SSL: A theory of how people learn to select strategies

Journal of Experimental Psychology: General

Volumn 135, Issue 2, 2006, Pages 207-236

  Rieskamp, Jörg ^a   Otto, Philipp E ^b  

^a MAX PLANCK INSTITUTE FOR HUMAN DEVELOPMENT   (Germany)

^b UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Exemplar model; Learning theory; Probabilistic inferences; Reinforcement learning; Strategy selection

Indexed keywords

ADAPTIVE BEHAVIOR; ADULT; ARTICLE; ASSOCIATION; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DECISION MAKING; FEMALE; GERMANY; HUMAN; LEARNING; MALE; PROBABILITY; PROBLEM SOLVING; PSYCHOLOGICAL THEORY; RANDOMIZED CONTROLLED TRIAL; REINFORCEMENT;

ADULT; CUES; DECISION MAKING; FEEDBACK, PSYCHOLOGICAL; FEMALE; GERMANY; HUMANS; LEARNING; MALE; PROBABILITY; PROBLEM SOLVING; PSYCHOLOGICAL THEORY; REINFORCEMENT (PSYCHOLOGY);

EID: 33745060418     PISSN: 00963445     EISSN: None     Source Type: Journal    
DOI: 10.1037/0096-3445.135.2.207     Document Type: Article

References (124)

