交叉重叠类别结构的自主学习优势和集中学习劣势

本研究采用四类别交叉重叠结构,探索了不同学习方式(集中、交错、随机和自主)对基于规则和信息整合类别学习的影响,通过计算模型的数据分析方法对265名被试的学习策略进行了模型拟合。结果发现,在基于规则和信息整合任务中,自主学习者均能较多地使用最优策略,自主学习的分类正确率均显著高于集中学习的分类正确率。并没有出现前人发现的规则学习的集中学习优势和信息整合学习的交错学习优势。结果表明,自主学习存在学习效率上的优势而集中学习存在劣势,可能是因为交叉重叠类别结构对自主学习的影响相对少于对集中学习的影响。     

婚姻暴力加害人再犯危险评估量表之建立研究

"台湾婚姻暴力加害人致命危险评估量表已由作者修正美国之DA量表,于2003年以台湾北中南东7县市之婚姻暴力案件为样本以询问被害人研究完成(林明杰、史玉山、简蕾如,2003)。"再犯危险"评估之部分因同年研究时以加拿大之SARA量表修改,唯发现该表部分题目需询问加害人本人较佳,故难有完整之答案,而未能作再犯危险评估量表之确立。研究者发现SARA之部分题项与DA有重复,今将以同一样本研究所使用之量表询问同样本之被加害人在首次报案后是否再被施予身体或精神暴力,以建立预测有无再犯,而使能建立再犯量表。本研究之目的在建立预测有无再犯之婚姻暴力加害人再犯危险评估量表,使能提供全台湾社会工作、警政、司法及临床工作等相关人员在介入婚姻暴力案件时作为再犯危险评估工具之用。     

类别学习的机制:基于类别中心学习假设的解释

为了区分推理学习是学习类别内部结构还是学习类别对应特征的规则,该研究使用眼动仪研究了22名大学生被试在类别学习过程中的眼动情况。被试被随机分为相等的两组,一组进行分类学习,另一组进行推理学习。实验分为学习、测试两阶段,在学习阶段被试三个连续单元的正确率为90%或者30个单元做完则结束学习,在测试阶段检验学习的效果。结果表明推理学习者在学习过程中关注的是类别内部特征之间的关系,而不是类别对应特征的规则。     

不同类别学习方式的对比研究

55名被试被随机分配到两个组,分别通过分类学习与推理学习来学习两个类别,之后,两组被试对新的测试项目进行典型性评定,考察类别学习方式对类别表征的影响。研究结果表明,分类学习者仅仅依据项目的诊断性程度来评定,而推理学习者主要依据项目的典型性程度来评定。所以,诊断性信息在分类学习者的类别表征中占有重要位置,典型性信息在推理学习者的类别表征中占有重要位置,即分类学习与推理学习导致的类别表征不同。     

类别数量对基于规则和信息整合类别学习的影响

研究以大学生为被试,运用即时反馈训练范式,探讨类别数量对基于规则和信息整合两种结构的类别学习的影响,并探讨被试在类别学习过程中的反应策略。实验结果显示:(1)类别数量不仅影响基于规则结构的类别学习,也影响信息整合结构的类别学习,类别学习成绩随类别数量的增多而下降;(2)在基于规则类别学习中,大多数被试使用理想的分类规则进行类别判断;在信息整合类别学习中,大多数被试使用极端值策略。研究结果支持难度观,不支持多重系统理论。     

类别学习中分类学习与推理学习的对比研究

本研究对类别学习中分类学习与推理学习进行了对比.实验中要求被试从成对呈现的项目中选择一个更典型的A,并且要求被试将典型的A和典型的B画出来.测试中所呈现项目诊断性程度或典型性程度不同.研究结果表明,分类学习者主要受诊断性信息的影响,而推理学习者主要受典型性信息的影响.即,分类学习者关注类别间信息,而推理学习者关注类别内信息.     

