肯定性“重要度“语义量词的多级估量模糊集模型建立及其应用

在人员选拔和心理素质评价研究中,需要通过被研究者在特定的问题、事件或情境上所作出的评价,来了解他们的心理活动特点。为了确保评价的可靠性,在量表设定上多采用自然语言的回答方式,如是或否,或极重要、很重要、较重要、稍重要等.等级性自然语言可表征人们评价事物的心理量,称语义量词.但这些语义量词不是一个数值,而是一个边界不确定的数量范围,难以作数量化分析,一直是心理测量方法学研究的重要课题。马谋超提出的模糊评判的“多级估量法”［‘l为等级性自然语言的数量化研究提供了重要的理论依据。优秀军事指挥员心理选拔与…     

Kripke Frame with Graded Accessibility and Fuzzy Possible World Semantics

A possible world structure consist of a set W of possible worlds and an accessibility relation R. We take a partial function r(·,·) to the unit interval [0, 1] instead of R and obtain a Kripke frame with graded accessibility r Intuitively, r(x, y) can be regarded as the reliability factor of y from x We deal with multimodal logics corresponding to Kripke frames with graded accessibility in a fairly general setting. This setting provides us with a framework for fuzzy possible world semantics. The basic propositional multimodal logic gK (grated K) is defined syntactically. We prove that gK is sound and complete with respect to this semantics. We discuss some extensions of gK including logics of similarity relations and of fuzzy orderings. We present a modified filtration method and prove that gK and its extensions introduced here are decidable.     

Probability matrix decomposition models

In this paper, we consider a class of models for two-way matrices with binary entries of 0 and 1. First, we considerBoolean matrix decomposition, conceptualize it as alatent response model (LRM) and, by making use of this conceptualization, generalize it to a larger class of matrix decomposition models. Second,probability matrix decomposition (PMD) models are introduced as a probabilistic version of this larger class of deterministic matrix decomposition models. Third, an algorithm for the computation of the maximum likelihood (ML) and the maximum a posteriori (MAP) estimates of the parameters of PMD models is presented. This algorithm is an EM-algorithm, and is a special case of a more general algorithm that can be used for the whole class of LRMs. And fourth, as an example, a PMD model is applied to data on decision making in psychiatric diagnosis. This paper is based on a chapter of the first author's doctoral dissertation, written at the University of Leuven and supervised by Paul De Boeck.     

A study of the classification capabilities of neural networks using unsupervised learning: A comparison withK-means clustering

Several neural networks have been proposed in the general literature for pattern recognition and clustering, but little empirical comparison with traditional methods has been done. The results reported here compare neural networks using Kohonen learning with a traditional clustering method (K-means) in an experimental design using simulated data with known cluster solutions. Two types of neural networks were examined, both of which used unsupervised learning to perform the clustering. One used Kohonen learning with a conscience and the other used Kohonen learning without a conscience mechanism. The performance of these nets was examined with respect to changes in the number of attributes, the number of clusters, and the amount of error in the data. Generally, theK-means procedure had fewer points misclassified while the classification accuracy of neural networks worsened as the number of clusters in the data increased from two to five.Acknowledgements: Sara Dickson, Vidya Nair, and Beth Means assisted with the neural network analyses.     

Points of view analysis revisited: Fitting multidimensional structures to optimal distance components with cluster restrictions on the variables

Points of view analysis (PVA), proposed by Tucker and Messick in 1963, was one of the first methods to deal explicitly with individual differences in multidimensional scaling, but at some point was apparently superceded by the weighted Euclidean model, well-known as the Carroll and Chang INDSCAL model. This paper argues that the idea behind points of view analysis deserves new attention, especially as a technique to analyze group differences. A procedure is proposed that can be viewed as a streamlined, integrated version of the Tucker and Messick Process, which consisted of a number of separate steps. At the same time, our procedure can be regarded as a particularly constrained weighted Euclidean model. While fitting the model, two types of nonlinear data transformations are feasible, either for given dissimilarities, or for variables from which the dissimilarities are derived. Various applications are discussed, where the two types of transformation can be mixed in the same analysis; a quadratic assignment framework is used to evaluate the results.The research of the first author was supported by the Royal Netherlands Academy of Arts and Sciences (KNAW); the research of the second author by the Netherlands Organization for Scientific Research (NWO Grant 560-267-029). An earlier version of this paper was presented at the European Meeting of the Psychometric Society, Leuven, 1989. We wish to thank Willem J. Heiser for his stimulating comments to earlier versions of this paper, and we are grateful to the Editor and anonymous referees for their helpful suggestions.     

