Beyond the null Ritual formal modeling of psychological processes

Journal of Psychology

Volumn 217, Issue 1, 2009, Pages 49-60

  Marewski, Julian N ^a   Olsson, Henrik ^a  

^a MAX PLANCK INSTITUTE FOR HUMAN DEVELOPMENT   (Germany)

Author keywords

Formal models; Model selection; Null hypothesis significance testing

Indexed keywords

EID: 67649515772     PISSN: 00443409     EISSN: None     Source Type: Journal    
DOI: 10.1027/0044-3409.217.1.49     Document Type: Article

References (100)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrox and F. Caski (Eds.), Second International Symposium on Information Theory (pp. 267-281). Budapest, Hungary: Akademiai Kiado.
2. Altmann, G. (2007). Editorial. Journal policies and procedures. Cognition, 102, 1-6.
3. Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, NJ: Erlbaum.
4. Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
5. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., and Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111, 1036-1060.
6. Anderson, J. R., and Bower, G. H. (1973). Human associative memory. Washington, DC: Winston and Sons.
7. Anderson, J. R., and Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2, 396-408.
8. Ayton, P., and Fischer, I. (2004). The hot hand fallacy and the gambler's fallacy: Two faces of subjective randomness? Memory and Cognition, 32, 1369-1378.
9. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.
10. Berger, J. O. (1985). Statistical decision theory and Bayesian analysis (2nd ed.). New York: Springer.
11. Box, G. E. P. (1979). Robustness in the strategy of scientific model-building. In R. L. Launer and G. N. Wilkinson (Eds.), Robustness in statistics (pp. 201-236). New York: Academic Press.
12. Brighton, H. (2006). Robust inference with simple cognitive models. In C. Lebiere & B. Wray (Eds.), Betweena rock and a hard place: Cognitive science principles meet AI-hard problems. Papers from the AAAI Spring Symposium (AAAI Tech. Rep. No. SS-06-03, pp. 17-22). Menlo Park, CA: AAAI Press.
13. Browne, M. W. (2000). Cross-validation methods. Journal of Mathematical Psychology, 44, 108-132.
14. Brunswik, E. (1956). Perception and the representative design of psychological experiments (2nd ed.). Berkeley: University of California Press.
15. Burnham, K., and Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York: Springer.
16. Cohen, J. (1994). The earth is round (p <.05). American Psychologist, 49, 997-1003.
17. Cumming, G., and Fidler, F. (2009). Confidence intervals: Better answers to better questions. Zeitschrift für Psychologie /Journal of Psychology, 217, 15-26.
18. Danzinger, K. (1990). Constructing the subject. Cambridge, UK: Cambridge University Press.
19. Dhami, M. K. (2003). Psychological models of professional decision making. Psychological Science, 14, 175-180.
20. Dücker, B. (2007). Rituale. Formen, Funktionen, Geschichte. Eine Einführung in die Ritualwissenschaft [Rituals. Forms, functions, history. An introduction into the science of rituals]. Stuttgart: Metzler.
21. Dutton, J. M., and Starbuck, W. H. (Eds.). (1971). Computer simulation of human behavior: A history of an intellectual technology. New York: Wiley.
22. Edwards, W., Lindman, H., and Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological Review, 70, 193-242.
23. Efron, B., and Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman and Hall.
24. Fechner, G. T. (1966). Elements of psychophysics. (H. E. Adler, Trans.). New York: Holt, Rinehart and Winston. (Original work published 1860).
25. Forster, M. R. (2000). Key concepts in model selection: Performance and generalizability. Journal of Mathematical Psychology, 44, 205-231.
26. Frick, R. W. (1996). The appropriate use of null hypothesis testing. Psychological Methods, 4, 379-390.
27. Fum, D., Del Missier, F., & Stocco, A. (2007). The cognitive modeling of human behavior: Why a model is (sometimes) better than 10, 000 words. Cognitive Systems Research, 8, 135-142.
28. Gaissmaier, W., Schooler, L. J., and Mata, R. (2008). An ecological perspective to cognitive limits: Modeling environment-mind interactions with ACT-R. Judgment and Decision Making, 3, 278-291.
29. Gaissmaier, W., Schooler, L. J., and Rieskamp, J. (2006). Simple predictions fuelled by capacity limitations: When are they successful?Journal of Experimental Psychology:Learning, Memory, and Cognition, 32, 966-982.
30. Gigerenzer, G. (1991). From tools to theories: A heuristic of discovery in cognitive psychology. Psychological Review, 98, 254-267.
31. Gigerenzer, G. (1996). On narrow norms and vague heuristics: A reply to Kahneman and Tversky. Psychological Review, 103, 592-596.
