Multi-factor analysis in language production: Sequential sampling models mimic and extend regression results

References

• Alario, F.-X., Ferrand, L., Laganaro, M., New, B., Frauenfelder, U. H., & Segui, J. (2004). Predictors of picture naming speed. Behavior Research Methods, Instruments, & Computers, 36, 140–155. doi: 10.3758/BF03195559
   PubMedGoogle Scholar
• Alario, F.-X., & Moscoso del Prado Martín, F. (2010). On the origin of the “cumulative semantic inhibition” effect. Memory & Cognition, 38, 57–66. doi: 10.3758/MC.38.1.57
   PubMed Web of Science ®Google Scholar
• Anders, R., Alario, F.-X., & Van Maanen, L. (2016). The shifted Wald distribution for response time data analysis. Psychological Methods, 21, 309–327. doi: 10.1037/met0000066
   PubMed Web of Science ®Google Scholar
• Anders, R., & Batchelder, W. H. (2013). Cultural consensus theory for the ordinal data case. Psychometrika, 80, 151–181. doi: 10.1007/s11336-013-9382-9
   PubMed Web of Science ®Google Scholar
• Anders, R., Oravecz, Z., & Alario, F.-X. (2017). Improved information pooling for hierarchical cognitive models through multiple and covaried regression. Behavior Research Methods. In press.
   Web of Science ®Google Scholar
• Anders, R., Riès, S., van Maanen, L., & Alario, F.-X. (2015). Evidence accumulation as a model for lexical selection. Cognitive Psychology, 82, 57–73. doi: 10.1016/j.cogpsych.2015.07.002
   PubMed Web of Science ®Google Scholar
• Anders, R., Riès, S., van Maanen, L., & Alario, F.-X. (2017). Lesions to the left lateral prefrontal cortex impair decision threshold adjustment for lexical selection. Cognitive Neuropsychology, 34, 1–20. doi: 10.1080/02643294.2017.1282447
   PubMed Web of Science ®Google Scholar
• Baayen, R. H. (2004). Statistics in psycholinguistics: A critique of some current gold standards. Mental Lexicon Working Papers, 1, 1–47.
   Google Scholar
• Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390–412. doi: 10.1016/j.jml.2007.12.005
   Web of Science ®Google Scholar
• Baayen, R. H., & Milin, P. (2010). Analyzing reaction times. International Journal of Psychological Research, 3, 12–28. doi: 10.21500/20112084.807
   Google Scholar
• Baayen, H., Vasishth, S., Kliegl, R., & Bates, D. (2017). The cave of shadows: Addressing the human factor with generalized additive mixed models. Journal of Memory and Language, 94, 206–234. doi: 10.1016/j.jml.2016.11.006
   Web of Science ®Google Scholar
• Balota, D. A., & Yap, M. J. (2011). Moving beyond the mean in studies of mental chronometry the power of response time distributional analyses. Current Directions in Psychological Science, 20, 160–166. doi: 10.1177/0963721411408885
   Web of Science ®Google Scholar
• Balota, D. A., Yap, M. J., Cortese, M. J., & Watson, J. M. (2008). Beyond mean response latency: Response time distributional analyses of semantic priming. Journal of Memory and Language, 59, 495–523. doi: 10.1016/j.jml.2007.10.004
   Web of Science ®Google Scholar
• Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., … Treiman, R. (2007). The English lexicon project. Behavior Research Methods, 39, 445–459. doi: 10.3758/BF03193014
   PubMed Web of Science ®Google Scholar
• Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255–278. doi: 10.1016/j.jml.2012.11.001
   Web of Science ®Google Scholar
• Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4: Linear mixed-effects models using eigen and s4. r package version 1.1-7. This is computer program (R package). The URL of the package is: http://CRAN.R-project.org/package=lme4
   Google Scholar
• Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142–163. doi: 10.1037/1082-989X.11.2.142
   PubMed Web of Science ®Google Scholar
• Belke, E., & Stielow, A. (2013). Cumulative and non-cumulative semantic interference in object naming: Evidence from blocked and continuous manipulations of semantic context. The Quarterly Journal of Experimental Psychology, 66, 2135–2160. doi: 10.1080/17470218.2013.775318
   PubMed Web of Science ®Google Scholar
• Bernardo, J. M., & Smith, A. F. M. (2000). Bayesian theory. Chichester, UK: Wiley.
