工作记忆广度个体差异模型述评

探究工作记忆广度的个体差异对揭示工作记忆实质具有重要的意义,文章首先介绍了工作记忆广度任务的特点,其次,对目前工作记忆广度个体差异的主要理论模型进行阐述和评价,最后总结了可能影响工作记忆广度个体差异的主要因素。     

Rank‐based permutation approaches for non‐parametric factorial designs

Inference methods for null hypotheses formulated in terms of distribution functions in general non‐parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set‐up Wald‐type statistics and ANOVA‐type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal–Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability.     

More efficient parameter estimates for factor analysis of ordinal variables by ridge generalized least squares

Data in psychology are often collected using Likert‐type scales, and it has been shown that factor analysis of Likert‐type data is better performed on the polychoric correlation matrix than on the product‐moment covariance matrix, especially when the distributions of the observed variables are skewed. In theory, factor analysis of the polychoric correlation matrix is best conducted using generalized least squares with an asymptotically correct weight matrix (AGLS). However, simulation studies showed that both least squares (LS) and diagonally weighted least squares (DWLS) perform better than AGLS, and thus LS or DWLS is routinely used in practice. In either LS or DWLS, the associations among the polychoric correlation coefficients are completely ignored. To mend such a gap between statistical theory and empirical work, this paper proposes new methods, called ridge GLS, for factor analysis of ordinal data. Monte Carlo results show that, for a wide range of sample sizes, ridge GLS methods yield uniformly more accurate parameter estimates than existing methods (LS, DWLS, AGLS). A real‐data example indicates that estimates by ridge GLS are 9–20% more efficient than those by existing methods. Rescaled and adjusted test statistics as well as sandwich‐type standard errors following the ridge GLS methods also perform reasonably well.     

Evaluation of Multi-parameter Test Statistics for Multiple Imputation

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.     

Empirically Corrected Rescaled Statistics for SEM with Small N and Large p

Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. With typical nonnormally distributed data in practice, a rescaled statistic T_rml proposed by Satorra and Bentler was recommended in the literature of SEM. However, T_rml has been shown to be problematic when the sample size N is small and/or the number of variables p is large. There does not exist a reliable test statistic for SEM with small N or large p, especially with nonnormally distributed data. Following the principle of Bartlett correction, this article develops empirical corrections to T_rml so that the mean of the empirically corrected statistics approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics control type I errors reasonably well even when N is smaller than 2p, where T_rml may reject the correct model 100% even for normally distributed data. The application of the empirically corrected statistics is illustrated via a real data example.     

生态化微表情识别测验EMERT的建立——对JACBART微表情识别测验的改进与发展

经典JACBART微表情识别测验只考察平静表情背景下微表情识别,生态效度不高。本研究创建生态化微表情识别测验,考察所有7种基本表情背景下的6种微表情识别特征。结果发现:(1)该测验具有良好的重测信度、校标效度和生态效度,能够稳定有效地测量生态化微表情识别。(2)信效度检验揭示了生态化微表情识别特征。某些生态化微表情识别存在训练效应。生态化微表情与经典微表情或普通表情普遍相关。恐惧、悲伤、厌恶、愤怒微表情背景主效应显著;惊讶和愉快微表情背景主效应不显著,成对比较发现各背景下惊讶/愉快微表情差异不显著,但是与普通表情有广泛的显著差异。用不同背景下微表情识别正确率的标准差定义生态化微表情识别波动,发现生态化微表情识别具有稳定的波动性。     

Implicit health associations across the adult lifespan

Objective: Explicit reports of one’s health self-concept (e.g. rate your overall health) are commonly used in research and clinical practice. These measures predict important health outcomes, but rely on conscious introspection so may not fully capture the different components of the health self-concept (e.g. more automatic components) that relate to actual health. This study examined the health-implicit association test (health-IAT), and how it may add to our prediction of health from self-reports.

