Was it a car or a cat I saw? An Analysis of Response Times for Word Recognition

Peruggia, Mario; Zandt, Trisha Van; Chen, Meng

doi:10.1007/978-1-4612-2078-7_17

Cited by 26 publications

(25 citation statements)

References 10 publications

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“…There are alternative approaches that accommodate individual differences by specifying distributions of basic model parameters and then learning the "hyperparameters" of these distributions from data (see, e.g., Peruggia, Van Zandt, & Chen, 2002;Rouder & Lu, 2005;Rouder, Sun, Speckman, Lu, & Zhou, 2003). These models are often developed within a hierarchical Bayesian modeling framework.…”

Section: Discussionmentioning

confidence: 99%

Modeling individual differences in cognition

Lee

Webb

2005

Psychonomic Bulletin & Review

117

109

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Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility of important individual differences between subjects. Other evaluations are done at the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially provides. To overcome these weaknesses, we have developed a general approach to modeling individual differences using families of cognitive models in which different groups of subjects are identified as having different psychological behavior. Separate models with separate parameterizations are applied to each group of subjects, and Bayesian model selection is used to determine the appropriate number of groups. We evaluate this individual differences approach in a simulation study and show that it is superior in terms of the key modeling goals of prediction and understanding. We also provide two practical demonstrations of the approach, one using the ALCOVE model of category learning with data from four previously analyzed category learning experiments, the other using multidimensional scaling representational models with previously analyzed similarity data for colors. In both demonstrations, meaningful individual differences are found and the psychological models are able to account for this variation through interpretable differences in parameterization. The results highlight the potential of extending cognitive models to consider individual differences.

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Section: Discussionmentioning

confidence: 99%

Modeling individual differences in cognition

Lee

Webb

2005

Psychonomic Bulletin & Review

117

109

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“…The complete model is provided in Appendix C. This model is one of a family of additive models we have presented for psychological data (Lu, 2004;Lu, Sun, Speckman, & Rouder, 2005), and further statistical details have been presented there. Peruggia, Van Zandt, and Chen (2002) presented a similar approach with the two-parameter Weibull (shift set to zero), in which they also placed a linear model on logarithm of the rate.…”

Section: An Additive Hierarchical Modelmentioning

confidence: 99%

A hierarchical model for estimating response time distributions

Rouder

Speckman

et al. 2005

Psychonomic Bulletin & Review

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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Was it a car or a cat I saw? An Analysis of Response Times for Word Recognition

Cited by 26 publications

References 10 publications

Modeling individual differences in cognition

Modeling individual differences in cognition

A hierarchical model for estimating response time distributions

Modeling individual differences using Dirichlet processes

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