   Google Scholar
• Boehm, U., van Maanen, L., Forstmann, B., & van Rijn, H. (2014). Trial-by-trial fluctuations in CNV amplitude reflect anticipatory adjustment of response caution. NeuroImage, 96, 95–105. doi: 10.1016/j.neuroimage.2014.03.063
   PubMed Web of Science ®Google Scholar
• Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological), 26, 211–252. doi: 10.1111/j.2517-6161.1964.tb00553.x
   Web of Science ®Google Scholar
• Brown, S., & Heathcote, A. (2008). The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57, 153–178. doi: 10.1016/j.cogpsych.2007.12.002
   PubMed Web of Science ®Google Scholar
• Burbeck, S. L., & Luce, R. D. (1982). Evidence from auditory simple reaction times for both change and level detectors. Attention, Perception, & Psychophysics, 32, 117–133. doi: 10.3758/BF03204271
   Google Scholar
• Busemeyer, J. R., & Townsend, J. T. (1992). Fundamental derivations from decision field theory. Mathematical Social Sciences, 23, 255–282. doi: 10.1016/0165-4896(92)90043-5
   Web of Science ®Google Scholar
• Cavanagh, J. F., Wiecki, T. V., Cohen, M. X., Figueroa, C. M., Samanta, J., Sherman, S. J., & Frank, M. J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14, 1462–1467. doi: 10.1038/nn.2925
   PubMed Web of Science ®Google Scholar
• Chiarcos, C., Nordhoff, S., & Hellmann, S. (2012). Linked data in linguistics: Representing and connecting language data and language metadata. Berlin: Springer Science & Business Media.
   Google Scholar
• Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12, 335–359. doi: 10.1016/S0022-5371(73)80014-3
   Google Scholar
• Clarke, A., & Tyler, L. K. (2015). Understanding what we see: How we derive meaning from vision. Trends in Cognitive Sciences, 19, 677–687. doi: 10.1016/j.tics.2015.08.008
   PubMed Web of Science ®Google Scholar
• Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 7, 249–253. doi: 10.1177/014662168300700301
   Web of Science ®Google Scholar
• Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304–1312. doi: 10.1037/0003-066X.45.12.1304
   Web of Science ®Google Scholar
• Dang, Y., Zhang, Y., & Chen, H. (2010). A lexicon-enhanced method for sentiment classification: An experiment on online product reviews. IEEE Intelligent Systems, 25, 46–53. doi: 10.1109/MIS.2009.105
   Web of Science ®Google Scholar
• Dawson, M. R. (1988). Fitting the ex-gaussian equation to reaction time distributions. Behavior Research Methods, 20, 54–57.
   Web of Science ®Google Scholar
• Dell, G. S., & Gordon, J. K. (2003). Neighbors in the lexicon: Friend or foe? In N. O. Schiller & A. S. Meyer (Eds.), Phonetics and phonology in language comprehension and production: Differences and similarities (pp. 9–37). New York: Walter de Gruyter.
