行为评价对3岁儿童掌控动机的影响

运用情境实验法,考察了71名3岁儿童掌控动机的基本特点及行为评价对其影响。结果发现:3岁儿童:(1)对新奇玩具具有强烈的掌控动机;(2)丰富的视听反馈会更加激发其探索与操控;(3)当成人对其探索行为有积极评价时,儿童获得更多愉悦感;(4)掌控成败体验出现;(5)积极评价可增强掌控成功时的兴趣感,缓解失败后的不良情绪;(6)多数(94.42%)儿童愿意尝试挑战,并能坚持较长时间以期成功,但坚持性存在个体差异。     

Application of GIS and multicriteria evaluation in locating sustainable boundary between the tunari National Park and Cochabamba City (Bolivia)

Increased population growth and urbanization, has caused increase in demand for land around the city of Cochabamba, which has led to encroachment into the National Park and the construction of illegal settlements. This has created a number of problems, some of which are the direct consequences of the new settlements in the ‘Tunari’ foothills. These settlements constitute a threat to the environment and people because of their location in flood risk and aquifer recharge areas. In response to this problem the city authorities are considering relocating the boundary between the National Park and the city. This study has focused on the design and evaluation of alternative locations for a sustainable boundary between the north of ‘Cochabamba City’ and the southern boundary of the ‘Tunari National Park’. In this process, a rational and systematic approach of collaborative‐decision‐making, supported by geographical information systems (GIS) and multicriteria evaluation (MCE) has been employed and evaluated. Copyright © 2003 John Wiley & Sons, Ltd.     

基于遗传算法的模糊综合评价在心理测量中的应用

提出了运用模糊数学对利克特量表数据进行分析的方法, 探讨了人们在进行模糊综合评价时, 所采用的算子和对各个自变量的权重分配, 并且运用遗传算法(GA)来确定相关的权重。以大学生对康师傅红烧牛肉面的评价数据为例, 运用基于遗传算法的模糊综合评价方法, 发现男生采用了“最大最小”合成算子, 女生采用了“有界和、取小”合成算子。研究结果表明, 基于遗传算法的模糊综合评价方法可以对利克特量表的心理测量数据进行有效的分析。     

Place of Strength: Indigenous Artists and Indigenous Knowledge is Prevention Science

The Place of Strength (PoS) project represents an effort to Indigenize program evaluation with Indigenous communities by melding art with prevention science. We propose that Native artists as evaluators: (a) opens avenues of communication for Indigenous perspectives; (b) provides opportunities to capture spiritual, relational, and emotional impacts of prevention programming; and (c) maintains Indigenous processes, language, and values at the center of knowledge production. The New Mexico Tribal Prevention Project (NMTPP) funded seven Southwestern tribes to develop substance abuse prevention programs. In response to their expressed negative experiences with evaluation of prevention strategies, NMTPP piloted PoS. PoS shifted systematic knowledge paradigms to Tribal thought, values, and perspectives embodied in art. Art exists in Native communities as a way of documenting lifeways and historical experiences through various cultural forms. We share the process of collaborating with Native artists to document the impact of substance abuse prevention initiatives through their art within a community context. We offer concepts derived from this project as a community psychology model for re‐conceptualizing evaluation utilizing Indigenous knowledge.     

反馈评估的局部背景依赖效应：ERP研究

人脑对反馈的评估依赖于其所处背景。但是只能依赖整体背景(整个组块的结果范围), 还是可以依赖局部背景(单个试次的结果范围)进行评估, 目前并不清楚。本研究通过获益/损失线索操纵所在试次的反馈背景, 探讨反馈评估仅依赖整体背景, 还是可延伸到局部水平。25名被试参与实验。结果发现, 背景试次间变化时, 在获益背景下, ￥0 (负反馈)比+ ￥4 (正反馈)引发更负的FRN (feedback-related negativity); 在损失背景下, - ￥4 (负反馈)比￥0 (正反馈)引发更负的FRN。这一结果说明反馈评估以某线索背景中可能的结果为参考对象, FRN的背景依赖可以延伸到局部水平。结合前人研究, 推测任务类型和反馈真实性会调节背景依赖水平。在含真实反馈的主动性任务中, FRN的背景依赖效应可延伸至局部水平。     

Meta‐Analysis of Integrated Primary and Behavioral Health Care Interventions for Treating Substance Use Among Adults

This meta‐analysis detected low effects in reducing substance use (Hedges's g = –0.11) in favor of using integrated primary and behavioral health care (IPBH) compared with treatment‐as‐usual approaches for adult patients with substance use. The impact of IPBH was limited.     

Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease

An accurate diagnosis of Parkinson’s disease by specialists involves many neurological, psychological and physical examinations. The specialists investigate a number of symptoms and signs when examining the nervous system conditions of a person. The diagnosis involves reviewing the medical history and genetic factor of the person. The recent diagnosis methodology to Parkinson’s disease relies on voice disorders analysis. This methodology entails extracting feature sets of a recorded person’s voice then utilizing a machine learning technique to identify the healthy and Parkinson’s cases from the voice. This paper attempts to improve the diagnoses of Parkinson’s disease by testing multiple feature evaluation and classification machine learning methods based on the voice disorders analysis. The aim of this paper is to find the optimal solution to the problem by (i) proposing a new Multiple Feature Evaluation Approach (MFEA) of a multi-agent system (ii) implementing five independent classification schemas which are Decision Tree, Naïve Bayes, Neural Network, Random Forests, and Support Vector Machine on the Parkinson’s diagnosis before and after applying the MFEA, and (iii) evaluating the diagnosis accuracy of the results. The methodology of the tests encompasses 10-fold cross-validation to evaluate the learning of methods and track variation in their performance. The test results show that the MFEA of the multi-agent system finds the best set of features and improves the performance of the classifiers. The average rate of improvement in the diagnostic accuracy of the classifiers are Decision Tree 10.51%, Naïve Bayes 15.22%, Neural Network 9.19%, Random Forests 12.75%, and Support Vector Machine 9.13%. These results show that the MFEA makes a significant improvement to the classifiers’ diagnosis results.