin.Google Scholar
Gigerenzer, G. & Brighton, H. (2009) Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science 1:107–43.CrossRefGoogle ScholarPubMed
Gigerenzer, G. & Todd, P. M. (1999) Simple heuristics that make us smart. Oxford University Press.Google Scholar
Gold, J. I. & Shadlen, M. N. (2001) Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Sciences 5:1016.Google Scholar
Goodman, N. D., Baker, C. L., Bonawitz, E. B., Mansinghka, V. K., Gopnik, A., Wellman, H., Schulz, L. E. & Tenenbaum, J. B. (2006) Intuitive theories of mind: A rational approach to false belief. In: Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society, Vancouver, Canada, ed. Sun, R., p. 1382–87. Cognitive Science Society.Google Scholar
Goodman, N. D., Tenenbaum, J. B., Feldman, J. & Griffiths, T. L. (2008b) A rational analysis of rule-based concept learning. Cognitive Science 32(1):108–54.Google Scholar
Gottlieb, G. (1992) Individual development and evolution: The genesis of novel behavior. Oxford University Press.Google Scholar
Gould, S. J. & Lewontin, R. (1979) The spandrels of San Marco and the Panglossian paradigm: A critique of the adaptationist programme. Proceedings of the Royal Society of London Series B: Biological Sciences 205:581–98.Google Scholar
Green, D. M. & Swets, J. A. (1966) Signal detection theory and psychophysics. John Wiley.Google Scholar
Griffiths, T. L. & Ghahramani, Z. (2006) Infinite latent feature models and the Indian buffet process. In: Advances in neural information processing systems, vol. 18, ed. Weiss, J., Schölkopf, B. & Platt, J., p. 475–82. MIT Press.Google Scholar
Griffiths, T. L., Sanborn, A. N., Canini, K. R. & Navarro, D. J. (2008b) Categorization as nonparametric Bayesian density estimation. In: The probabilistic mind: Prospects for rational models of cognition, ed. Oaksford, M. & Chater, N.. Oxford University Press.Google Scholar
Griffiths, T. L., Steyvers, M. & Tenenbaum, J. B. (2007) Topics in semantic representation. Psychological Review 114:211–44.Google Scholar
Griffiths, T. L. & Tenenbaum, J. B. (2006) Optimal predictions in everyday cognition. Psychological Science 17(9):767–73.CrossRefGoogle ScholarPubMed
Griffiths, T. L. & Tenenbaum, J. B. (2009) Theory-based causal induction. Psychological Review 116:661716.CrossRefGoogle ScholarPubMed
Guttman, N. & Kalish, H. I. (1956) Discriminability and stimulus generalization. Journal of Experimental Psychology 51:7988.CrossRefGoogle ScholarPubMed
Hamilton, W. D. (1964) The genetical theory of social behavior. Journal of Theoretical Biology 7:152.Google Scholar
Hastings, W. K. (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57:97109.CrossRefGoogle Scholar
Hebb, D. O. (1949) The organization of behavior: A neuropsychological theory. John Wiley.Google Scholar
Hogarth, R. M. & Karelaia, N. (2005) Ignoring information in binary choice with continuous variables: When is less “more”? Journal of Mathematical Psychology 49:115–24.CrossRefGoogle Scholar
Horgan, J. (1999) The undiscovered mind: How the human brain defies replication, medication, and explanation. Psychological Science 10:470–74.Google Scholar
Hornik, K., Stinchcombe, M. & White, H. (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2:359–66.CrossRefGoogle Scholar
Huber, D. E., Shiffrin, R. M., Lyle, K. B. & Ruys, K. I. (2001) Perception and preference in short-term word priming. Psychological Review 108:149–82.CrossRefGoogle ScholarPubMed
Hummel, J. E. & Biederman, I. (1992) Dynamic binding in a neural network for shape recognition. Psychological Review 99:480517.Google Scholar
Jaynes, E. T. (1968) Prior probabilities. IEEE Transactions on Systems Science and Cybernetics 4:227–41.Google Scholar
Jeffreys, H. (1946) An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences 186:453–61.Google Scholar
Joanisse, M. F. & Seidenberg, M. S. (1999) Impairments in verb morphology after brain injury: A connectionist model. Proceedings of the National Academy of Sciences USA 96:7592–97.Google Scholar
