Models of causation and the semantics of causal verbs

This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb cause itself. According to focal set models of causation (; ), the concept of CAUSE should be more similar to the concepts of ENABLE and PREVENT than either is to each other. According to a model based on theory of force dynamics, the force dynamic model, the concepts of CAUSE, ENABLE, and PREVENT should be roughly equally similar to one another. The relationship between these predictions and the meaning of causal verbs was examined by having participants sort causal verbs and rate them with respect to the dimensions specified by the two models. The results from five experiments indicated that the force dynamic model provides a better account of the meaning of causal verbs than do focal set models of causation. Implications for causal inference and induction are discussed.     

Developmental continuity in the processes that underlie spatial recall

This study investigated whether children's spatial recall performance shows three separable characteristics: (1) biases away from symmetry axes (geometric effects); (2) systematic drift over delays; and (3) biases toward the exemplar distribution experienced in the task (experience-dependent effects). In Experiment 1, the location of one target within each geometric category was varied. Children's responses showed biases away from a midline axis that increased over delays. In Experiment 2, multiple targets were placed within each category at the same locations used in Experiment 1. After removing geometric effects, 6-year-olds'--but not 11-year-olds'--responses were biased toward the average remembered location over learning. In Experiment 3, children responded to one target more frequently than the others. Both 6- and 11-year-olds showed biases toward the most frequent target over learning. These results provide a bridge between the performance of younger children and adults, demonstrating continuity in the processes that underlie spatial memory abilities across development.     

Bayesian modeling of measurement error in predictor variables using item response theory

It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between the latent variables and dichotomous observed variables, which may be responses to tests or questionnaires. It will be shown that the multilevel model with measurement error in the observed predictor variables can be estimated in a Bayesian framework using Gibbs sampling. In this article, handling measurement error via the normal ogive model is compared with alternative approaches using the classical true score model. Examples using real data are given.This paper is part of the dissertation by Fox (2001) that won the 2002 Psychometric Society Dissertation Award.     

A hierarchical bayesian statistical framework for response time distributions

This paper provides a statistical framework for estimating higher-order characteristics of the response time distribution, such as the scale (variability) and shape. Consideration of these higher order characteristics often provides for more rigorous theory development in cognitive and perceptual psychology (e.g., Luce, 1986). RT distribution for a single participant depends on certain participant characteristics, which in turn can be thought of as arising from a distribution of latent variables. The present work focuses on the three-parameter Weibull distribution, with parameters for shape, scale, and shift (initial value). Bayesian estimation in a hierarchical framework is conceptually straightforward. Parameter estimates, both for participant quantities and population parameters, are obtained through Markov Chain Monte Carlo methods. The methods are illustrated with an application to response time data in an absolute identification task. The behavior of the Bayes estimates are compared to maximum likelihood (ML) estimates through Monte Carlo simulations. For small sample size, there is an occasional tendency for the ML estimates to be unreasonably extreme. In contrast, by borrowing strength across participants, Bayes estimation shrinks extreme estimates. The results are that the Bayes estimators are more accurate than the corresponding ML estimators.We are grateful to Michael Stadler who allowed us use of his data. This research is supported by (a) National Science Foundation Grant SES-0095919 to J. Rouder, D. Sun, and P. Speckman, (b) University of Missouri Research Board Grant 00-77 to J. Rouder, (c) National Science Foundation grant DMS-9972598 to Sun and Speckman, and (d) a grant from the Missouri Department of Conservation to D. Sun.     

Long-term efficacy of exercise across diagnostically heterogenous mental disorders and the mediating role of affect regulation skills

BackgroundExercise interventions are efficacious in reducing disorder-specific symptoms in various mental disorders. However, little is known about long-term transdiagnostic efficacy of exercise across heterogenous mental disorders and the potential mechanisms underlying treatment effects.MethodsPhysically inactive outpatients, with depressive disorders, anxiety disorders, insomnia or attention deficit hyperactivity disorder were randomized to a standardized 12-week exercise intervention, combining moderate exercise with behavior change techniques (BCTs) (n = 38), or a passive control group (n = 36). Primary outcome was global symptom severity (Symptom Checklist-90, SCL-90-R) and secondary outcomes were self-reported exercise (Physical Activity, Exercise, and Sport Questionnaire), exercise-specific affect regulation (Physical Activity-related Health Competence Questionnaire) and depression (SCL-90-R) assessed at baseline (T1), post-treatment (T2) and one year after post-treatment (T3). Intention-to-treat analyses were conducted using linear mixed models and structural equations modeling.ResultsFrom T1 to T3, the intervention group significantly improved on global symptom severity (d = −0.43, p = .031), depression among a depressed subsample (d = −0.62, p = .014), exercise (d = 0.45, p = .011) and exercise-specific affect regulation (d = 0.44, p = .028) relative to the control group. The intervention group was more likely to reveal clinically significant changes from T1 to T3 (p = .033). Increases in exercise-specific affect regulation mediated intervention effects on global symptom severity (ß = −0.28, p = .037) and clinically significant changes (ß = −0.24, p = .042).ConclusionsThe exercise intervention showed long-term efficacy among a diagnostically heterogeneous outpatient sample and led to long-lasting exercise behavior change. Long-term increases in exercise-specific affect regulation within exercise interventions seem to be essential for long-lasting symptom reduction.     

