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Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning

  • Mike Oaksford (a1) and Nick Chater (a2)
Abstract

According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining the end-point of cognitive development; and contemporary psychology of reasoning has focussed on comparing human reasoning against logical standards.

Bayesian Rationality argues that rationality is defined instead by the ability to reason about uncertainty. Although people are typically poor at numerical reasoning about probability, human thought is sensitive to subtle patterns of qualitative Bayesian, probabilistic reasoning. In Chapters 1–4 of Bayesian Rationality (Oaksford & Chater 2007), the case is made that cognition in general, and human everyday reasoning in particular, is best viewed as solving probabilistic, rather than logical, inference problems. In Chapters 5–7 the psychology of “deductive” reasoning is tackled head-on: It is argued that purportedly “logical” reasoning problems, revealing apparently irrational behaviour, are better understood from a probabilistic point of view. Data from conditional reasoning, Wason's selection task, and syllogistic inference are captured by recasting these problems probabilistically. The probabilistic approach makes a variety of novel predictions which have been experimentally confirmed. The book considers the implications of this work, and the wider “probabilistic turn” in cognitive science and artificial intelligence, for understanding human rationality.

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Adams, E. W. (1975) The logic of conditionals: An application of probability to deductive logic. Reidel.
Adams, E. W. (1998) A primer of probability logic. CLSI Publications.
Anderson, J. R. (1990) The adaptive character of thought. Erlbaum.
Anderson, J. R. (1991a) Is human cognition adaptive? Behavioral and Brain Sciences 14:471–84; discussion 485–517.
Anderson, J. R. (1991b) The adaptive nature of human categorization. Psychological Review 98:409–29.
Anderson, J. R. & Matessa, M. (1998) The rational analysis of categorization and the ACT-R architecture. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., pp. 197217. Oxford University Press.
Anderson, J. R. & Milson, R. (1989) Human memory: An adaptive perspective. Psychological Review 96:703–19.
Anderson, J. R. & Schooler, L. J. (1991) Reflections of the environment in memory. Psychological Science 2:396408.
Aristotle, (1980) Nicomachean ethics, trans. Ross, W. D.. Clarendon Press.
Baron, J. (1981) An analysis of confirmation bias. Paper presented at the 22nd Annual Meeting of the Psychonomic Society. 6–8 November, 1981, Philadelphia, PA.
Baron, J. (1985) Rationality and intelligence. Cambridge University Press.
Barwise, J. & Cooper, R. (1981) Generalized quantifiers and natural language. Linguistics and Philosophy 4:159219.
Bennett, J. (2003) A philosophical guide to conditionals. Oxford University Press.
Bernoulli, J. (1713/2005) Ars conjectandi The art of conjecture, trans. Sylla, E. D.. Johns Hopkins University Press.
Boole, G. (1854/1958) An investigation of the laws of thought. Macmillan/Dover. (Reprinted by Dover, 1958).
Bovens, L. & Hartmann, S. (2003) Bayesian epistemology. Clarendon Press.
Braine, M. D. S. (1978) On the relation between the natural logic of reasoning and standard logic. Psychological Review 85:121.
Chater, N., Crocker, M. & Pickering, M. (1998) The rational analysis of inquiry: The case of parsing: In: Rational models of cognition, ed. Oaksford, M. & Chater, N., pp. 441–68. Oxford University Press.
Chater, N. & Manning, C. D. (2006) Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences 10:335–44.
Chater, N. & Oaksford, M. (1999b) The probability heuristics model of syllogistic reasoning. Cognitive Psychology 38:191258.
Cheng, P. W. & Holyoak, K. J. (1985) Pragmatic reasoning schemas. Cognitive Psychology 17:391416.
Chomsky, N. (1957) Syntactic structures. Mouton.
Chomsky, N. (1965) Aspects of the theory of syntax. MIT Press.
Cohen, L. J. (1981) Can human irrationality be experimentally demonstrated? Behavioral and Brain Sciences 4:317–70.
Copeland, D. E. & Radvansky, G. A. (2004) Working memory and syllogistic reasoning. Quarterly Journal of Experimental Psychology 57A:1437–57.
Cosmides, L. (1989) The logic of social exchange: Has natural selection shaped how humans reason? Studies with the Wason selection task. Cognition 31:187276.
Cosmides, L. & Tooby, J. (2000) Evolutionary psychology and the emotions. In: Handbook of emotions, 2nd edition, ed. Lewis, M. & Haviland-Jones, J. M., pp. 91115. Guilford.
