Empirical research on the identification of singular,multiple and subordinate argumentation

To examine to what degree argumentation skills are mastered by pupils who attend the vocational, general and academic streams in Dutch secondary education various subtests were constructed. The theoretical study of argumentation as exposed by Van Eemeren and Grootendorst was the basis for this test construction. In this article tests for the identification of singular, multiple and subordinate argumentation are described. Also an account is given of a pretest of these three subtests.     

Some properties of estimated scale invariant covariance structures

Scale invariance is a property shared by many covariance structure models employed in practice. An example is provided by the well-known LISREL model subject only to classical normalizations and zero constraints on the parameters. It is shown that scale invariance implies that the estimated covariannce matrix must satisfy certain equations, and the nature of these equations depends on the fitting function used. In this context, the paper considers two classes of fitting functions: weighted least squares and the class of functions proposed by Swain.Constructive comments by the referees are greatly appreciated. The author gratefully acknowledges Michael Browne's interest in his work.     

Finite mixtures in confirmatory factor-analysis models

In this paper, various types of finite mixtures of confirmatory factor-analysis models are proposed for handling data heterogeneity. Under the proposed mixture approach, observations are assumed to be drawn from mixtures of distinct confirmatory factor-analysis models. But each observation does not need to be identified to a particular model prior to model fitting. Several classes of mixture models are proposed. These models differ by their unique representations of data heterogeneity. Three different sampling schemes for these mixture models are distinguished. A mixed type of the these three sampling schemes is considered throughout this article. The proposed mixture approach reduces to regular multiple-group confirmatory factor-analysis under a restrictive sampling scheme, in which the structural equation model for each observation is assumed to be known. By assuming a mixture of multivariate normals for the data, maximum likelihood estimation using the EM (Expectation-Maximization) algorithm and the AS (Approximate-Scoring) method are developed, respectively. Some mixture models were fitted to a real data set for illustrating the application of the theory. Although the EM algorithm and the AS method gave similar sets of parameter estimates, the AS method was found computationally more efficient than the EM algorithm. Some comments on applying the mixture approach to structural equation modeling are made.Note: This paper is one of the Psychometric Society's 1995 Dissertation Award papers.—EditorThis article is based on the dissertation of the author. The author would like to thank Peter Bentler, who was the dissertation chair, for guidance and encouragement of this work. Eric Holman, Robert Jennrich, Bengt Muthén, and Thomas Wickens, who served as the committee members for the dissertation, had been very supportive and helpful. Michael Browne is appreciated for discussing some important points about the use of the approximate information in the dissertation. Thanks also go to an anonymous associate editor, whose comments were very useful for the revision of an earlier version of this article.     

Generalized Rough Sets (Preclusivity Fuzzy-Intuitionistic (BZ) Lattices)

The standard Pawlak approach to rough set theory, as an approximation space consisting of a universe U and an equivalence (indiscernibility) relation R U x U, can be equivalently described by the induced preclusivity ("discernibility") relation U x U \ R, which is irreflexive and symmetric.We generalize the notion of approximation space as a pair consisting of a universe U and a discernibility or preclusivity (irreflexive and symmetric) relation, not necessarily induced from an equivalence relation. In this case the "elementary" sets are not mutually disjoint, but all the theory of generalized rough sets can be developed in analogy with the standard Pawlak approach. On the power set of the universe, the algebraic structure of the quasi fuzzy-intuitionistic "classical" (BZ) lattice is introduced and the sets of all "closed" and of all "open" definable sets with the associated complete (in general nondistributive) ortholattice structures are singled out.The rough approximation of any fixed subset of the universe is the pair consisting of the best "open" approximation from the bottom and the best "closed" approximation from the top. The properties of this generalized rough approximation mapping are studied in the context of quasi-BZ lattice structures of "closed-open" ordered pairs (the "algebraic logic" of generalized rough set theory), comparing the results with the standard Pawlak approach. A particular weak form of rough representation is also studied.     