1. Akaike, H. (1973). Information theory as an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.). Proceedings of the Second. International Symposium on Information Theory (pp. 267-281). Budapest, Hungary: Akademiai Kiado.
2. Andersen, J. R. (1991). Is human cognition adaptive? Behavioral & Brain Sciences, 14, 471-517.
3. Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlhaum.
4. Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Erlbaum.
5. Ashby, F. G., & Maddox, W. T. (1992). Complex decision rules in categorization: Contrasting novice and experienced performance. Journal of Experimental Psychology: Human Perception and Performance, 18, 50-71.
6. Ball, C. T., Langholtz, H. J., Auble, J., & Sopchak, B. (1998). Resource-allocation strategies: A verbal protocol analysis. Organizational Behavior & Human Decision Processes, 76, 70-88.
7. Balzer, W. K., Doherty, M. E., & O'Connor, R. (1989). Effects of cognitive feedback on performance. Psychological Bulletin, 106, 410-433.
8. Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3, 439-449.
9. Börgers, T., & Sarin, R. (1997). Learning through reinforcement and replicator dynamics. Journal of Economic Theory, 77, 1-14.
10. Bozdogan, H. (2000). Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology, 44, 62-91.
11. Brehmer, B. (1980). In one word: Not from experience. Acta Psychologica, 45, 223-241.
12. 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.
13. Bröder, A. (2003). Decision making with the "adaptive toolbox": Influence of environmental structure, intelligence, and working memory load. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 611-625.
14. Bröder, A., & Schiffer, S. (2003). "Take The Best" versus simultaneous feature matching: Probabilistic inferences from memory and effects of representation format. Journal of Experimental Psychology: General, 132, 277-293.
15. Brown, G. W. (1951). Iterative solutions of games by fictitious play. In T. C. Koopmans (Ed.), Activity analysis of production and allocation (pp. 374-376). New York: Wiley.
16. Brown, N. R. (1995). Estimation strategies and the judgment of event frequency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 1539-1553.
17. Brown, N. R., Cui, X., & Gordon, R. D. (2002). Estimating national populations: Cross-cultural differences and availability effects. Applied Cognitive Psychology, 16, 811-827.
18. Browne, M. W. (2000). Cross-validation methods. Journal of Mathematical Psychology, 44, 108-132.
19. Burnham, K. P., & Anderson, D. R. (1998). Model selection and multi-model inference. New York: Springer.
20. Busemeyer, J. R. (1993). Violations of the speed-accuracy tradeoff relation: Decreases in decision accuracy with increases in decision time. In O. Svenson & A. J. Maule (Eds.), Time pressure and stress in human judgment and decision making (pp. 181-193). New York: Plenum Press.
21. Busemeyer, J. R., & Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121, 177-194.
22. Bush, R. R., & Mosteller, F. (1955). Stochastic models for learning. New York: Wiley.
23. Camerer, C., & Ho, T.-H. (1999a). Experience-weighted attraction learning in games: Estimates from weak-link games. In David V. Budescu & I. Erev (Eds.), Games and human behavior: Essays in honor of Amnon Rapoport (pp. 31-51). Mahwah, NJ: Erlbaum.
24. Camerer, C., & Ho, T.-H. (1999b). Experience-weighted attraction learning in normal form games. Econometrica, 67, 827-874.
25. Chater, N. (2000). How smart can simple heuristics be? Behavioral & Brain Sciences, 23, 745-746.
26. Cheung, Y.-W., & Friedman, D. (1997). Individual learning in normal form games: Some laboratory results. Games & Economic Behavior, 19, 46-76.
27. Christensen-Szalanski, J. J. (1978). Problem solving strategies: A selection mechanism, some implications, and some data. Organizational Behavior & Human Performance, 22, 307-323.
28. Chu, P. C., & Spires, E. E. (2003). Perceptions of accuracy and effort of decision strategies. Organizational Behavior & Human Decision Processes, 91, 203-214.
29. Connolly, T., & Gilani, N. (1982). Information search in judgment tasks: A regression model and some preliminary findings. Organizational Behavior & Human Decision Processes, 30, 330-350.
30. Connolly, T., & Wholey, D. R. (1988). Information mispurchase in judgment tasks: A task-driven causal mechanism. Organizational Behavior & Human Decision Processes, 42, 75-87.
31. Costa-Gomes, M., Crawford, V. P., & Broseta, B. (2001). Cognition and behavior in normal-form games: An experimental study. Econometrica, 69, 1193-1235.
32. Coyle, T. R., Read, L. E., Gaultney, J. F., & Bjorklund, D. F. (1998). Giftedness and variability in strategic processing on a multitrial memory task: Evidence for stability in gifted cognition. Learning & Individual Differences, 10, 273-290.
33. Czienskowski, U. (2004). The Oil Drilling Experiment [Computer software]. Berlin, Germany: Max Planck Institute for Human Development.
34. Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81, 95-106.
35. Dougherty, M. R. P., Gettys, C. F., & Ogden, E. E. (1999). MINERVADM: A memory processes model for judgments of likelihood. Psychological Review, 106, 180-209.
36. Einhorn, H. J. (1970). The use of nonlinear, noncompensatory models in decision making. Psychological Bulletin, 73, 221-230.
37. Einhorn, H. J., & Hogarth, R. M. (1981). Behavioral decision theory: Processes of judgment and choice. Annual Review of Psychology, 32, 53-88.
38. Einhorn, H. J., Kleinmuntz, D. N., & Kleinmuntz, B. (1979). Linear regression and process-tracing models of judgment. Psychological Review, 86, 465-485.
39. Eisenberg, P., & Becker, C. A. (1982). Semantic context effects in visual word recognition, sentence processing, and reading: Evidence for semantic strategies. Journal of Experimental Psychology: Human Perception and Performance, 8, 739-756.
