Annotated diffusion model papers list

(PDF version available here)

Original RT distribution paper – argues that distributions are necessary for evaluation reaction time models.
Ratcliff, R. & Murdock, B. B., Jr. (1976). Retrieval processes in recognition memory. Psychological Review, 83, 190-214.

Original diffusion model paper. Lots of stuff in this that is missed. Drift (relatedness) criterion p 65 & p106), within vs. between blocks designs and how RT results can switch around (Expt 1), individual differences p. 98), scaling of RT and accuracy versus drift (p66), information entering the decision process (p62), speed-accuracy RT partitioning (p66), model freedom (p69).
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59-108.

Diffusion model when drift rate changes during the time course of processing (modest results mainly from the literature).
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.

Diffusion model and letter string matching – a model of representation plus the decision process (variability in starting point is mentioned as a was of producing fast errors). Response signal functions also fit.
Ratcliff, R. (1981). A theory of order relations in perceptual matching. Psychological Review, 88, 552-572.

Evidence for drift rate changing during processing (diffusion model fits).
Ratcliff, R. & McKoon, G. (1982). Speed and accuracy in the processing of false statements about semantic information. Journal ofExperimental Psychology: Learning, Memory and Cognition, 8, 16-36.

Drift bias and bias in starting points first discussed in detail (noted earlier – but implicitly).
Ratcliff, R. (1985). Theoretical interpretations of speed and accuracy of positive and negative responses. Psychological Review, 92, 212-225.

More on the above.
Ratcliff, R. (1987). More on the speed and accuracy of positive and negative responses. Psychological Review, 94, 277-280.

Another nonmonotonic time course study.
Ratcliff, R. & McKoon, G. (1989). Similarity information versus relational information: Differences in the time course of retrieval. CognitivePsychology, 21, 139-155.

Surprise! the diffusion model passes a test for serial inserted processes!
Ratcliff, R. (1988). A note on the mimicking of additive reaction time models. Journal of Mathematical Psychology, 32, 192-204.

Fitting Meyer et al’s data. Introduces implicit boundaries for the response signal paradigm.
Ratcliff, R. (1988). Continuous versus discrete information processing: Modeling the accumulation of partial information. PsychologicalReview, 95, 238-255.

Non monotonic time course in recognition memory.
Gronlund, S.D. & Ratcliff, R. (1989). Time course of item and associative information: Implications for global memory models. Journal ofExperimental Psychology: Learning, Memory, and Cognition, 15, 846-858.

Monte Carlo studies of how to deal with outlier reaction times.
Ratcliff R. (1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114, 510-532.

You can deconvolve RT distributions, with plausible but wrong results.
Sheu, C-F, & Ratcliff, R. (1995). The application of Fourier deconvolution to reaction time data: A cautionary note. Psychological Bulletin,116, 285-299.

Examines the basis for variability in parameters across trials.
Van Zandt, T., & Ratcliff, R. (1995). Statistical mimicking of reaction time data: Single-process models, parameter variability, and mixtures.Psychonomic Bulletin and Review, 2, 20-54.

A model for implicit memory tasks that is actually a multi-alternative random walk model.
Ratcliff, R., & McKoon, G. (1997). A counter model for implicit priming in perceptual word identification. Psychological Review, 104, 319-343.

The second rebirth of the models (where we got errors and correct responses and parameter invariances) – this paper was submitted years after the 1999 Psych Rev paper, but beat it out.
Ratcliff, R., & Rouder, J.N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9, 347-356.

The first paper that got error RTs versus correct RTs right. Discusses drift bias and criterion bias, also discusses fast criterion setting which is too fast for any adaptive method based on data.
Ratcliff, R., Van Zandt, T., & McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261-300.

Masking and letter discrimination.
Ratcliff, R., & Rouder, J.N. (2000). A diffusion model account of masking in two-choice letter identification. Journal of Experimental Psychology: Human Perception and Performance, 26, 127-140.

Diffusion model and aging – setting the stage for empirical work
Ratcliff, R., Spieler, D. & McKoon, G. (2000). Explicitly modeling the effects of aging on response time. Psychonomic Bulletin and Review, 7, 1-25.

Modest but had a lot of work in it – has random walk model at the end.
Rouder, J.N., Ratcliff, R., & McKoon, G. (2000). A neural network model of implicit memory for object recognition. Psychological Science, 11, 13-19.

Critique of Carpenter’s deterministic reaction time model.
Ratcliff, R. (2001). Putting noise into neurophysiological models of simple decision making (letter). Nature Neuroscience, 4, 336.

Aging and RT: Signal detection (numerosity discrimination). Looks at individual differences as do most of the other aging and RT papers.
Ratcliff, R., Thapar, A., & McKoon, G. (2001). The effects of aging on reaction time in a signal detection task. Psychology and Aging, 16, 323-341.

Numerical methods for simulating the diffusion process.
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.

How to fit the model to data.
Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin and Review, 9, 438-481.

Shows that the diffusion model cannot predict everything!! Very important point.
Ratcliff, R. (2002). A diffusion model account of reaction time and accuracy in a brightness discrimination task: Fitting real data and failing to fit fake but plausible data. Psychonomic Bulletin and Review, 9, 278-291.

Diffusion model and single cell recording.
Ratcliff, R., Cherian, A., & Segraves, M. (2003). A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of simple two-choice decisions. Journal of Neurophysiology, 90, 1392-1407.

Aging and RT: letter discrimination.
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.