交叉分类及其对刻板印象的影响

社会分类(分类)是群体认知和印象评价的依据, 交叉分类作为一种认知过程, 是指在知觉或评价他人时, 同时在两个或多个类别维度上进行社会分类, 形成在多个类别维度上内外群体身份的交叉。研究发现交叉分类既可以降低刻板印象, 也可以增强刻板印象。对此, 类别化视角的解释强调群体身份的作用, 而个体化视角的解释强调多重类别背景下去类别化的作用。现有的研究在如何理解并考察这一影响的不同方向上仍存在分歧, 同时, 也未能认识到刻板印象对于个体认知与认同的意义, 存在明显不足。今后的研究应重点探讨类别化视角的动机与认知过程, 个体化视角的优势类别与加工方式, 以及以自我归类为基础的整合。     

K-means、潜在类别模型和混合Rasch模型的比较

基于模拟研究比较了K-means方法、潜在类别模型和混合Rasch模型在二分外显变量情境下的聚类效果.结果表明：（1）潜在类别数量、变量数量、样本量、样本平衡和变量间相关对K-means方法、潜在类别模型和混合Rasch模型的分类准确性均有影响且因素间的交互作用存在;（2）除了在2个潜在类别的样本不平衡条件下K-means方法表现较差外,在其他条件下与潜在类别模型和混合Rasch模型的表现相当;（3）混合Rasch模型的分类一致性在2个潜在类别的情境下要好于潜在类别模型,但是在4个潜在类别的情境下要差于潜在类别模型.     

汉语自闭症儿童分类能力的实验研究

为探查自闭症儿童的分类能力,采用类别判断任务,比较了刺激的典型性对12名汉语自闭症儿童和相匹配控制组儿童分类正确率和反应时的影响。研究发现,自闭症组在典型刺激上的分类能力与控制组大致相同,但在不典型刺激上的分类能力明显更差,表明自闭症儿童既有一定的分类能力,又存在内在的缺陷。研究结果支持自闭症"弱的中心统合理论"。     

分类精确性指数Entropy在潜剖面分析中的表现:一项蒙特卡罗模拟研究

本研究通过蒙特卡洛模拟考查了分类精确性指数Entropy及其变式受样本量、潜类别数目、类别距离和指标个数及其组合的影响情况。研究结果表明:(1)尽管Entropy值与分类精确性高相关,但其值随类别数、样本量和指标数的变化而变化,很难确定唯一的临界值;(2)其他条件不变的情况下,样本量越大,Entropy的值越小,分类精确性越差;(3)类别距离对分类精确性的影响具有跨样本量和跨类别数的一致性;(4)小样本(N=50~100)的情况下,指标数越多,Entropy的结果越好;(5)在各种条件下Entropy对分类错误率比其它变式更灵敏。     

Human classifier: Observers can deduce task solely from eye movements

Computer classifiers have been successful at classifying various tasks using eye movement statistics. However, the question of human classification of task from eye movements has rarely been studied. Across two experiments, we examined whether humans could classify task based solely on the eye movements of other individuals. In Experiment 1, human classifiers were shown one of three sets of eye movements: Fixations, which were displayed as blue circles, with larger circles meaning longer fixation durations; Scanpaths, which were displayed as yellow arrows; and Videos, in which a neon green dot moved around the screen. There was an additional Scene manipulation in which eye movement properties were displayed either on the original scene where the task (Search, Memory, or Rating) was performed or on a black background in which no scene information was available. Experiment 2 used similar methods but only displayed Fixations and Videos with the same Scene manipulation. The results of both experiments showed successful classification of Search. Interestingly, Search was best classified in the absence of the original scene, particularly in the Fixation condition. Memory also was classified above chance with the strongest classification occurring with Videos in the presence of the scene. Additional analyses on the pattern of correct responses in these two conditions demonstrated which eye movement properties successful classifiers were using. These findings demonstrate conditions under which humans can extract information from eye movement characteristics in addition to providing insight into the relative success/failure of previous computer classifiers.     