32. Gigerenzer, G. (1998a). Surrogates for theories. Theory and Psychology, 8, 195-204.
33. Gigerenzer, G. (1998b). We need statistical thinking, not statistical rituals. Behavioral and Brain Sciences, 21, 199-200.
34. Gigerenzer, G., and Brighton, H. (2008). Homo heuristicus: Why biased minds make better inferences. Manuscript submitted for publication.
35. Gigerenzer, G., and Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 104, 650-669.
36. Gigerenzer, G., Hoffrage, U., and Goldstein, D. G. (2008). Fast and frugal heuristics are plausible models of cognition: Reply to Dougherty, Franco-Watkins, and Thomas. Psychological Review, 115, 230-239.
37. Gigerenzer, G., Krauss, S., and Vitouch, O. (2004). The null ritual: What you always wanted to know about significance testing but were afraid to ask. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 391-408). Thousand Oaks, CA: Sage.
38. Gigerenzer, G., and Murray, D. J. (1987). Cognition as intuitive statistics. Hillsdale, NJ: Erlbaum.
39. Gigerenzer, G., and Richter, H. R. (1990). Context effects and their interaction with development: Area judgments. Cognitive Development, 5, 235-264.
40. Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., and Krüger, L. (1989). The empire of chance. How probability changed science and everyday life. Cambridge, UK: Cambridge University Press.
41. Gigerenzer, G., Todd, P. M., and the ABC Research Group. (1999). Simple heuristics that make us smart. New York: Oxford University Press.
42. Grainger, J., and Jacobs, A. M. (1996). Orthographic processing in visual word recognition: A multiple read-out model. Psychological Review, 103, 518-565.
43. Grünwald, P. D. (2007). The minimum description length principle. Cambridge, MA: MIT Press.
44. Hintzman, D. L. (1991). Why are formal models useful in psychology? In W. E. Hockley and S. Lewandowsky (Eds.), Relating theory and data: Essays on human memory in honor of Bennet B. Murdock (pp. 39-56). Hillsdale, NJ: Erlbaum.
45. Hogarth, R. M., and Karelaia, N. (2007). Heuristics and linear models of judgment: Matching rules and environments. Psychological Review, 114, 733-758.
46. Holland, J. H. (1998). Emergence: From chaos to order. Redwood City, CA: Addison-Wesley.
47. Jacobs, A. M., and Grainger, J. (1994). Models of visual word recognition. Sampling the state of the art. Journal of Experimental Psychology: Human Perception and Performance, 20, 1311-1334.
48. Kahneman, D., and Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin, and D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 49-81). New York: Cambridge University Press.
49. Kahneman, D., and Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3, 430-454.
50. Kelley, H. H. (1967). Attribution theory in social psychology. In D. Levine (Ed.), Nebraska Symposium on Motivation (Vol. 15). Lincoln: University of Nebraska Press.
51. Lewandowsky, S. (1993). The rewards and hazards of computer simulations. Psychological Science, 4, 236-243.
52. Loftus, G. R. (1993). Editorial comment. Memory and Cognition, 21, 1-3.
53. Loftus, G. R., and Masson, M. E. (1994). Using confidence intervals in within-subject designs. Psychonomic Bulletin and Review, 1, 476-490.
54. Loftus, G. R., Oberg, M. A., and Dillon, A. M. J. (2004). Linear theory, dimensional theory, and the face-inversion effect. Psychological Review, 111, 835-863.
55. Luce, R. D. (1999). Where is mathematical modeling in psychology headed? Theory and Psychology, 9, 723-737.
56. Marr, D. (1982). Vision. San Francisco: W. H. Freeman.
57. Masson, M. E. J., and Loftus, G. R. (2003). Using confidence intervals for graphically based data interpretation. Canadian Journal of Experimental Psychology, 57, 203-220.
58. Maynard Smith, J. (1982). Evolution and the theory of games. Cambridge, UK: Cambridge University Press.
59. Meehl, P. E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46, 806-834.
60. Melton, A. W. (1962). Editorial. Journal of Experimental Psychology, 64, 553-557.
61. Morrison, D. E., and Henkel, R. E. (Eds.). (1970). The significance test controversy. Chicago: Aldine.
62. Myung, I. J., Balasubramanian, V., and Pitt, M. A. (2000). Counting probability distributions: Differential geometry and model selection. Proceedings of the National Academy of Sciences USA, 97, 11170-11175.
63. Myung, I. J., and Pitt, M. A. (1997). Applying Occam's razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin and Review, 4, 79-95.
64. Navarro, D. J., Pitt, M. A., and Myung, I. J. (2004). Assessing the distinguishability of models and the informativeness of data. Cognitive Psychology, 49, 47-84.
