Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving

We hypothesize that effective collaboration is facilitated when individuals and environmental components form a synergy where they work together and regulate one another to produce stable patterns of behavior, or regularity, as well as adaptively reorganize to form new behaviors, or irregularity. We tested this hypothesis in a study with 32 triads who collaboratively solved a challenging visual computer programming task for 20 min following an introductory warm‐up phase. Multidimensional recurrence quantification analysis was used to examine fine‐grained (i.e., every 10 s) collective patterns of regularity across team members' speech rate, body movement, and team interaction with the shared user interface. We found that teams exhibited significant patterns of regularity as compared to shuffled baselines, but there were no systematic trends in regularity across time. We also found that periods of regularity were associated with a reduction in overall behavior. Notably, the production of irregular behavior predicted expert‐coded metrics of collaborative activity, such as teams' ability to construct shared knowledge and effectively negotiate and coordinate execution of solutions, net of overall behavioral production and behavioral self‐similarity. Our findings support the theory that groups can interact to form interpersonal synergies and indicate that information about system‐level dynamics is a viable way to understand and predict effective collaborative processes.     

Anxiety sensitivity in traumatized Cambodian refugees: a discriminant function and factor analytic investigation

We examined the psychometric properties and factor structure of a Cambodian translation of the Anxiety Sensitivity Index (ASI) and an Augmented ASI (the ASI supplemented with a 9-item addendum that assesses additional Cambodian concerns about anxiety-related sensations). Both the ASI and the Augmented ASI distinguished among three diagnostic groups: highest score, PTSD with panic disorder (PP group); next, panic disorder without PTSD (P group); and then, other disorders than PTSD or panic disorder (O group). In the discriminant function analysis using the Augmented ASI, the best classificatory predictor (PP vs. P vs. O) was an Addendum item (“It scares me when I stand up and feel dizzy”). The principal component analysis (oblimin rotation) of the ASI yielded a 3-factor solution (I, Weak Heart Concerns; II, Control Concerns; III, Social Concerns) and of the Augmented ASI, a 4-factor solution (I, Weak Heart Concerns; II, Control Concerns; III, Wind Attack Concerns; IV, Social Concerns). The item clustering within the factor solution of both the ASI and Augmented ASI illustrates the role of cultural syndromes in generating fear of mental and bodily events.     

Pseudo maximum likelihood estimation and a test for misspecification in mean and covariance structure models

Using the theory of pseudo maximum likelihood estimation the asymptotic covariance matrix of maximum likelihood estimates for mean and covariance structure models is given for the case where the variables are not multivariate normal. This asymptotic covariance matrix is consistently estimated without the computation of the empirical fourth order moment matrix. Using quasi-maximum likelihood theory a Hausman misspecification test is developed. This test is sensitive to misspecification caused by errors that are correlated with the independent variables. This misspecification cannot be detected by the test statistics currently used in covariance structure analysis.For helpful comments on a previous draft of the paper we are indebted to Kenneth A. Bollen, Ulrich L. Küsters, Michael E. Sobel and the anonymous reviewers of Psychometrika. For partial research support, the first author wishes to thank the Department of Sociology at the University of Arizona, where he was a visiting professor during the fall semester 1987.     

Establishment and replication of emotion regulation profiles: implications for psychological health

Background and Objectives: A promising method of capturing the complex nature of emotion regulation is to assess composite profiles of regulation (i.e., default pattern of regulation across multiple strategies). However, it remains unclear whether regulatory profiles demonstrate consistency across samples and in relation to mental health. Design: Two studies are presented here. Both utilized a cross-sectional design, and the second study presents a replication of the first. Method: Both studies utilized self-report data from independent undergraduate samples to perform latent profile analyses of emotion regulation use. Results: Studies 1 and 2 demonstrated evidence for four replicable regulatory profiles: Adaptive, Accepting (with or without Suppression), Non-accepting, and Maladaptive. Profiles were also related to symptoms of depression and anxiety, such that those consistently relying on adaptive strategies reported lower symptoms than those relying more heavily on maladaptive strategies. Conclusions: These findings clarify previous work which tied regulatory profiles to psychological health by extending a person-centered approach to understanding the ways in which individuals regulate their emotions.     