Courville, A. C., Daw, N. D. & Touretzky, D. S. (2006) Bayesian theories of conditioning in a changing world. Trends in Cognitive Sciences 10:294300.
Daston, L. (1988) Classical probability in the enlightenment. Princeton University Press.
Davidson, D. (1984) Inquiries into truth and interpretation. Oxford University Press.
Earman, J. (1992) Bayes or bust? MIT Press.
Edgington, D. (1995) On conditionals. Mind 104:235329.
Evans, J. St. B. T. (1972) Reasoning with negatives. British Journal of Psychology 63:213–19.
Evans, J. St. B. T. & Handley, S. J. (1999) The role of negation in conditional inference. Quarterly Journal of Experimental Psychology 52A 739–69.
Evans, J. St. B. T., Handley, S. J., Harper, C. N. J. & Johnson-Laird, P. N. (1999) Reasoning about necessity and possibility: A test of the mental model theory of deduction. Journal of Experimental Psychology: Learning, Memory, and Cognition 25:14951513.
Evans, J. St .B. T., Handley, S. J. & Over, D. E. (2003) Conditionals and conditional probability. Journal of Experimental Psychology: Learning, Memory and Cognition 29321–55.
Evans, J. St. B. T. & Lynch, J. S. (1973) Matching bias in the selection task. British Journal of Psychology 64:391–97.
Evans, J. St. B. T., Newstead, S. E. & Byrne, R. J. (1993) Human reasoning. Erlbaum.
Evans, J. St. B. T. & Over, D. E. (1996a) Rationality and reasoning. Psychology Press.
Evans, J. St. B. T. & Over, D. E. (1996b) Rationality in the selection task: Epistemic utility versus uncertainty reduction. Psychological Review 103:356–63.
Evans, J. St. B. T. & Over, D. E. (2004) If. Oxford University Press.
Fodor, J. A. (1983) The modularity of mind. MIT Press.
Geurts, B. (2003) Reasoning with quantifiers. Cognition 86:223–51.
Gigerenzer, G. & Goldstein, D. (1996) Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review 103:650–69.
Gigerenzer, G. & Hoffrage, U. (1995) How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review 102(4):684704.
Gigerenzer, G., Swijinck, Z., Porter, T., Daston, L., Beatty, J. & Kruger, L. (1989) The empire of chance. Cambridge University Press.
Gigerenzer, G., Todd, P. & the ABC Research Group. (1999) Simple heuristics that make us smart. Oxford University Press.
Green, D. W. & Over, D. E. (1997) Causal inference, contingency tables and the selection task. Current Psychology of Cognition 16:459–87.
Green, D. W. & Over, D. E. (2000) Decision theoretical effects in testing a causal conditional. Current Psychology of Cognition 19:5168.
Griffiths, T. L. & Tenenbaum, J. B. (2005) Structure and strength in causal induction. Cognitive Psychology 51:354–84.
Hacking, I. (1975) The emergence of probability. Cambridge University Press.
Hacking, I. (1990) The taming of chance. Cambridge University Press.
Hattori, M. (2002) A quantitative model of optimal data selection in Wason's selection task. Quarterly Journal of Experimental Psychology 55A:1241–72.
Henle, M. (1978) Foreword. In: Human reasoning, ed. Revlin, R. & Mayer, R. E.. Winston.
Horwich, P. (1982) Probability and evidence. Cambridge University Press.
Howson, C. & Urbach, P. (1993) Scientific reasoning: The Bayesian approach, 2nd edition. Open Court.
Inhelder, B. & Piaget, J. (1955) De la logique de l'enfant à la logique de l'adolescent. Presses Universitaires de France. (English version: The growth of logical thinking from childhood to adolescence. Routledge, 1958).
Jeffrey, R. C. (1983) The logic of decision, 2nd edition. University of Chicago Press.
Johnson-Laird, P. N. (1983) Mental models. Cambridge University Press.
Johnson-Laird, P. N. & Byrne, R. M. J. (1991) Deduction. Erlbaum.
Johnson-Laird, P. N. & Byrne, R. M. J. (2002) Conditionals: A theory of meaning, pragmatics, and inference. Psychological Review 109:646–78.
Kahneman, D., Slovic, P. & Tversky, A., eds. (1982) Judgment under uncertainty: Heuristics and biases. Cambridge University Press.
Kakade, S. & Dayan, P. (2002) Acquisition and extinction in autoshaping. Psychological Review 109:533–44.
Kant, E. (1787/1961) Critique of pure reason, First edition, second impression, trans. Smith, N. K.. Macmillan.
