Capacity and Procedural Accounts of Impaired Memory in Depression

Findings of impaired memory in states of dysphoria or depression are summarized and subsumed under different accounts of mood-related memory deficits. Theoretical accounts based on the assumption of a storage system of limited capacity are compared to accounts which emphasize the role of procedures and strategies in attending and remembering. Two reanalyses of a recent experiment in the process-dissociation paradigm are reported. They address issues of dysphoria-related differences in automatic versus controlled uses of memory in a task of word-stem completion. The two reanalyses rest on different assumptions about the relation between automatic and controlled components, but they converge in highlighting the advantages of a procedural rather than capacity-based view of memory deficits. finally. similarities to other research domains and theoretical approaches are outlined

Using recursive partitioning to account for parameter heterogeneity in multinomial processing tree models

In multinomial processing tree (MPT) models, individual differences between the participants in a study lead to heterogeneity of the model parameters. While subject covariates may explain these differences, it is often unknown in advance how the parameters depend on the available covariates, that is, which variables play a role at all, interact, or have a nonlinear influence, etc. Therefore, a new approach for capturing parameter heterogeneity in MPT models is proposed based on the machine learning method MOB for model-based recursive partitioning. This recursively partitions the covariate space, leading to an MPT tree with subgroups that are directly interpretable in terms of effects and interactions of the covariates. The pros and cons of MPT trees as a means of analyzing the effects of covariates in MPT model parameters are discussed based on a simulation experiment as well as on two empirical applications from memory research. Software that implements MPT trees is provided via the mpttree function in the psychotree package in R

Improved EM algorithm for MPT model analysis

Multinomialprocessing tree (MPT) models are in wide use as measurement models for analyzing categorical data in cognitive experiments. The approach involves estimating parameters and conducting hypothesis tests involving parameters that are arrayed in a tree structure designed to represent latent cognitive processes. The standard inference algorithm for these models is based on the well-known expectationmaximization (EM) algorithm. On the basis of the original use of the EMalgorithm for MPT models, this article presents an approach that accelerates the convergence speed of the algorithm by (1) adjusting suitable initial positions for certain parameters to reduce required iterative times and (2) using a series of operations between/among a set of matrices that are specific to the original model structure and information to reduce the time required for a single iteration. As compared with traditional algorithms, the simulation results show that the proposed algorithm has superior efficiency in interpreted languages and also has better algorithm readability and structure flexibility. © 2011 Psychonomic Society, Inc

Recognition memory models and binary-response ROCs: A comparison by minimum description length

Model comparison in recognition memory has frequently relied on receiver operating characteristics (ROC) data. We present a meta-analysis of binary-response ROC data that builds on previous such meta-analyses and extends them in several ways. Specifically, we include more data and consider a much more comprehensive set of candidate models. Moreover, we bring to bear modern developments in model selection on the current selection problem. The new methods are based on the minimum description length framework, leading to the normalized maximum likelihood (NML) index for assessing model performance, taking into account differences between the models in flexibility due to functional form. Overall, NML results for individual ROC data indicate a preference for a discrete-state model that assumes a mixture of detection and guessing states

Spinoza’s error: Memory for truth and falsity

Two theoretical frameworks have been proposed to account for the representation of truth and falsity in human memory: the Cartesian model and the Spinozan model. Both models presume that during information processing a mental representation of the information is stored along with a tag indicating its truth value. However, the two models disagree on the nature of these tags. According to the Cartesian model, true information receives a “true” tag and false information receives a “false” tag. In contrast, the Spinozan model claims that only false information receives a “false” tag, whereas untagged information is automatically accepted as true. To test the Cartesian and Spinozan models, we conducted two source memory experiments in which participants studied true and false trivia statements from three different sources differing in credibility (i.e., presenting 100% true, 50% true and 50% false, or 100% false statements). In Experiment 1, half of the participants were informed about the source credibility prior to the study phase. As compared to a control group, this precue group showed improved source memory for both true and false statements, but not for statements with an uncertain validity status. Moreover, memory did not differ for truth and falsity in the precue group. As Experiment 2 revealed, this finding is replicated even when using a 1-week rather than a 20-min retention interval between study and test phases. The results of both experiments clearly contradict the Spinozan model but can be explained in terms of the Cartesian model

Sensitivity to the prototype in children with high-functioning autism spectrum disorder: An example of Bayesian cognitive psychometrics

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