   Google Scholar
• Dell, G. S., Lawler, E. N., Harris, H. D., & Gordon, J. K. (2004). Models of errors of omission in aphasic naming. Cognitive Neuropsychology, 21, 125–145. doi: 10.1080/02643290342000320
   PubMed Web of Science ®Google Scholar
• Dell, G. S., Schwartz, M. F., Martin, N., Saffran, E. M., & Gagnon, D. A. (1997). Lexical access in aphasic and nonaphasic speakers. Psychological Review, 104, 801–838. doi: 10.1037/0033-295X.104.4.801
   PubMed Web of Science ®Google Scholar
• DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition? Neuron, 73, 415–434. doi: 10.1016/j.neuron.2012.01.010
   PubMed Web of Science ®Google Scholar
• Dillon, D. G., Wiecki, T., Pechtel, P., Webb, C., Goer, F., Murray, L., … Weissman, M. (2015). A computational analysis of flanker interference in depression. Psychological Medicine, 45, 2333–2344. doi: 10.1017/S0033291715000276
   PubMed Web of Science ®Google Scholar
• Donkin, C., Averell, L., Brown, S., & Heathcote, A. (2009). Getting more from accuracy and response time data: Methods for fitting the linear ballistic accumulator. Behavior Research Methods, 41, 1095–1110. doi: 10.3758/BRM.41.4.1095
   PubMed Web of Science ®Google Scholar
• Donkin, C., & Van Maanen, L. (2014). Pie ´ron’s law is not just an artifact of the response mechanism. Journal of Mathe- Matical Psychology, 62, 22–32. doi: 10.1016/j.jmp.2014.09.006
   Web of Science ®Google Scholar
• Dunham, J., Cook, G., & Horner, J. (2014). Lingsync & the online linguistic database: New models for the collection and management of data for language communities, linguists and language learners. Proceedings of the 2014 workshop on the use of computational methods in the study of endangered languages (pp. 24–33).
   Google Scholar
• Ferrand, L., & Alario, F.-X. (1998). Normes d’associations verbales pour 366 noms d’objets concrets. L’Année Psychologique, 98, 659–709. doi: 10.3406/psy.1998.28564
   Google Scholar
• Folks, J., & Chhikara, R. (1978). The inverse Gaussian distribution and its statistical application–a review. Journal of the Royal Statistical Society. Series B (Methodological), 40(3), 263–289. doi:10.1111/j.2517-6161.1978.tb01039.x
   Google Scholar
• Forstmann, B. U., Ratcliff, R., & Wagenmakers, E.-J. (2016). Sequential sampling models in cognitive neuroscience: Advantages, applications, and extensions. Annual Review of Psychology, 67, 641–666. doi: 10.1146/annurev-psych-122414-033645
   PubMed Web of Science ®Google Scholar
• Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2016). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 1–18. doi:10.1017/s0305000916000209
   Web of Science ®Google Scholar
• Frank, M. J., Gagne, C., Nyhus, E., Masters, S., Wiecki, T. V., Cavanagh, J. F., & Badre, D. (2015). FMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. The Journal of Neuroscience, 35, 485–494. doi: 10.1523/JNEUROSCI.2036-14.2015
   PubMed Web of Science ®Google Scholar
• Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 1, 515–534. doi: 10.1214/06-BA117A
   Web of Science ®Google Scholar
• Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed). Boca Raton, FL: Chapman & Hall/CRC.
   Google Scholar
• Glaser, W. R. (1992). Picture naming. Cognition, 42, 61–105. doi: 10.1016/0010-0277(92)90040-O
   PubMed Web of Science ®Google Scholar
• Gluth, S., & Rieskamp, J. (2017). Variability in behavior that cognitive models do not explain can be linked to neuroimaging data. Journal of Mathematical Psychology, 76, 104–116. doi: 10.1016/j.jmp.2016.04.012
   Web of Science ®Google Scholar
• Goldstein, H. (2011). Multilevel statistical models. Chichester: John Wiley & Sons.
   Google Scholar
• Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.
