A comparison of four methods for simulating the diffusion process

Behavior Research Methods, Instruments, and Computers

Volumn 33, Issue 4, 2001, Pages 443-456

  Tuerlinckx, Francis ^a,d   Maris, Eric ^b   Ratcliff, Roger ^c   De Boeck, Paul ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^c NORTHWESTERN UNIVERSITY   (United States)

^d Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; COMPARATIVE STUDY; COMPUTER SIMULATION; EVALUATION; HUMAN; MONTE CARLO METHOD; PROBABILITY; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ANALYSIS OF VARIANCE; COMPUTER SIMULATION; HUMANS; MODELS, STATISTICAL; MONTE CARLO METHOD; PROBABILITY; STOCHASTIC PROCESSES;

EID: 0035523028     PISSN: 07433808     EISSN: None     Source Type: Journal    
DOI: 10.3758/BF03195402     Document Type: Review

References (27)

1. BHATTACHARYA, R. N., & WAYMIRE, E. C. (1990). Stochastic processes with applications. New York: Wiley.
2. BOULEAU, N., & LÉPINOLE, D. (1994). Numerical methods for stochastic processes. New York: Wiley.
3. BURDEN, R. L., & FAIRES, J. D. (1997). Numerical analysis (6th ed.). Pacific Grove, CA: Brooks/Cole.
4. BUSEMEYER, J. R., & TOWNSEND, J. T. (1992). Fundamental derivations from decision field theory. Mathematical Social Sciences, 23, 255-282.
5. COX, D. R., & MILLER, H. D. (1970). The theory of stochastic processes. London: Methuen.
6. DIEDERICH, A. (1997). Dynamic stochastic models for decision making under time constraints. Journal of Mathematical Psychology, 41, 260-274.
7. FAHRMEIR, L. (1976). On the simulation of stochastic processes with continuous state and parameter space. In L. Dekker (Ed.), Simulation of systems (pp. 67-71 ). Amsterdam: North-Holland.
8. FELLER, W. (1968). An introduction to probability theory and its applications (Vol. 1, 3rd ed.). New York: Wiley.
9. HANES, D. P., & SCHALL, J. D. (1996). Neural control of voluntary movement initiation. Science, 274, 427-430.
10. HEATH, R. A., & KELLY, L. (1988). An application of a discriminability index for the assessment of individual differences. In R. A. Heath (Ed.), Current issues in cognitive development and mathematical psychology (pp. 180-196). Newcastle, NSW: University of Newcastle.
11. KARLIN, S., & TAYLOR, H. M. (1981). A second course in stochastic processes. New York: Academic Press.
12. LICHTERS, R., FRICKE, T., & SCHNAKENBERG, J. (1995). Stochastic simulation of diffusion with absorbing and reflecting boundary conditions [On-line]. Available: http://www.physik.rwth-aachen.de/group/thphys/ tpd/Werke/absrefl.html
13. LUCE, R. D. (1986). Response times. New York: Oxford University Press.
14. MOOD, A. M., GRAYBILL, F. A., & BOES, D. C. (1974). Introduction to the theory of statistics. New York: McGraw-Hill.
15. PRESS, W. H., FLANNERY, B. P., TEUKOLSKY, S. A., & VETTERLING, W. T. (1986). Numerical recipes: The art of scientific computing. New York: Cambridge University Press.
16. RATCLIFF, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59-108.
17. RATCLIFF, R. (1980). A note on modeling accumulation of information when the rate of accumulation changes over time. Journal of Mathematical Psychology, 21, 178-184.
18. RATCLIFF, R., & ROUDER, J. (1998). Modeling response times for two-choice decisions. Psychological Science, 9, 347-356.
19. RATCLIFF, R., & ROUDER, J. (2000). A diffusion model account of masking in two-choice letter identification. Journal of Experimental Psychology: Human Perception & Performance, 26, 127-140.
20. RATCLIFF, R., & TUERLINCKX, F. (2001). Estimating the parameters of the diffusion model. Manuscript submitted for publication.
21. RATCLIFF, R., VAN ZANDT, T., & MCKOON, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261-300.
22. ROSS, S. M. (1996). Stochastic processes (2nd ed.). New York: Wiley.
23. SCHWARZ, W. A. (1993). A diffusion model of early visual search: Theoretical analysis and experimental results. Psychological Research, 55, 200-207.
24. SMITH, P. L. (1990). A note on the distribution of response times for a random walk with Gaussian increments. Journal of Mathematical Psychology, 34, 445-459.
25. SMITH, P. L. (2000). Stochastic dynamic models of response time and accuracy: A foundational primer. Journal of Mathematical Psychology, 44, 408-463.
26. TANNER, M. A. (1996). Tools for statistical inference: Methods for the exploration of posterior distributions and likelihood functions (2nd ed.). New York: Springer-Verlag.
27. WALD, A. (1947). Sequential analysis. New York: Wiley.