幽门螺杆菌根除失败原因及补救策略分析

幽门螺杆菌（Hp）与胃炎、消化性溃疡、胃黏膜相关淋巴组织淋巴瘤及胃癌关系密切。随着根除Hp治疗在临床的普遍开展,Hp根除率逐渐下降,其主要原因是日益突出的抗生素耐药及患者对药物依从性差。对根除治疗失败者进行补救治疗已成为Hp治疗研究领域的新热点。由于各地的Hp耐药率及感染者个体情况存在很大差异,目前尚未形成统一规范的补救策略,遵循个体化的补救策略已逐渐被大家所接受。     

Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals

ABSTRACT— Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes are organized in the brain. Using a variety of classifier techniques, we achieved cross-validated classification accuracy of 80% across individuals (chance = 13%). Using a neural network classifier, we recovered a low-dimensional representation common to all the cognitive-perceptual tasks in our data set, and we used an ontology of cognitive processes to determine the cognitive concepts most related to each dimension. These results revealed a small organized set of large-scale networks that map cognitive processes across a highly diverse set of mental tasks, suggesting a novel way to characterize the neural basis of cognition.     

Sequential sampling models of human text classification

Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper develops two models of human text document classification based on random walk and accumulator sequential sampling processes. The models are evaluated using data from an experiment where participants classify text documents presented one word at a time under task instructions that emphasize either speed or accuracy, and rate their confidence in their decisions. Fitting the random walk and accumulator models to these data shows that the accumulator provides a better account of the decisions made, and a “balance of evidence” measure provides the best account of confidence. Both models are also evaluated in the applied information retrieval context, by comparing their performance to established machine learning techniques on the standard Reuters‐21578 corpus. It is found that they are almost as accurate as the benchmarks, and make decisions much more quickly because they only need to examine a small proportion of the words in the document. In addition, the ability of the accumulator model to produce useful confidence measures is shown to have application in prioritizing the results of classification decisions.     

Nearest vertex attraction for actively reducing loss

In recent years the neural networks have received considerable success on classification tasks. The randomly initialized network is generally expected to adjust the weights for reducing the loss, which is also indicated as the total distance from the samples to the corresponding vertices. However, the output space of the conventional neural networks suffers from the fixed relation between the labels and vertices during learning. This case forces the mapped points around the unexpected vertex across the decision boundary into the neighborhood of the correct vertex, and simultaneously the boundary points cannot obtain the substantial rectification. Therefore, this study proposes a novel nearest vertex attraction (NVA) to actively adjust the relation between the categories and the output vertices for improving the neural network classifiers. The best relation allows that the data points can be attracted by the nearest vertices to minimize the total moving distances. In this way, the mapped points that are near to the vertices and the decision boundaries obtain the decent management. We evaluated the NVA with several conventional classification techniques and other neural network classifiers on 12 public UCI datasets. The numerical experiments demonstrate that the proposed method improves performance of the neural network classifiers on the involved benchmarks.     

机器学习在自闭症儿童早期识别和诊断领域的应用

早发现、早诊断、早干预是开展自闭症儿童教育康复工作的共识, 但传统识别和诊断方法局限及专业人员缺乏常导致自闭症儿童错失最佳干预期。为改善现状, 近年来机器学习凭借其客观准确、简便灵活等方面的优势, 逐渐被应用到自闭症的早期预测、筛查、诊断和评估过程管理中, 积累了较为丰富的成果。但是机器学习也在研究对象选取、分类数据采集和理论模型应用等方面存在局限性。未来研究应推动构建孕产期和新生儿病理生理信息追踪数据库和标准化模型分类指标体系, 同时继续优化算法, 加快智能化自闭症识别和诊断理论成果向实践转化。     

Bayesian inference for the information gain model

One of the most popular paradigms to use for studying human reasoning involves the Wason card selection task. In this task, the participant is presented with four cards and a conditional rule (e.g., “If there is an A on one side of the card, there is always a 2 on the other side”). Participants are asked which cards should be turned to verify whether or not the rule holds. In this simple task, participants consistently provide answers that are incorrect according to formal logic. To account for these errors, several models have been proposed, one of the most prominent being the information gain model (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). This model is based on the assumption that people independently select cards based on the expected information gain of turning a particular card. In this article, we present two estimation methods to fit the information gain model: a maximum likelihood procedure (programmed in R) and a Bayesian procedure (programmed in WinBUGS). We compare the two procedures and illustrate the flexibility of the Bayesian hierarchical procedure by applying it to data from a meta-analysis of the Wason task (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). We also show that the goodness of fit of the information gain model can be assessed by inspecting the posterior predictives of the model. These Bayesian procedures make it easy to apply the information gain model to empirical data. Supplemental materials may be downloaded along with this article from .     

