A hierarchical bayesian statistical framework for response time distributions

Rouder, Jeffrey N.; Sun, Dongchu; Speckman, Paul L.; Lu, Jun; Zhou, Duo

doi:10.1007/bf02295614

Cited by 130 publications

(156 citation statements)

References 58 publications

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“…We used hierarchical Bayesian modeling (see, e.g., Farrell & Ludwig, 2008;Gelman & Hill, 2007;Rouder, Lu, Speckman, Sun, & Jiang, 2005;Rouder, Sun, Speckman, Lu, & Zhou, 2003;Shiffrin, Lee, Kim, & Wagenmakers, 2008) to fit the exGaussian and shifted Wald distributions to the lexical decision data. We used a hierarchical Bayesian approach to fit the descriptive distributions because the individual participant data obtained from the lexical decision tasks were considerably noisier than the synthetic data used in the previous section.…”

Section: Validation Of the Ex-gaussian And Shifted Wald Parameters Usmentioning

confidence: 99%

Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis

Matzke

Wagenmakers

2009

Psychonomic Bulletin & Review

439

470

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Section: Validation Of the Ex-gaussian And Shifted Wald Parameters Usmentioning

confidence: 99%

Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis

Matzke

Wagenmakers

2009

Psychonomic Bulletin & Review

439

470

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“…The model that we will discuss was first presented by Rouder, Sun, Speckman, Lu, and Zhou (2003). They provided a detailed and formal account.…”

Section: The Modelmentioning

confidence: 99%

A hierarchical model for estimating response time distributions

Rouder

Speckman

et al. 2005

Psychonomic Bulletin & Review

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171

191

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We present a statistical model for inference with response time (RT) distributions. The model has the following features. First, it provides a means of estimating the shape, scale, and location (shift) of RT distributions. Second, it is hierarchical and models between-subjects and within-subjects variability simultaneously. Third, inference with the model is Bayesian and provides a principled and efficient means of pooling information across disparate data from different individuals. Because the model efficiently pools information across individuals, it is particularly well suited for those common cases in which the researcher collects a limited number of observations from several participants. Monte Carlo simulations reveal that the hierarchical Bayesian model provides more accurate estimates than several popular competitors do. We illustrate the model by providing an analysis of the symbolic distance effect in which participants can more quickly ascertain the relationship between nonadjacent digits than that between adjacent digits. THEORETICAL AND REVIEW ARTICLES

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“…In a stochastic parameters model (e.g., Peruggia et al, 2002;Rouder et al, 2003), every participant is assumed to have a unique parameter value y that is sampled from a parametric distribution, as illustrated in Fig. 3a.…”

Section: Hierarchical Bayesian Models For Individual Differencesmentioning

confidence: 99%

“…Because of these difficulties, a number of authors have considered more sophisticated ways of expressing individual differences within models of cognitive processes (e.g., Lee & Webb, in press;Peruggia, Van Zandt, & Chen, 2002;Rouder, Sun, Speckman, Lu, & Zhou, 2003;Steyvers, Tenenbaum, Wagenmakers, & Blum, 2003;Webb & Lee, 2004). The central innovation is to provide an explicit model for the kinds of individual differences that might appear in the data, in much the same way as established methods in psychometric models like Item Response Theory (e.g., Hoskens & de Boeck, 2001;Junker & Sijtsma, 2001;Lord, 1980).…”

Section: Introductionmentioning

confidence: 99%

Modeling individual differences using Dirichlet processes

Navarro

Griffiths

Steyvers

et al. 2006

Journal of Mathematical Psychology

112

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We introduce a Bayesian framework for modeling individual differences, in which subjects are assumed to belong to one of a potentially infinite number of groups. In this model, the groups observed in any particular data set are not viewed as a fixed set that fully explains the variation between individuals, but rather as representatives of a latent, arbitrarily rich structure. As more people are seen, and more details about the individual differences are revealed, the number of inferred groups is allowed to grow. We use the Dirichlet process-a distribution widely used in nonparametric Bayesian statistics-to define a prior for the model, allowing us to learn flexible parameter distributions without overfitting the data, or requiring the complex computations typically required for determining the dimensionality of a model. As an initial demonstration of the approach, we present three applications that analyze the individual differences in category learning, choice of publication outlets, and web-browsing behavior. r 2005 Elsevier Inc. All rights reserved.

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A hierarchical bayesian statistical framework for response time distributions

Abstract: Bayesian analysis, hierarchical models, response time, MCMC, Weibull distribution,

Cited by 130 publications

References 58 publications

Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis

Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis

A hierarchical model for estimating response time distributions

Modeling individual differences using Dirichlet processes

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