Aging and RT: brightness discrimination.
Ratcliff, R., Thapar, A. & McKoon, G. (2003). A diffusion model analysis of the effects of aging on brightness discrimination. Perception and Psychophysics, 65, 523-535.

Diffusion model for lexical decision.
Ratcliff, R., Gomez, P., & McKoon, G. (2004). A diffusion model account of the lexical decision task. Psychological Review, 111, 159-182.

Shows how “1/f” noise could be generated in sequential sampling models.
Wagenmakers, E-J., Farrell, S., & Ratcliff, R. (2004). Estimation and interpretation of 1/f noise in human cognition. Psychonomic Bulletin and Review, 11, 579-615.

Major comparison among sequential sampling models.
Ratcliff, R. & Smith, P.L. (2004).  A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111, 333-367.

Reply to Myerson’s critique of aging and RT.
Ratcliff, R., Spieler, D. & McKoon, G. (2004). Analysis of group differences in processing speed: Where are the models of processing? Psychonomic Bulletin and Review, 11, 755-769.

Diffusion model fit to lexical decision data from Aphasics.
Ratcliff, R., Perea, M., Colangelo, A. & Buchanan, L. (2004). A diffusion model account of normal and impaired readers. Brain and Cognition, 55, 374-382.

Estimating variances and covariances in model parameters and discriminating between models: examples include RT models.
Wagenmakers, E-J., Ratcliff, R., Gomez, P. & Iverson, G.J. (2004). Assessing model mimicry using the parametric bootstrap. Journal of Mathematical Psychology, 48, 28-50.

Aging and RT: Recognition memory.
Ratcliff, R., Thapar, A., & McKoon, G. (2004). A diffusion model analysis of the effects of aging on recognition memory. Journal of Memory and Language, 50, 408-424.

Aging and RT: Lexical decision. Talks about RT scaling – priming effects are larger for old than young, but this is purely due to scaling effects. Also looks at how large discrepancies need to be for significant chi-square values.
Ratcliff, R., Thapar, A., Gomez, P. & McKoon, G. (2004). A diffusion model analysis of the effects of aging in the lexical-decision task. Psychology and Aging, 19, 278-289.

Philip put a front end onto a masking and attention experiment with a diffusion model back end.
Smith, P.L., Ratcliff, R. & Wolfgang, B.J. (2004). Attention orienting and the time course of perceptual decisions: Response time distributions with masked and unmasked displays. Vision Research, 44, 1297-1320.

If you publish a paper, you get to write a review in this domain.
Smith, P.L., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in Neuroscience, 27, 161-168.

Applying the models to data previously fit only by the diffusion model.
Ratcliff, R., Thapar, A., Smith, P. L., & McKoon, G. (2005). Aging and response times: A comparison of sequential sampling models. In, Measuring the Mind: Speed, Control, and Age, (pp. 3-32), J. Duncan, P. McLeod, & L. Phillips (Eds.), Oxford University Press: Oxford, England.

Vary speed accuracy and word/nonword probabilities in 2 expts.
Wagenmakers, E-J., Ratcliff, R., Gomez, P., & McKoon, G. (2008). A diffusion model account of criterion shifts in the lexical decision task. Journal of Memory and Language, 58, 140-159.

Go/no-go task require an implicit boundary – the first model of this task.
Gomez, P., Ratcliff, R., & Perea, M. (2007). A model of the go/no-go task. Journal of Experimental Psychology: General, 136, 389-413.

How to model response signal and reaction time data simultaneously.
Ratcliff, R. (2006). Modeling response signal and response time data. Cognitive Psychology, 53, 195-237..

ERP data and single trial ERP analyses match diffusion model processes (had the cover of the issue)
Philiastides, M.G., Ratcliff, R., & Sajda, P. (2006). Neural representation of task difficulty and decision-making during perceptual categorization: A timing diagram. Journal of Neuroscience, 26, 8965-8975.

Aging and RT: Individual differences.
Ratcliff, R., Thapar, A., & McKoon, G. (2006). Aging and individual differences in rapid two-choice decisions. Psychonomic Bulletin and Review, 13, 626-635.

Aging and RT: Practice effects.
Ratcliff, R., Thapar, A., & McKoon, G. (2006). Aging, practice, and perceptual tasks: A diffusion model analysis. Psychology and Aging, 21, 353-371.

Methods for numerically approximating sequential sampling models.
Brown, S., Ratcliff, R., & Smith, P.L. (2006). Evaluating methods for approximating stochastic differential equations. Journal of Mathematical Psychology, 50, 402-410.

A model for the brightness discrimination task which fits single cell recording data.
Ratcliff, R., Hasegawa, Y.T., Hasegawa, Y.P., Smith, P.L., & Segraves, M.A. (2007). Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task. Journal of Neurophysiology, 97, 1756-1774.

Aging and very old (75-90 year olds)
Ratcliff, R., Thapar, A., & McKoon, G. (2007). Application of the diffusion model to two-choice tasks for adults 75-90 years old. Psychology and Aging, 22, 56-66.

IQ and the worst performance rule
Ratcliff, R., Schmiedek, F., & McKoon, G. (2008). A diffusion model explanation of the worst performance rule for reaction time and IQ.Intelligence, 36, 10-17.

Rapid criterion shift in monkeys and humans
Ferrera, V. P., Grinband, J., Xiao, Q, Hirsch, J., Ratcliff, R. (2006). Distinguishing evidence accumulation from response bias in categorical decision making. Society for Neuroscience Abstracts.