Publications

Osth, A. F., Dennis, S., & Heathcote, A. (2017). Likelihood ratio sequential sampling models of recognition memory. Cognitive Psychology, 92, 101–126. [Data & Code]

Evans, N. J., Howard, Z. L., Heathcote, A., & Brown, S. D. (2017). Model flexibility analysis does not measure the persuasiveness of a fit. Psychological Review, 124(3), 339–345.

Bushmakin, M. A., Eidels, A., & Heathcote, A. (2017). Breaking the rules in perceptual information integration. Cognitive Psychology, 95, 1–16.

Hawkins, G. E., Mittner, M., Forstmann, B. U., & Heathcote, A. (2017). On the efficiency of neurally-informed cognitive models to identify latent cognitive states. Journal of Mathematical Psychology, 76, 142–155. [R Code]

Thiele, J. E., Haaf, J. M., & Rouder, J. N. (2017). Is there variation across individuals in processing? Bayesian analysis for systems factorial technology. Journal of Mathematical Psychology, 81, 40-54. [Github]

Sense, F., Morey, C. C., Prince, M., Heathcote, A., & Morey, R. D. (2017). Opportunity for verbalization does not improve visual change detection performance: A state-trace analysis. Behavior Research Methods, 49(3), 853–862.

Haaf, J. M., & Rouder, J. N. (2017). Developing Constraint in Bayesian Mixed Models. Psychological Methods, 22(4), 779-798. [GitHub repository]

Houpt, J. W., Heathcote, A., & Eidels, A. (2017). Bayesian analyses of cognitive architecture. Psychological Methods, 22(2), 288–303.

Tillman, G., Osth, A. F., van Ravenzwaaij, D., & Heathcote, A. (2017). A diffusion decision model analysis of evidence variability in the lexical decision task. Psychonomic Bulletin & Review, 24(6), 1949–1956.

Tillman, G., Strayer, D., Eidels, A., & Heathcote, A. (2017). Modeling cognitive load effects of conversation between a passenger and driver. Attention, Perception, & Psychophysics, 79(6), 1795–1803. [Supplementary Materials]

Strickland, L., Heathcote, A., Remington, R. W., & Loft, S. (2017). Accumulating evidence about what prospective memory costs actually reveal. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(10), 1616–1629. [Supplementary Materials]

Osth, A. F., Bora, B., Dennis, S., & Heathcote, A. (2017). Diffusion vs. linear ballistic accumulation: Different models, different conclusions about the slope of the zROC in recognition memory. Journal of Memory and Language, 96, 36–61.

Grootswagers, T., Ritchie, J. B., Wardle, S. G., Heathcote, A., & Carlson, T. A. (2017). Asymmetric compression of representational space for object animacy categorization under degraded viewing conditions. Journal of Cognitive Neuroscience, 29(12), 1995–2010.

Evans, N. J., Hawkins, G. E., Boehm, U., Wagenmakers, E.-J. & Brown, S. D. (2017). The computations
that support simple decision-making: A comparison between the diffusion and urgency-gating models. Scientific Reports, 7 (1), 16433.

Matzke, D., Love, J., & Heathcote A. (2017). A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm. Behavior Research Methods, 49, 267-281. Download Supplementary Materials.

Wagenmakers, E.-J., Verhagen, A.J., Ly, A., Matzke, D., Steingroever, H., Rouder, J.N., & Morey, R.D. (2017). The need for Bayesian hypothesis testing in psychological science. In S.O. Lilienfeld & I. Waldman (Eds.), Psychological science under scrutiny: Recent challenges and proposed solutions (pp. 123-138). John Wiley and Sons.

Dutilh, G., Vandekerckhove, J., Ly, A., Matzke, D., Pedroni, A., Frey, R., et al. (2017). A test of the diffusion model explanation of the worst performance rule using preregistration and blinding. Attention, Perception, & Psychophysics, 79, 713-725.

Matzke, D., Hughes, M., Badcock, J.C., Michie, P., & Heathcote, A. (2017). Failures of cognitive control or attention? The case of stop-signal deficits in schizophrenia. Attention, Perception, & Psychophysics, 79, 1078-1086.

Krypotos, A.-M., Blanken, T.F., Arnaudova, I., Matzke, D., & Beckers, T. (2017). A primer on Bayesian analysis for experimental psychopathologists. Journal of Experimental Psychopathology, 8, 140-157.

Matzke, D., Ly, A., Selker, R., Weeda, W. D., Scheibehenne, B., Lee, M.D., & Wagenmakers, E.-J. (2017). Bayesian inference for correlations in the presence of estimation uncertainty and measurement error. Collabra: Psychology, 3(1), 25. [Supplementary Materials]

Gronau, Q. F., Sarafoglou, A., Matzke, D., Ly, A., Boehm, U., Marsman, M., et al. (2017). A tutorial on bridge sampling. Journal of Mathematical Psychology, 81, 80-97.

Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76(B), 117-130.

Nunez, M. D., Gosai, A., Vandekerckhove, J. & Srinivasan, R. (2017). EEG measures of neural processing speed reflect human visual encoding time. Conference on Cognitive Computational Neuroscience. New York, New York. September 2017.

Gronau, Q. F., Erp, S. V., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E.-J. (2017). A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power. Comprehensive Results in Social Psychology, 2(1), 123–138.

Scheibehenne, B., Gronau, Q. F., Jamil, T., & Wagenmakers, E.-J. (2017). Fixed or random? A resolution through model averaging: Reply to Carlsson, Schimmack, Williams, and Bürkner (2017). Psychological Science, 28(11), 1698–1701.

Gronau, Q. F., Duizer, M., Bakker, M., & Wagenmakers, E.-J. (2017). Bayesian mixture modeling of significant p values: A meta-analytic method to estimate the degree of contamination from H₀. Journal of Experimental Psychology: General, 146(9), 1223–1233.

Sutton, K., Heathcote, A., & Bore, M. (2007). Measuring 3-D understanding on the Web and in the laboratory. Behavior Research Methods, 39(4), 926–939.

Heathcote, A., & Mewhort, D. J. K. (1990). Is unbounded visual search intractable? Comment on Tsotsos, J. K., Analysing vision at the complexity level. Behavioral and Brain Sciences, 13(3), 449–449.