GPT.EXE: A powerful tool for the visualization and analysis of general processing tree models

Hu, Xiangen; Phillips, Glenn

doi:10.3758/bf03207714

Cited by 57 publications

(64 citation statements)

References 11 publications

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“…The processing tree model was specified and tested in the program GPT (Hu & Phillips, 1999), available on the Internet at http://xhuoffice.psyc. memphis.edu/gpt/.…”

Section: A Multinomial Modelmentioning

confidence: 99%

Conceptual and perceptual factors in the picture superiority effect

Stenberg

2006

European Journal of Cognitive Psychology

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The picture superiority effect, i.e., better memory for pictures than for corresponding words, has been variously ascribed to a conceptual or a perceptual processing advantage. The present study aimed to disentangle perceptual and conceptual contributions. Pictures and words were tested for recognition in both their original formats and translated into participants' second language. Multinomial Processing Tree (Batchelder & Riefer, 1999) and MINERVA (Hintzman, 1984) models were fitted to the data, and parameters corresponding to perceptual and conceptual recognition were estimated. Over three experiments, orienting tasks were varied, with neutral (Exp. 1), semantic (Exp. 2), and perceptual (Exp. 3) instructions, and the encoding manipulations were used to validate the parameters. Results indicate that there is picture superiority in both conceptual and perceptual memory, but conceptual processing makes a stronger contribution to the advantage of pictures over words in recognition.

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“…The processing tree model was specified and tested in the program GPT (Hu & Phillips, 1999), available on the Internet at http://xhuoffice.psyc. memphis.edu/gpt/.…”

Section: A Multinomial Modelmentioning

confidence: 99%

Conceptual and perceptual factors in the picture superiority effect

Stenberg

2006

European Journal of Cognitive Psychology

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“…The implementation of the parametric bootstrap in multi Tree is similar to the one in GPT (Hu & Phillips, 1999). Given an MPT model, the desired number of observations, and means and standard deviations of the parameters, a random parameter vector is obtained from a multivariate beta distribution (see below).…”

Section: Pdmentioning

confidence: 99%

“…If the standard deviations of the parameters differ, this analysis can be used to evaluate the robustness of a model against violations of the identical distribution (iid) assumption (cf. Hu & Phillips, 1999). For example, MPT models assume that the parameters are identically distributed for all items across all participants.…”

Section: Pdmentioning

confidence: 99%

“…Several computer programs have been developed in the past decade that can be used to estimate MPT modelsnamely, AppleTree (Rothkegel, 1999), GPT (Hu & Phillips, 1999) and HMMTree (Stahl & Klauer, 2007). Moreover, the parameters of MPT models can be estimated using the SOLVER library from Microsoft Excel with macros provided by Dodson, Prinzmetal, and Shimamura (1998).…”

Section: Moshagen Moshagen@uni-mannheimdementioning

confidence: 99%

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multiTree: A computer program for the analysis of multinomial processing tree models

Moshagen

2010

Behavior Research Methods

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352

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“…The statistical theory of MPT models, including maximum likelihood (ML) parameter estimation, overall model testing, and tests of specific hypotheses within models, has been discussed by Hu and Batchelder (1994) and by Riefer and Batchelder (1988). Flexible software to fit MPT models by means of ML estimation has been developed by Hu and Phillips (1999), Moshagen (2010), Rothkegel (1999), and Stahl and Klauer (2007. MPT models entail a reparameterization of the cell probabilities of the multinomial or product-multinomial distribution (Andersen, 1980;Bishop, Fienberg, & Holland, 1975) in terms of parameters assumed to represent the probabilities of underlying cognitive processes.…”

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confidence: 99%

Parameter identification in multinomial processing tree models

Schmittmann

Dolan

Raijmakers

et al. 2010

Behavior Research Methods

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Multinomial processing tree (MPT) models form an increasingly popular class of stochastic models for categorical data that have applications in a variety of research areas in cognitive, differential, and social psychology (e.g., Batchelder & Riefer, 1999Erdfelder et al., 2009;Stahl, 2006). For example, Batchelder and Riefer (1999) discussed over 80 applications of MPT models in various areas of psychology, including memory, perception, and reasoning. The statistical theory of MPT models, including maximum likelihood (ML) parameter estimation, overall model testing, and tests of specific hypotheses within models, has been discussed by Hu and Batchelder (1994) and by Riefer and Batchelder (1988). Flexible software to fit MPT models by means of ML estimation has been developed by Hu and Phillips (1999), Moshagen (2010), Rothkegel (1999), and Stahl and Klauer (2007. MPT models entail a reparameterization of the cell probabilities of the multinomial or product-multinomial distribution (Andersen, 1980;Bishop, Fienberg, & Holland, 1975) in terms of parameters assumed to represent the probabilities of underlying cognitive processes. The underlying cognitive architecture is represented as a rooted tree, in which each branch represents a processing sequence leading to an observable categorical response. More than one branch may terminate in a given response.In MPT models, as in all statistical models, global model identification (the ability to infer unique parameter values from data) is an important issue. A common cause of nonidentifiability of a model is parameter redundancy, in which case the likelihood of the model can be expressed as a function of fewer than the original number of parameters (Catchpole & Morgan, 1997). If a model is not parameter redundant, it is locally, and possibly even globally, identified; we go into the details of local and global identification in terms of MPT models below.In an MPT model, there is a function f ( ) that maps the model's parameter space into the set of possible multinomial probability distributions. Global identification can be established by proving that inequality of two parameter vectors implies inequality of the modeled multi nomial cell probability vectors f ( ) f ( ) for all parameter vectors and in the parameter space Meiser, 2005;Riefer & Batchelder, 1988; see also Bishop et al., 1975, p. 510). The required calculations for establishing global identification can become tedious, and even intractable, in complex models. In this case, the next best thing is to establish local identification; that is, implies f ( ) f ( ) for all in an open neighborhood around . One can empirically investigate local identification at a specific point G in the parameter space by fitting the generating MPT model to artificial (simulated) data generated by G , or to exact summary statistics-that is, the expected cell counts, derived from the probabilities f ( G ) implied by the generating MPT model. Obtaining parameter estimates sufficiently close to the true values G (in the case of simulated...

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GPT.EXE: A powerful tool for the visualization and analysis of general processing tree models

Cited by 57 publications

References 11 publications

Conceptual and perceptual factors in the picture superiority effect

Conceptual and perceptual factors in the picture superiority effect

multiTree: A computer program for the analysis of multinomial processing tree models

Parameter identification in multinomial processing tree models

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