Kirby, K. N. (1994) Probabilities and utilities of fictional outcomes in Wason's four card selection task. Cognition 51:128.
Klauer, K. C. (1999) On the normative justification for information gain in Wason's selection task. Psychological Review 106:215–22.
Knill, D. & Richards, W., eds. (1996) Perception as Bayesian inference. Cambridge University Press.
Kuhn, T. (1962) The structure of scientific revolutions. University of Chicago Press.
Lakatos, I. (1970) Falsification and the methodology of scientific research programmes. In: Criticism and the growth of knowledge, ed. Lakatos, I. & Musgrave, A., pp. 91196. Cambridge University Press.
Lindley, D. V. (1956) On a measure of the information provided by an experiment. Annals of Mathematical Statistics 27:9861005.
Manktelow, K. I. & Over, D. E. (1987) Reasoning and rationality. Mind and Language 2:199219.
Manktelow, K. I. & Over, D. E. (1991) Social roles and utilities in reasoning with deontic conditionals. Cognition 39:85105.
Manktelow, K. I., Sutherland, E. J. & Over, D. E. (1995) Probabilistic factors in deontic reasoning. Thinking and Reasoning 1:201–20.
McCarthy, J. & Hayes, P. J. (1969) Some philosophical problems from the standpoint of artificial intelligence. In: Machine intelligence, vol. 4, ed. Meltzer, B. & Michie, D.. Edinburgh University Press.
McClelland, J. L. (1998) Connectionist models and Bayesian inference. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., pp. 2153. Oxford University Press.
McKenzie, C. R. M., Ferreira, V. S., Mikkelsen, L. A., McDermott, K. J. & Skrable, R. P. (2001) Do conditional statements target rare events? Organizational Behavior and Human Decision Processes 85:291309.
McKenzie, C. R. M. & Mikkelsen, L. A. (2000) The psychological side of Hempel's paradox of confirmation. Psychonomic Bulletin and Review 7:360–66.
McKenzie, C. R. M. & Mikkelsen, L. A. (2007) A Bayesian view of covariation assessment. Cognitive Psychology 54:3361.
Nelson, J. D. (2005) Finding useful questions: On Bayesian diagnosticity, probability, impact, and information gain. Psychological Review 112(4):979–99.
Newell, A., Shaw, J. C. & Simon, H. A. (1958) Chess-playing programs and the problem of complexity. IBM Journal of Research and Development 2:320–25.
Newell, A. & Simon, H. A. (1972) Human problem solving. Prentice-Hall.
Newstead, S. E., Handley, S. J. & Buck, E. (1999) Falsifying mental models: Testing the predictions of theories of syllogistic reasoning. Memory and Cognition 27:344–54.
Nickerson, R. S. (1996) Hempel's paradox and Wason's selection task: Logical and psychological puzzles of confirmation. Thinking and Reasoning 2:132.
Novick, L. R. & Cheng, P. W. (2004) Assessing interactive causal influence. Psychological Review 111:455–85.
Oaksford, M. (2004a) Conditional inference and constraint satisfaction: Reconciling probabilistic and mental models approaches? Paper presented at the 5th International Conference on Thinking, July 22–24, 2004. University of Leuven, Leuven, Belgium.
Oaksford, M. (2004b) Reasoning. In: Cognitive psychology, ed. Braisby, N. & Gellatly, A., pp. 418–55. Oxford University Press.
Oaksford, M. & Chater, N. (1991) Against logicist cognitive science. Mind and Language 6:138.
Oaksford, M. & Chater, N. (1994) A rational analysis of the selection task as optimal data selection. Psychological Review 101:608–31.
Oaksford, M. & Chater, N. (1996) Rational explanation of the selection task. Psychological Review 103:381–91.
Oaksford, M. & Chater, N. (1998a) Rationality in an uncertain world. Psychology Press.
Oaksford, M. & Chater, N. eds. (1998b) Rational models of cognition. Oxford University Press.
Oaksford, M. & Chater, N. (2003a) Conditional probability and the cognitive science of conditional reasoning. Mind and Language 18:359–79.
Oaksford, M. & Chater, N. (2003b) Optimal data selection: Revision, review, and reevaluation. Psychonomic Bulletin and Review 10:289318.
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.
Oaksford, M. & Chater, N. (2008) Probability logic and the Modus Ponens–Modus Tollens asymmetry in conditional inference. In: The probabilistic mind: Prospects for Bayesian cognitive science, ed. Chater, N. & Oaksford, M., pp. 97120. Oxford University Press.
