Fast, frugal, and rational: How rational norms explain behavior

Organizational Behavior and Human Decision Processes

Volumn 90, Issue 1, 2003, Pages 63-86

  Chater, Nick ^a   Oaksford, Mike ^b   Nakisa, Ramin ^c   Redington, Martin ^c  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b CARDIFF UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0038733989     PISSN: 07495978     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0749-5978(02)00508-3     Document Type: Review

References (192)

1. Akerlof, G., & Yellen, J. (1985). Can small deviations from rationality make significant differences to economic equilibria? American Economic Review, 75, 708-720.
2. Allais, M. (1953). Le comportement de l'homme rationnel devant le risque: Critique des postulats et axiomes de l'école américaine. Econometrica, 21, 503-546.
3. Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
4. Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum.
5. Anderson, J. R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471-517.
6. Anderson, J. R., & Milson, R. (1989). Human memory: An adaptive perspective. Psychological Review, 96, 703-719.
7. Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 1, 396-408.
8. Anderson, N. H. (1981). Foundations of information integration theory. New York: Academic Press.
9. Anscombe, F., & Aumann, R. (1963). A definition of subjective probability. Annals of Mathematical Statistics, 34, 199-205.
10. Arrow, K. J., Colombatto, E., Perlman, M., & Schmidt, C. (Eds.). (1996). The rational foundations of economic behavior. London: Macmillan.
11. Ashby, F. G., & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216-233.
12. Ayton, P. (2000). Do the birds and bees need cognitive reform? Behavioral and Brian Sciences, 23, 666-667.
13. Ayton, P., & Hardman, D. (1997). Are two rationalities better than one? Current Psychology of Cognition, 16, 39-51.
14. Barkow, J., Cosmides, L., & Tooby, J. (1992). The adapted mind: Evolutionary psychology and the generation of culture. New York: Oxford University Press.
15. Berger, J. O. (1995). Statistical decision theory and Bayesian analysis. New York: Springer-Verlag.
16. Bernado, J. M., & Smith, A. F. M. (1995). Bayesian theory. Chichester, Sussex: Wiley.
17. Braine, M. D. S. (1978). On the relation between the natural logic of reasoning and standard logic. Psychological Review, 85, 1-21.
18. Bröder, A. (2000). Assessing the empirical validity of the "take-the-best" heuristic as a model of human probabilistic inference. Journal of Experimental Psychology: Learning, Memory and Cognition, 26, 1332-1346.
19. Brown, G. D. A. (1998). The endpoint of reading instruction: The ROAR model. In J. L. Metsala & L. C. Ehri (Eds.), Word recognition in beginning literacy (pp. 121-138). Mahwah, NJ: Erlbaum.
20. Brunner, D., Kacelnik, A., & Gibbons, J. (1992). Optimal foraging and timing processes in the starling, Sturnus vulgaris: Effect of inter-capture interval. Animal Behavior, 44, 597-613.
21. Brunswick, E. (1934). Wahrnehmung und Gegenstandswelt: Grundlegung einer Psychologie vom Gegenstand her. Leipzig: Deuticke.
22. Bullinaria, J. A. (1994). Internal representations of a connectionist model of reading aloud. In Proceedings of the sixteenth annual conference of the cognitive science society (pp. 84-89). Hillsdale, NUJ: Erlbaum.
23. Chater, N. (1995). Neural networks: The new statistical models of mind. In J. P. Levy, D. Bairaktaris, J. A. Bullinaria, & P. Cairns (Eds.), Connectionist models of memory and language (pp. 207-228). London: UCL Press.
24. Chater, N. (1996). Reconciling simplicity and likelihood principles in perceptual organization. Psychological Review, 103, 566-581.
25. Chater, N., & Oaksford, M. (1990). Autonomy, implementation and cognitive architecture: A reply to Fodor and Pylyshyn. Cognition, 34, 93-107.
26. Chater, N., & Oaksford, M. (2000). The rational analysis of mind and behavior. Synthese, 122, 93-131.
27. Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367-405.
28. Cherniak, C. (1986). Minimal rationality. Cambridge, MA: MIT Press.
29. Chew, S. H. (1983). A generalization of the quasilinear mean with applications to the measurement of income inequality and decision theory resolving the Allais paradox. Econometrica, 51, 1065-1092.