Joanisse, M. F. & Seidenberg, M. S. (2003) Phonology and syntax in specific language impairment: Evidence from a connectionist model. Brain and Language 86:4056.Google Scholar
Johnson, M. H. (1998) The neural basis of cognitive development. In: Handbook of child psychology, vol. 2: Cognition, perception, and language, ed. Kuhm, D. & Siegler, R. S., p. 149. Wiley.Google Scholar
Jones, M. & Sieck, W. R. (2003) Learning myopia: An adaptive recency effect in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 29:626–40.Google Scholar
Jones, M. & Zhang, J. (2003) Which is to blame: Instrumental rationality, or common knowledge? Behavioral and Brain Sciences 26:166–67.Google Scholar
Kant, I. (1787/1961) Critique of pure reason, trans. Smith, N. K.. St. Martin's Press. (Original work published in 1787).Google Scholar
Kemp, C. & Tenenbaum, J. B. (2008) The discovery of structural form. Proceedings of the National Academy of Sciences USA 105:10687–692.CrossRefGoogle ScholarPubMed
Kemp, C., Perfors, A. & Tenenbaum, J. B. (2007) Learning overhypotheses with hierarchical Bayesian models. Developmental Science 10:307–21.Google Scholar
Köver, H., Bao, S. (2010) Cortical plasticity as a mechanism for storing Bayesian priors in sensory perception. PLoS ONE 5(5):e10497.Google Scholar
Krugman, P. (2009) How did economists get it so wrong? New York Times, MM36, September 2.Google Scholar
Kurz, E. M. & Tweney, R. D. (1998) The practice of mathematics and science: From calculus to the clothesline problem. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., p. 415–38. Oxford University Press.Google Scholar
Lee, M. D. & Sarnecka, B. W. (2010) A model of knower-level behavior in number-concept development. Cognitive Science 34:5167.Google Scholar
Love, B. C. (2002) Comparing supervised and unsupervised category learning. Psychonomic Bulletin and Review 9:829–35.Google Scholar
Lucas, C., Griffiths, T. L., Xu, F. & Fawcett, C. (2009) A rational model of preference learning and choice prediction by children. Advances in Neural Information Processing Systems 21:985–92.Google Scholar
Luce, R. D. (1963) Detection and recognition. In: Handbook of mathematical psychology, ed. Luce, R. D., Bush, R. R. & Galanter, E., p. 103–89. John Wiley.Google Scholar
Machery, E. & Barrett, C. (2006) Debunking adapting minds. Philosophy of Science 73:232–46.Google Scholar
Marcus, G. F. (1998) Rethinking eliminative connectionism. Cognitive Psychology 37:243–82.Google Scholar
Marcus, G. F. (2008) Kluge: The haphazard construction of the human mind. Houghton Mifflin.Google Scholar
Markman, A. B. & Ross, B. H. (2003) Category use and category learning. Psychological Bulletin 129:592615.Google Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.Google Scholar
Mayr, E. (1982) The growth of biological thought: Diversity, evolution, and inheritance. Harvard University Press.Google Scholar
McClelland, J. L., Rumelhart, D. E. & the PDP Research Group. (1986) Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 2: Psychological and biological models. MIT Press.Google Scholar
McCulloch, W. & Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 7:115–33.CrossRefGoogle Scholar
McKenzie, C. R. M. & Mikkelsen, L. A. (2007) A Bayesian view of covariation assessment. Cognitive Psychology 54:3361.Google Scholar
McNamara, J. M. & Houston, A. I. (2009) Integrating function and mechanism. Trends in Ecology and Evolution 24:670–75.Google Scholar
Michaels, C. F. & Carello, C. (1981) Direct perception. Prentice-Hall.Google Scholar
Miller, E. K. & Cohen, J. D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience 24:167202.Google Scholar
Miller, G. A. (2003) The cognitive revolution: A historical perspective. Trends in Cognitive Sciences 7:141–44.CrossRefGoogle ScholarPubMed
Minsky, M. & Papert, S. A. (1969) Perceptrons: An introduction to computational geometry. MIT Press.Google Scholar
