A hierarchical model for estimating response time distributions

Rouder, Jeffrey N.; Lu, Jun; Speckman, Paul L.; Sun, Dongchu; Jiang, Yi

doi:10.3758/bf03257252

Cited by 169 publications

(191 citation statements)

References 101 publications

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“…The model makes joint predictions not just of the probability of making a particular response, but also the probability that the response is made within each of the ranges, making it straightforward to calculate the multinomial likelihood assigned by the model to the vectors of observed counts (Ratcliff & Tuerlincx, 2002). Admittedly, Vincentizing on its own is prone to inconsistency (Rouder & Speckman, 2004), but combined with QML and a sufficient number of observations, the parameters obtained are still quite stable without needing to re-describe RT distributions in terms of a parametric family (Rouder, Lu, Speckman, Sun, & Jiang, 2005, although we will resort to parametric models of RT distributions when the number of observations is small or only summary statistics are available).…”

Section: Quantile Maximum Likelihoodmentioning

confidence: 99%

A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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ii Acknowledgements First, I extend my thanks to my committee members for their time, consideration, and guidance. I would especially like to thank my advisors, Rich Shiffrin and Mike Jones, for first convincing me to come to Indiana, making me feel welcome, and guiding me into the world I now inhabit. I must also thank Isaiah Harbison, Michael Dougherty, and Sharona Atkins for introducing me to the very idea of mathematical psychology and for teaching me and trusting me. show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process.

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Section: Quantile Maximum Likelihoodmentioning

confidence: 99%

A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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“…All of the above parameters are estimated simultaneously according to the hierarchical Bayesian model described in Appendix C (Rouder et al, 2005). The model was implemented in JAGS (Plummer, 2013), which was used to obtain 10,000 posterior samples split over 10 parallel chains after 2000 iterations of "burn-in" each.…”

Section: Bayesian Modelmentioning

confidence: 99%

Parallel interactive retrieval of item and associative information from event memory

Cox

Criss

2017

Cognitive Psychology

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Memory contains information about individual events (items) and combinations of events (associations). Despite the fundamental importance of this distinction, it remains unclear exactly how these two kinds of information are stored and whether different processes are used to retrieve them. We use both model-independent qualitative properties of response dynamics and quantitative modeling of individuals to address these issues. Item and associative information are not independent and they are retrieved concurrently via interacting processes. During retrieval, matching item and associative information mutually facilitate one another to yield an amplified holistic signal. Modeling of individuals suggests that this kind of facilitation between item and associative retrieval is a ubiquitous feature of human memory.

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“…For example, for modeling skewed distributions such as response times, a Weibull distribution could be used (Rouder, Lu, Speckman, Sun, & Jiang, 2005). If the analyst desires a likelihood function other than one already built into JAGS, the Poisson zeros trick can be used to specify virtually any likelihood function (e.g., Ntzoufras, 2009, p. 276).…”

Section: Appendix B Modifying the Program For Other Priors Or Likelihmentioning

confidence: 99%

Bayesian Estimation Supersedes the t Test

Kruschke¹

2012

PsycEXTRA Dataset

311

411

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Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms.Keywords: Bayesian statistics, effect size, robust estimation, Bayes factor, confidence interval One of the most frequently encountered scientific procedures is a comparison of two groups (e.g., du Prel, Röhrig, Hommel, & Blettner, 2010;Fritz, Morris, & Richler, 2012;Wetzels et al., 2011). Given data from two groups, researchers ask various comparative questions: How much is one group different from another? Can we be reasonably sure that the difference is non-zero? How certain are we about the magnitude of difference? These questions are difficult to answer because data are contaminated by random variability despite researchers' efforts to minimize extraneous influences on the data. Because of "noise" in the data, researchers rely on statistical methods of probabilistic inference to interpret the data. When data are interpreted in terms of meaningful parameters in a mathematical description, such as the difference of mean parameters in two groups, it is Bayesian analysis that provides complete information about the credible parameter values. Bayesian analysis is also more intuitive than traditional methods of null hypothesis significance testing (e.g., Dienes, 2011).This article introduces an intuitive Bayesian approach to the analysis of data from two groups. The method yields complete distributional information about the means and standard deviations of the groups. In particular, the analysis reveals the relative credibility of every possible difference of means, every possible difference of standard deviations, and all possible effect sizes. From this explicit distribution of credible parameter values, inferences about null values can be made without ever referring to p values as in null hypothesis significance testing (NHST). Unlike NHST, the Bayesian method can accept the null value, not only reject it, when certainty in the estimate is high. The new method handles outliers by describing the data as heavy tailed distributions instead of normal distributions, to the extent implied by the data. The new method also implements power analysis in both retrospective and prospective forms.The analysis is implemented in the widely used and free programming languages R and JAGS and can be run on Macintosh, Linux, and Windows operating systems. Complete installation instructions are provided, along with working examples. The programs can also be flexibly extended to other types of data and analyses. Thus, the software can be used by virtually ...

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A hierarchical model for estimating response time distributions

Cited by 169 publications

References 101 publications

A Dynamic Approach to Recognition Memory

A Dynamic Approach to Recognition Memory

Parallel interactive retrieval of item and associative information from event memory

Bayesian Estimation Supersedes the t Test

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