Fitting the Ratcliff diffusion model to experimental data

Psychonomic Bulletin and Review

Volumn 14, Issue 6, 2007, Pages 1011-1026

  Vandekerckhove, Joachim ^a,b   Tuerlinckx, Francis ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b Department of Psychology ^*  (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38149140824     PISSN: 10699384     EISSN: None     Source Type: Journal    
DOI: 10.3758/BF03193087     Document Type: Review

References (45)

1. BARENHOLTZ, E., COHEN, E., FELDMAN, J., & SINGH, M. (2003). Detection of change in shape: An advantage for concavities. Cognition, 89, 1-9.
2. BISHOP.Y., FlENBERG, S., & HOLLAND, P. (1975). Discrete multivariate analysis: Theory and practice, Cambridge, MA: MIT Press.
3. BROWN, S., & HEATHCOTE, A. (2003). QMLE: Fast, robust and efficient estimation of distribution functions based on quantiles. Behavior Research Methods, Instruments, & Computers, 35, 485-492.
4. CHANDRA, M. (2001). Statistical quality control. New York: CRC Press.
5. DE BOECK, P., & WILSON, M. (2004). Explanatory item response models: A generalized linear and nonlinear approach. New York: Springer.
6. DONDERS, F. C. (1969). On the speed of mental processes. Acta Psychologica, 30, 412-431. (Translation of Die Schnelligkeit psychischer Processe, first published in 1868)
7. HEATHCOTE, A., & BROWN, S. (2004). Reply to Speckman and Rouder: A theoretical basis for QML. Psychonomic Bulletin & Review, 11, 577-578.
8. HEATHCOTE, A., BROWN, S., & MEWHORT, D. J. K. (2002). Quantite maximum likelihood estimation of response time distributions. Psychonomic Bulletin & Review, 9, 394-401.
9. HURVICH, C. M., & TSAI, C.-L. ( 1989). Regression and time series model selection in small samples. Biometrika, 76, 297-307.
10. LAMING, D. (1968). Information theory of choice reaction time. New York: Wiley.
11. LEE, M. D., FUSS, I. G., & NAVARRO, D. J. (2007). A Bayesian approach to diffusion models of decision-making and response time. In B. Schölkopf, J. C. Platt, & T. Hoffman (Eds.), Advances in neural information processing systems. Vol. 19. Cambridge, MA: MIT Press.
12. LINK, S., & HEATH, R. (1975). A sequential theory of psychological discrimination. Psychometrika, 40, 77-105.
13. LITTELL, R. C., STROUP, W., & FREUND, R. J. (2002). SAS system for linear models (4th ed.). Cary, NC: SAS Institute.
14. LUCE, D. (1986). Response times. New York: Oxford University Press.
15. NELDER, J. A., & MEAD, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308-313.
16. RATCLIFF, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59-108.
17. RATCLIFF, R. ( 1981 ). A theory of order relations in perceptual matching. Psychological Review, 88, 552-572.
18. RATCLIFF, R. (1988). Continuous versus discrete information processing: Modeling the accumulation of partial information. Psychological Review, 95, 238-255.
19. RATCLIFF, R. (1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114, 510-532.
20. RATCLIFF, R. (2002). A diffusion model account of response time and accuracy in a brightness discrimination task: Fitting real data and failing to fit fake but plausible data. Psychonomic Bulletin & Review, 9, 278-291.
21. RATCLIFF, R., GOMEZ, P., & MCKOON, G. (2004). A diffusion model account of the lexical-decision task. Psychological Review, 111, 159-182.
22. RATCLIFF, R., & ROUDER, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9, 347-356.
23. RATCLIFF, R., & ROUDER, J. N. (2000). A diffusion model account of masking in letter identification. Journal of Experimental Psychology: Human Perception & Performance, 26, 127-140.
24. RATCLIFF, R., THAPAR, A., & MCKOON, G. (2004). A diffusion model analysis of the effects of aging on recognition memory. Journal of Memory & Language, 50, 408-424.
25. RATCLIFF, R., & TUERLINCKX, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9, 438-481.
26. RATCLIFF, R., VAN ZANDT, T., & MCKOON, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261-300.
27. READ, T., & CRESSIE, N. (1988). Goodness-of-fit statistics for discrete multivariate data. New York: Springer.
28. ROBERTS, S. (1959). Control chart tests based on geometric moving averages. Technometrics, 1, 239-250.
29. SCHWARZ, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461 -464.
30. SCHWARZ, W. (2001). The ex-Wald distribution as a descriptive model of response times. Behavior Research Methods, Instruments, & Computers, 33, 457-469.
31. SPECKMAN, P. L., & ROUDER, J. N. (2004). A comment on Heathcote, Brown, and Mewhort's QMLE method for response time distributions. Psychonomic Bulletin & Review, 11, 574-576.
32. STRAM, D. O., & LEE, J. W. (1994). Variance components testing in the longitudinal mixed effects model. Biometrics, 50, 1171-1177.
33. STRAYER, D. L., & KRAMER, A. F. (1994). Strategies and automaticity: I. Basic findings and conceptual framework. Journal of Experimental Psychology: Learning, Memory, A Cognition, 20, 318-341.
34. THAPAR, A., RATCLIFF, R., & MCKOON, G. (2003). A diffusion model analysis of the effects of aging on letter discrimination. Psychology & Aging, 18, 415-429.
35. TUERLINCKX, F. (2004). The efficient computation of the cumulative distribution and probability density functions in the diffusion model. Behavior Research Methods, Instruments, & Computers, 36, 702-716.
36. TUERLINCKX, F., MARIS, E., RATCLIFF, R., & DE BOECK, P. (2001). A comparison of four methods for simulating the diffusion process. Behavior Research Methods, Instruments, & Computers, 33, 443-456.
37. ULRICH, R., & MILLER, J. (1994). Effects of truncation on reaction time analysis. Journal of Experimental Psychology: General, 123, 34-80.
38. VANDEKERCKHOVE, J., PANIS, S., & WAGEMANS, J. (in press). The concavity effect is a compound of local and global effects. Perception & Psychophysics.
39. VANDEKERCKHOVE, J., & TUERLINCKX, F. (2007). The Diffusion Model Analysis Toolbox [Software and manual]. Retrieved from ppwkuleuven .be/okp/dmatoolbox.
40. VANDEKERCKHOVE, J., & TUERLINCKX, F. (in press). Diffusion model analysis with MATLAB: A DMAT primer. Behavior Research Methods.
41. VOSS, A., ROTHERMUND, K., & VOSS, J. (2004). Interpreting the parameters of the diffusion model: An empirical validation. Memory & Cognition, 32, 1206-1220.
42. VOSS, A., & VOSS, J. (2007a). Fast-dm: A free program for efficient diffusion model analysis. Behavior Research Methods, 39, 767-775.
43. VOSS, A., & VOSS, J. (2007b). A fast numerical algorithm for the estimation of diffusion-model parameters. Manuscript submitted for publication.
44. WAGENMAKERS, E.-J., RATCLIFF, R., GOMEZ, P., & McKOON, G. (in press). A diffusion model account of criterion manipulations in the lexical decision task. Journal of Memory & Language.
45. WAGENMAKERS, E.-J., VAN DER MAAS, H. L., & GRASMAN, R. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14, 3-22.