1 |
Ratcliff, R., Gomez, P, & McKoon, G. (2004). A diffusion model account of the lexical decision task. Psychological Review, 111(1), 159-182
DOI
|
2 |
Ratcliff, R. & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Compuation, 20, 873-922.
DOI
|
3 |
Ratcliff, R. & Smith, P. L. (2015). Modeling simple decisions and applications using a diffusion model. In J. R. Busemeyer, Z. Wang, J. T. Townsend, & A. Eidels(Eds.), Oxford Handbook of Computational and Mathematical Psychology. New York, NY: Oxford University Press.
|
4 |
Ratcliff, R. & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to the contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9(3), 438-481.
DOI
|
5 |
Smelser N. J. & P. B. Baltes, eds. (2001). International encyclopedia of the social and behavioral sciences. Oxford: Elsevier Science
|
6 |
Thapar, A., Ratcliff, R., & McKoon, G. (2003). A diffusion model analysis of the effects of aging on letter discrimination. Psychology and Aging, 18, 415-429.
DOI
|
7 |
Tuerlinckx, F., Maris, E., Ratcliff, R., & De Boeck, P. (2001). A comparison of four methods for simulating the diffusion process. Behavior Research Methods, Instruments, and Computers, 33, 443-456.
DOI
|
8 |
Vandekerckhove, J., & Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review, 14, 1011-1026
DOI
|
9 |
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59-108.
DOI
|
10 |
Vandekerckhove, J., & Tuerlinckx, F.(2008). Diffusion model analysis with MATLAB: A DMAT primer. Behavior Research Methods, 40, 61-72.
DOI
|
11 |
Van Orden, G., Holden, J., & Turvey, M. (2003). Self organization of cognitive performance. Journal of Experimental Psychology: General, 132, 331-350.
DOI
|
12 |
Van Zandt, T., Colonius, H., & Proctor, R. W. (2000). A comparison of two response time models applied to perceptual matching. Psychonomic Bulletin & Review, 7(2), 208-256.
DOI
|
13 |
Verdonck S., Meers K., Tuerlinckx F.(2016). Efficient simulation of diffusion-based choice RT models on CPU and GPU. Behavior Research Methods. 48(1), 13-27.
DOI
|
14 |
Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion Models in Experimental Psychology: A Practical Introduction. Experimental Psychology, 60, 385-402.
DOI
|
15 |
Voss, A., & Voss, J. (2007). Fast-Dm: a Free Programm for Efficient Diffusion Model Analysis. Behavior Research Methods, 39(4), 767-775.
DOI
|
16 |
Wagenmakers, E.-J., van der Maas, H. L. J., & Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14, 3-22.
DOI
|
17 |
Berkson, J. (1980) Minimum Chi-Square, Not Maximum Likelihood!". Annals of Statistics. 8 (3): 457-487.
DOI
|
18 |
Brown, S., Ratcliff, R., & Smith, P. L. (2006). Evaluating methods for approximating stochastic differential equations. Journal of Mathematical Psychology, 50, 402-410.
DOI
|
19 |
Feller, W. (1968). An introduction to probability theory and its applications. New York: Wiley.
|
20 |
Heathcote, A., Brown, S., & Mewhort, D. J. K. (2002). Quantile Maximum Likelihood Estimation of Response Time Distributions. Psychonomic Bulletin and Review, 9, 394-401.
DOI
|
21 |
Luersen, M. A., Le Riche, R., & Guyon, F. (2004). A constrained, globalized, and bounded Nelder-Mead method for engineering optimization. Structural and Multidisciplinary Optimization, 27(1), 43-54.
DOI
|
22 |
Mulder, M., Wagenmakers, E., Ratcliff, R., Boekel, W., & Forstmann, B. (2012). Bias in the brain: A diffusion analysis of prior probability and potential payoff. The Journal of Neuroscience, 32(7), 2335-2343.
DOI
|
23 |
Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308-313.
DOI
|