Measuring Situation Awareness in Virtual Environment-Based Training

We evaluated the efficacy of a computer-based situation awareness (SA) measurement system for training dismounted infantry SA in an urban terrain virtual reality (VR) simulation. Based on past research, we hypothesized that the SA measures would be sensitive to individual (squad leader) differences, and that the frequency of specific probes would reveal differences in critical SA requirements among scenarios. Three infantry squads performed multiple trials across two different scenarios. A confederate platoon leader posed probes to squad leaders during trials and experts made ratings afterward. Results revealed squad leaders had similar responses to probes, despite differences in combat experience. Analysis of probe frequency revealed different high priority SA elements and decisions for each scenario. The SA behavior and communication ratings revealed differences among squads, which trended with experience. Measures of SA were also consistent across the test scenario as a result of similar mission types and task difficulties. We discuss the implication of our findings for future research and theory within this area.     

PC-Based Game Features That Influence Instruction and Learner Motivation

PC-based games are currently being used for military training, but the instructional and motivational features of such technology are not well understood. To identify features of training games that influence instruction and motivation, a popular first-person-perspective game with a military theme was analyzed empirically. Twenty-one participants played the “basic training” portion of the game, which included Army background information, virtual marksmanship training, an obstacle course, virtual weapons familiarization, and an urban terrain training mission. The results of this research provide useful information to individuals developing training games, desktop simulations, and interactive multimedia courseware to meet optimal training objectives and strategies.     

Network structure influences speech production

Network science provides a new way to look at old questions in cognitive science by examining the structure of a complex system, and how that structure might influence processing. In the context of psycholinguistics, clustering coefficient-a common measure in network science-refers to the extent to which phonological neighbors of a target word are also neighbors of each other. The influence of the clustering coefficient on spoken word production was examined in a corpus of speech errors and a picture-naming task. Speech errors tended to occur in words with many interconnected neighbors (i.e., higher clustering coefficient). Also, pictures representing words with many interconnected neighbors (i.e., high clustering coefficient) were named more slowly than pictures representing words with few interconnected neighbors (i.e., low clustering coefficient). These findings suggest that the structure of the lexicon influences the process of lexical access during spoken word production.     

动态系统研究方法在社会心理学中的应用

文章综述了动态系统研究方法在社会心理学中的应用。社会心理学现象具有复杂性、时间性和动态性特点,动态分析方法如动态系统行为模式确定、动态系统稳定性分析、人工神经网络和元细胞自动机等计算机模型的应用,可以有效的捕捉到人类思维和行为的这些特点。目前动态分析方法已经在社会心理学的动态属性研究中取得了一定的进展。     

Verb gapping: An action-gap compatibility study

This study addresses the processing of verb-gapping sentences, e.g., John closes a juice bottle and Jim [ ] a lemonade bottle. The goal was to explore if there would be an interaction between language comprehension and motor action not only for overt action verbs but also for gapped verbs. Participants read gapping sentences that either described clockwise or counter-clockwise manual rotations (e.g., closes vs. opens a juice bottle). Adopting a paradigm developed by Zwaan and Taylor (2006), sentence presentation was frame-by-frame. Participants proceeded from frame to frame by turning a knob either clockwise or counter clockwise. Analyses of the frame reading-times yielded a significant effect of compatibility between the linguistically conveyed action and the knob turning for the overt-verb (e.g., closes/opens a juice bottle) as well as for the gapped-verb frame (e.g., a lemonade bottle) – with longer reading times in the match condition than in the mismatch condition – but not for any of the other frames (e.g., and Jim). The results are promising in providing novel evidence for the real-time reactivation of gapped verbs and in suggesting that action simulation is not bound to the processing of overt verbs.     

Mechanisms for Robust Cognition

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition.     

Kinetics of interstitial segregation in Cottrell atmospheres and grain boundaries

Trapping of interstitial (e.g. carbon) atoms is driven by the reduction in energy in the system. Diffusion of interstitials, together with their trapping in dislocation cores and/or grain boundaries, is studied by the thermodynamic extremal principle. In addition to the total Gibbs energy, a well-established formulation of the total dissipation is applied. Dimension-free evolution equations are derived, whose solution is well approximated by an easy to handle kinetic equation. Cottrell’s power law can be verified in the initial stage.     

Selecting the number of classes under latent class regression: a factor analytic analogue

Recently, the regression extension of latent class analysis (RLCA) model has received much attention in the field of medical research. The basic RLCA model summarizes shared features of measured multiple indicators as an underlying categorical variable and incorporates the covariate information in modeling both latent class membership and multiple indicators themselves. To reduce complexity and enhance interpretability, one usually fixes the number of classes in a given RLCA. Often, goodness of fit methods comparing various estimated models are used as a criterion to select the number of classes. In this paper, we propose a new method that is based on an analogous method used in factor analysis and does not require repeated fitting. Two ideas with application to many settings other than ours are synthesized in deriving the method: a connection between latent class models and factor analysis, and techniques of covariate marginalization and elimination. A Monte Carlo simulation study is presented to evaluate the behavior of the selection procedure and compare to alternative approaches. Data from a study of how measured visual impairments affect older persons’ functioning are used for illustration.This work was supported by National Institute on Aging (NIA) Program Project P01-AG-10184-03. The author wishes to thank Dr. Karen Bandeen-Roche for her stimulating comments and helpful discussions, and Drs. Gary Rubin and Sheila West for kindly making the Salisbury Eye Evaluation data available.     

学习支持对基于计算机模拟的发现学习的影响

研究了针对发现活动的意义性的解释性支持 (IS)、针对其系统逻辑性的实验性支持 (ES)以及学习者的推理能力对基于模拟的科学发现学习的影响。设计开发了关于浮力的模拟软件 ,被试为北京十四中初二学生 80名 ,采用 2 (有 /无IS)× 2 (有 /无ES)× 3(推理能力 )的混合实验设计。结果发现 ,推理能力在原理性知识、直觉性理解测验以及学习者所设计实验的质量上有显著的主效应。IS在原理性知识和灵活应用测验上有显著的主效应。ES与推理能力在原理性知识测验上有显著的交互作用 ,ES在学习者所设计实验的质量上有显著的主效应。这一结果说明 ,发现活动的意义性和系统逻辑性对基于模拟的发现学习有重要的制约作用 ,应该针对这两个侧面设计相应的学习支持     

Residual Normality Assumption and the Estimation of Multiple Membership Random Effects Models

While conventional hierarchical linear modeling is applicable to purely hierarchical data, a multiple membership random effects model (MMrem) is appropriate for nonpurely nested data wherein some lower-level units manifest mobility across higher-level units. Although a few recent studies have investigated the influence of cluster-level residual nonnormality on hierarchical linear modeling estimation for purely hierarchical data, no research has examined the statistical performance of an MMrem given residual non-normality. The purpose of the present study was to extend prior research on the influence of residual non-normality from purely nested data structures to multiple membership data structures. Employing a Monte Carlo simulation study, this research inquiry examined two-level MMrem parameter estimate biases and inferential errors. Simulation factors included the level-two residual distribution, sample sizes, intracluster correlation coefficient, and mobility rate. Results showed that estimates of fixed effect parameters and the level-one variance component were robust to level-two residual non-normality. The level-two variance component, however, was sensitive to level-two residual non-normality and sample size. Coverage rates of the 95% credible intervals deviated from the nominal value assumed when level-two residuals were non-normal. These findings can be useful in the application of an MMrem to account for the contextual effects of multiple higher-level units.