Current status of the motor program: Revisited

The motor program is a concept that has had a major influence on theorizing in the field of motor control. However, there has been a lack of consensus as to what exactly is a motor program and its role in movement organization and execution. In 1994 Morris, Summers, Matyas, and Iansek concluded from a review of the application of the motor program concept in the field of physical therapy that continued use of the term may impede progress in the field. In this paper we examine what has happened to the motor program concept in the thirteen years since the previous evaluation. The review indicates that although the term is still being used in different ways, the theoretical existence of a motor program appears to be generally accepted by researchers in experimental psychology, movement science, and neurophysiology. The recent development of powerful brain imaging techniques may allow determination of whether the motor program should be regarded as a metaphorical or literal concept.     

Labels as features (not names) for infant categorization: a neurocomputational approach

A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorization processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. We distinguish between two competing accounts: supervised name-based categorization and unsupervised feature-based categorization. We describe a neurocomputational model of infant visual categorization, based on self-organizing maps, that implements the unsupervised feature-based approach. The model successfully reproduces experiments demonstrating the impact of labeling on infant visual categorization reported in Plunkett, Hu, and Cohen (2008) . It mimics infant behavior in both the familiarization and testing phases of the procedure, using a training regime that involves only single presentations of each stimulus and using just 24 participant networks per experiment. The model predicts that the observed behavior in infants is due to a transient form of learning that might lead to the emergence of hierarchically organized categorical structure and that the impact of labels on categorization is influenced by the perceived similarity and the sequence in which the objects are presented. The results suggest that early in development, say before 12 months old, labels need not act as invitations to form categories nor highlight the commonalities between objects, but they may play a more mundane but nevertheless powerful role as additional features that are processed in the same fashion as other features that characterize objects and object categories.     

Community types and mental health: a multilevel study of local environmental stress and coping

Research has found that neighborhood structural characteristics can influence residents' mental health. Few studies, however, have explored the proximal reasons behind such influences. This study investigates how different types of communities, in terms of environmental stressors (social and physical disorder and fear of crime) and social resources (informal ties and formal organizational participation), affect well-being, depression, and anxiety in adult residents. Data are from a survey of 412 residents nested in 50 street blocks. Block stressors and resources were cluster analyzed to identify six block types. After controlling for several individual- and block-level characteristics, results from multilevel models suggest that in communities facing relatively few stressors, higher levels of formal participation are associated with better mental health. Because high levels of formal participation were not found in communities with higher levels of stressors, the impact of participation in such contexts could not be examined. However, results suggest that in communities where stressors are more common, isolation from neighbors may have a protective effect on mental health.     

A general method for computing hierarchical component structures by Goldberg’s Bass-Ackwards method

Goldberg [Goldberg, L. R.(in press) Doing it all Bass-Ackwards: The development of hierarchical factor structures from the top down. Journal of Research in Personality] has recently described a novel method for computing hierarchical component structures via a “top down” design. He calls his method the “Bass Ackwards” approach to distinguish it from more common “bottom up” methods for obtaining hierarchical solutions. Using simple matrix equations, in this commentary I demonstrate that the end result of a Bass Ackwards analysis—that is, the correlations between component scores from different levels of a hierarchy—can be calculated without calculating the actual component scores. By avoiding component-score calculation, Goldberg’s method (a) can be applied to any data set in which a correlation matrix is available, and (b) can be applied to factor scores rather than estimated factor scores. Goldberg suggests that the Bass Ackwards method represents a useful tool for elucidating the underlying dimensionality and latent structure (rotation in hyperspace) of a data set. Computer code is reported in the Appendix A of this article to help personality researchers more fully evaluate this claim in empirical and simulated data.     

Trait anhedonia is a transdiagnostic correlate of internalizing problems during adolescence

Developmental research documents that anhedonia, or diminished interest in usual activities, is associated with a diverse array of emotional problems in childhood and adolescence. Meanwhile, official nosologies desginate anhedonia as a more specific characteristic of major depressive disorder. Using a quantitative model of the internalizing domain, we compared the strength of transdiagnostic versus diagnosis-specific pathways from anhedonia to major depression (and other internalizing conditions) during adolescence. We recruited 241 youth ages 14–17 who completed semistructured interviews of anxiety and depressive disorders, as well as several self-report surveys of trait anhedonia and neuroticism. Confirmatory factor analysis of diagnostic correlations revealed good fit for a unidimensional model of the 10 internalizing conditions we assessed. This overarching internalizing dimension was statistically significantly correlated with trait anhedonia (r = 0.17) and neuroticism (r = 0.59). In contrast, anhedonia was virtually unrelated to major depression (r = −0.02), net the internalizing dimension. Thus, in this sample, the connection between anhedonia and major depression was explained by a transdiagnostic dimension presumed to underlie all internalizing problems. Compared to neuroticism, however, anhedonia had a more limited association with internalizing, consistent with established personality models of anxiety and depression. We conclude that these data are consistent with conceptualizing anhedonia predominantly as a transdiagnostic correlate of internalizing conditions, rather than a specific marker of major depression, in developmental psychopathology research and clinical interventions for young people.     

