The complexity of developmental predictions from dual process models

Drawing developmental predictions from dual-process theories is more complex than is commonly realized. Overly simplified predictions drawn from such models may lead to premature rejection of the dual process approach as one of many tools for understanding cognitive development. Misleading predictions can be avoided by paying attention to several cautions about the complexity of developmental extrapolations. The complexity of developmental predictions follows from the fact that overall normative responding at a given age derives from several different mental characteristics: (1) the developmental course of Type 1 processing, (2) the developmental course of Type 2 processing, (3) the acquisition of mindware usable by Type 1 processing, (4) the acquisition of mindware usable by Type 2 processing, and (5) the practicing of the mindware available to Type 2 processing to the extent that it is available to be processed in an autonomous manner. The complexity of all these interacting processes and sources of information can sometimes result in U-shaped developmental functions on some heuristics and biases tasks, making younger children look like they are responding more optimally than older children. This is particularly true when the youngest groups are ill-equipped to even understand the task and thus respond randomly. A final caution concerns terminology: The terms normative or rational should be reserved for responses and not attributed to subpersonal processes.     

Inspection times and the selection task: What do eye-movements reveal about relevance effects?

Three experiments are reported that used eye-movement tracking to investigate the inspection-time effect predicted by Evans' (1996) heuristic-analytic account of the Wason selection task. Evans' account proposes that card selections are based on the operation of relevance-determining heuristics, whilst analytic processing only rationalizes selections. As such, longer inspection times should be associated with selected cards (which are subjected to rationalization) than with rejected cards. Evidence for this effect has been provided by Evans (1996) using computer- presented selection tasks and instructions for participants to indicate (with a mouse pointer) cards under consideration. Roberts (1998b) has argued that mouse pointing gives rise to artefactual support for Evans' predictions because of biases associated with the task format and the use of mouse pointing. We eradicated all sources of artefact by combining careful task constructions with eye-movement tracking to measure directly on-line attentional processing. All three experiments produced good evidence for the robustness of the inspection-time effect, supporting the predictions of the heuristic-analytic account.     

Is Work Conducive to Self-Destruction?

ABSTRACT: While the current literature contains numerous studies and even more numerous assumptions linking aspects of the work setting, stress, and mortality; no systematic investigation has been made of possible patterns of stress-induced self-destructive behaviors among the work settings per se. This research paper attempts to help fill that gap by reporting on an analysis of data on industry, age and mortality rates for seven stress-related causes of death (suicide, homocide, hypertensive heart disease, cirrhosis of the liver, arteriosclerotic heart disease, ulcer of the stomach, and hypertension). Using available United States' mortality statistics, a consistent pattern is found for all of the stress-related types of deaths by industry and age. A suggested explanation of this pattern is based on status integration theory.     

Reinforcement sensitivity theory at work: punishment sensitivity as a dispositional source of job-related stress

Gray's reinforcement sensitivity theory (RST) describes two important personality constructs; sensitivity to reward and sensitivity to punishment. In two studies, we examine whether these constructs can be considered dispositions to work stress. Results of Study 1 (N = 105 employees in different occupations) indicated that employees with strong punishment sensitivity reacted more strongly to work stressors than others. This idea was confirmed in a longitudinal design in Study 2. Reward sensitivity was unrelated to stress in both studies. Overall, results strongly support the idea that punishment sensitivity is a dispositional source of work stress. Results further confirm that RST and its derived personality measures can contribute to theorizing about personality–environment interactions in a highly relevant daily setting, namely the working environment. Copyright © 2007 John Wiley & Sons, Ltd.     

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation

Abstract

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x²) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.