30. Chomsky, N. (1980). Rules and representations. Cambridge, MA: MIT Press.
31. Christiansen, M. H., & Chater, N. (Eds.). (2001). Connectionist psycholinguistics. Westport, CT: Ablex.
32. Christiansen, M., Chater, N., & Seidenberg, M. (Eds.) (1999). Special issue on connectionist natural language processing. Cognitive Science, 23(4).
33. Cohen, L. J. (1981). Can human irrationality be experimentally demonstrated? Behavioral and Brain Sciences, 4, 317-370.
34. Colman, A. M. (1995). Game theory and its applications in the social and biological sciences (2nd ed.). Oxford: Butterworth-Heinemann.
35. Coltheart, M., Curtis, B,, Atkins, P., & Haller, M. (1993). Models of reading aloud: Dual-route and parallel-distributed-processing approaches. Psychological Review, 100, 589-608.
36. Cooper, G., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
37. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21-27.
38. Crawford, C., Smith, M., & Krebs, D. (1987). Sociobiology and psychology. Hillsdale, NJ: Erlbaum.
39. Cyert, R., & de Groot, M. (1974). Rational expectations and Bayesian analysis. Journal of Political Economy, 82, 521-536.
40. Czerlinski, J., Gigerenzer, G., & Goldstein, D. G. (1999). How good are simple heuristics? In G. Gigerenzer, P. Todd, & The ABC Group (Eds.), Simple heuristics that make us smart (pp. 97-118). Oxford: Oxford University Press.
41. Davidson, D. (1984). Inquiries into truth and interpretation. Oxford: Clarendon Press.
42. Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81, 95-106.
43. de Canio, S. (1979). Rational expectations and learning from experience. Quarterly Journal of Economics, 93, 47-57.
44. Dhami, M. K., & Ayton, P. (2001). Bailing and jailing the fast and frugal way. Journal of Behavioral Decision Making, 14, 141-168.
45. Dhami, M. K., & Harries, C. (2002). Fast and frugal versus regression models of human judgment. Thinking and Reasoning, 7, 5-27.
46. Dougherty, M. R. P., Gettys, C. F., & Ogden, E. E. (1999). MINERVA-DM: A memory process model for judgments of likelihood. Psychological Review, 106, 180-209.
47. Duda, R. O., & Hart, P. E. (1973). Patterns classification and scene analysis. New York: Wiley.
48. Einhorn, H. J. (1970). The use of nonlinear, noncompensatory models in decision making. Psychological Bulletin, 73, 221-230.
49. Einhorn, H. J. (1971). Use of nonlinear, noncompensatory models as a function of task and amount of information. Organizational Behavior and Human Performance, 6, 1-27.
50. Einhorn, H. J. (1972). Expert measurement and mechanical combination. Organizational Behavior and Human Performance, 13, 171-192.
51. Ellsberg, D. (1961). Risk, ambiguity and the savage axioms. Quarterly Journal of Economics, 75, 643-669.
52. Elster, J. (Ed.). (1986). Rational choice. Oxford: Basil Blackwell.
53. Evans, J. St. B. T. (1982). The psychology of deductive reasoning. London: Routledge & Kegan Paul.
54. Evans, J. St. B. T. (1989). Bias in human reasoning: Causes and consequences. Hillsdale, NJ: Erlbaum.
55. Evans, J. St. B. T., & Over, D. E. (1996)., Rationality and reasoning. Hove, Sussex: Psychology Press.
56. Evans, J. St. B. T., & Over, D. (1997). Rationality in reasoning: The problem of deductive competence. Cahiers de Psychologie Cognitive, 16, 1-35.
57. Evans, J. St. B. T., Newstead, S. E., & Byrne, R. M. J. (1993). Human reasoning. Hillsdale, NJ: Erlbaum.
58. Fishburn, P. C. (1983). Transitive measurable utility. Journal of Economic Theory, 31, 293-317.
59. Flood, M. M. (1958). Some experimental games. Management Science, 5, 5-26.
60. Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71.
61. Frey, B. (1998). Graphical models for machine learning and digital communication. Cambridge, MA: MIT Press.
62. Friedman, M. (1953). Methodology of positive economics. Chicago: Chicago University Press.
63. Friedman, N., & Goldszmit, M. (1996). Learning Bayesian networks with local structure. In Proceedings of the 12th conference on uncertainty in artificial intelligence (pp. 252-262). San Mateo, CA: Morgan Kaufmann.