   Google Scholar
• Hawkins, G. E., Forstmann, B. U., Wagenmakers, E.-J., Ratcliff, R., & Brown, S. D. (2015). Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making. The Journal of Neuroscience, 35, 2476–2484. doi: 10.1523/JNEUROSCI.2410-14.2015
   PubMed Web of Science ®Google Scholar
• Heathcote, A. (2004). Fitting Wald and ex-Wald distributions to response time data: An example using functions for the S-PLUS package. Behavior Research Methods, Instruments, & Computers, 36, 678–694. doi: 10.3758/BF03206550
   PubMedGoogle Scholar
• Heathcote, A., Popiel, S. J., & Mewhort, D. (1991). Analysis of response time distributions: An example using the stroop task. Psychological Bulletin, 109, 340–347. doi: 10.1037/0033-2909.109.2.340
   Web of Science ®Google Scholar
• Howard, D., Nickels, L., Coltheart, M., & Cole-Virtue, J. (2006). Cumulative semantic inhibition in picture naming: Experimental and computational studies. Cognition, 100, 464–482. doi: 10.1016/j.cognition.2005.02.006
   PubMed Web of Science ®Google Scholar
• Jiang, Y., Rouder, J. N., & Speckman, P. L. (2004). A note on the sampling properties of the vincentizing (quantile averaging) procedure. Journal of Mathematical Psychology, 48, 186–195. doi: 10.1016/j.jmp.2004.01.002
   Web of Science ®Google Scholar
• Kelly, S. P., & O’Connell, R. G. (2013). Internal and external influences on the rate of sensory evidence accumulation in the human brain. The Journal of Neuroscience, 33, 19434–19441. doi: 10.1523/JNEUROSCI.3355-13.2013
   PubMed Web of Science ®Google Scholar
• Klauer, K. C., & Kellen, D. (2018). RT-MPTs: Process models for response-time distributions based on multinomial processing trees with applications to recognition memory. Journal of Mathematical Psychology, 82, 111–130. doi: 10.1016/j.jmp.2017.12.003
   Web of Science ®Google Scholar
• Kruschke, J. K. (2011). Doing Bayesian data analysis: A tutorial with R and BUGS. New York: Academic Press.
   Google Scholar
• Laming, D. R. J. (1968). Information theory of choice-reaction times. London: Academic Press.
   Google Scholar
• Lee, M. D. (2011). How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology, 55, 1–7. doi: 10.1016/j.jmp.2010.08.013
   Web of Science ®Google Scholar
• Leite, F. P., & Ratcliff, R. (2010). Modeling reaction time and accuracy of multiple-alternative decisions. Attention, Perception, & Psychophysics, 72, 246–273. doi: 10.3758/APP.72.1.246
   PubMed Web of Science ®Google Scholar
• Lewandowski, D., Kurowicka, D., & Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100, 1989–2001. doi: 10.1016/j.jmva.2009.04.008
   Web of Science ®Google Scholar
• Luce, R. D. (1986). Response times: Their role in inferring elementary mental organization. New York: Oxford University Press.
   Google Scholar
• Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. New York: W.H. Freeman and Company.
   Google Scholar
• Matzke, D., & Wagenmakers, E.-J. (2009). Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis. Psychonomic Bulletin & Review, 16, 798–817. doi: 10.3758/PBR.16.5.798
   PubMed Web of Science ®Google Scholar
• McFall, R. M., Treat, T. A., & Viken, R. J. (1997). Contributions of cognitive theory to new behavioral treatments. Psychological Science, 8, 174–176. doi: 10.1111/j.1467-9280.1997.tb00406.x
   Web of Science ®Google Scholar
• McNally, R. J., & Reese, H. E. (2009). Information-processing approaches to understanding anxiety disorders. In Oxford Handbook of anxiety and related disorders (pp. 136–152). New York: Oxford University Press.