Mortimer, D., Feldner, J., Vaughan, T., Vetter, I., Pujic, Z., Rosoff, W. J., Burrage, K., Dayan, P., Richards, L. J. & Goodhill, G. J. (2009) Bayesian model predicts the response of axons to molecular gradients. Proceedings of the National Academy of Sciences USA 106:10296–301.Google Scholar
Mozer, M. C., Pashler, H. & Homaei, H. (2008) Optimal predictions in everyday cognition: The wisdom of individuals or crowds? Cognitive Science 32:1133–47.Google Scholar
Murphy, G. L. (1993) A rational theory of concepts. Psychology of Learning and Motivation 29:327–59.CrossRefGoogle Scholar
Nersessian, N. J. (1986) A cognitive-historical approach to meaning in scientific theories. In: The process of science: Contemporary philosophical approaches to understanding scientific practice, ed. Nersessian, N. J.. Martinus Nijhoff.Google Scholar
Newell, A. & Simon, H. A. (1972) Human problem solving. Prentice-Hall.Google Scholar
Newport, E. L. (1990) Maturational constraints on language learning. Cognitive Science 14:1128.Google Scholar
Nosofsky, R. M., Palmeri, T. J. & Mckinley, S. C. (1994) Rule-plus-exception model of classification learning. Psychological Review 104:266300.Google Scholar
Oaksford, M. & Chater, N. (1994) A rational analysis of the selection task as optimal data selection. Psychological Review 101:608–31.CrossRefGoogle Scholar
Oaksford, M. & Chater, N. (1998a) An introduction to rational models of cognition. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., p. 118. Oxford University Press.Google Scholar
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.Google Scholar
Oaksford, M. & Chater, N. (2010) Conditionals and constraint satisfaction: Reconciling mental models and the probabilistic approach? In: Cognition and conditionals: Probability and logic in human thinking, ed. Oaksford, M. & Chater, N., p. 309–34. Oxford University Press.Google Scholar
Oppenheim, R. W. (1981) Ontogenetic adaptations and retrogressive processes in the development of the nervous system and behavior. In: Maturation and development: Biological and psychological perspectives, ed. Connolly, K. J. & Prechtl, H. F. R., p. 73108. International Medical.Google Scholar
Pinker, S. (1995) The language instinct: How the mind creates language. Perennial.Google Scholar
Pinker, S. (2002) The blank slate: The modern denial of human nature. Viking.Google Scholar
Pinker, S. & Prince, A. (1988) On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition 28:73193.Google Scholar
Pitt, M. A., Myung, I. J. & Zhang, S. (2002) Toward a method of selecting among computational models of cognition. Psychological Review 109:472–91.CrossRefGoogle Scholar
Plaut, D. C., McClelland, J. L., Seidenberg, M. S. & Patterson, K. (1996) Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review 103:56115.Google Scholar
Pollack, J. B. (1990) Recursive distributed representations. Artificial Intelligence 46:77105.CrossRefGoogle Scholar
Pothos, E. M. & Chater, N. (2002) A simplicity principle in unsupervised human categorization. Cognitive Science 26:303–43.Google Scholar
Rachman, S. (1997) The evolution of cognitive behaviour therapy. In: Science and practice of cognitive behaviour therapy, ed. Clark, D., Fairburn, C. G. & Gelder, M. G., p. 126. Oxford University Press.Google Scholar
Raiffa, H. & Schlaifer, R (1961) Applied statistical decision theory. Harvard University Press.Google Scholar
Ramscar, M., Yarlett, D., Dye, M., Denny, K. & Thorpe, K. (2010) The effects of feature-label-order and their implications for symbolic learning. Cognitive Science 34:149.Google Scholar
Ravi, S. & Knight, K. (2009) Minimized models for unsupervised part-of-speech tagging. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics (ACL) and the 4th International Joint Conference on Natural Language Processing of the AFNLP, ed. Su, K.-Y., p. 504–12. Association for Computational Linguistics. Available at: http://www.aclweb.org/anthology-new/P/P09/P09-1057.pdf Google Scholar
Ricci, G. & Levi-Civita, T. (1900) Méthodes de calcul différentiel absolu et leurs applications [Methods of absolute differential calculus and their applications]. Mathematische Annalen 54(1–2):125201.Google Scholar
