Explaining color term typology with an evolutionary model

An expression-induction model was used to simulate the evolution of basic color terms to test Berlin and Kay's (1969) hypothesis that the typological patterns observed in basic color term systems are produced by a process of cultural evolution under the influence of biases resulting from the special properties of universal focal colors. Ten agents were simulated, each of which could learn color term denotations by generalizing from examples using Bayesian inference, and for which universal focal red, yellow, green, and blue were especially salient, but unevenly spaced in the perceptual color space. Conversations between these agents, in which agents would learn from one another, were simulated over several generations, and the languages emerging at the end of each simulation were investigated. The proportion of color terms of each type correlated closely with the equivalent frequencies found in the World Color Survey, and most of the emergent languages could be placed on one of the evolutionary trajectories proposed by Kay and Maffi (1999). The simulation therefore demonstrates how typological patterns can emerge as a result of learning biases acting over a period of time.     

Test theory without an answer key

A general model is presented for homogeneous, dichotomous items when the answer key is not known a priori. The model is structurally related to the two-class latent structure model with the roles of respondents and items interchanged. For very small sets of respondents, iterative maximum likelihood estimates of the parameters can be obtained by existing methods. For other situations, new estimation methods are developed and assessed with Monte Carlo data. The answer key can be accurately reconstructed with relatively small sets of respondents. The model is useful when a researcher wants to study objectively the knowledge possessed by members of a culturally coherent group that the researcher is not a member of.This research was supported by NSF Grant No. SES-8320173 to the authors. We gratefully acknowledge comments and suggestions from John Boyd, Tarow Indow, and Kathy Maher as well as the editor and several anonymous referees.     

A general model for the analysis of multilevel data

A general model is developed for the analysis of multivariate multilevel data structures. Special cases of the model include repeated measures designs, multiple matrix samples, multilevel latent variable models, multiple time series, and variance and covariance component models.We would like to acknowledge the helpful comments of Ruth Silver. We also wish to thank the referees for helping to clarify the paper. This work was partly carried out with research funds provided by the Economic and Social Research Council (U.K.).     

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.     

A latent class analysis of positive psychosis symptoms based on the British Psychiatric Morbidity Survey

This study aimed to describe the distribution of positive psychosis symptoms in a large scale, population-based sample. Data from the British Psychiatric Morbidity Survey was used to examine homogeneous sub-types of participants based on their responses to the five probe items of the Psychosis Screening Questionnaire. Latent class analysis was used to identify the underlying class structure of psychosis. Four classes of psychosis were identified. Associations between the latent classes and the demographic risk factors, clinical variables and reports of traumatic experiences showed significantly higher risks for the psychosis class, the paranoid class, and the intermediate class compared to the normative class. Four classes that appeared to represent an underlying continuum could best explain psychosis symptom expression at the population level.     

A class of multidimensional IRT models for testing unidimensionality and clustering items

We illustrate a class of multidimensional item response theory models in which the items are allowed to have different discriminating power and the latent traits are represented through a vector having a discrete distribution. We also show how the hypothesis of unidimensionality may be tested against a specific bidimensional alternative by using a likelihood ratio statistic between two nested models in this class. For this aim, we also derive an asymptotically equivalent Wald test statistic which is faster to compute. Moreover, we propose a hierarchical clustering algorithm which can be used, when the dimensionality of the latent structure is completely unknown, for dividing items into groups referred to different latent traits. The approach is illustrated through a simulation study and an application to a dataset collected within the National Assessment of Educational Progress, 1996. The author would like to thank the Editor, an Associate Editor and three anonymous referees for stimulating comments. I also thank L. Scaccia, F. Pennoni and M. Lupparelli for having done part of the simulations.     

科学主义的尴尬与中医学的多向度发展

近５０年的中医现代化研究暴露了科学主义试图消解其他向度存在的独尊倾向,而经络实质研究、证客观化研究等方面的失败,宣告了科学主义并不是解决中医学所有问题的灵丹妙药。科学主义所遇到的这种尴尬局面表明,中医学应该有多种向度的发展,除了现代化研究这一模式之外,至少还应当有两种向度的存在,即原有形态的传统中医学体系应继续存在下去,传统中医学在技术上与西医学的结合应当受到鼓励。     

All on the Same Side: Reflections on the Dialogue Between Science and Religion

The 'war' between religion and science is winding down, creating new opportunities for fruitful dialogue. The foundations of indirect religion-science dialogue, where the perspectives of the two disciplines illuminate some third subject, are not well established. A detailed comparison of the Roman Catholic bishops' dialogues and a similar program within the American Association for the Advancement of Science illustrates the variety in formalscience-religion interactions and reveals much about the promise, achievements, and limitations of different approaches. Success depends in large part on controlling the diversity of the dialogue group, choosing topics carefully, and adopting positive and cooperative attitudes.     

Relations of Tripartite Dimensions of Emotion to Childhood Anxiety and Mood Disorders

This study examined preliminary higher-order models relating tripartite dimensions of emotion to severity of anxiety and depressive disorders in 100 clinically referred children and adolescents. In light of the accumulating support for multifactor models of vulnerability and negative emotion in children, the present investigation was designed to establish preliminary estimates of the structure and magnitude of the relations of three emotion factors with dimensions of social anxiety, depression, panic, generalized anxiety, obsessions/compulsions, and separation anxiety. Results were consistent with structures that minimally specified two higher-order emotion factors, yet only some parameter estimates were consistent with theory regarding the tripartite model. Problems with the measurement of tripartite factors and possibilities for further research are outlined.     

现代医学模式形成与疾病治疗

现代生物心理社会医学模式已经成为卫生界领导及广大医务人员的共识 ,并以此指导临床医疗实践。临床诊断正在增添新的内容 ,疾病治疗这个概念也拓宽了她的外延 ,人文科学在医学中越来越显示出她独特的不可替代的地位和作用 ,生物治疗、心理治疗、哲学治疗是未来的三大块治疗模式