Oaksford, M., Chater, N. & Grainger, B. (1999) Probabilistic effects in data selection. Thinking and Reasoning 5:193244.
Oaksford, M., Chater, N., Grainger, B. & Larkin, J. (1997) Optimal data selection in the reduced array selection task (RAST). Journal of Experimental Psychology: Learning, Memory and Cognition 23:441–58.
Oaksford, M., Chater, N. & Larkin, J. (2000) Probabilities and polarity biases in conditional inference. Journal of Experimental Psychology: Learning, Memory and Cognition 26:883–89.
Oaksford, M. & Moussakowski, M. (2004) Negations and natural sampling in data selection: Ecological vs. heuristic explanations of matching bias. Memory and Cognition 32:570–81.
Oaksford, M., Roberts, L. & Chater, N. (2002) Relative informativeness of quantifiers used in syllogistic reasoning. Memory and Cognition 30:138–49.
Oaksford, M. & Stenning, K. (1992) Reasoning with conditionals containing negated constituents. Journal of Experimental Psychology: Learning, Memory and Cognition 18:835–54.
Oaksford, M. & Wakefield, M. (2003) Data selection and natural sampling: Probabilities do matter. Memory and Cognition 31:143–54.
Oberauer, K., Weidenfeld, A. & Hörnig, R. (2004) Logical reasoning and probabilities: A comprehensive test of Oaksford and Chater (2001) Psychonomic Bulletin and Review 11:521–27.
Oberauer, K. & Wilhelm, O. (2003) The meaning(s) of conditionals: Conditional probabilities, mental models and personal utilities. Journal of Experimental Psychology: Learning, Memory, and Cognition 29:680–93.
Oberauer, K., Wilhelm, O. & Diaz, R. R. (1999) Bayesian rationality for the Wason selection task? A test of optimal data selection theory. Thinking and Reasoning 5:115–44.
Over, D. E. & Evans, J. St. B. T (1994) Hits and misses: Kirby on the selection task. Cognition 52:235–43.
Over, D. E. & Jessop, A. (1998) Rational analysis of causal conditionals and the selection task. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., pp. 399414. Oxford University Press.
Pearl, J. (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann.
Pearl, J. (2000) Causality: Models, reasoning and inference. Cambridge University Press.
Perham, N. & Oaksford, M. (2005) Deontic reasoning with emotional content: Evolutionary psychology or decision theory? Cognitive Science 29:681718.
Politzer, G. & Braine, M. D. (1991) Responses to inconsistent premises cannot count as suppression of valid inferences. Cognition 38:103–08.
Popper, K. R. (1935/1959) The logic of scientific discovery. Basic Books.
Putnam, H. (1974) The “corroboration” of theories. In: The philosophy of Karl Popper, vol. 2, ed. Schilpp, A.. Open Court.
Pylyshyn, Z., ed. (1987) The robot's dilemma: The frame problem in artificial intelligence. Ablex.
Quine, W. V. O. (1953) From a logical point of view. Harvard University Press.
Ramsey, F. P. (1931/1990b) The foundations of mathematics and other logical essays. Routledge and Kegan Paul.
Reiter, R. (1980) A logic for default reasoning, Artificial Intelligence 13:81132.
Rips, L. J. (1983) Cognitive processes in propositional reasoning. Psychological Review 90:3871.
Rips, L. J. (1994) The psychology of proof. MIT Press.
Rosch, E. (1975) Cognitive representation of semantic categories. Journal of experimental psychology: General 104:192233.
Schroyens, W. & Schaeken, W. (2003) A critique of Oaksford, Chater and Larkin's (2000) conditional probability model of conditional reasoning. Journal of Experimental Psychology: Learning, Memory and Cognition 29:140–49.
Sober, E. (2002) Intelligent design and probability reasoning. International Journal for Philosophy of Religion 52:6580.
Stewart, N., Chater, N. & Brown, G. D. A. (2006) Decision by sampling. Cognitive Psychology 53:126.
Wagner, C. G. (2004) Modus tollens probabilized. British Journal for Philosophy of Science 55:747–53.
Yama, H. (2001) Matching versus optimal data selection in the Wason selection task. Thinking and Reasoning 7:295311.
Yuille, A. L. & Kersten, D. (2006) Vision as Bayesian Inference: Analysis by Synthesis? Trends in Cognitive Sciences 10:301–08.
Zeelenberg, M., Van Dijk, W. W., Manstead, A. S. R. & Van der Pligt, J. (2000) On bad decisions and disconfirmed expectancies: The psychology of regret and disappointment. Cognition and Emotion 14:521–41.
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