   Google Scholar
• Meyer, A. S. (1992). Investigation of phonological encoding through speech error analyses: Achievements, limitations, and alternatives. Cognition, 42, 181–211. doi: 10.1016/0010-0277(92)90043-H
   PubMed Web of Science ®Google Scholar
• Miletić, S., Turner, B. M., Forstmann, B. U., & van Maanen, L. (2017). Parameter recovery for the leaky competing accumulator model. Journal of Mathematical Psychology, 76, 25–50. doi: 10.1016/j.jmp.2016.12.001
   Web of Science ®Google Scholar
• Miller, R., Scherbaum, S., Heck, D. W., Goschke, T., & Enge, S. (2017). On the relation between the (censored) shifted Wald and the Wiener distribution as measurement models for choice response times. Applied Psychological Measurement, 42(2), 116–135. doi:10.1177/0146621617710465
   PubMed Web of Science ®Google Scholar
• Mirman, D., Strauss, T. J., Brecher, A., Walker, G. M., Sobel, P., Dell, G. S., & Schwartz, M. F. (2010). A large, search- able, web-based database of aphasic performance on picture naming and other tests of cognitive function. Cognitive Neuropsychology, 27, 495–504. doi: 10.1080/02643294.2011.574112
   PubMed Web of Science ®Google Scholar
• Mulder, M. J., Bos, D., Weusten, J. M., van Belle, J., van Dijk, S. C., Simen, P., … Durston, S. (2010). Basic impairments in regulating the speed-accuracy tradeoff predict symptoms of attention-deficit/hyperactivity disorder. Biological Psychiatry, 68, 1114–1119. doi: 10.1016/j.biopsych.2010.07.031
   PubMed Web of Science ®Google Scholar
• Mulder, M., Van Maanen, L., & Forstmann, B. (2014). Perceptual decision neurosciences–a model-based review. Neuroscience, 277, 872–884. doi: 10.1016/j.neuroscience.2014.07.031
   PubMed Web of Science ®Google Scholar
• Myung, J. I., Tang, Y., & Pitt, M. A. (2009). Evaluation and comparison of computational models. Methods in Enzymology, 454, 287–304. doi: 10.1016/S0076-6879(08)03811-1
   PubMed Web of Science ®Google Scholar
• Nakahara, H., Nakamura, K., & Hikosaka, O. (2006). Extended LATER model can account for trial-by-trial variability of both pre-and post-processes. Neural Networks, 19, 1027–1046. doi: 10.1016/j.neunet.2006.07.001
   PubMed Web of Science ®Google Scholar
• Navarrete, E., Del Prato, P., & Mahon, B. Z. (2012). Factors determining semantic facilitation and interference in the cyclic naming paradigm. Frontiers in Psychology, 3, 38. doi: 10.3389/fpsyg.2012.00038
   PubMed Web of Science ®Google Scholar
• Neidle, C., & Vogler, C. (2012). A new web interface to facilitate access to corpora: Development of the asllrp data access interface (dai). In Proc. 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon, LREC.
   Google Scholar
• Nozari, N., & Hepner, C. R. (2018). To select or to wait? The importance of criterion setting in debates of competitive lexical selection. Cognitive Neuropsychology. Advance online publication. doi:10.1080/02643294.2018.1476335
   PubMed Web of Science ®Google Scholar
• Oganian, Y., Froehlich, E., Schlickeiser, U., Hofmann, M. J., Heekeren, H. R., & Jacobs, A. M. (2016). Slower perception followed by faster lexical decision in longer words: A diffusion model analysis. Frontiers in Psychology, 6, 1958. doi: 10.3389/fpsyg.2015.01958
   PubMed Web of Science ®Google Scholar
• Oldfield, R., & Wingfield, A. (1964). The time it takes to name an object. Nature, 202, 1031–1032. doi: 10.1038/2021031a0
   PubMed Web of Science ®Google Scholar
• Oppenheim, G. M. (2017). A blind spot in correct naming latency analyses. Cognitive Neuropsychology, 34, 33–41. doi: 10.1080/02643294.2017.1338563
   PubMed Web of Science ®Google Scholar
• Oppenheim, G. M., Dell, G. S., & Schwartz, M. F. (2010). The dark side of incremental learning: A model of cumulative semantic interference during lexical access in speech production. Cognition, 114, 227–252. doi: 10.1016/j.cognition.2009.09.007
   PubMed Web of Science ®Google Scholar
• Oravecz, Z., Anders, R., & Batchelder, W. H. (2013). Hierarchical Bayesian modeling for test theory without an answer key. Psychometrika, 80(2), 341–364. doi:10.1007/s11336-013-9379-4
   PubMed Web of Science ®Google Scholar
