Hierarchical modeling of contingency-based source monitoring: A test of the probability-matching account

Arnold, Nina R.; Bayen, Ute J.; Kuhlmann, Beatrice G.; Vaterrodt, Bianca

doi:10.3758/s13423-012-0342-7

Cited by 31 publications

(54 citation statements)

References 18 publications

(41 reference statements)

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“…Some of these techniques have been applied to source memory data (Arnold, Bayen, Kuhlmann, & Vaterrodt, 2013). There are also advances in metrics for model fit, such as the Fisher Information Approximation (Singmann & Kellen, 2013) and Minimum Description Length (Kellen et al., 2013).…”

Section: Discussionmentioning

confidence: 99%

Assumptions behind scoring source versus item memory: Effects of age, hippocampal lesions and mild memory problems

Cooper

Greve

Henson

2017

Cortex

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Source monitoring paradigms have been used to separate: 1) the probability of recognising an item (Item memory) and 2) the probability of remembering the context in which that item was previously encountered (Source memory), conditional on it being recognised. Multinomial Processing Tree (MPT) models are an effective way to estimate these conditional probabilities. Moreover, MPTs make explicit the assumptions behind different ways to parameterise Item and Source memory. Using data from six independent groups across two different paradigms, we show that one would draw different conclusions about the effects of age, age-related memory problems and hippocampal lesions on Item and Source memory, depending on the use of: 1) standard accuracy calculation vs MPT analysis, and 2) two different MPT models. The MPT results were more consistent than standard accuracy calculations, and furnished additional parameters that can be interpreted in terms of, for example, false recollection or missed encoding. Moreover, a new MPT structure that allowed for separate memory representations (one for item information and one for item-plus-source information; the Source-Item model) fit the data better, and provided a different pattern of significant differences in parameters, than the more conventional MPT structure in which source information is a subset of item information (the Item-Source model). Nonetheless, there is no theory-neutral way of scoring data, and thus proper examination of the assumptions underlying the scoring of source monitoring paradigms is necessary before theoretical conclusions can be drawn.

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Section: Discussionmentioning

confidence: 99%

Assumptions behind scoring source versus item memory: Effects of age, hippocampal lesions and mild memory problems

Cooper

Greve

Henson

2017

Cortex

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“…As an empirical example, we reanalyze data by Arnold, Bayen, Kuhlmann, and Vaterrodt ( 2013 ). Eighty-four participants had to learn statements that were presented by either a doctor or a lawyer (Source) and were either typical for doctors, typical for lawyers, or neutral (Expectancy).…”

Section: Multinomial Processing Tree Modelsmentioning

confidence: 99%

“…In previous analyses, parameter estimates for the beta-MPT model were often similar to those for the latent-trait MPT model (e.g., Arnold, Bayen, & Smith, 2015 ). Table 1 shows the results of both analyses for the retrieval condition from Arnold et al ( 2013 ). To facilitate the comparison, we transformed the probit mean μ and variance σ 2 in the latent-trait MPT to the probability scale using the TreeBUGS function probitInverse.…”

Section: Treebugsmentioning

confidence: 99%

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TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling

2017

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Multinomial processing tree (MPT) models are a class of measurement models that account for categorical data by assuming a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution. We provide an accessible introduction to hierarchical MPT modeling and present the user-friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity—the beta-MPT approach (Smith & Batchelder, Journal of Mathematical Psychology 54:167-183, 2010) and the latent-trait MPT approach (Klauer, Psychometrika 75:70-98, 2010). TreeBUGS reads standard MPT model files and obtains Markov-chain Monte Carlo samples that approximate the posterior distribution. The functionality and output are tailored to the specific needs of MPT modelers and provide tests for the homogeneity of items and participants, individual and group parameter estimates, fit statistics, and within- and between-subjects comparisons, as well as goodness-of-fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (as either fixed or random effects) in the latent-trait MPT model.

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“…B. Smith and Batchelder applied the beta-MPT approach to the MPT model of pair clustering (Batchelder & Riefer, 1986) and provided a WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, 2000) implementation. We previously applied the beta-MPT approach to the MPT model of source monitoring (Arnold, Bayen, Kuhlmann, & Vaterrodt, 2013) and to the MPT model of PM (Arnold, Bayen, & Böhm, in press). …”

Section: Hierarchical Mpt Modelingmentioning

confidence: 99%

Hierarchical Multinomial Modeling Approaches

Arnold

Bayen

Smith

2015

Experimental Psychology

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Hierarchical extensions of multinomial processing tree (MPT) models have been developed to deal with heterogeneity in participants or items. In this study, the beta-MPT model (J. B. Smith & Batchelder, 2010) and the latent-trait approach (Klauer, 2010) were used to estimate individual model parameters for prospective and retrospective components of prospective memory (PM), which requires remembering to perform an action in the future. The data from two experiments investigating the relationship between PM and working memory (R. E. Smith & Bayen, 2005, Experiment 1; R. E. Smith, Persyn, & Butler, 2011) were reanalyzed using the two hierarchical modeling approaches, both of which provide parameter estimates for individual participants. The results showed a positive correlation of the prospective component of PM with working-memory span and provide the first direct comparisons of the two hierarchical extensions of an MPT model.

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Hierarchical modeling of contingency-based source monitoring: A test of the probability-matching account

Cited by 31 publications

References 18 publications

Assumptions behind scoring source versus item memory: Effects of age, hippocampal lesions and mild memory problems

Assumptions behind scoring source versus item memory: Effects of age, hippocampal lesions and mild memory problems

TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling

Hierarchical Multinomial Modeling Approaches

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