64. Gallistel, C. R. (1990). The organization of learning. Cambridge, MA: MIT Press.
65. Ganzach, Y. (1995). Nonlinear models of clinical judgment: Meehl's data revisited. Psychological Bulletin, 118, 422-429.
66. Garey, M. R., & Johnson, D. S. (1979). Computers and intractability: A guide to the theory of NP-completeness. San Francisco: W.H. Freeman.
67. Gibson, J. J. (1979). The ecological approach to visual perception. Boston, MA: Houghton Mifflin.
68. Gigerenzer, G. (2000). Adaptive thinking: Rationality in the real world. Oxford: Oxford University Press.
69. Gigerenzer, G. (2001). The adaptive toolbox. In G. Gigerenzer & R. Selten (Eds.), Bounded rationality: The adaptive toolbox (pp. 37-50). Cambridge, MA: MIT Press.
70. Gigerenzer, G., & Goldstein, D. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650-669.
71. Gigerenzer, G., & Goldstein, D. G. (1999). Betting on one good reason. In G. Gigerenzer, P. Todd, & The ABC Group (Eds.), Simple heuristics that make us smart (pp. 75-95). Oxford: Oxford University Press.
72. Gigerenzer, G., Hell, W., & Blank, H. (1988). Presentation and content: The use of base-rates as a continuous variable. Journal of Experimental Psychology: Human Perception and Performance, 14, 513-525.
73. Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102, 684-704.
74. Gigerenzer, G., & Murray, D. J. (1987). Cognition as intuitive statistics. Hillsdale, NJ: Erlbaum.
75. Gigerenzer, G., & Selten, R. (2001). Rethinking rationality. In G. Gigerenzer & R. Selten (Eds.), Bounded rationality: The adaptive toolbox (pp. 1-13). Cambridge, MA: MIT Press.
76. Gigerenzer, G., Todd, P. & The ABC Group (1999a). Simple heuristics that make us smart. Oxford: Oxford University Press.
77. Gigerenzer, G., & Todd, P. (1999b). Fast and frugal heuristics: The adaptive toolbox. In G. Gigerenzer, P. Todd, & The ABC Group (Eds.), Simple heuristics that make us smart (pp. 3-34). Oxford: Oxford University Press.
78. Goldstein, D. G., & Gigerenzer, G. (1999). The recognition heuristic: How ignorance makes us smart. In G. Gigerenzer, P. Todd, & The ABC Group (Eds.), Simple heuristics that make us smart (pp. 37-58). Oxford: Oxford University Press.
79. Good, I. J. (1971). Twenty seven principles of rationality. In V. P. Godambe & D. A. Sprott (Eds.), Foundations of statistical inference. Toronto: Holt, Rhinehart & Winston.
80. Hahn, U., & Chater, N. (1998). Similarity and rules: Distinct? Exhaustive? Empirically distinguishable? Cognition, 65, 197-230.
81. Hammond, K. R., & Summers, D. A. (1965). Cognitive dependence on linear and non-linear cues. Psychological Review, 72, 215-224.
82. Harsanyi, J. C., & Selten, R. (1988). A general theory of equilibrium selection in games. Cambridge, MA: MIT Press.
83. Hertwig, R. (2000). The questionable utility of "cognitive ability" in explaining cognitive illusions. Behavioral and Brian Sciences, 23, 678-679.
84. Hinton, G. E. (1989). Connectionist learning procedures. Artificial Intelligence, 40, 185-234.
85. Hintzman, D. L. (1984). MINERVA2: A simulation model of human memory. Behavior Research Methods, Instruments and Computers, 16, 96-101.
86. Hintzman, D. L. (1986). "Schema abstraction" in a multiple trace memory model. Psychological Review, 93, 411-428.
87. Hintzman, D. L. (1988). Judgments of frequency and recognition memory in a multiple-trace memory model. Psychological Review, 95, 528-551.
88. Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York: Basic Books.
89. Intrator, N. (1993). On the use of projection pursuit constraints for training neural networks. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in neural information processing systems (Vol. 5, pp. 3-10). San Mateo, CA: Morgan Kaufman.
90. Jeannerod, M. (1988). The neural and behavioral organisation of goal-directed movements. Oxford: Oxford University Press.
91. Juslin, P. & Persson, M. (2001). PROBabilities from EXemplars (PROBEX): A "Lazy" algorithm for probabilistic inference from generic knowledge. Unpublished manuscript, Department of Psychology, Uppsala University, Sweden.