65. Neumann, J. von, and Morgenstern, O. (1944). Theory of games and economic behavior. Princeton: Princeton University Press.
66. Newell, A. (1973a). You can't play 20 questions with nature and win: Projective comments on the papers of this symposium. In W. G. Chase (Ed.), Visual information processing (pp. 283-310). New York: Academic Press.
67. Newell, A. (1973b). Production systems: Models of control structures. In W. G. Chase (Ed.), Visual information processing (pp. 463-526). New York: Academic Press.
68. Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241-301.
69. Nilsson, H., Olsson, H., and Juslin, P. (2005). The cognitive substrate of subjective probability. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 600-620.
70. Payne, J. W., Bettman, J. R., and Johnson, E. J. (1993). The adaptive decision maker. New York: Cambridge University Press.
71. Pitt, M. A., Kim, W., Navarro, D. J., and Myung, J. I. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57-83.
72. Pitt, M. A., and Myung, I. J. (2000). When a good fit can be bad. Trends in Cognitive Sciences, 6, 421-425.
73. Pitt, M. A., Myung, I. J., and Zhang, S. (2002). Toward a method for selecting among computational models for cognition. Psychological Review, 109, 472-491.
74. Polk, T. A., and Seifert, C. M. (Eds.). (2002). Cognitive modeling. Cambridge, MA: MIT Press.
75. Raaijmakers, J. G. W., and Shiffrin, R. M. (1981). Search of associative memory. Psychological Review, 88, 93-134.
76. Ratcliff, R., Van Zandt, T., and McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261-300.
77. Rieskamp, J., and Otto, P. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207-236.
78. Ritter, S., Anderson, J. R., Koedinger, K. R., and Corbett, A. (2007). Cognitive tutor: Applied research in mathematics education. Psychonomic Bulletin and Review, 14, 249-255.
79. Roberts, S., and Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.
80. Rosnow, R. L., and Rosenthal, R. (2009). Effect sizes: Why, when, and how to use them. Zeitschrift für Psychologie /Journal of Psychology, 217, 6-14.
81. Rumelhart, D. E., McClelland, J. L., and the PDP Research Group. (Eds.). (1986). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. I). Cambridge, MA: MIT Press.
82. Salvucci, D. D. (2006). Modeling driver behavior in a cognitive architecture. Human Factors, 48, 362-380.
83. Schelling, T. (1969). Models of segregation. American Economic Review, 59, 499-493.
84. Schmidt, F. L. (1992). What do data really mean? Research findings, meta-analysis, and cumulative knowledge in psychology. American Psychologist, 47, 1172-1181.
85. Schmidt, F. L. (1996). Statistical significance testing and cumulative knowledge in psychology: Implications for training of researchers. Psychological Methods, 1, 115-129.
86. Schooler, L. J., and Hertwig, R. (2005). How forgetting aids heuristic inference. Psychological Review, 112, 610-628.
87. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
88. Sedlmeier, P., and Gigerenzer, G. (1989). Do studies of statistical power have an effect on the power of studies? Psychological Bulletin, 105, 309-316.
89. Sedlmeier, P., and Renkewitz, F. (2007). Forschungsmethoden und Statistik in der Psychologie [Research methods and statistics in psychology]. Munich: Pearson Education.
90. Shiffrin, R. M., Lee, M. D., Kim, W., and Wagenmakers, E.-J. (2008). A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32, 1248-1284.
91. Stevens, S. S. (1957). On the psychophysical law. Psychological Review, 64, 153-181.
92. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions (with discussion). Journal of the Royal Statistical Society, Series B, 36, 111-147.
93. Stone, M. (1977). Asymptotics for and against cross-validation. Biometrika, 64, 29-35.
94. Thorngate, W. (2000). Teaching social simulation with Matlab. Journal of Artificial Societies and Social Simulation, 3. Retrieved August 3, 2008, from http://www.soc.surrey.ac.uk /JASSS/3/1forum/1.html
95. Townsend, J. T. (1975). The mind-body problem revisited. In C. Cheng (Ed.), Philosophical aspects of the mind-body problem (pp. 200-218). Honolulu, HI: Honolulu University Press.
96. Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.
97. Wagenmakers, E.-J. (2003). How many parameters does it take to fit an elephant? Journal of Mathematical Psychology, 47, 580-586.
98. Wagenmakers, E.-J. (2007). A practical solution to the pervasive problems of p values. Psychonomic Bulletin and Review, 14, 779-804.
99. Wilkinson, L., and the Task Force on Statistical Inference. (1999). Statistical methods in psychology journals. Guidelines and explanations. American Psychologist, 54, 594-604.
100. Woodworth, R. (1938). Experimental psychology. New York: Holt.