成人脑涨落图年龄变化特点的研究

该工作研究了１２０名２０－７９岁正常成人脑涨落图的年龄变化特点。结果表明：（１）从２０岁到５０岁,Ｓ系谱线活动向低频方向集中,６０岁后谱线又向高频方向有所移动;（２）部分谐振系的随龄变化表现明显,其中以Ｓ１７系变化最为敏感;（３）脑区间相干活动和各脑区的连频活动随年龄增强,脑能量分布的有序性逐渐破坏;（４）增龄性变化主要反映在优势脑区（左前脑）和优势脑轴（左前／右后脑）上,与脑高级机能活动密切相关的左前脑的退行性变化最为明显。上述结果反映了脑功能的减退是一个非线性的多层次的复杂过程。     

A note on the investigation of local and global identifiability

In general, nonlinear models such as those commonly employed for the analysis of covariance structures, are not globally identifiable. Any investigation of local identifiability must either yield a mapping of identifiability onto the entire parameter space, which will rarely be feasible in any applications of interest, or confine itself to the neighbourhood of such points of special interest as the maximum likelihood point.The author would like to thank J. Jack McArdle and Colin Fraser for their comments on this paper.     

Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs

Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of Cognitive Architectures (CAs). CAs are an approach for modeling human intelligence and behavior. This paper presents an functional bioinspired computational decision-making model to satisfy the physiological needs of hunger and thirst. Our proposal considers as black boxes other cognitive functions that are part of a general CA (named Cuäyöllötl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need.     

Declarative working memory: A bio-inspired cognitive architecture proposal

Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory. Working memory refers to the capacity to maintain temporarily a limited amount of information in mind, which can then be used to support various abilities, including learning, reasoning, planning and decision-making. Unlike short-term memory, working memory is not only a storage site, but it is also a framework of interacting processes that involve the temporary storage and manipulation of information in the service of performing complex cognitive activities. Declarative memory is a type of long-term memory related with the storage of facts and events. This research focuses on the development of a cognitive architecture for the type of working memory that maintains and manipulates declarative information. The construction of the model was grounded in theoretical evidence taken from cognitive sciences such as neuroscience and psychology, which gave us the components and their processes. The model was evaluated through a case study that covers the encoding, storing, and retrieval stages. Our hypothesis is that a virtual creature endowed with our working memory model will provide faster access to the information needed for the ongoing task. Therefore, it improves the planning and decision-making processes.     

Satisfiability Testing for Boolean Formulas Using Δ-trees

The tree-based data structure of -tree for propositional formulas is introduced in an improved and optimised form. The -trees allow a compact representation for negation normal forms as well as for a number of reduction strategies in order to consider only those occurrences of literals which are relevant for the satisfiability of the input formula. These reduction strategies are divided into two subsets (meaning- and satisfiability-preserving transformations) and can be used to decrease the size of a negation normal form A at (at most) quadratic cost. The reduction strategies are aimed at decreasing the number of required branchings and, therefore, these strategies allow to limit the size of the search space for the SAT problem.     

Processing homonymy and polysemy: effects of sentential context and time-course following unilateral brain damage

The present study investigated the abilities of left-hemisphere-damaged (LHD) non-fluent aphasic, right-hemisphere-damaged (RHD), and normal control individuals to access, in sentential biasing contexts, the multiple meanings of three types of ambiguous words, namely homonyms (e.g., "punch"), metonymies (e.g., "rabbit"), and metaphors (e.g., "star"). Furthermore, the predictions of the "suppression deficit" and "coarse semantic coding" hypotheses, which have been proposed to account for RH language function/dysfunction, were tested. Using an auditory semantic priming paradigm, ambiguous words were incorporated in dominant- or subordinate-biasing sentence-primes followed after a short (100 ms) or long (1,000 ms) interstimulus interval (ISI) by dominant-meaning-related, subordinate-meaning-related or unrelated target words. For all three types of ambiguous words, both the effects of context and ISI were obvious in the performance of normal control subjects, who showed multiple meaning activation at the short ISI, but eventually, at the long ISI, contextually appropriate meaning selection. Largely similar performance was exhibited by the LHD non-fluent aphasic patients as well. In contrast, RHD patients showed limited effects of context, and no effects of the time-course of processing. In addition, although homonymous and metonymous words showed similar patterns of activation (i.e., both meanings were activated at both ISIs), RHD patients had difficulties activating the subordinate meanings of metaphors, suggesting a selective problem with figurative meanings. Although the present findings do not provide strong support for either the "coarse semantic coding" or the "suppression deficit" hypotheses, they are viewed as being more consistent with the latter, according to which RH damage leads to deficits suppressing alternative meanings of ambiguous words that become incompatible with the context.