92. Kacelnik, A. (1998). Normative and descriptive models of decision making: Time discounting and risk sensitivity. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 54-70). Oxford: Oxford University Press.
93. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.
94. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-291.
95. Kolodner, J. L. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufman.
96. Kreps, D. M. (1990). A course in microeconomic theory. New York: Harvester Wheatsheaf.
97. Kripke, S. (1982). Wittgenstein on rules and private languages: An elementary exposition. Oxford: Blackwell.
98. Ledyard, J. O. (1995). Public goods: A survey of experimental research. In J. E. Kagel & A. E. Roth (Eds.), The handbook of experimental economics (pp. 111-194). Princeton: Princeton University Press.
99. Leeuwenberg, E., & Boselie, F. (1988). Against the likelihood principle in visual form perception. Psychological Review, 95, 485-491.
100. Libby, R. (1976). Man versus model of man: Some conflicting evidence. Organizational Behavior and Human Performance, 16, 1-12.
101. Lindley, D. V. (1956). On a measure of the information provided by an experiment. Annals of Mathematical Statistics, 27, 986-1005.
102. Ling, C. X., & Marinov, M. (1993). Answering the connectionist challenge: A symbolic model of learning the past tense of English verbs. Cognition, 49, 235-290.
103. Ling, C. X., & Marinov, M. (1994). A symbolic model of the nonconscious acquisition of information. Cognitive Science, 18, 595-621.
104. Loewenstein, G. (1992). The fall and rise of psychological explanations in the economics of intertemporal choice. In G. Loewenstein & J. Elster (Eds.), Choice over time (pp. 3-34). New York: Russell Sage Foundation.
105. Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. Economic Journal, 92, 805-824.
106. McClelland, J. L. (1998). Connectionist models of Bayesian inference. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 21-53). Oxford: Oxford University Press.
107. MacLeod, P., Plunkett, K., & Rolls, E. T. (1998). Introduction to connectionist modelling of cognitive processes. Oxford: Oxford University Press.
108. McCloskey, D. N. (1985). The rhetoric of economics. Madison: University of Wisconsin Press.
109. MacDonald, M. C., Pearlmutter, N. J., & Seidenberg, M. S. (1994). Lexical nature of syntactic ambiguity resolution. Psychological Review, 101, 676-703.
110. McDermott, D. (1987). A critique of pure reason. Computational Intelligence, 3, 151-160.
111. McFarland, D., & Houston, A. (1981). Quantitative ethology: The state space approach. London: Pitman.
112. Mackay, D. J. C. (1992). A practical Bayesian framework for backpropagation networks. Neural computation, 4, 448-462.
113. Machina, M. J. (1982). "Expected utility" analysis without the independence axiom. Econometrica, 39, 277-323.
114. Marr, D. (1982). Vision. San Francisco: W.H. Freeman.
115. Martignon, L., & Hoffrage, U. (1999). Why does one reason decision making work? A case study in ecological rationality. In G. Gigerenzer, P. Todd, & The ABC Group (Eds.), Simple heuristics that make us smart (pp. 119-140). Oxford: Oxford University Press.
116. Martignon, L., & Laskey, K. (1999). Bayesian benchmarks for fast and frugal heuristics. In G. Gigerenzer, P. Todd, & The ABC Group (Eds.), Simple heuristics that make us smart (pp. 169-188). Oxford: Oxford University Press.
117. Massaro, D. W. (1987). Speech perception by ear and eye. Hillsdale, NJ: Erlbaum.
118. May, K. O. (1954). Intransitivity, utility, and the aggregation of preference patterns. Econometrica, 22, 1-13.
119. Maynard-Smith, J., & Price, G. R. (1973). The logic of animal conflict. Nature, 246, 15-18.
120. Meehl, P. E. (1954). Clinical versus statistical predictions: A theoretical analysis and revision of the literature. Minneapolis: University of Minnesota Press.
121. Messick, D. M. (1991). On the evolution of group-based altruism. In R. Selten (Ed.), Game equilibrium models I: Evolution and game dynamics (pp. 304-328). Berlin: Springer-Verlag.
122. Minsky, M., & Papert, S. (1969). Perceptrons: An introduction to computational geometry. Cambridge, MA: MIT Press.
123. Morton, J. (1969). The interaction of information in word recognition. Psychological Review, 76, 165-178.
124. Movellan, J. R., & Chadderdon, G. (1996). Cognition and the statistics of natural signals. In G. Cottrell (Ed.), Proceedings of the eighteenth annual conference of the cognitive science society (pp. 381-394). Hillsdale, NJ: Erlbaum.