Rogers, T. T. & Plaut, D. C. (2002) Connectionist perspectives on category-specific deficits. In: Category-specificity in brain and mind, ed. Forde, E. & Humphreys, G. W., p. 251–89. Psychology Press.Google Scholar
Rosch, E. (1978) Principles of categorization. In: Cognition and categorization, ed. Rosch, E. & Lloyd, B. B., p. 2748. Erlbaum.Google Scholar
Rosenblatt, F. (1962) Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Spartan Books.Google Scholar
Rougier, N. P., Noelle, D., Braver, T. S., Cohen, J. D. & O'Reilly, R. C. (2005) Prefrontal cortex and the flexibility of cognitive control: Rules without symbols. Proceedings of the National Academy of Sciences USA 102:7338–43.Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986) Learning representations by back-propagating errors. Nature 323:533–36.Google Scholar
Rumelhart, D. E., McClelland, J. L. & the PDP research group. (1986) Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations. MIT Press.Google Scholar
Sakamoto, Y., Jones, M. & Love, B. C. (2008) Putting the psychology back into psychological models: Mechanistic versus rational approaches. Memory and Cognition 36(6):1057–65.Google Scholar
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2010a) Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 117:1144–67.Google Scholar
Sanborn, A. N., Griffiths, T. L. & Shiffrin, R. M. (2010b) Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology 60:63106.Google Scholar
Sargent, T. J. (1993) Bounded rationality in macroeconomics. Oxford University Press.Google Scholar
Savage, L. J. (1954) The foundations of statistics. John Wiley/Dover.Google Scholar
Schwarz, G. E. (1978) Estimating the dimension of a model. Annals of Statistics 6(2):461–64.Google Scholar
Shafto, P., Kemp, C., Mansinghka, V. M. & Tenenbaum, J. B. (2011) A probablistic model of cross-categorization. Cognition. 120:125.Google Scholar
Shiffrin, R. M. & Steyvers, M. (1998) The effectiveness of retrieval from memory. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., p. 7395. Oxford University Press.Google Scholar
Simon, H. A. (1957a) A behavioral model of rational choice. In: Models of man, social and rational: Mathematical essays on rational human behavior in a social setting, ed. Simon, H. A., p. 241–60. John Wiley.Google Scholar
Skinner, B. F. (1938) The behavior of organisms: An experimental analysis. Appleton-Century.Google Scholar
Skinner, B. F. (1957) Verbal behavior. Appleton-Century-Crofts.Google Scholar
Skinner, B. F. (1958) Reinforcement today. American Psychologist 13:9499.Google Scholar
Sloman, S. A. & Fernbach, P. M. (2008) The value of rational analysis: An assessment of causal reasoning and learning. In: The probabilistic mind: Prospects for rational models of cognition, ed. Chater, N. & Oaksford, M, p. 485500. Oxford University Press.CrossRefGoogle Scholar
Smith, D. L. (2007) Beyond Westemarck: Can shared mothering or maternal phenotype matching account for incest avoidance? Evolutionary Psychology 5:202–22.Google Scholar
Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L. & Samuelson, L. (2002) Object name learning provides on-the-job training for attention. Psychological Science 13:1319.CrossRefGoogle ScholarPubMed
Smith, P. K. (1982) Does play matter? Functional and evolutionary aspects of animal and human play. Behavioral and Brain Sciences 5:139–84.Google Scholar
Smolensky, P. (1988) On the proper treatment of connectionism. Behavioral and Brain Sciences 11:123.Google Scholar
Smolin, L. (2006) The trouble with physics: The rise of string theory, the fall of a science, and what comes next. Houghton Mifflin Harcourt.Google Scholar
Sobel, D. M., Tenenbaum, J. B. & Gopnik, A. (2004) Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. Cognitive Science 28(3):303–33.Google Scholar
Soltani, A. & Wang, X.-J. (2010) Synaptic computation underlying probabilistic inference. Nature Neuroscience 13(1):112–19.Google Scholar
