Returning to the present moment: Thinking about one’s childhood increases focus on the hedonistic present

Children seem to live more in the present moment than adults. But is it possible to make the present time perspective more available among adults? Four experiments demonstrated that activating childhood selves can lead adults to be more embedded in the hedonistic present. Studies 1 and 2 showed that recalling memories from childhood, either in an open-ended or a structured form, increased participants’ focus on the hedonistic present. Study 3 showed that this effect also occurred after an implicit childhood manipulation. Study 4 revealed that taking the perspective of a child significantly increased orientation to the hedonistic present, compared to taking the perspective of an adult. The effects of activation of childhood selves were not mediated by mood, nostalgia, impatience, concentration, difficulty of the task, enjoyment connected with the task, or involvement in the task. Evaluation of one’s childhood and tendency to relive childhood memories did not affect the results.     

The interactive effect of neuroticism and extraversion on the daily variability of affective states

The interaction between neuroticism and extraversion is thought to predict affective variability. In this study, neuroticism and extraversion were assessed with questionnaires, and affects were measured by experience sampling, with five daily assessments over 2 weeks. Affective variability was studied within a three-dimensional affective space whose three axes were oriented along the main affective dimensions: positive affect, negative affect, and activation. Quantile regression mixed-effects models allowed predicting zones in which affective states were most likely to occur according to personality. Beyond the well-known effect of personality on affect level, high neuroticism and/or high extraversion were accompanied by heightened affective variability. Results were interpreted as potentially reflecting positive feedback loops oriented toward negative affect for neuroticism, and toward positive affect and activation for extraversion.     

纳入式分类分析法在潜在剖面模型的后续多元回归中的应用

纳入式分类分析法能克服传统的分类分析法对后续一元回归模型参数的低估,发挥潜在类别模型的后续分析简化变量间交互作用的功能。本文进一步将纳入式分类分析法拓展至潜在剖面模型后续的多元统计分析中。通过蒙特卡洛模拟实验,比较各种纳入变量的方法思路与后续分析模型在四种常见的多元回归模型中参数估计的表现。结果发现,纳入式分类分析法所需纳入的变量取决于后续分析中与因变量、潜类别变量的关系,且后续分析使用含交互作用的模型更为稳健。     

Prediction of overt aggression experienced by college students

We sought to predict overt aggression among college students from their intra- and interpersonal relationships self-perceptions. Respondents were 311 students (females = 69.8%; black = 65.9%, white = 16.7%; mean age = 26.38 years, SD = 9.57 years) from a large South African university. Results following multivariate inferential analysis indicated Negative, destructive interpersonal relationships; Negative, destructive self-concept; and Positive inclination towards others to predict overt aggression among the university students. By contrast, Positive interaction with self and others; and Submissiveness towards others can be used to predict overt aggression among the university students. Our hypothesis that the more constructive (higher) students’ self-concept and intrapersonal relationships, interpersonal relationships, the lower will their levels of overt aggression, physical aggression and verbal aggression become was supported. As such, the finding was that: Overt Aggression = a(Negative destructive interpersonal relationships) + b(Negative destructive self-concept) ? c(Positive inclination towards others).     

Tests of Simple Slopes in Multiple Regression Models with an Interaction: Comparison of Four Approaches

In multiple regression researchers often follow up significant tests of the interaction between continuous predictors X and Z with tests of the simple slope of Y on X at different sample-estimated values of the moderator Z (e.g., ±1 SD from the mean of Z). We show analytically that when X and Z are randomly sampled from the population, the variance expression of the simple slope at sample-estimated values of Z differs from the traditional variance expression obtained when the values of X and Z are fixed. A simulation study using randomly sampled predictors compared four approaches: (a) the Aiken and West (1991 Aiken, L. S., &; West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.[Crossref] , [Google Scholar]) test of simple slopes at fixed population values of Z, (b) the Aiken and West test at sample-estimated values of Z, (c) a 95% percentile bootstrap confidence interval approach, and (d) a fully Bayesian approach with diffuse priors. The results showed that approach (b) led to inflated Type 1 error rates and 95% confidence intervals with inadequate coverage rates, whereas other approaches maintained acceptable Type 1 error rates and adequate coverage of confidence intervals. Approach (c) had asymmetric rejection rates at small sample sizes. We used an empirical data set to illustrate these approaches.     

Should metacognition be measured by logistic regression?

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria.     

Authentic leadership influences on organisational commitment in a South African state hospital

This study investigated the influence of authentic leadership (AL) on employee organisational commitment (OC) in a South African state hospital. A convenience sample of 222 employees (female = 81%; black = 82%) completed the Authentic Leadership Questionnaire on their perceptions of AL behaviours among their leaders; namely self-awareness, relational transparency, internalised moral perspective, and balanced processing. The employees also completed the Organisational Commitment Questionnaire to self-report on their own OC; namely affective, continuance, and normative commitment. Data were analysed utilising regression analysis to predict employees’ self-reported OC from perceived AL behaviours. Findings suggest AL scores to predict OC scores as well as the OC dimensions of continuance and normative commitment. AL scores did not predict affective commitment. Employees whose leaders apply AL may be able to retain employees through increased OC.     

Identification of abnormal movements with 3D accelerometer sensors for seizure recognition

Human-activity recognition and seizure-detection techniques have gathered pace with the widespread availability of wearable devices. A study of the literature shows various studies for 3D accelerometer-based seizure detection that describe the selection of acceleration variables and controlled transformations, while discarding the remaining input variable contributions. The aim of this research is to evaluate feature extraction based on different techniques and with the advantage of an overview of all information on the problem. Three feature extraction techniques – namely, Locally Linear Embedding, Principal Component Analysis (PCA) and a Distance-Based PCA – are analyzed and their outcomes compared against K-Nearest Neighbor and Decision Trees. A realistic experimentation simulating epileptic mioclonic convulsions was performed. The PCA-based methods were found to produce solutions that managed the problem perfectly well, either learning specific models for each individual or learning generalized models.     

Regularized Multiple-Set Canonical Correlation Analysis

Multiple-set canonical correlation analysis (Generalized CANO or GCANO for short) is an important technique because it subsumes a number of interesting multivariate data analysis techniques as special cases. More recently, it has also been recognized as an important technique for integrating information from multiple sources. In this paper, we present a simple regularization technique for GCANO and demonstrate its usefulness. Regularization is deemed important as a way of supplementing insufficient data by prior knowledge, and/or of incorporating certain desirable properties in the estimates of parameters in the model. Implications of regularized GCANO for multiple correspondence analysis are also discussed. Examples are given to illustrate the use of the proposed technique. The work reported in this paper is supported by Grants 10630 and 290439 from the Natural Sciences and Engineering Research Council of Canada to the first and the second authors, respectively. The authors would like to thank the two editors (old and new), the associate editor, and four anonymous reviewers for their insightful comments on earlier versions of this paper. Matlab programs that carried out the computations reported in the paper are available upon request.     

Criteria instability and the isolated option effect

In two-stage choices, decision makers often compare a new (isolated) option with the winner from the first stage. Previous research has identified a choice advantage for an isolated option, ostensibly due to loss aversion. We propose an alternative mechanism suggesting that instability of the criteria used in each choice stage is the main driver of the isolated option effect. Results from a series of experiments support the criteria instability account and not loss aversion as the explanation for the isolated option effect.