I last updated this file in March 2021.
Lee, M. D., Criss, A. H., Devezer, B., Donkin, C., Etz, A., Leite, F. P., et al. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2, 141–153.
Vandekerckhove, J., White, C. N., Trueblood, J. S., Rouder, J. S., Matzke, D., Leite, F. P., et al. (2019). Robust diversity in cognitive modeling. Computational Brain & Behavior, 2, 271–276.
The first and last articles as bookends of what became a special issue about practices that cognitive modelers can use to contribute to building model-based inferential knowledge in ways that support robust advances.
Starns, J. J., Cataldo, A. M., Rotello, C. M., et al. (2019). Assessing theoretical conclusions with blinded inference to investigate a potential inference crisis. Advances in Methods and Practices in Psychological Science, 2, 335–349.
A collaborative and large-scale assessment of the validity of the inferences drawn from cognitive models, applied to recognition memory. Each of the contributing labs modeled the data and reported results independently. I joined as a one-person lab.
Dutilh, G., Annis, J., Brown, S. D., et al. (2019). The quality of response time data inference: A blinded, collaborative assessment of the validity of cognitive models. Psychonomic Bulletin & Review, 26, 1051–1069.
The first blinded, collaborative, and large-scale assessment of the validity of the inferences drawn from cognitive models. Each of the contributing labs modeled the data and reported results independently. I joined as a one-person lab. Nearly all of the work was done by 2018, but the publication did not come out until 2019.
Independent records of my scholarly publications and citations can be found at Google Scholar.
Contribution to the first registered replication report to make it to print. This project involved researchers from 31 labs across 11 countries, setting an excellent example for large-scale replication efforts.
Leite, F. P. (2012). A Comparison of Two Diffusion-Process Models in Accounting for Payoff and Stimulus Frequency Manipulations. Attention, Perception, & Psychophysics, 74, 1366-1382.
According to a reviewer, “this paper presents a nice detailed test of how payoff and stimulus frequency impact the decision process (assuming the decision process is a sequential sampling process).”
Leite, F. P. & Ratcliff, R. (2011). What Cognitive Processes Drive Response Biases? A Diffusion Model analysis. Judgment and Decision Making, 6(7), 651-687.
According to a reviewer, this paper makes “a potentially important and novel contribution” by studying which parameters of the diffusion model account for the behavioral changes associated with changes in stimulus frequency, relative payoffs, and decision threshold.
Leite, F. P. (2011). Larger Reward Values Alone Are Not Enough to Entice More Cooperation. Thinking & Reasoning, 17, 82-103.
According to a reviewer, this “manuscript elegantly demonstrates that increased cooperation for numeric (but not monetary) reward increases is not ubiquitous and that the prevalence of cooperative behavior depends on the strategies against which the participants play.”
Leite, F. P. & Ratcliff, R. (2010). Modeling Reaction Time and Accuracy of Multiple-Alternative Decisions. Attention, Perception, & Psychophysics, 72, 246-273.
According to reviewers, this “paper takes up an ambitious and worthy task in attempting to extend the already extensive work that had been accomplished thus far with diffusion models … to data from a multiple choice task … [exploring] a variety of other model types … and [comparing] between several classes of models.”
An example of the models in the article, coded in C, can be found here.