Effects of primary task predictability and secondary task modality on lane maintenance

The objective of this work was to investigate the effects of auditory and visuo-spatial secondary tasks on variability in lane position in predictable and unpredictable driving conditions. Sixty-six participants drove a simulated vehicle. Predictability was manipulated by adding wind gusts, and the secondary task load by either an auditory task (Experiment 1) or a visuo-spatial task (Experiment 2). Results demonstrated that in the predictable driving condition, lane position variability decreased when auditory secondary task load was given, but it was not affected by visuo-spatial secondary task load. In the unpredictable driving condition; however, while the auditory secondary task load did not affect lane position variability, visuo-spatial secondary task load increased it. The data suggests that the effects of cognitive load on lane maintenance may depend on the type of the secondary task beside predictability of primary task.     

学龄早期词语阅读习得对阅读理解能力的预测作用：初始水平和发展速率的贡献

本研究测查了183名儿童在一年级期间（学年初,学年中,学年末）的词语阅读能力（词语解码,阅读流畅性）及一年级末和二年级末的阅读理解能力,采用分层线性模型估计每名儿童在一年级期间词语阅读能力的初始水平和发展速率,以考察二者对阅读理解能力的独立预测作用。结果表明,词语识别的初始水平和增长速率都能独立预测一年级末和二年级末的阅读理解能力,阅读流畅性的初始水平和增长速率只能独立预测二年级末的阅读理解能力,但可以预测阅读理解能力在一年级到二年级的增长。     

学校氛围对学生数学成绩影响的跨文化比较:基于多水平分析的结果

学校氛围是学校中被成员所体验并对其行为产生影响的、相对持久而稳定的学校环境特征。它以成员的共同行为感知为基础,对学校和学生的发展具有重要作用。以PISA2003测试的数据为基础,采用多水平线性模型,考察了中国香港、日本、美国和土耳其的15岁学生及其所在学校的校长知觉的学校氛围对学生数学成绩的影响。结果显示:(1)学生知觉的师生关系、数学课堂秩序,校长知觉的学生士气、学校消极行为对数学成绩的显著预测作用具有较高的跨文化一致性;(2)数学课堂上的教师支持、教师负面状态、数学教师间的共识能显著预测部分国家/地区的学生数学成绩;(3)学生知觉的对学校的看法、校长知觉的教师士气对四个国家/地区的学生数学成绩均无显著预测作用。     

Hierarchical growing grid networks for skeleton based action recognition

In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed of the learning phase. Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network. These layers are designed to solve the action recognition problem. The first-layer growing grid receives the input data of human actions and the neural map generates an action pattern vector representing each action sequence by connecting the elicited activation of the trained map. The pattern vectors are then sent to the ordered vector representation layer to build the time-invariant input vectors of key activations for the second-layer growing grid. The second-layer growing grid categorizes the input vectors to the corresponding action clusters/sub-clusters and finally the one-layer supervised neural network labels the shaped clusters with action labels. Three experiments using different datasets of actions show that the system is capable of learning to categorize the actions quickly and efficiently. The performance of the growing grid architecture is compared with the results from a system based on Self-Organizing Maps, showing that the growing grid architecture performs significantly superior on the action recognition tasks.     

Analyzing driver's response to the yellow onset at signalized intersections

Intersections are critical points within the highway system at which the risk of crashes increases. This study seeks to better understand drivers’ behavior at an intersection by examining the relationship between their observed driving behavior, psychological attributes and decision to proceed through an intersection. A driving simulator and self-report questionnaire were used to understand driver decision-making at the onset of the yellow phase across several signalized intersections. The simulator measured driving outcomes such as speed, braking, and throttle as drivers cross through four increasingly difficult intersections. The questionnaires measured demographics, psychological traits including mindfulness and impulsiveness along with self-reported driving behaviors. A total of 102 participants completed the questionnaire as well as the driving simulator experiment. Hierarchical clustering served to classify drivers into four groups on the basis of their observed driving in the simulator: the safest drivers, safe drivers, speed demons, and aggressive drivers. These driving styles moderated the relationship between the drivers’ psychological traits and their decision to stop or proceed at each intersection. Results showed that mindfulness was highly related to the safest drivers’ decision to stop at the first intersection, while impulsiveness and anxiety were related to the speed demons’ decision to stop at the third intersection. These findings lay a strong foundation for developing progressive educational campaigns incorporating driver psychology in their methodology. Findings also provide support for research linking driving performance and psychological traits with implications for intersection design.