• Pe, M. L., Vandekerckhove, J., & Kuppens, P. (2013). A diffusion model account of the relationship between the emotional flanker task and rumination and depression. Emotion, 13, 739–747. doi: 10.1037/a0031628
   PubMed Web of Science ®Google Scholar
• Pike, R. (1973). Response latency models for signal detection. Psychological Review, 80, 53–68. doi: 10.1037/h0033871
   PubMed Web of Science ®Google Scholar
• Protopapas, A. (2007). Check vocal: A program to facilitate checking the accuracy and response time of vocal responses from DMDX. Behavior Research Methods, 39, 859–862. doi: 10.3758/BF03192979
   PubMed Web of Science ®Google Scholar
• Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108. doi: 10.1037/0033-295X.85.2.59
   Web of Science ®Google Scholar
• Ratcliff, R. (1979). Group reaction time distributions and an analysis of distribution statistics. Psychological Bulletin, 86, 446–461. doi: 10.1037/0033-2909.86.3.446
   PubMed Web of Science ®Google Scholar
• Ratcliff, R. (1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114, 510–532. doi: 10.1037/0033-2909.114.3.510
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., Gomez, P., & McKoon, G. (2004). A diffusion model account of the lexical decision task. Psychological Review, 111, 159–182. doi: 10.1037/0033-295X.111.1.159
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20, 873–922. doi: 10.1162/neco.2008.12-06-420
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., & Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111, 333–367. doi: 10.1037/0033-295X.111.2.333
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Sciences, 20, 260–281. doi: 10.1016/j.tics.2016.01.007
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9, 438–481. doi: 10.3758/BF03196302
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., & Van Dongen, H. P. (2011). Diffusion model for one-choice reaction-time tasks and the cognitive effects of sleep deprivation. Proceedings of the National Academy of Sciences, 108, 11285–11290. doi: 10.1073/pnas.1100483108
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., Van Zandt, T., & McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261–300. doi: 10.1037/0033-295X.106.2.261
   PubMed Web of Science ®Google Scholar
• Ricciardi, L. M. (1977). Diffusion processes and related topics in biology. Berlin: Springer-Verlag.
   Google Scholar
• Riès, S., Janssen, N., Burle, B., & Alario, F.-X. (2013). Response-locked brain dynamics of word production. PloS one, 8, e58197. doi: 10.1371/journal.pone.0058197
   PubMed Web of Science ®Google Scholar
• Roelofs, A. (1992). A spreading-activation theory of lemma retrieval in speaking. Cognition, 42, 107–142. doi: 10.1016/0010-0277(92)90041-F
   PubMed Web of Science ®Google Scholar
• Roelofs, A. (2008). Dynamics of the attentional control of word retrieval: Analyses of response time distributions. Journal of Experimental Psychology: General, 137, 303–323. doi: 10.1037/0096-3445.137.2.303
   PubMed Web of Science ®Google Scholar
• Roelofs, A., & Piai, V. (2017). Distributional analysis of semantic interference in picture naming. The Quarterly Journal of Experimental Psychology, 70, 782–792. doi: 10.1080/17470218.2016.1165264
   PubMed Web of Science ®Google Scholar
• Rouder, J. N. (2005). Are unshifted distributional models appropriate for response time? Psychometrika, 70, 377–381. doi: 10.1007/s11336-005-1297-7
   Web of Science ®Google Scholar
• Rouder, J. N., & Lu, J. (2005). An introduction to Bayesian hierarchical mdoels with an application in the theory of signal detection. Psychonomic Bulletin and Review, 12, 573–604. doi: 10.3758/BF03196750
   PubMed Web of Science ®Google Scholar
• Rouder, J. N., Lu, J., Speckman, P., Sun, D., & Jiang, Y. (2005). A hierarchical model for estimating response time distributions. Psychonomic Bulletin & Review, 12, 195–223. doi: 10.3758/BF03257252
   PubMed Web of Science ®Google Scholar
• Rouder, J. N., Lu, J., Sun, D., Speckman, P., Morey, R., & Naveh-Benjamin, M. (2007). Signal detection models with random participant and item effects. Psychometrika, 72, 621–642. doi: 10.1007/s11336-005-1350-6
   Web of Science ®Google Scholar
• Rouder, J. N., Morey, R. D., & Pratte, M. S. (2013). Hierarchical Bayesian models. Practice, 1, 10.
