变长CD-CAT中的曝光控制与终止规则

本研究借鉴传统计算机化自适应测验的思想, 并结合认知诊断的特点, 在认知诊断框架下提出了4种变长CD-CAT的终止规则, 分别是属性标准误法(SEA)、邻近后验概率之差法(DAPP)、二等分法(HA)以及混合法(HM)。在未控制曝光和采用不同曝光控制条件下, 与HSU法及KL法进行了比较。研究结果表明：(1) 终止条件越严格, 平均测验长度越长, 按测验长度最大值终止的测验百分比越大, 模式判准率越高。(2) 当未加入曝光控制时, 4种新的终止规则均有较好表现, 与HSU法十分接近。随着最大后验概率预设值的增加或e的减小, 模式判准率呈上升趋势, 平均测验长度逐渐增加, 但在题库使用率方面均较差。(3) 当加入项目曝光控制时, 6种变长终止规则下的题库使用率有了极大的提升, 仍能保持较高的模式判准率, 并且不同的曝光控制方法对终止规则的影响是不同的。其中, 相对标准终止规则极易受到曝光控制方法的影响。(4) 综合来看, SEA、HM以及HA法在各项指标上的表现与HSU法基本一致, 其次为KL法和DAPP法。     

利用脑成像多体素模式分析解码认知的神经表征:原理和应用

多体素模式分析(multi-voxel pattern analysis,MVPA)是一种基于机器学习理论发展出来的新的功能磁共振数据分析技术。MVPA技术通过训练分类器,对由不同认知状态引起的多体素信号模式进行分类。与传统的基于单个体素的分析方法相比,该技术可更敏感地检测脑对认知状态的表征,并使得从神经信号解码认知状态成为可能。文章介绍MVPA技术的基本原理、分析步骤以及可以用MVPA来解决的科学问题,并对应用中可能面临的问题提供了参考建议。     

Classifying simulated air threats with fast and frugal heuristics

In three experiments, subjects learned to classify simulated aircraft using four probabilistic cues then classified test sets designed to contrast predictions of several compensatory and non-compensatory heuristics. Results indicated that a proportion of subjects appeared to use a non-compensatory fast and frugal heuristic but that significant proportions used less frugal compensatory decision rules. The relative proportions of subjects exhibiting responses consistent with the fast and frugal heuristic versus other decision rules was largely unaffected by manipulations of time pressure and perceived cue uncertainty. An extreme time constraint, however, did result in significant differences in the proportions of subjects who preferred non-compensatory to compensatory decision rules. These results suggest that it is possible to model multi-attribute decision tasks like threat assessment with a fast and frugal heuristic although no single heuristic is a general model for the simulated threat assessment task. Copyright © 2006 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.     

修订的布卢姆目标分类与加涅和安德森学习结果分类的比较

本文首先阐述了对新修订的布卢姆认知教育目标分类学与加涅和安德森的认知学习结果分类进行比较研究的缘由;然后分别概述这三个分类理论;接着就他们分类的异同进行比较,以沟通_三者关系;最后表明,应该将教学目标与学习结果分类理论相沟通才能有效运用目标分类学指导教学目标、策略和评估的设计.     

The knowledge content of statistical data

An information-theoretic framework is used to analyze the knowledge content in multivariate cross classified data. Several related measures based directly on the information concept are proposed: the knowledge content (S) of a cross classification, its terseness (Zeta), and the separability (Gamma _X ) of one variable, given all others. Exemplary applications are presented which illustrate the solutions obtained where classical analysis is unsatisfactory, such as optimal grouping, the analysis of very skew tables, or the interpretation of well-known paradoxes. Further, the separability suggests a solution for the classic problem of inductive inference which is independent of sample size.     

Trend in correlated proportions

A random effects probit model is developed for the case in which the same units are sampled repeatedly at each level of an independent variable. Because the observed proportions may be correlated under these conditions, estimating their trend with respect to the independent variable is no longer a standard problem for probit, logit or loglinear analysis. Using a qualitative analogue of a random regressions model, we employ instead marginal maximum likelihood to estimate the average latent trend line. Likelihood ratio tests of the hypothesis of no trend in the average line, and the hypothesis of no differences in average trend lines between experimental treatments, are proposed. We illustrate the model both with simulated data and with observed data from a clinical experiment in which psychiatric patients on two drug therapies are rated on five occasions for the presence or absence of symptoms.Supported by a grant from the MacArthur Foundation and National Science Foundation Grant BNS85-11774.The authors are indebted to James Heckman for calling our attention to the Clark algorithm.     

Measuring concept semantic relatedness through common spatial pattern feature extraction on EEG signals

We study the semantic relationship between pairs of nouns of concrete objects such as “HORSE - SHEEP” and “SWING - MELON” and how this relationship activity is reflected in EEG signals. We collected 18 sets of EEG records; each set containing 150 events of stimulation. In this work we focus on feature extraction algorithms. Particularly, we highlight Common Spatial Pattern (CSP) as a method of feature extraction. Based on these latter, different classifiers were trained in order to associate a set of signals to a previously learned human answer, pertaining to two classes: semantically related, or not semantically related. The results of classification accuracy were evaluated comparing with other four methods of feature extraction, and using classification algorithms from five different families. In all cases, classification accuracy was benefited from using CSP instead of FDTW, LPC, PCA or ICA for feature extraction. Particularly with the combination CSP-Naïve Bayes we obtained the best average precision of 84.63%.     

肿瘤临床治疗中应关注肝脏

抗肿瘤细胞毒性药物有不同程度毒副作用,有些严重毒副反应是限制药物剂量或使用的直接原因。肝脏是药物代谢的主要器官,也是药物损伤的主要靶器官。随着临床肿瘤治疗关注治疗带来的副作用与关注疗效同等重要的发展,本文对抗肿瘤细胞毒性药物与肝损伤、发生机制、诊断、分型和临床治疗进展进行了阐述。