Wide-Coverage Probabilistic Sentence Processing

This paper describes a fully implemented, broad-coverage model of human syntactic processing. The model uses probabilistic parsing techniques, which combine phrase structure, lexical category, and limited subcategory probabilities with an incremental, left-to-right pruning mechanism based on cascaded Markov models. The parameters of the system are established through a uniform training algorithm, which determines maximum-likelihood estimates from a parsed corpus. The probabilistic parsing mechanism enables the system to achieve good accuracy on typical, garden-variety language (i.e., when tested on corpora). Furthermore, the incremental probabilistic ranking of the preferred analyses during parsing also naturally explains observed human behavior for a range of garden-path structures. We do not make strong psychological claims about the specific probabilistic mechanism discussed here, which is limited by a number of practical considerations. Rather, we argue incremental probabilistic parsing models are, in general, extremely well suited to explaining this dual nature—generally good and occasionally pathological—of human linguistic performance.     

“Oilfield Trash or Oilfield Treasure? A Pastoral Response to Living in the Extraction Economy of the Permian Basin” by Rev. Dr. Dawn Darwin Weaks

The climate crisis is witnessed on a global scale and it is also experienced in the local communities that work in extractive industries. In this article, a pastor in the Permian Basin explores the term “oilfield trash” as it is used for oilfield workers in the Permian Basin, connecting the epithet with the negative conditions for quality of life there, and comparing it to treatment of oilfield workers in Norway. Treatment of workers is identified as essential to esteem of communities and land. Renaming workers “treasure” in keeping with the tradition of Isaiah 62:4 is identified as part of the healing needed for extractive communities to transition away from fossil fuels. Four avenues of congregational ministry within mining economies are identified, with the way of “partnership” with the workers in the industry recommended as offering hope for churches to help relieve the climate crisis.     

Bayesian hierarchical response time modelling—A tutorial

Response time modelling is developing rapidly in the field of psychometrics, and its use is growing in psychology. In most applications, component models for response times are modelled jointly with component models for responses, thereby stabilizing estimation of item response theory model parameters and enabling research on a variety of novel substantive research questions. Bayesian estimation techniques facilitate estimation of response time models. Implementations of these models in standard statistical software, however, are still sparse. In this accessible tutorial, we discuss one of the most common response time models—the lognormal response time model—embedded in the hierarchical framework by van der Linden (2007). We provide detailed guidance on how to specify and estimate this model in a Bayesian hierarchical context. One of the strengths of the presented model is its flexibility, which makes it possible to adapt and extend the model according to researchers' needs and hypotheses on response behaviour. We illustrate this based on three recent model extensions: (a) application to non-cognitive data incorporating the distance-difficulty hypothesis, (b) modelling conditional dependencies between response times and responses, and (c) identifying differences in response behaviour via mixture modelling. This tutorial aims to provide a better understanding of the use and utility of response time models, showcases how these models can easily be adapted and extended, and contributes to a growing need for these models to answer novel substantive research questions in both non-cognitive and cognitive contexts.     

Further evaluation of treatment duration on the resurgence of destructive behavior

Translation of promising procedures for mitigating treatment relapse has received considerable attention recently from researchers across the basic–applied continuum. One procedure that has demonstrated mixed support involves increasing the duration of treatment as a strategy for blunting resurgence. In a recent translational study, Greer et al. (2020) failed to detect a mitigation effect of increased treatment duration on the resurgence of destructive behavior. However, design limitations may have been responsible. The present study corrected these limitations by (a) employing a sequential design to decrease the possibility of multiple-treatment interference, (b) evaluating more treatment durations, (c) arranging treatments of fixed durations, and (d) conducting treatments of more extreme duration in a different clinical sample. Despite these improvements in experimental rigor and the testing of more extreme boundary conditions, the present study also failed to detect a mitigation effect of increased treatment duration. Likely explanations are discussed.     

Thurstonian modeling of ranking data via mean and covariance structure analysis

Although Thurstonian models provide an attractive representation of choice behavior, they have not been extensively used in ranking applications since only recently efficient estimation methods for these models have been developed. These, however, require the use of special-purpose estimation programs, which limits their applicability. Here we introduce a formulation of Thurstonian ranking models that turns an idiosyncratic estimation problem into an estimation problem involving mean and covariance structures with dichotomous indicators. Well-known standard solutions for the latter can be readily applied to this specific problem, and as a result any Thurstonian model for ranking data can be fitted using existing general purpose software for mean and covariance structure analysis. Although the most popular programs for covariance structure analysis (e.g., LISREL and EQS) cannot be presently used to estimate Thurstonian ranking models, other programs such as MECOSA already exist that can be straightforwardly used to estimate these models.This paper is based on the author's doctoral dissertation. Ulf Böckenholt was my advisor. The author is indebted to Ulf Böckenholt for his comments on a previous version of this paper and to Gerhard Arminger for his extensive support on the use of MECOSA. The final stages of this research took place while the author was at the Department of Statistics and Econometrics, Universidad Carlos III de Madrid. Conversations with my colleague there, Adolfo Hernández, helped to greatly improve this paper.