A thurstonian pairwise choice model with univariate and multivariate spline transformations

A probabilistic choice model is developed for paired comparisons data about psychophysical stimuli. The model is based on Thurstone's Law of Comparative Judgment Case V and assumes that each stimulus is measured on a small number of physical variables. The utility of a stimulus is related to its values on the physical variables either by means of an additive univariate spline model or by means of multivariate spline model. In the additive univariate spline model, a separate univariate spline transformation is estimated for each physical dimension and the utility of a stimulus is assumed to be an additive combination of these transformed values. In the multivariate spline model, the utility of a stimulus is assumed to be a general multivariate spline function in the physical variables. The use of B splines for estimating the transformation functions is discussed and it is shown how B splines can be generalized to the multivariate case by using as basis functions tensor products of the univariate basis functions. A maximum likelihood estimation procedure for the Thurstone Case V model with spline transformation is described and applied for illustrative purposes to various artificial and real data sets. Finally, the model is extended using a latent class approach to the case where there are unreplicated paired comparisons data from a relatively large number of subjects drawn from a heterogeneous population. An EM algorithm for estimating the parameters in this extended model is outlined and illustrated on some real data.The first author is supported as Bevoegdverklaard Navorser of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek. The authors are indebted to Ulf Böckenholt and Yoshio Takane for useful comments on an earlier draft of this paper.     

AUDITORY SUCCESSIVE CONDITIONAL DISCRIMINATION AND AUDITORY STIMULUS EQUIVALENCE CLASSES

This paper describes an experimental demonstration of stimulus equivalence classes consisting entirely of auditory stimuli. Stimuli were digitized arbitrary syllables (e.g., “cug,” “vek”) presented via microcomputer. Training and testing were conducted with a two-choice auditory successive conditional discrimination procedure. On each trial, auditory samples and comparisons were presented successively. As each comparison was presented, a response location (a rectangle) appeared on the computer screen. After all stimuli for a trial were presented, subjects selected one of the response locations. Six subjects acquired the conditional discrimination baseline, 4 subjects demonstrated the formation of three-member auditory equivalence classes resulting from sample-S+ relations, and 1 subject demonstrated equivalence classes resulting from sample-S— relations. Four subjects received additional training and subsequently demonstrated expansion of the three-member classes to four members each.     

An adaptive approach for modelling multiattribute choice

An adaptive approach for modelling individual-level choice among multiattribute alternatives using the binary logit model is presented. The algorithm involves the collection of paired comparison data. In an effort to maximize the amount of information obtainable from each response, it is based on the experimental design criterion of D-optimality. A simulation study indicates that the proposed algorithm outperforms other sequential selection approaches in terms of estimation accuracy and predictive efficiency under certain circumstances. The results appear to encourage the use of such an adaptive algorithm for individual-level modelling in light of the potential reduction in data requirements without significant loss in predictive accuracy.     

On the Bock-Aitkin procedure—From an EM algorithm perspective

The relationship between the EM algorithm and the Bock-Aitkin procedure is described with a continuous distribution of ability (latent trait) from an EM-algorithm perspective. Previous work has been restricted to the discrete case from a probit-analysis perspective. The author is grateful to Bradley A. Hanson for valuable discussion and comments. Thanks also go to Terry A. Ackerman, Meichu Fan, Subrata Kundu, and Robert K. Tsutakawa for their help and encouragement in this study.     

Generalized latent trait models

In this paper we discuss a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. A unified maximum likelihood method for estimating the parameters of the generalized latent trait model will be presented. We discuss in addition the scoring of individuals on the latent dimensions. The general framework presented allows, not only the analysis of manifest variables all of one type but also the simultaneous analysis of a collection of variables with different distributions. The approach used analyzes the data as they are by making assumptions about the distribution of the manifest variables directly.     

On Learnability of Restricted Classes of Categorial Grammars

In this paper we present learning algorithms for classes of categorial grammars restricted by negative constraints. We modify learning functions of Kanazawa [10] and apply them to these classes of grammars. We also prove the learnability of intersection of the class of minimal grammars with the class of k-valued grammars. Presented by Wojciech Buszkowski     

汉字通透性算法以及对结构方式效应的解释

在先前提出的汉字通透性算法中 ,包括了水平通透性和垂直通透性 ,而汉字作为整体 ,其通透性也应该是一个整体 ,所以 ,当前研究对这一算法进行了更正 ,然后运用新的算法对先前实验材料和基本汉字集 (6 72 4 )中的配对上下字和左右字进行通透性分析 ,证明了它的可行性。在实验研究中 ,采用新算法选择实验用字 ,以达到使上下字的通透性大于左右字的通透性或者使它们相等的目的 ,以此为前提观察到了逆转的结构方式效应以及结构方式效应消失。这一实验结果表明 ,结构方式本身并不影响汉字识别 ,影响汉字识别的直接因素是隐藏在结构方式之下的汉字通透性     

Model specification search using a genetic algorithm with factor reordering for a simple structure factor analysis model

Abstract:  Many techniques for automated model specification search based on numerical indices have been proposed, but no single decisive method has yet been determined. In the present article, the performance and features of the model specification search method using a genetic algorithm (GA) were verified. A GA is a robust and simple metaheuristic algorithm with great searching power. While there has already been some research applying metaheuristics to the model fitting task, we focus here on the search for a simple structure factor analysis model and propose a customized algorithm for dealing with certain problems specific to that situation. First, implementation of model specification search using a GA with factor reordering for a simple structure factor analysis is proposed. Then, through a simulation study using generated data with a known true structure and through example analysis using real data, the effectiveness and applicability of the proposed method were demonstrated.     

Latent variable models for analyzing children's processing ability

In this paper, we develop a latent processing ability model to analyze the speed of processing ability data. Our approach can not only effectively evaluate the effects of covariates on the latent processing ability, but also estimate the latent trait of each child by calculating its posterior mean. In addition, we derive the correlations structure of latent traits among different age groups. Simulations are conducted to evaluate the performance of our proposed model. The results indicated that the estimation of model parameters is satisfactory overall. The method is evaluated using real data from children aged 4–7 years in Changchun, China.     

医疗情感计算应用的多维困境及其法治实现

情感计算技术依托算法的数据处理能力和自主学习能力已经在医疗健康领域有所实践。但由于医疗领域的特殊性,情感计算技术医疗应用过程中存在着道德合理性风险、规制盲区以及技术羁绊等多重隐患,亟须法律规制。首先,应当坚守“拟主体”身份的道德底线,追求伦理与法治的最佳耦合;其次,应构建权责明晰的算法责任嵌入机制,建立多元协同的算法权力治理机制;最后,应建立健全医疗情感计算技术审查机制,完善情感计算算法风险等级治理体系。