人类颜色视觉的计算理论

该文有机地结合了计算视觉理论和生态学视觉理论,指出颜色信息处理的根本任务是检测环境中的光不变量。在此基础上,作者提出了颜色视觉的计算理论以及计算理论本身的生物学标准。初级视觉计算是典型的不适定问题,动物的视觉系统则利用视环境中存在的条件将该不适定问题转化为定解问题。本文引入颜色视觉计算的免要条件,客观性约束,以及颜色认知的神经表象,证明了上述约束下颜色算法的存在性。本文给出了构造颜色知觉的基本假设。同时,该文还讨论了与上述问题密切相关的几个基本问题:神经表象的完备性,主观色觉的客观性,明度知觉和颜色知觉的统一,人类主观色觉的实现方式。     

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.     

The asymptotic posterior normality of the latent trait in an IRT model

It has long been part of the item response theory (IRT) folklore that under the usual empirical Bayes unidimensional IRT modeling approach, the posterior distribution of examinee ability given test response is approximately normal for a long test. Under very general and nonrestrictive nonparametric assumptions, we make this claim rigorous for a broad class of latent models.This research was partially supported by Office of Naval Research Cognitive and Neural Sciences Grant N0014-J-90-1940, 442-1548, National Science Foundation Mathematics Grant NSF-DMS-91-01436, and the National Center for Supercomputing Applications. We wish to thank Kumar Joag-dev and Zhiliang Ying for enlightening suggestions concerning the proof of the basic result.The authors wish to thank Kumar Joag-Dev, Brian Junker, Bert Green, Paul Holland, Robert Mislevy, and especially Zhiliang Ying for their useful comments and discussions.     

分时距认知特点的研究

本研究探讨了分时距估计的认知特点,检验了SS模型、PT模型和CS模型的预测效度,结果表明：分时距估计主要是在信息提取阶段采用分段推算策略对时间信息进行重建的过程,变化／分割和注意分配对分时距估计有显著影响;SS模型的预测效度最低,PT、CS模型的预测效度较高。     

卡车驾驶员速度估计研究

用速度知觉测试仪,对251名男性卡车驾驶员在四种实验条件下的速度估计准确性进行了测量。结果发现：安全组卡车驾驶员的速度估计准确性优于事故组卡车驾驶员,两者在低速条件下差异不显著,高速条件下差异显著;事故组卡车驾驶员的高估次数高于安全组卡车驾驶员,低速条件下两者不显著.高速条件下差异显著;事故多发组与安全组卡车驾驶员的速度估计准确性在高速及低速长距离条件下差异显著;速度及距离对速度估计准确性有显著影响,且两者之间存在显著的交互作用;年龄对速度估计准确性无显著影响。     

Shrinkage estimation of linear combinations of true scores

This paper is concerned with combining observed scores from sections of tests. It is demonstrated that in the presence of population information a linear combination of true scores can be estimated more efficiently than by the same linear combination of the observed scores. Three criteria for optimality are discussed, but they yield the same solution which can be described and motivated as a multivariate shrinkage estimator.Input from Eric Bradlow, Charles Lewis, and Linda Zeger is acknowledged. Research for this paper was funded by the Program Research Council (ETS). Suggestions of the Editor and of anonymous referees were instrumental in several improvements of the paper.     

A marginalization model for the multidimensional unfolding analysis of ranking data

A marginalization model for the multidimensional unfolding analysis of ranking data is presented. A subject samples one of a number of random points that are multivariate normally distributed. The subject perceives the distances from the point to all the stimulus points fixed in the same multidimensional space. The distances are error perturbed in this perception process. He/she produces a ranking dependent on these error-perturbed distances. The marginal probability of a ranking is obtained according to this ranking model and by integrating out the subject (ideal point) parameters, assuming the above distribution. One advantage of the model is that the individual differences are captured using the posterior probabilities of subject points. Three sets of ranking data are analyzed by the model.     

Upward versus Downward Anchoring in Frequency Judgments of Social Facts

Frequency estimation of social facts in two methods of judgment elicitation was investigated. In the “narrow-range” condition, subjects answered questions in the format: “Out of 100 incidents, how many belong to category X?” In the “wide-range” condition, the frequency for the same event was assessed with respect to “Out of 10,000”. Judged frequencies in the wide-range condition were divided by 100, and were compared with the corresponding judgments in the narrow-range condition. Such comparisons were made for low-frequency and high-frequency events. Previous research has shown that, for low-frequency events, judged frequencies are proportionally greater in the narrow-range than in the wide-range condition. These results reflect cognitive processes of implicit anchoring, whereby judged frequencies lie close to small numbers within the response ranges provided. I call this process “downward anchoring,” and predicted that this tendency would be replicated in the present study. Moreover, I predicted that assessments about high-frequency events would evoke similar cognitive processes operating in the opposite direction. By such “upward anchoring,” judged frequencies would lie close to relatively larger numbers within the given response ranges. Consequently, I predicted that judged frequencies for high-frequency events would be proportionally greater in the wide-range condition than in the narrow-range condition. These predictions were confirmed.     

Robust estimation of ability in the Rasch model

Estimating ability parameters in latent trait models in general, and in the Rasch model in particular is almost always hampered by noise in the data. This noise can be caused by guessing, inattention to easy questions, and other factors which are unrelated to ability. In this study several alternative formulations which attempt to deal with these problems without a reparameterization are tested through a Monte Carlo simulation. It was found that although no one of the tested schemes is uniformly superior to all others, a modified jackknife stood out as the best one in general, it was also super efficient (more efficient than the asymptotically optimal estimator) for tests with forty or fewer items. It is proposed that this sort of jackknifing scheme for estimating ability be considered for practical work.This research was funded through a grant from the Law Enforcement Assistance Administration (78-NI-AX-0047) to the Bureau of Social Science Research, Howard Wainer, Principal Investigator. We would like to thank Ronald Mead, Anne Morgan and James Ramsay for kind, generous, and invaluable help at various stages of the project.     

A bayesian approach to confirmatory factor analysis

Confirmatory factor analysis is considered from a Bayesian viewpoint, in which prior information on parameter is incorporated in the analysis. An iterative algorithm is developed to obtain the Bayes estimates. A numerical example based on longitudinal data is presented. A simulation study is designed to compare the Bayesian approach with the maximum likelihood method.Computer facilities were provided by the Computer Services Center, The Chinese University of Hong Kong.