125. Movellan, J. R., & McClelland, J. L. (1995). Stochastic interactive processing, channel separability and optimal perceptual inference: An examination of Morton's Law. Technical Report PDP.CNS.95.4, Carnegie Mellon University, Pittsburgh, PA.
126. Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica, 29, 315-335.
127. Nash, J. (1950). The bargaining problem. Econometrica, 28, 155-162.
128. Neal, R. (1993). Bayesian learning via stochastic dynamics. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in neural information processing systems (Vol. 5, pp. 475-482). San Mateo, CA: Morgan Kaufman.
129. Nelson, R., & Winter, S. (1982). An evolutionary theory of economic capabilities and behavior. Cambridge, MA: Harvard University Press.
130. Newell, A. (1982). The knowledge level. Artificial Intelligence, 18, 87-127.
131. Newell, A. (1991). Unified theories of cognition. Cambridge, UK: Cambridge University Press.
132. Newell, B. R., & Shanks, D. R. (2001). Take the best or look at the rest? Factors influencing 'one-reason' decision-making. Unpublished manuscript, Department of Psychology, University College London.
133. Nosofsky, R. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
134. Nosofsky, R. M. (1990). Relations between exemplar similarity and likelihood models of classification. Journal of Mathematical Psychology, 34, 393-418.
135. Oaksford, M., & Chater, N. (1991). Against logicist cognitive science. Mind and Language, 6, 1-38.
136. Oaksford, M., & Chater, N. (1993). Reasoning theories and bounded rationality. In K. I. Manktelow & D. E. Over (Eds.), Rationality (pp. 31-60). London: Routledge.
137. Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608-631.
138. Oaksford, M., & Chater, N. (1995). Theories of reasoning and the computational explanation of everyday inference. Thinking and Reasoning, 1, 121-152.
139. Oaksford, M., & Chater, N. (1998a). Rationality in an uncertain world: Essays in the cognitive science of human reasoning. Hove, Sussex: Psychology Press.
140. Oaksford, M., & Chater, N. (Eds.). (1998b). Rational models of cognition. Oxford: Oxford University Press.
141. Oaksford, M., Chater, N., & Stenning, K. (1990). Connectionism, classical cognitive science and experimental psychology. AI & Society, 4, 73-90.
142. Paris, J. (1992). The uncertain reasoner's companion. Cambridge: Cambridge University Press.
143. Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 35, 1065-1076.
144. Payne, J. W. (1976). Task complexity and contingent processing in decision making: An information search and protocol analysis. Organizational Behavior and Human Performance, 16, 366-387.
145. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory and Cognition, 14, 534-552.
146. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1990). The adaptive decision maker: Effort and accuracy in choice. In R. M. Hogarth (Ed.), Insights in decision making: A tribute to Hillel J. Einhorn (pp. 129-153). Chicago: Chicago University Press.
147. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. New York: Cambridge University Press.
148. Payne, J. W., Bettman, J. R., & Luce, M. F. (1996). When time is money: Decision behavior under opportunity-cost time pressure. Organizational Behavior and Human Decision Processes, 66, 131-152.
149. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufman.
150. Persson, M., & Juslin, P. (1999). The ecological rationality of PROBEX. Unpublished manuscript, Department of Psychology, Uppsala University, Sweden.
151. Pinker, S. (1998). How the mind works. Harmondsworth, UK: Penguin.
152. Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. E. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103, 56-115.
153. Pomerantz, J. R., & Kubovy, M. (1987). Theoretical approaches to perceptual organization. In K. R. Boff, L. Kaufman, & J. P. Thomas (Eds.), Handbook of perception and human performance, Volume II: Cognitive processes and performance (pp. 36.1-36.46). New York: Wiley.
154. Pylyshyn, Z. W. (1987). The robot's dilemma: The frame problem in artificial intelligence. Norwood, NJ: Ablex.
155. Quine, W. V. O. (1960). Word and object. Cambridge, MA: MIT Press.
156. Quinlan, J. R. (1993). C4.5: Programs for machine learning. Los Altos: Morgan Kaufmann.
157. Reiner, R. (1995). Arguments against the possibility of perfect rationality. Minds and Machines, 5, 373-389.
158. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness in the variation of reinforcement and non reinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditional II: Current research and theory (pp. 64-99). New York: Appleton-Century-Crofts.