飞行员情境认知的模糊综合评判

借用环境科学中环境质量评价的概念和方法,建立了飞行员情境认知的多级模糊综合评判模型,并用该模型对飞行员的情境认知进行了综合评判。研究表明,用模糊综合评判方法评价飞行员的情境认知可以提供真实、全面的飞行认知信息,为飞行员的训练有效性及安全性提供可靠依据。     

Gaussian model‐based partitioning using iterated local search

The emergence of Gaussian model‐based partitioning as a viable alternative to K‐means clustering fosters a need for discrete optimization methods that can be efficiently implemented using model‐based criteria. A variety of alternative partitioning criteria have been proposed for more general data conditions that permit elliptical clusters, different spatial orientations for the clusters, and unequal cluster sizes. Unfortunately, many of these partitioning criteria are computationally demanding, which makes the multiple‐restart (multistart) approach commonly used for K‐means partitioning less effective as a heuristic solution strategy. As an alternative, we propose an approach based on iterated local search (ILS), which has proved effective in previous combinatorial data analysis contexts. We compared multistart, ILS and hybrid multistart–ILS procedures for minimizing a very general model‐based criterion that assumes no restrictions on cluster size or within‐group covariance structure. This comparison, which used 23 data sets from the classification literature, revealed that the ILS and hybrid heuristics generally provided better criterion function values than the multistart approach when all three methods were constrained to the same 10‐min time limit. In many instances, these differences in criterion function values reflected profound differences in the partitions obtained.     

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.     

The Gist of Delay of Gratification: Understanding and Predicting Problem Behaviors

Delay of gratification captures elements of temptation and self‐denial that characterize real‐life problems with money and other problem behaviors such as unhealthy risk taking. According to fuzzy‐trace theory, decision makers mentally represent social values such as delay of gratification in a coarse but meaningful form of memory called “gist.” Applying this theory, we developed a gist measure of delay of gratification that does not involve quantitative trade‐offs (as delay discounting does) and hypothesize that this construct explains unique variance beyond sensation seeking and inhibition in accounting for problem behaviors. Across four studies, we examine this Delay‐of‐gratification Gist Scale by using principal components analyses and evaluating convergent and divergent validity with other potentially related scales such as Future Orientation, Propensity to Plan, Time Perspectives Inventory, Spendthrift‐Tightwad, Sensation Seeking, Cognitive Reflection, Barratt Impulsiveness, and the Monetary Choice Questionnaire (delay discounting). The new 12‐item measure captured a single dimension of delay of gratification, correlated as predicted with other scales, but accounted for unique variance in predicting such outcomes as overdrawing bank accounts, substance abuse, and overall subjective well‐being. Results support a theoretical distinction between reward‐related approach motivation, including sensation seeking, and inhibitory faculties, including cognitive reflection. However, individuals' agreement with the qualitative gist of delay of gratification, as expressed in many cultural traditions, could not be reduced to such dualist distinctions nor to quantitative conceptions of delay discounting, shedding light on mechanisms of self‐control and risk taking. Copyright © 2016 John Wiley & Sons, Ltd.     

Clustering and switching in unconstrained,phonemic and semantic verbal fluency: the role of age and school type

We investigated the role of age and school type on clustering and switching in verbal fluency tasks (VFTs) with Brazilian children. The children were administered unconstrained, phonemic and semantic VFTs with a duration of 150 or 120 s, respectively. Both age and school type influenced all variables and in terms of performance over time. Older children and private school students outperformed the remainder of the sample, with the first 30 s of each VFT usually being the most productive. Although the size of the clusters produced did not differ between groups, the types of clusters did show some variations, with semantic clusters being the most frequent. Our results revealed strong correlations between switching ability and word production in all three VFTs. In conclusion, the executive functions known as planning and cognitive flexibility play a crucial role in word production by organising and facilitating the recall of lexical information from memory.