40. Erev, I. (1998). Signal detection by human observers: A cutoff reinforcement learning model of categorization decisions under uncertainty, Psychological Review, 105, 280-298.
41. Erev, I., & Roth, A. E. (1998). Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review, 88, 848-881.
42. Erev, I., & Roth, A. E. (2001). Simple reinforcement learning models and reciprocation in the prisoner's dilemma game. In G. Gigerenzer & R. Selten (Eds.), Bounded rationality: The adaptive toolbox (pp. 215-231). Cambridge, MA: MIT Press.
43. Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107-140.
44. Estes, W. K. (1950). Toward a statistical theory of learning. Psychological Review, 57, 94-107.
45. Estes, W. K. (1976). The cognitive side of probability learning. Psychological Review, 83, 37-64.
46. Fishburn, P. C. (1980). Lexicographic additive differences. Journal of Mathematical Psychology, 21, 191-218.
47. Fiske, A. P. (1992). The four elementary forms of sociality: Framework for a unified theory of social relations. Psychological Review, 99, 689-723.
48. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650-669.
49. Gigerenzer, G., Todd, P. M., & the ABC Research Group. (1999). Simple heuristics that make us smart. New York: Oxford University Press.
50. Ginossar, Z., & Trope, Y. (1987). Problem solving in judgment under uncertainty. Journal of Personality and Social Psychology, 52, 464-474.
51. Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, 227-247.
52. Gonzalez, C. Lerch, J. F., & Lebiere, C. (2003). Instance-based learning in dynamic decision making. Cognitive Science, 27, 591-635.
53. Gonzalez-Vallejo, C. (2002). Making trade-offs: A new probabilistic and context sensitive model of choice behavior. Psychological Review, 109, 137-155.
54. Hammond, K. R. (1955). Probabilistic functioning and the clinical method. Psychological Review, 62, 255-262.
55. Hammond, K. R., & Summers, D. A. (1972). Cognitive control. Psychological Review, 79, 58-67.
56. Hintzman, D. L. (1988). Judgments of frequency and recognition memory in a multiple-trace memory model. Psychological Review, 95, 528-551.
57. Huber, O. (1980). The influence of some task variables on cognitive operations in an information-processing decision model. Acta Psychologica, 45, 187-196.
58. Johnson, E. J., & Payne, J. W. (1985). Effort and accuracy in choice. Management Science, 31, 394-414.
59. Juslin, P., Jones, S., Olsson, H., & Winman, A. (2003). Cue abstraction and exemplar memory in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 924-941.
60. Juslin, P., Olsson, H., & Olsson, A.-C. (2003). Exemplar effects in categorization and multiple-cue judgment. Journal of Experimental Psychology: General, 132, 133-156.
61. Juslin, P., & Persson, M. (2002). PROBabilities from EXemplars (PROBEX): A "lazy" algorithm for probabilistic inference from generic knowledge. Cognitive Science, 26, 563-607.
62. Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.
63. Kubovy, M., & Healy, A. F. (1980). Process models of probabilistic categorization. In T. S. Wallsten (Ed.), Cognitive processes in choice and decision behavior (pp. 239-262). Hillsdale, NJ: Erlbaum.
64. Lamberts, K. (2000). Information-accumulation theory of speeded categorization. Psychological Review, 107, 227-260.
65. Lemaire, P., & Siegler, R. S. (1995). Four aspects of strategic change: Contributions to children's learning of multiplication. Journal of Experimental Psychology: General, 124, 83-97.
66. Lerner, J. S., & Tetlock, P. E. (1999). Accounting for the effects of accountability. Psychological Bulletin, 125, 255-275.
67. Lipshitz, R. (2000). Two cheers for bounded rationality. Behavioral & Brain Sciences, 23, 756-757.
68. Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492-527.
69. Logan, G. D. (2002). An instance theory of attention and memory. Psychological Review, 109, 376-400.
70. Lovett, M. (1998). Choice. In J. R. Anderson & C. Lebiere (Eds.), The atomic components of thought (pp. 255-296). Mahwah. NJ: Erlbaum.
71. Luce, R. D. (1959). Individual choice behavior. New York: Wiley.
72. Martignon, L., & Laskey, K. (1999). Bayesian benchmarks for fast and frugal heuristics. In G. Gigerenzer, P. M. Todd, & the ABC Research Group, Simple heuristics that make us smart (pp. 169-188). New York: Oxford University Press.
73. Massaro, D. W., & Friedman, D. (1990). Models of integration given multiple sources of information. Psychological Review, 97, 225-252.
74. Medin, D. L., Altom, M. W., & Murphy, T. D. (1984). Given versus induced category representations: Use of prototype and exemplar information in classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 333-352.
75. Medin, D. L.. & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238.
76. Mellers, B. A., & Biagini, K. (1994). Similarity and choice. Psychological Review, 101, 505-518.
77. Myung, I. J., & Pitt, M. A. (1997). Applying Occam's razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin & Review, 4, 79-95.
78. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308-313.
79. Nellen, S. (2003). The use of the "Take The Best" heuristic under different conditions, modeled with ACT-R. Paper presented at the Fifth International Conference on Cognitive Modeling. Bamberg, Germany.
80. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall.
81. Newell, B. R., & Shanks, D. R. (2003). Take the best or look at the rest? Factors influencing "one-reason" decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 53-65.
82. Newell, B. R., Weston, N. J., & Shanks, D. R. (2003). Empirical tests of a fast-and-frugal heuristic: Not everyone "takes-the-best." Organizational Behavior & Human Decision Processes, 91, 82-96.
83. Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
