Hierarchical Hidden Markov Models for Response Time Data
Tóm tắt
Từ khóa
Tài liệu tham khảo
Bastian, M., & Sackur, J. (2013). Mind wandering at the fingertips: automatic parsing of subjective states based on response time variability. Frontiers in Psychology, 4, 573.
Beal, M. J., Ghahramani, Z., & Rasmussen, C. E. (2001). The infinite hidden Markov model. In Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, NIPS’01 (pp. 577–584). MIT Press.
Bhar, R., & Hamori, S. (2004). Hidden Markov models: applications to financial economics. New York: Springer.
Borst, J. P., & Anderson, J. R. (2015). The discovery of processing stages: analyzing EEG data with hidden semi-Markov models. NeuroImage, 108, 60–73.
Craigmile, P. F., Peruggia, M., & Van Zandt, T. (2010). Hierarchical Bayes models for response time data. Psychometrika, 75, 613–632.
Craigmile, P. F., Peruggia, M., & Van Zandt, T. (2012). A Bayesian hierarchical model for response time data providing evidence for criteria changes over time. In M. C. Edwards & R. C. MacCallum (Eds.), Current issues in the theory and application of latent variable models (pp. 42–61). New York: Taylor and Francis.
Dillard, M. B., Warm, J. S., Funke, G. J., Funke, M. E., Victor S. Finomore, J., Matthews, G., Shaw, T. H., and Parasuraman, R. (2014). The sustained attention to response task (SART) does not promote mindlessness during vigilance performance. Human Factors, 56:1364–1379.
Falmagne, J. (1965). Stochastic models for choice reaction time with applications to experimental results. Journal of Mathematical Psychology, 2, 77–124.
Falmagne, R. (1968). A direct investigation of hypothesis-making behavior in concept identification. Psychonomic Science, 13, 335–336.
Foulsham, T., Farley, J., & Kingstone, A. (2013). Canadian journal of experimental psychology/revue canadienne de psychologie expérimentale. Human Factors, 61, 51–59.
Franklin, M. S., Broadway, J. M., Mrazek, M. D., Smallwood, J., & Schooler, J. W. (2013). Window to the wandering mind: pupillometry of spontaneous thought while reading. Quarterly Journal of Experimental Psychology, 66, 2289–2294.
Frühwirth-Schnatter, S. (2006). Finite mixture and Markov switching models. New York: Springer.
Gales, M., & Young, S. (2007). The application of hidden Markov models in speech recognition. Foundations and Trends in Signal Processing, 1, 195–304.
Gelman, A. (2007). Comment: Bayesian checking of the second levels of hierarchical models. Statistical Science, 22, 349–352.
Hawkins, G., Mittner, M., Boekel, W., Heathcote, A., & Forstmann, B. (2015). Toward a model-based cognitive neuroscience of mind wandering. Neuroscience, 310, 290–305.
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 Model-based Cognitive Neuroscience.
Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. Technometrics, 33, 251–272.
Kim, S., Potter, K., Craigmile, P. F., Peruggia, M., & Van Zandt, T. (2017). A Bayesian race model for recognition memory. Journal of the American Statistical Association, 112, 77–91.
Kofler, M. J., Sarver, D. E., Spiegel, J. A., Day, T. N., Harmon, S. L., & Wells, E. L. (2017). Heterogeneity in ADHD: neurocognitive predictors of peer, family, and academic functioning. Child Neuropsychology, 23, 733–759.
Kunkel, D., Potter, K., Craigmile, P. F., Peruggia, M., & Van Zandt, T. (2019). A bayesian race model for response times under cyclic stimulus discriminability. The Annals of Applied Statistics, 13, 271–296.
Lindsen, J. P., & de Jong, R. (2010). Distinguishing between the partial-mapping preparation hypothesis and the failure-to-engage hypothesis of residual switch costs. Journal of Experimental Psychology: Human Perception and Performance, 36, 1207–1226.
Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492–527.
Logan, G. D. (1992). Shapes of reaction-time distributions and shapes of learning curves: a test of the instance theory of automaticity. Journal of Experimental Psychology. Learning, Memory, and Cognition, 18, 883–914.