159. Rieskamp, J., & Hoffrage, U. (1999). When do people use simple heuristics and how can we tell? In G. Gigerenzer, P. Todd, & The ABC Group (Eds.), Simple heuristics that make us smart (pp. 141-168). Oxford: Oxford University Press.
160. Rips, L. J. (1994). The psychology of proof. Cambridge, MA: MIT Press.
161. Roth, A. (1996). Individual rationality as a useful approximation: Comments on Tversky's "Rational theory of constructive choice". In K. J. Arrow, E. Colombatto, M. Perlman, & C. Schmidt (Eds.), The rational foundations of economic behavior (pp. 198-202). London: Macmillan.
162. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536.
163. Rumelhart, D. E., McClelland, J. L., & The PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructures of cognition (Vols. 1 & 2). Cambridge, MA: MIT Press.
164. Samuelson, P. (1937). A note on the measurement of utility. Review of Economic Studies, 4, 155-161.
165. Savage, L. J. (1954). The foundations of statistics. New York: Wiley.
166. Sawyer, J. (1966). Measurement and prediction, clinical and statistical. Psychological Bulletin, 66, 178-200.
167. Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523-568.
168. Shanks, D. R. (1995a). Is human learning rational? Quarterly Journal of Experimental Psychology, 48A, 257-279.
169. Shanks, D. (1995b). The psychology of associative learning. Cambridge: Cambridge Univerisity Press.
170. Shepard, R. N. (1967). On subjectively optimum selections among multi-attribute alternatives. In W. Edwards & A. Tversky (Eds. , Decision making (pp. 257-283). Harmondsworth, UK: Penguin.
171. Simon, H. A. (1959). Theories of decision-making in economics and behavioral science. American Economic Review, 49, 253-283.
172. Simon, H. A. (1992). Economics, bounded rationality and the cognitive revolution. Aldershot: Elgar.
173. Stanovich, K. E. (1999). Who is rational? Studies of individual differences in reasoning. Hillsdale, NJ: Erlbaum.
174. Stanovich, K. E., & West, R. F. (1998a). Cognitive ability and variation in selection task performance. Thinking and Reasoning, 4, 193-230.
175. Stanovich, K. E., & West, R. F. (1998b). Individual differences in framing and conjunction effects. Thinking and Reasoning, 4, 289-317.
176. Stanovich, K. E., & West, R. F. (1998c). Individual differences in rational thought. Journal of Experimental Psychology: General, 127, 161-188.
177. Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences, 23, 645-726.
178. Stein, E. (1996). Without good reason: The rationality debate in philosophy and cognitive science. Oxford: Oxford University Press.
179. Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton, NJ: Princeton University Press.
180. Stich, S. (1990). The fragmentation of reason. Cambridge, MA: MIT Press.
181. Taraban, R., & McClelland, J. L. (1988). Constituent attachment and thematic role assignment in sentence processing: Influences of content-based expectations. Journal of Memory and Language, 27, 597-632.
182. Todd, I. A., & Kacelnik, A. (1993). Psychological mechanisms and the Marginal Value Theorem: Dynamics of scalar memory for travel time. Animal Behavior, 46, 765-775.
183. Todd, P., & Gigerenzer, G. (1999). What we have learned (so far). In G. Gigerenzer, P. Todd, & The ABC Group (Eds.), Simple heuristics that make us smart (pp. 357-365). Oxford: Oxford University Press.
184. Tversky, A. (1969). Intransitivity of preferences. Psychological Review, 76, 31-48.
185. Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 79, 281-299.
186. Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases. Science, 125, 1124-1131.
187. Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. Journal of Business, 59, 251-278.
188. van Damme, E. (1991). Stability and the perfection of Nash equilibria. Berlin: Springer-Verlag.
189. van der Helm, P. A., & Leeuwenberg, E. L. J. (1996). Goodness of visual regularities: A non-transformational approach. Psychological Review, 103, 429-456.
190. von Helmholtz, H. (1910/1962). In J. P. Southall (Ed. and translation). Treatise on physiological optics (Vol. 3). New York: Dover.
191. von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton: Princeton University Press.
192. Weber, E. U., Goldstein, W. M., & Barlas, S. (1995). And let us not forget memory: The role of memory processes and techniques in the study of judgment and choice. The Psychology of Learning and Motivation, 32, 33-81.