Reading Emotion From Mouse Cursor Motions: Affective Computing Approach

Affective computing research has advanced emotion recognition systems using facial expressions, voices, gaits, and physiological signals, yet these methods are often impractical. This study integrates mouse cursor motion analysis into affective computing and investigates the idea that movements of the computer cursor can provide information about emotion of the computer user. We extracted 16–26 trajectory features during a choice‐reaching task and examined the link between emotion and cursor motions. Participants were induced for positive or negative emotions by music, film clips, or emotional pictures, and they indicated their emotions with questionnaires. Our 10‐fold cross‐validation analysis shows that statistical models formed from “known” participants (training data) could predict nearly 10%–20% of the variance of positive affect and attentiveness ratings of “unknown” participants, suggesting that cursor movement patterns such as the area under curve and direction change help infer emotions of computer users.     

An interactional “live eye tracking” study in autism spectrum disorder: combining qualitative and quantitative approaches in the study of gaze

Recent studies on gaze behaviours in individuals with autism spectrum disorder (ASD) have utilised “live eye tracking.” Such research has focused on generating quantitative eye tracking measurements, which provide limited (if any) qualitative contextual details of the actual interactions in which gaze occurs. This article presents a novel methodological approach that combines live eye tracking with qualitative interaction analysis, multimodally informed conversation analysis. Drawing on eye tracking and wide-angle video recordings, this combination renders visible some of the functions, or what gaze “does,” in interactional situations. The participants include three children with ASD and their adult co-participants during body-movement gaming sessions. The article demonstrates how quantitative eye tracking research can be extended qualitatively using a microanalytic interaction analysis to recontextualise the gaze shifts identified. The findings in this article show that the co-participants treat a child’s gaze shifts differently depending on when these occur in a stream of other action. The study suggests that introducing this qualitative dimension to eye tracking research could increase its ecological validity and offer new insight into gaze behaviours in ASD.     

运动情境四维成就目标定向量表的初步编制——以第十二届特奥会中国籍智力残疾运动员为被试

该研究在文献分析和访谈的基础上,确定智力残疾运动领域成就目标定向的四维结构并编制初测问卷。对400名特奥会运动员进行了施测,对数据进行项目分析、验证性因素分析和信度分析。验证性因素分析和信度分析显示,问卷具有较好的效度和信度,可以作为运动领域四维目标定向的测量工具。     

A Bayesian Approach to More Stable Estimates of Group-Level Effects in Contextual Studies

Multilevel analyses are often used to estimate the effects of group-level constructs. However, when using aggregated individual data (e.g., student ratings) to assess a group-level construct (e.g., classroom climate), the observed group mean might not provide a reliable measure of the unobserved latent group mean. In the present article, we propose a Bayesian approach that can be used to estimate a multilevel latent covariate model, which corrects for the unreliable assessment of the latent group mean when estimating the group-level effect. A simulation study was conducted to evaluate the choice of different priors for the group-level variance of the predictor variable and to compare the Bayesian approach with the maximum likelihood approach implemented in the software Mplus. Results showed that, under problematic conditions (i.e., small number of groups, predictor variable with a small ICC), the Bayesian approach produced more accurate estimates of the group-level effect than the maximum likelihood approach did.     

非词复述测验的编制和初步分析

本研究编制了一种用于测量儿童语音工作记忆的测验———非词复述测验,并在48名四年级小学生中初步进行信度、效度检验和项目分析。结果表明:(1)该测验有较高的重测信度;(2)该测验具有较高的结构效度和效标效度;(3)分测验二的项目难度分布比较合理,多数项目鉴别力较高,而分测验一的项目难度分布和项目鉴别力则有待于在今后的研究中进一步提高。