   Google Scholar
• Rouder, J. N., Province, J. M., Morey, R. D., Gomez, P., & Heathcote, A. (2015). The lognormal race: A cognitive-process model of choice and latency with desirable psychometric properties. Psychometrika, 80, 491–513. doi: 10.1007/s11336-013-9396-3
   PubMed Web of Science ®Google Scholar
• Scaltritti, M., Navarrete, E., & Peressotti, F. (2015). Distributional analyses in the picture–word interference paradigm: Exploring the semantic interference and the distractor frequency effects. The Quarterly Journal of Experimental Psychology, 68, 1348–1369. doi: 10.1080/17470218.2014.981196
   PubMed Web of Science ®Google Scholar
• Scheibehenne, B., & Pachur, T. (2015). Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice. Psychonomic Bulletin & Review, 22, 391–407. doi: 10.3758/s13423-014-0684-4
   PubMed Web of Science ®Google Scholar
• Townsend, J. T., & Ashby, F. G. (1983). Stochastic modeling of elementary psychological processes. Cambridge: Cambridge University Press.
   Google Scholar
• Treat, T. A., & Dirks, M. A. (2007). Integrating clinical and cognitive science. In Psychological clinical science: Papers in honor of Richard M. McFall (pp. 289–318). New York: Psychology Press.
   Google Scholar
• Turner, B. M., Van Maanen, L., & Forstmann, B. U. (2015). Informing cognitive abstractions through neuroimaging: The neural drift diffusion model. Psychological Review, 122, 312–336. doi: 10.1037/a0038894
   PubMed Web of Science ®Google Scholar
• Ulrich, R., & Miller, J. (1994). Effects of truncation on reaction time analysis. Journal of Experimental Psychology: General, 123, 34–80. doi: 10.1037/0096-3445.123.1.34
   PubMed Web of Science ®Google Scholar
• Usher, M., Olami, Z., & McClelland, J. L. (2002). Hick’s law in a stochastic race model with speed–accuracy tradeoff. Journal of Mathematical Psychology, 46, 704–715. doi: 10.1006/jmps.2002.1420
   Web of Science ®Google Scholar
• Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2011). Hierarchical diffusion models for two-choice response times. Psychological Methods, 16, 44–62. doi: 10.1037/a0021765
   PubMed Web of Science ®Google Scholar
• Van Maanen, L., Brown, S. D., Eichele, T., Wagenmakers, E.-J., Ho, T., Serences, J., & Forstmann, B. U. (2011). Neural correlates of trial-to-trial fluctuations in response caution. Journal of Neuroscience, 31, 17488–17495. doi: 10.1523/JNEUROSCI.2924-11.2011
   PubMed Web of Science ®Google Scholar
• Van Maanen, L., & Van Rijn, H. (2007). An accumulator model of semantic interference. Cognitive Systems Research, 8, 174–181. doi: 10.1016/j.cogsys.2007.05.002
   Web of Science ®Google Scholar
• Van Maanen, L., Van Rijn, H., & Taatgen, N. (2012). Race/a: An architectural account of the interactions between learning, task control, and retrieval dynamics. Cognitive Science, 36, 62–101. doi: 10.1111/j.1551-6709.2011.01213.x
   PubMed Web of Science ®Google Scholar
• Van Ravenzwaaij, D., Donkin, C., & Vandekerckhove, J. (2017). The ez diffusion model provides a powerful test of simple empirical effects. Psychonomic Bulletin & Review, 24, 547–556. doi: 10.3758/s13423-016-1081-y
   PubMed Web of Science ®Google Scholar
• Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7, 424–465. doi: 10.3758/BF03214357
   PubMed Web of Science ®Google Scholar
• Vincent, S. B. (1912). The functions of the vibrissae in the behavior of the white rat, volume 1. Chicago: University of Chicago.