Majoros, W. (2007). Methods for computational gene prediction. Cambridge: Cambridge University Press.
Meyer, D. E., Osman, A. M., Irwin, D. E., & Yantis, S. (1988). Modern mental chronometry. Biological Psychology, 26, 3–67.
Molenaar, D., & Boeck, P. (2018). Response mixture modeling: accounting for heterogeneity in item characteristics across response times. Psychometrika, 83, 279–297.
Nigg, J. T., Willcutt, E. G., Doyle, A. E., & Sonuga-Barke, E. J. (2005). Causal heterogeneity in attention-deficit/hyperactivity disorder: do we need neuropsychologically impaired subtypes? Biological Psychiatry, 57, 1224–1230.
Palmer, E. M., Horowitz, T. S., Torralba, A., & Wolfe, J. M. (2011). What are the shapes of response time distributions in visual search? Journal of experimental psychology: Human perception and performance, 37, 58–71.
Peruggia, M., Van Zandt, T., & Chen, M. (2002). Was it a car or a cat I saw? An analysis of response times for word recognition. In Case Studies in Bayesian Statistics (Vol. 6, pp. 319–334). New York: Springer.
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. In Proceedings of the IEEE (Vol. 7, pp. 257–286).
Ranger, J., Wolgast, A., & Kuhn, J. (2018). Robust estimation of the hierarchical model for responses and response times. British Journal of Mathematical and Statistical Psychology, 72, 83–107.
Rouder, J. N., Sun, D., Speckman, P. L., Lu, J., & Zhou, D. (2003). A hierarchical Bayesian statistical framework for response time distributions. Psychometrika, 68, 589–606.
Rouder, J. N., Lu, J., Speckman, P., Sun, D., & Jiang, Y. (2005). A hierarchical model for estimating response time distributions. Psychonomic Bulletin and Review, 12, 195–223.
Sarkar, A., Chabout, J., Macopson, J. J., Jarvis, E. D., & Dunson, D. B. (2018). Bayesian semiparametric mixed effects Markov models with application to vocalization syntax. Journal of the American Statistical Association, 113(524), 1515–1527.
Sederberg, P. (2016). SMILE: State Machine Interface Library for Experiments. Retrieved from https://github.com/compmem/smile/.
Smallwood, J., & Schooler, J. W. (2015). The science of mind wandering: empirically navigating the stream of consciousness. Annual Review of Psychology, 66, 487–518.
Smith, M. R. (2017). Ternary: an R package for creating ternary plots. Zenodo. doi: https://doi.org/10.5281/zenodo.1068996.
Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2005). Sharing clusters among related groups: hierarchical Dirichlet processes. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in Neural Information Processing Systems 17 (pp. 1385–1392). MIT Press.
Thaler, N. S., Bello, D. T., & Etcoff, L. M. (2013). WISC-IV profiles are associated with differences in symptomatology and outcome in children with ADHD. Journal of Attention Disorders, 17(4), 291–301.
Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013). Speech synthesis based on hidden Markov models. Proceedings of the IEEE, 101, 1234–1252.
Vandekerckhove, J., Tuerlinckx, F., and Lee, M. (2008). A Bayesian approach to diffusion process models of decision-making. Pages 1429–1434. Cognitive science society; Austin, TX.
Wagenmakers, E.-J., Farrell, S., & Ratcliff, R. (2004). Estimation and interpretation of 1/f noise in human cognition. Psychonomic Bulletin & Review, 11, 579–615.
Wang, Z., Chen, Y., & Li, Y. (2004). A brief review of computational gene prediction methods. Genomics, Proteomics & Bioinformatics, 2, 216–221.
Yantis, S., & Meyer, D. E. (1988). Dynamics of activation in semantic and episodic memory. Journal of Experimental Psychology: General, 117, 130.
Yellott, J. I. (1971). Correction for fast guessing and the speed-accuracy tradeoff in choice reaction time. Journal of Mathematical Psychology, 8, 159–199.