Design: 1004 participants (ages 18–85) completed this web-based study with the health-IAT (assessing self-healthy implicit associations) and explicit assessments of health.

Main outcome measures: Self-reported measures of physical functioning.

Results: The health-IAT was valid and reliable. Older age was correlated with stronger self-healthy implicit associations. Although the health-IAT did not incrementally predict self-reported markers of physical functioning when only controlling for explicit health self-concept, it was an incremental predictor once age was entered for all four models tested.

Conclusions: The health-IAT appears to be a valid and reliable new measure that assesses implicit self-concept relating to physical health. Results reveal the potential value of assessing implicit health self-concept in both research and practice, especially when taking into account age.     

On Being Found: How Habitual Patterns of Thought Influence Creative Interest,Behavior, and Ability

This study on 138 undergraduate students used path analysis to investigate the relationship between creativity (interest, measured by a creative activities survey; and ability: fluency, originality, and elaboration) and different aspects of thought patterns presumed to influence the preparation and illumination phase of the creative process: habitual patterns of thought (ruminative brooding, ruminative self-reflection), thought suppression, thought intrusion, mind wandering, and associative ability. Such relationship was hinted at in Wallas’s classical model of the creative sequence, but is rarely investigated. We found that creative behavior/interest was driven by self-reflection, thought intrusion, and the lack of a need for thought suppression; creative ability was fueled mainly by associative ability. The only variable that influenced both aspects of creativity was the lack of resistance to thought suppression; this distinguished the creativity variables from dysphoria, which was associated with a desire for thought suppression. The results suggest that what drives the need to create is not creative ability per se, but rather self-focused attention, as well as the feeling or experience of being found by thought, rather than finding it. That is, the need to create is associated with having thoughts that interrupt one’s ordinary stream of consciousness and that are seen as welcome rather than interfering.     

The cognitive reflection test revisited: exploring the ways individuals solve the test

Individuals’ propensity not to override the first answer that comes to mind is thought to be a crucial cause behind many failures in reasoning. In the present study, we aimed to explore the strategies used and the abilities employed when individuals solve the cognitive reflection test (CRT), the most widely used measure of this tendency. Alongside individual differences measures, protocol analysis was employed to unfold the steps of the reasoning process in solving the CRT. This exploration revealed that there are several ways people solve or fail the test. Importantly, 77% of the cases in which reasoners gave the correct final answer in our protocol analysis, they started their response with the correct answer or with a line of thought which led to the correct answer. We also found that 39% of the incorrect responders reflected on their first response. The findings indicate that the suppression of the first answer may not be the only crucial feature of reflectivity in the CRT and that the lack of relevant knowledge is a prominent cause of the reasoning errors. Additionally, we confirmed that the CRT is a multi-faceted construct: both numeracy and reflectivity account for performance. The results can help to better apprehend the “whys and whens” of the decision errors in heuristics and biases tasks and to further refine existing explanatory models.     

Body perception and meal type across age and gender on a Mediterranean island (Sardinia)

Our aim was to describe the differences in consumption of types of meal (complete/incomplete) eaten at lunch and dinner by gender in people of different age groups (children, young adults and seniors). In particular, we wanted to investigate the differences in choice of meal based on perceived weight. The study involved 516 participants, of which 156 were children (8-12y), 187 were young adults (19-30y) and 173 were elderly (65-90y). A cross-sectional analysis was carried out into the choice of meal type based on the different age groups and genders, on the three Body Mass Index categories (under, normal, overweight), on the three levels of self-perception (slim, normal and heavy) and on the degrees of perception of one’s body weight (underestimation, accurate estimation, overestimation). The food eaten was grouped into three types of daily meal: (a) incomplete, (b) complete lunch or dinner, (c) always complete. Differences emerged in the type of meal eaten according to age, gender and perceived weight. The results show gender differences in associations with meal type according to the way in which body mass is measured or perceived. The choice of complete/incomplete meal type changes according to whether real weight or perceived weight is considered.