   Google Scholar
• Wabersich, D., & Vandekerckhove, J. (2014). The rwiener package: An r package providing distribution functions for the Wiener diffusion model. The R Journal, 6, 49–56. doi: 10.32614/RJ-2014-005
   Google Scholar
• Wagenmakers, E.-J., & Brown, S. (2007). On the linear relation between the mean and the standard deviation of a response time distribution. Psychological Review, 114, 830–841. doi: 10.1037/0033-295X.114.3.830
   PubMed Web of Science ®Google Scholar
• 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. doi: 10.1016/j.jml.2007.04.006
   PubMed Web of Science ®Google Scholar
• Wagenmakers, E.-J., Van Der Maas, H. L., & Grasman, R. P. (2007). An ez-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14, 3–22. doi: 10.3758/BF03194023
   PubMed Web of Science ®Google Scholar
• Wald, A. (1947). Sequential analysis. New York: John Wiley.
   Google Scholar
• Walker, G. M., Hickok, G., & Fridriksson, J. (2018). A cognitive psychometric model for assessment of picture naming abilities in aphasia. Psychological Assessment, 30(6), 809–826.
   PubMed Web of Science ®Google Scholar
• White, C. N., Ratcliff, R., Vasey, M. W., & McKoon, G. (2010a). Anxiety enhances threat processing without competition among multiple inputs: A diffusion model analysis. Emotion, 10, 662–677. doi: 10.1037/a0019474
   PubMed Web of Science ®Google Scholar
• White, C. N., Ratcliff, R., Vasey, M. W., & McKoon, G. (2010b). Using diffusion models to understand clinical disorders. Journal of Mathematical Psychology, 54, 39–52. doi: 10.1016/j.jmp.2010.01.004
   PubMed Web of Science ®Google Scholar
• Wiecki, T. V., Poland, J., & Frank, M. J. (2015). Model-based cognitive neuroscience approaches to computational psychi- atry: Clustering and classification. Clinical Psychological Science, 3, 378–399. doi: 10.1177/2167702614565359
   Web of Science ®Google Scholar
• Wiecki, T. V., Sofer, I., & Frank, M. J. (2013). HDDM: Hierarchical Bayesian estimation of the drift-diffusion model in python. Frontiers in Neuroinformatics, 7, 14. doi: 10.3389/fninf.2013.00014
   PubMed Web of Science ®Google Scholar
• Winkel, J., Keuken, M. C., Van Maanen, L., Wagenmakers, E.-J., & Forstmann, B. U. (2014). Early evidence affects later decisions: Why evidence accumulation is required to explain response time data. Psychonomic Bulletin & Review, 21, 777–784.
   PubMed Web of Science ®Google Scholar
• Zandbelt, B., Purcell, B. A., Palmeri, T. J., Logan, G. D., & Schall, J. D. (2014). Response times from ensembles of accumulators. Proceedings of the National Academy of Sciences, 111, 2848–2853. doi: 10.1073/pnas.1310577111
   PubMed Web of Science ®Google Scholar