Associations Between Infant Brain Activity and Recall Memory

Long‐term explicit memory is thought to involve a complex neural circuit including the medial temporal lobe, the medial diencephalon, the prefrontal cortex, and association cortices. When this memory system and associated neural circuitry develops is of great interest to developmental psychologists and developmental cognitive neuroscience researchers. In the present report, we provide evidence of a relation between behavioral and neurophysiological measures of long‐term explicit memory in 9‐month‐old infants. These measures provide converging evidence of the development of long‐term explicit memory at least by the end of the first year of life.     

Multiple Realizability,Constraints, and Identity

Shapiro has suggested that the empirical plausibility of the multiple realizability of human‐like minds is dubious, because a contrary thesis, the Mental Constraint Thesis, enjoys positive empirical evidence. The Mental Constraint Thesis states that, given the actual physical laws, there is only one way to realize a human‐like mind. I will suggest, however, that the Mental Constraint Thesis is not a contrary to the empirical multiple realizability thesis relevant to psychological reduction or autonomy and, as a consequence, has no bearing on those classificatory issues.     

Editorial overview: Analytic and methodological innovations for suicide‐focused research

This editorial overview provides an introduction to the Suicide and Life‐Threatening Behaviors Special Issue: “Analytic and Methodological Innovations for Suicide‐Focused Research.” We outline several challenges faced by modern suicidologists, such as the need to integrate different analytical and methodological techniques from other fields with the unique data problems in suicide research. Therefore, the overall aim of this issue was to provide up‐to‐date methodological and analytical guidelines, recommendations, and considerations when conducting suicide‐focused research. The articles herein present this information in an accessible way for researchers/clinicians and do not require a comprehensive background in quantitative methods. We introduce the topics covered in this special issue, which include how to conduct power analyses using simulations, work with large data sets, use experimental therapeutics, and choose covariates, as well as open science considerations, decision‐making models, ordinal regression, machine learning, network analysis, and measurement considerations. Many of the topics covered in this issue provide step‐by‐step walkthroughs using worked examples with the accompanied code in free statistical programs (i.e., R). It is our hope that these articles provide suicidologists with valuable information and strategies that can help overcome some of the past limitations of suicide research, and improve the methodological rigor of our field.     

Impact of assertive and accusatory communication of distress and anger: A verbal component analysis

Forty undergraduate women were asked to imagine discussing an important problem in their relationship with a personally close partner and to rate likely emotional and behavioral reactions to four kinds of statements: 1) assertive communication of distress, 2) assertive communication of anger, 3) accusatory communication of distress, and 4) accusatory communication of anger. Accusatory “you” statements were rated as more aversive and evoked more negative emotional and behavioral response inclinations than did assertive “I” messages. Statements including “anger” words evoked more negative emotional and behavioral response inclinations than statements including “distress” words. Findings were related to research on cue-controlled aggression and the work of Staats, which predicts ways that verbal labels (language) exert control over emotions and behavior.     

Abstract: Comparing Semiparametric and Parametric Methods for Modeling Interactions Among Latent Variables

Many approaches have been proposed to estimate interactions among latent variables. These methods often assume a specific functional form for the interaction, such as a bilinear interaction. Theory is seldom specific enough to provide a functional form for an interaction, however, so a more exploratory, diagnostic approach may often be required. Bauer (2005) proposed a semiparametric approach that allows for the estimation of interaction effects of unknown functional form among latent variables. A structural equation mixture model (SEMM) is first fit to the data. Then an approximation of the interaction is obtained by aggregating over the mixing components. A simulation study is used to examine the performance of this semiparametric approach to two parametric approaches: the latent moderated structures approach (Klein & Moosbrugger, 2000) and the unconstrained product-indicator approach (Marsh, Wen, & Hau, 2004). Data were generated from four functional forms: main effects only, quadratic trend, bilinear interaction, and exponential interaction. Estimates of bias and root mean squared error of approximation were calculated by comparing the surface used to generate the data and the model-implied surface constructed from each approach. As expected, the parametric approaches were more efficient than the SEMM. For the main effects model, bias was similar for both the SEMM and parametric approaches. For the bilinear interaction, the parametric approaches provided nearly identical results, although the SEMM approach was slightly more biased. When the parametric approaches assumed a bilinear interaction and the data were generated from a quadratic trend or an exponential interaction, the parametric approaches generated biased estimates of the true surface. The SEMM approach approximated the true data generation surface with a similarly low level of bias for all the nonlinear surfaces. For example, Figure 1 shows the true surface for the bilinear interaction along with the SEMM estimated average surface. The results suggest that the SEMM approach can provide a relatively unbiased approximation to variety of nonlinear relationships among latent variables.     

Observations on the Use of Growth Mixture Models in Psychological Research

Psychologists are applying growth mixture models at an increasing rate. This article argues that most of these applications are unlikely to reproduce the underlying taxonic structure of the population. At a more fundamental level, in many cases there is probably no taxonic structure to be found. Latent growth classes then categorically approximate the true continuum of individual differences in change. This approximation, although in some cases potentially useful, can also be problematic. The utility of growth mixture models for psychological science thus remains in doubt. Some ways in which these models might be more profitably used are suggested.