Loving and angry? Happy and sad? Understanding and reporting of mixed emotions in mother–child relationships by 6‐ to 12‐year‐olds

The Evaluative Space Model of emotions allows for the coactivation of positive‐appetitive and negative‐avoidant systems, but few studies have examined mixed emotions in child development. Existing research suggests children's understanding of opposite valence emotion combinations emerges by approximately 11 years of age. However, it is not yet clear whether various opposite valence combinations are understood at different ages, nor whether children can understand them in others before they have experienced such mixed emotions themselves. Semi‐structured interviews with 97 children investigated whether they regarded six combinations of opposite valence mixed emotions as possible, could provide reasons for them, and report their own experience of each in the context of mother–child relationships. Both understanding that such combinations are possible and ability to provide reasons for them increased after age 6 and up to age 11, but were still incomplete in 12‐year‐olds. Understanding of different opposite valence combinations developed at different rates. At each age, fewer children who showed understanding of these combinations in others reported having had a similar experience themselves. The findings suggest a need to systematically examine a range of mixed emotions in order to develop a comprehensive theory of the development of mixed emotion understanding. They also suggest extending research into adolescence.     

Extending exploratory diagnostic classification models: Inferring the effect of covariates

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.     

« Conciliation vie privée vie professionnelle » : plusieurs termes pour une même réalité ? Proposition de modélisation à travers l’usage français

The aim of this paper is to define the vocabulary used about interactions between people's work and their private lives. Several terms exist and are used as synonyms despite of their slightly different meanings are different: balance, conflict, interaction, enrichment; personal life, family life… Based on an analysis of 45 French articles in psychology, management and human resources, we notice that the terms used are rarely defined. In a thematic analysis of these articles, we suggest a model which defines all words usually used.     

演绎推理的形式和内容启动加工

聚焦推理加工初期,研究通过指导语操控主观加工倾向,考察不同指导语条件下内容可信度和形式有效性对演绎推理结果的影响,检验内容和形式两类加工过程的启动加工动态变化性。结果表明,指导语条件显著影响推理初期加工。在无提示指导语和形式判定指导语条件下,推理结果受到了形式有效性和内容可信度的影响;内容判定指导语下只受到了内容可信度的影响。证明主观加工倾向影响了内容和形式的启动加工关系,支持了动态变化性的假设。     

Toward a social psychophysiology of vagally mediated heart rate variability: Concepts and methods in self‐regulation,emotion, and interpersonal processes

Theory and research on self‐regulation, emotional adjustment, and interpersonal processes focus increasingly on parasympathetic functioning, using measures of vagally mediated heart rate variability (vmHRV) or respiratory sinus arrhythmia (RSA). This review describes models of vmHRV in these areas, and issues in measurement and analysis. We propose a framework organizing theory and research as examining (a) vmHRV as an individual difference or a situational response, and (b) resting, reactive, or recovery levels. Evidence supports interpretation of individual differences in resting vmHRV as a broad biomarker for adaptive functioning, but its specificity and underlying mechanisms require elaboration. Individual differences in vagal reactivity (i.e., trait‐like differences in vmHRV decreases during challenge or stress) are less commonly studied in adults and results are mixed. Many stressors and challenges evoke temporary decreases in vmHRV, and in some research self‐regulatory effort evokes increases. In a smaller literature, positive interpersonal experiences and some restorative processes increase resting vmHRV, whereas depletion of self‐regulatory capacity through related effort decreases it. Greater attention to conceptual distinctions regarding vmHRV constructs and several methodological issues will strengthen future research. Importantly, researchers should exercise caution in equating vmHRV with specific psychosocial constructs, especially in the absence of converging assessments and precise experimental manipulations.     

Assessing the distinguishability of models and the informativeness of data

A difficulty in the development and testing of psychological models is that they are typically evaluated solely on their ability to fit experimental data, with little consideration given to their ability to fit other possible data patterns. By examining how well model A fits data generated by model B, and vice versa (a technique that we call landscaping), much safer inferences can be made about the meaning of a model's fit to data. We demonstrate the landscaping technique using four models of retention and 77 historical data sets, and show how the method can be used to: (1) evaluate the distinguishability of models, (2) evaluate the informativeness of data in distinguishing between models, and (3) suggest new ways to distinguish between models. The generality of the method is demonstrated in two other research areas (information integration and categorization), and its relationship to the important notion of model complexity is discussed.     

Global and local covert visual attention: Evidence from a bayesian hidden markov model

Psychological, psychophysical and physiological research indicates that people switch between two covert attention states, local and global attention, while visually exploring complex scenes. The focus in the local attention state is on specific aspects and details of the scene, and on examining its content with greater visual detail. The focus in the global attention state is on exploring the informative and perceptually salient areas of the scene, and possibly on integrating the information contained therein. The existence of these two visual attention states, their relative prevalence and sequence in time has remained empirically untested to date. To fill this gap, we develop a psychometric model of visual covert attention that extends recent work on hidden Markov models, and we test it using eye-movement data. The model aims to describe the observed time series of saccades typically collected in eye-movement research by assuming a latent Markov process, indicative of the brain switching between global and local covert attention. We allow subjects to be in either state while exploring a stimulus visually, and to switch between them an arbitrary number of times. We relax the no-memory-property of the Markov chain. The model that we develop is estimated with MCMC methodology and calibrated on eye-movement data collected in a study of consumers' attention to print advertisements in magazines.We thank Dominique Claessens of Verify International for making the data available to us.