A two-part evaluation approach for measuring the usability and user experience of an Augmented Reality-based assistance system to support the temporal coordination of spatially dispersed teams

One key predictor of team performance and productivity is the teams’ ability to coordinate its subtasks as precisely as possible over time. Thereby, the quality of the temporal coordination in teams is highly dependent on several cognitive team skills, like for instance the ability to build a precise and stable mental model of the situation (shared situation awareness) and of the team task process (task state awareness/ TSA). To support these cognitive skills in teams, various methods and trainings already exist. However, considering teams that work de-located, the prerequisites and therefore the requirements for team supportive methods change. This work presents the results of an empirical usability and user experience (UX) study of an interface for an Augmented Reality-based assistance system for spatially dispersed teams, called Ambient Awareness Tool (AAT). The AAT interface consists of graphical information about the teamwork process and aims at enhancing the TSA of the team, thereby supporting its temporal coordination. Within the framework of a participatory design process we conducted a two-part expert-user-study, comprising a laboratory experiment for the evaluation of the interface usability and a follow-up online survey to additionally investigate the UX. By means of the usability scores we first inferred three interface configuration clusters. A subsequent UX evaluation then allowed us to designate the configuration cluster with the highest usability and UX scores. As a result, we defined one interface configuration that will be investigated concerning its actual impact on the temporal coordination of spatially dispersed teams in a further study.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

基于网络理论的物质成瘾新视角

物质成瘾是一种涉及生理、心理和环境等多因素的复杂现象, 但是当前基于生物还原论的解释对物质成瘾现象整体性的理解和康复研究造成了阻碍。网络理论聚焦于心理障碍变量之间相互作用形成的反馈环路, 从整体视角为研究物质成瘾提供了新的理论框架。将网络理论应用于物质成瘾的研究中将有利于:(1)理解症状之间的相互关系和影响; (2)理解症状网络的整体性和系统性动态变化过程; 以及(3)将多层次和多水平因素整合到统一的理论框架中。从网络理论视角来理解物质成瘾, 也将对未来的干预和治疗提供了理论支持。目前, 网络理论仍处于言语模型阶段, 未来需要进一步提出更具体、可验证的统计模型, 以完善对于物质成瘾机制的了解, 更加有效地推进物质成瘾的治疗与恢复。     

动作视频游戏对发展性阅读障碍者阅读技能的影响及其内在机制

发展性阅读障碍是一种在获得阅读技能方面的特殊困难, 这种障碍会严重影响个体的发展, 如何帮助发展性阅读障碍者改善其阅读技能是近年来研究的焦点。传统的干预方法主要针对发展性阅读障碍者的语音缺陷, 这类方法存在一些问题, 如费时费力、给阅读障碍者带来阅读压力等。近年来, 大部分研究表明通过趣味性的动作视频游戏训练可以显著地提高发展性阅读障碍者的阅读技能, 但是其背后的机制尚不明确。基于大细胞通路缺陷理论框架, 从视觉空间注意、注意跨通道转换、视觉运动加工等方面来梳理动作视频游戏与阅读之间的关系, 揭示了动作视频游戏训练对阅读效率影响的可能内在机制。未来的研究可以在大细胞通路缺陷理论的框架下, 深入分析动作视频游戏改善阅读的神经机制, 并尝试开发更适合发展性阅读障碍者的干预程序。     

混合情绪能促进心理健康吗？

摘 要 积极和消极情绪的同时体验,即混合情绪,是生活中常见的情绪状态。健康应对的共激活模型、情绪动态模型和积极比模型一致认为混合情绪与心理健康之间呈现非线性的关系,适当的混合情绪有利于个体在困境中全面加工和整合信息,寻求意义,获得控制感,进而实现健康应对和心理症状改善,获得幸福感。未来研究应关注对非线性关系的精确描述;低压力或非压力情境下混合情绪的意义;进一步研究混合情绪促进心理健康的内部机制和影响因素。     

Termination of information evaluation in auditing

A screening decision is about whether a candidate fails to meet a decision maker's criteria and must be rejected, or whether it meets the criteria and can be accepted. Image theory contends that screening decisions are based on a simple, noncompensatory process that focuses solely upon the candidate's negative attributes. This contention has received empirical support in a job screening context in which students role-played a hypothetical person seeking a job. But how robust is image theory's prediction? The present work was done in the context of professional auditors screening prospective clients. The client screening context is a particularly interesting setting to test the implications of image theory. If auditors reject clients with negative attributes, as predicted by image theory, this will indicate that some clients are being denied access to a socially useful service. Further, auditors can accept prospective clients with negative attributes and simply adjust the audit plan and fees to compensate for the client's riskiness. Thus, it is not clear that the preliminary findings obtained in a job screening context will generalize to the audit context. On the other hand, results consistent with the image theory prediction would suggest a robust theory. Even in the client screening context, where auditors used their own criteria to screen prospective clients, we found overwhelming support for image theory's view of screening. The results indicate the potential payoffs of image theory as a robust framework to address audit and other substantive problems. Implications of a noncompensatory, negative oriented, client screening strategy are discussed.     

Selecting a new technology strategy using multiattribute,multilevel decision trees

Research indicates that selecting a strategy to best exploit a new technology is a complex decision-making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision-making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.     

Preference structures and threshold models

Preferences are often represented in terms of a function, in the deterministic case as well as in the probabilistic case. In the present paper we develop a new numerical representation of preference structures for which the strict preference relation (P) is without circuit but not necessarily transitive. Moreover, we investigate the consequences of the representation for the usual preference structures. In particular, we propose new formulations for the numerical representation of the interval order structure.