Rare Constructions Are More Often Sentence‐Initial

Main clause phenomena (MCPs) are syntactic constructions that occur predominantly or exclusively in main clauses. I propose a processing explanation for MCPs. Sentence processing is easiest at the beginning of the sentence (requiring less search); this follows naturally from widely held assumptions about sentence processing. Because of this, a wider variety of constructions can be allowed at the beginning of the sentence without overwhelming the sentence‐processing mechanism. Unlike pragmatic and grammatical accounts of MCPs, the processing account predicts avoidance of MCPs in non‐initial main clauses (non‐initial coordinate clauses and premodified clauses). A corpus study supports these predictions, but it is somewhat inconclusive. A further corpus study examines another type of syntactic construction, premodifying adjunct phrases (“openers”); the prediction here is that less common types of opener will be especially avoided in non‐initial contexts. The prediction is confirmed, supporting the processing view of rare constructions.     

A reformulation of the general Euclidean model for the external analysis of preference data

This paper discusses least squares methods for fitting a reformulation of the general Euclidean model for the external analysis of preference data. The reformulated subject weights refer to a common set of reference vectors for all subjects and hence are comparable across subjects. If the rotation of the stimulus space is fixed, the subject weight estimates in the model are uniquely determined. Weight estimates can be guaranteed nonnegative. While the reformulation is a metric model for single stimulus data, the paper briefly discusses extensions to nonmetric, pairwise, and logistic models. The reformulated model is less general than Carroll's earlier formulation.The author is grateful to Christopher J. Nachtsheim for his helpful suggestions.     

A method of optimal scaling for multivariate ordinal data and its extensions

This paper develops a method of optimal scaling for multivariate ordinal data, in the framework of a generalized principal component analysis. This method yields a multidimensional configuration of items, a unidimensional scale of category weights for each item and, optionally, a multidimensional configuration of subjects. The computation is performed by alternately solving an eigenvalue problem and executing a quasi-Newton projection method. The algorithm is extended for analysis of data with mixed measurement levels or for analysis with a combined weighting of items. Numerical examples and simulations are provided. The algorithm is discussed and compared with some related methods.Earlier results of this research appeared in Saito and Otsu (1983). The authors would like to acknowledge the helpful comments and encouragement of the editor.     

Hierarchical classes: Model and data analysis

A discrete, categorical model and a corresponding data-analysis method are presented for two-way two-mode (objects × attributes) data arrays with 0, 1 entries. The model contains the following two basic components: a set-theoretical formulation of the relations among objects and attributes; a Boolean decomposition of the matrix. The set-theoretical formulation defines a subset of the possible decompositions as consistent with it. A general method for graphically representing the set-theoretical decomposition is described. The data-analysis algorithm, dubbed HICLAS, aims at recovering the underlying structure in a data matrix by minimizing the discrepancies between the data and the recovered structure. HICLAS is evaluated with a simulation study and two empirical applications.This research was supported in part by a grant from the Belgian NSF (NFWO) to Paul De Boeck and in part by NSF Grant BNS-83-01027 to Seymour Rosenberg. We thank Iven Van Mechelen for clarifying several aspects of the Boolean algebraic formulation of the model and Phipps Arabie for his comments on an earlier draft.     

Imputation of missing categorical data by maximizing internal consistency

This paper suggests a method to supplant missing categorical data by reasonable replacements. These replacements will maximize the consistency of the completed data as measured by Guttman's squared correlation ratio. The text outlines a solution of the optimization problem, describes relationships with the relevant psychometric theory, and studies some properties of the method in detail. The main result is that the average correlation should be at least 0.50 before the method becomes practical. At that point, the technique gives reasonable results up to 10–15% missing data.We thank Anneke Bloemhoff of NIPG-TNO for compiling and making the Dutch Life Style Survey data available to use, and Chantal Houée and Thérèse Bardaine, IUT, Vannes, France, exchange students under the COMETT program of the EC, for computational assistance. We also thank Donald Rubin, the Editors and several anonymous reviewers for constructive suggestions.     

影响频度记忆的几个因素

本研究通过两个实验探讨了刺激呈现频度、刺激的形式及其性质、加工深度、操作难度、意向性诸因素对频度记忆的影响,结果表明频度编码并不是一个独立、自动的加工系统,对其它识记过程有影响的因素同时也能改变被试的频度估计水平;意向的作用主要体现在当识记任务有一定难度且又缺乏有效的回忆线索时,能够在有限的范围内提高频度估计的水平;记忆痕迹的深度和广度对于频度信息的提取来说是两个彼此关联的线索。此外,本文还就频度记忆领域几个主要理论观点作了简要评述。     

量词肯定句和否定句的理解

本研究试图探索人们对舍有“所有”、“每一个”、“有一些”等量词的肯定句、否定句和双重否定句的理解。要求成人被试尽快判断呈现在计算机屏幕上的句子和哪一幅图画的内容相符。结果表明,影响反应时的因素为(1)句子的表层结构和它的底层意义的一致程度;(2)句子的意义和从图画中得出的命题的一致程度;(3)句子中使用的量词。结果还表明,否定句的判断时间不一定全都比肯定句的长。     

Parallel distributed processing and neuropsychology: A neural network model of Wisconsin card sorting and verbal fluency

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.     

Computerized assessment in neuropsychology: A review of tests and test batteries

This article contains detailed reviews of 13 computerized neuropsychological and performance test batteries and six stand-alone computer tests. Tasks found on these instruments are described and tables illustrate which batteries employ which measures. In addition to issues of reliability and validity, special considerations apply to computerized assessment. These issues are discussed and readers are provided information to help them assess computerized tests in relation to their particular clinical and research needs. Since many computerized tests were developed as performance assessment tools, the relationship between performance and neuropsychological assessment is examined.