84. Nosofsky, R. M. (1988). Similarity, frequency, and category representations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 54-65.
85. Nosofsky, R. M. (1992). Exemplars, prototypes, and similarity rules. In A. F. Healy, S. M. Kosslyn, & R. Shiffrin (Eds.), Essays in honor of William K. Estes (pp. 149-167). Hillsdale, NJ: Erlbaum.
86. Nosofsky, R. M., Clark, S. E., & Shin, H. J. (1989). Rules and exemplars in categorization, identification, and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 282-304.
87. Nosofsky, R. M., & Johansen, M. K. (2000). Exemplar-based accounts of "multiple-system" phenomena in perceptual categorization. Psychonomic Bulletin & Review, 7, 375-402.
88. Nosofsky, R. M., Kruschke, J. K., & McKinley, S. C. (1992). Combining exemplar-based category representations and connectionist learning rules. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 211-233.
89. Patalano, A. L., Smith, E. E., Jonides, J., & Koeppe, R. A. (2001). PET evidence for multiple strategies of categorization. Cognitive, Affective & Behavioral Neuroscience, 1, 360-370.
90. Payne, J. W. (1976). Task complexity and contingent processing in decision making: An information search and protocol analysis. Organizational Behavior & Human Performance, 16, 366-387.
91. 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.
92. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. New York: Cambridge University Press.
93. Pitt, M. A., & Myung, I. J. (2002). When a good fit can be bad. Trends in Cognitive Sciences, 6, 421-425.
94. Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472-491.
95. Pitz, G. F., & Sachs, N. J. (1984). Judgment and decision: Theory and applications. Annual Review of Psychology, 35, 139-163.
96. Rakow, T., Newell, B. R., Fayers, K., & Hersby, M. (2005). Evaluating three criteria for establishing cue-search hierarchies in inferential judgment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 1088-1104.
97. Rapoport, A., & Wallsten, T. S. (1972). Individual decision behavior. Annual Review of Psychology, 23, 131-176.
98. Restle, F. (1962). The selection of strategies in cue learning. Psychological Review, 69, 329-343.
99. Rieskamp, J., Busemeyer, J. R.. & Laine, T. H. (2003). How do people learn to allocate resources? Comparing two learning theories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1066-1081.
100. Rieskamp, J., Busemeyer, J. R., & Mellers, B. A. (in press). Extending the bounds of rationality: A review of research on preferential choice. Journal of Economic Literature.
101. Rieskamp, J., & Hoffrage, U. (1999). When do people use simple heuristics, and how can we tell? In G. Gigerenzer, P. M. Todd, & the ABC Research Group (Eds.), Simple heuristics that make us smart (pp. 141-167). New York: Oxford University Press.
102. Rieskamp, J., & Hoffrage, U. (2005). The use of simple heuristics: Inferences and preferences under time pressure. Manuscript submitted for publication.
103. Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.
104. Rommelfanger, H., & Unterharnscheid, D. (1985). Entwicklung einer Hierarchie gewichteter Bonitätskriterien [Development of a hierarchy of weighted credit-worthiness criteria]. Österreichisches Bankarchiv, 12, 419-427.
105. Shanks, D. R. (1991). Categorization by a connectionist network. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 433-443.
106. Sieck, W. R., & Yates, J. F. (2001). Overconfidence effects in category learning: A comparison of connectionist and exemplar memory models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1003-1021.
107. Siegler, R. S. (1999). Strategic development. Trends in Cognitive Sciences, 3, 430-435.
108. Siegler, R. S., & Shipley, C. (1995). Variation, selection, and cognitive change. In T. J. Simon & G. S. Halford (Eds.). Developing cognitive competence: New approaches to process modeling (pp. 31-76). Hillsdale, NJ: Krlbaum.
109. Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63, 129-138.
110. Smedslund, J. (1955). Multiple probability learning. Oslo, Norway: Oslo University Press.
111. Smith, V. L., & Walker, J. M. (1993). Monetary rewards and decision cost in experimental economics. Economic Inquiry, 31, 245-261.
112. Stahl, D. O. (1996). Boundedly rational rule learning in a guessing game. Games & Economic Behavior, 16, 303-330.
113. Stahl, D. O. (2000). Rule learning in symmetric normal-form games: Theory and evidence. Games & Economic Behavior, 32, 105-138.
114. Sutton, R. S.. & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
115. Svenson, O. (1979). Process descriptions of decision making. Organizational Behavior & Human Performance, 23, 86-112.
116. Svenson, O. (1992). Differentiation and consolidation theory of human decision making: A frame of reference for the study of pre and post-decision processes. Acta Psychologies 80, 143-168.
117. Taatgen, N. A., & Wallach, D. (2002). Whether skill acquisition is rule or instance based is determined by the structure of the task. Cognitive Science Quarterly, 2, 163-204.
118. Thurstone, L. L. (1930). The learning function. Journal of General Psychology, 3, 469-493.
119. Tversky, A. (1969). Intransitivity of preferences. Psychological Review, 76, 31-48.
120. Wainer, H. (1976). Estimating coefficients in linear models: It don't make no nevermind. Psychological Bulletin, 83, 213-217.
121. Wallsten, T. S. (1968). Failure of predictions from subjectively expected utility theory in a Bayesian decision task. Organizational Behavior & Human Decision Processes, 3, 239-252.
122. Wickens, T. D. (1989). Multiway contingency tables analysis for the social sciences. Hillsdale, NJ: Erlbaum.
123. Yates, J. F. (1990). Judgment and decision making. Old Tappan, NJ: Prentice Hall.
124. Young, M. E., & Wasserman, E. A. (2005). Theories of learning. In K. Lamberts & R. L. Goldstone (Eds.), The handbook of cognition. (pp. 161-182). London: Sage.