Spencer, J. P., Perone, S. & Johnson, J. S. (2009) The dynamic field theory and embodied cognitive dynamics. In: Toward a unified theory of development: Connectionism and dynamic systems theory reconsidered, ed. Spencer, J. P., Thomas, M. S. & McClelland, J. L., p. 86118. Oxford University Press.Google Scholar
Sperber, D. & Hirschfeld, L. A. (2003) The cognitive foundations of cultural stability and diversity. Trends in Cognitive Sciences 8:4046.Google Scholar
Stankiewicz, B. J., Legge, G. E., Mansfield, J. S. & Schlicht, E. J. (2006) Lost in virtual space: Studies in human and ideal spatial navigation. Journal of Experimental Psychology: Human Perception and Performance 32:688704.Google Scholar
Steyvers, M., Lee, M. D. & Wagenmakers, E.-J. (2009) A Bayesian analysis of human decision-making on bandit problems. Journal of Mathematical Psychology 53:168–79.Google Scholar
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E.-J. & Blum, B. (2003) Inferring causal networks from observations and interventions. Cognitive Science 27:453–89.CrossRefGoogle Scholar
Stigler, S. M. (1961) The economics of information. Journal of Political Economy 69:213–25.Google Scholar
Tenenbaum, J. B. & Griffiths, T. L. (2001) Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences 24(4):629–40.Google Scholar
Tenenbaum, J. B., Griffiths, T. L. & Kemp, C. (2006) Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences 10:309–18.Google Scholar
Thagard, P. (1989) Explanatory coherence. Behavioral and Brain Sciences 12:435502.Google Scholar
Thaler, R. H. & Sunstein, C. R. (2008) Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.Google Scholar
Thibaux, R. & Jordan, M. I. (2007) Hierarchical beta processes and the Indian buffet process. In: Proceedings of the Tenth Conference on Artificial Intelligence and Statistics (AISTATS), ed. Meila, M. & Shen, X.. Society for Artificial Intelligence and Statistics. (Online Publication). Available at: http://www.stat.umn.edu/%7Eaistat/proceedings/start.htm Google Scholar
Thompson-Schill, S., Ramscar, M. & Chrysikou, M. (2009) Cognition without control: When a little frontal lobe goes a long way. Current Directions in Psychological Science 8:259–63.Google Scholar
Tooby, J. & Cosmides, L. (2005) Conceptual foundations of evolutionary psychology. In: The handbook of evolutionary psychology, ed. Buss, D. M., p. 567. Wiley.Google Scholar
Tversky, A. & Kahneman, D. (1974) Judgment under uncertainty: Heuristics and biases. Science 185:1124–31.Google Scholar
Vul, E., Frank, M. C., Alvarez, G. A. & Tenenbaum, J. B. (2009) Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. Advances in Neural Information Processing Systems 22:1955–63.Google Scholar
Watson, J. B. (1913) Psychology as the behaviorist views it. Psychological Review 20:158–77.Google Scholar
Wertheimer, M. (1923/1938) Laws of organization in perceptual forms. In: A source book of Gestalt psychology, ed. & trans. Ellis, W., p. 7188. Routledge & Kegan Paul. (Original work published in 1923).Google Scholar
Wilder, M. H., Jones, M. & Mozer, M. C. (2009) Sequential effects reflect parallel learning of multiple environmental regularities. Advances in Neural Information Processing Systems 22:2053–61.Google Scholar
Woit, P. (2006) Not even wrong: The failure of string theory and the search for unity in physical law. Basic Books.Google Scholar
Wolpert, D. (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8:1341–90.Google Scholar
Wood, J. N. & Grafman, J. (2003) Human prefrontal cortex: Processing and representational perspectives. Nature Reviews: Neuroscience 4:129–47.Google Scholar
Xu, F. & Tenenbaum, J. B. (2007b) Word learning as Bayesian inference. Psychological Review 114(2):245–72.Google Scholar
Yamauchi, T. & Markman, A. B. (1998) Category learning by inference and classification. Journal of Memory and Language 39:124–48.CrossRefGoogle Scholar
Yu, A. & Cohen, J. (2008) Sequential effects: Superstition or rational behavior? Advances in Neural Information Processing Systems 21:1873–80.Google Scholar