Psychiatric diagnoses of infants and toddlers referred to a community mental health system

There has been increased interest in improving mental health assessment and treatment of young children. However, there are limited data regarding the prevalence of psychiatric disorders among infants and toddlers. The recent development of measures and classification systems that are specific to this age group has enabled researchers to begin to investigate the social, emotional, and behavioral problems of young children. The present study reviews the major research that has been conducted in this area and examines the prevalence of psychiatric diagnoses of infants and toddlers referred to a public community mental health system. The current investigation also compares the frequency of diagnoses of children who have been evaluated based on the Diagnostic and Statistical Manual (4th edition) with another group of children who were diagnosed based on the Diagnostic Classification: 0-3 system. Although there were some differences between the two samples and classification systems, analysis indicated that parent-child relational problems, adjustment difficulties, regulatory problems, and developmental disorders are some of the more common diagnoses prevalent among young children referred for outpatient treatment. As diagnostic measures and classification systems continue to improve, clinicians will have a better understanding of the mental health problems and treatment needs of young children.     

Development of classification: The use of similarity and dimensional relations

Findings about perceptual development indicate that overall similarity is the primary perceptual relation by which young children compare complex objects. Traditional studies of classification, however, did not focus on children's organizational use of holistic relations but rather on their ability to classify by dimensions or criterial attributes. The results from such traditional studies suggest that young children are deficient classifiers. The present research investigated the possibility, contrary to the traditional view, that 4- to 6-year-old children are competent and systematic classifiers at least by overall similarity. In three experiments, preschoolers and kindergarteners classified various sets of multidimensional stimuli that could be organized into categories by overall similarity or by dimensional attributes. Consistent with the research in perceptual development, the children were highly attentive to overall similarity. However, the preschoolers in particular showed marked difficulty in using this relation to form categories of more than two objects. The children's difficulties were highly reminiscent of traditional claims about early classification. Analyses of the classification strategies used by the children, however, suggest that even the youngest children understood the purpose of a classification. The developmentla changes appear to be in the ability to execute a classification. Importantly, type of classification strategy was independent of type of category organization. Individual children used the same strategies both when classifying by overall similarity and by dimensional attributes. These results strongly suggest that it is the classification skills themselves, and not just the ability to classify by particular relations, that change with age.     

Supervisors’ work-related and nonwork information sharing: Integrating research on information sharing,information seeking,and trust using self-disclosure theory

Although significant scholarly attention has been devoted to understanding subordinates’ information seeking from supervisors, researchers have not paid adequate attention to information sharing by supervisors. Moreover, research on supervisors’ information sharing behavior has focused almost exclusively on work-related information sharing, disregarding supervisors’ sharing of information not related to work (e.g., that related to family). Drawing on self-disclosure theory, we argue that supervisors share both work-related and nonwork information with their subordinates and propose that these two forms of information sharing are conceptually distinct. Furthermore, to unravel the role of supervisors’ nonwork information sharing, we develop an interactive model to test how it may be associated with important employee outcomes. We conducted pilot studies using five samples and, through a sixth study, tested the hypothesized model using a four-wave data collection design. This study makes three major contributions to research and theory. First, it integrates information sharing and information seeking literatures. Second, it underlines the importance of supervisors’ nonwork information sharing in organizations by testing its direct and interactive effects. Third, it contributes to theory by presenting trust as a mechanism that links information disclosure in dyads. Results obtained using structural equation modeling generally supported the proposed model.