The role of impulses in shaping decisions

This paper explores the extent to which decision behavior is shaped by short‐lived reactions to the outcome of the most recent decision. We inspected repeated decision‐making behavior in two versions of each of two decision‐making tasks, an individual task and a strategic one. By regressing behavior onto the outcomes of recent decisions, we found that the upcoming decision was well predicted by the most recent outcome alone, with the tendency to repeat a previous action being affected both by its actual outcome and by the outcomes of actions not taken. Because the goodness of predictions based on the most recent outcome did not diminish as participants gained experience with the task, we conclude that repeated decisions are continuously affected by impulsive reactions. Copyright © 2010 John Wiley & Sons, Ltd.     

人文关怀：ICU不容忽视的问题

ICU挽救了大量重症患者的生命,但也让很多患者留下了痛苦的回忆,ICU内人文关怀的缺失是发生这种现象的重要原因。疾病及医疗护理行为、ICU的环境、失眠、生命尊严与求生欲望之间的矛盾都使患者产生痛苦,由此提出要从制度层面完善学科设置,明确学科定位,加强专科人才培养,合理配置人力资源,完善管理政策,技术层面要对患者进行有效的镇静、镇痛治疗,重视患者诉求,减少有创操作,开展ICU床旁早期活动和康复治疗,从心理层面建立友好的ICU环境,注重医护患沟通,从多角度实施临终关怀,帮助患者减少痛苦。     

Describing the elephant: Structure and function in multivariate data

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain.     

An analysis and synthesis of multiple correspondence analysis,optimal scaling,dual scaling,homogeneity analysis and other methods for quantifying categorical multivariate data

We discuss a variety of methods for quantifying categorical multivariate data. These methods have been proposed in many different countries, by many different authors, under many different names. In the first major section of the paper we analyze the many different methods and show that they all lead to the same equations for analyzing the same data. In the second major section of the paper we introduce the notion of a duality diagram, and use this diagram to synthesize the many superficially different methods into a single method.The ideas in this paper were worked out by the first author, with some suggestions provided by the second. The current version of this paper has evolved from three previous versions, the first two written by the first author.     

The direct product model for the mtmm matrix parameterized as a second order factor analysis model

The composite direct product model for the multitrait-multimethod matrix is reparameterized as a second-order factor analysis model. This facilitates the use of widely available computer programs such as LISREL and LISCOMP for fitting the model.Bruce Bloxom. Paul Horst and Karl Jöreskog contributed helpful comments to an earlier version of this paper. Their suggestions are gratefully acknowledged.     

Conformity of two sociometric relations

The problem of comparing two sociometric relations or measurements (A andB) recorded in distinct sociomatrices was originally discussed by Katz and Powell in the early 1950's and Hubert and Baker in the late 1970's. The problem is considered again using a probabilistic model designed specifically for discrete-valued network measurements. The model allows for the presence of various structural tendencies, such as reciprocity and differential popularity. A parameter that isolates the tendency for actors to choose other actors on both relations simultaneously is introduced, and estimated conditional on the presence of other parameters that reflect additional important network properties. The parameter is presented as a symmetric index but is also generalized to the predictive (A onB orB onA) situation. This approach to the problem is illustrated with the same data used by the earlier solutions, and the unique nature of the two relations in the data set (A = received choices,B = perceived choices), as it affects the modeling, is discussed. Significance tests for the parameter and related parameters are described, as well as an extension to more than two relations.Research support provided by National Science Grant #SES84-08626 to the University of Illinois at Urbana-Champaign. I am grateful to Dawn Iacobucci and Sheila Weaver for assistance with the research reported here, and to Carolyn Anderson, James Green, David Holtgrave, and four anonymous referees for comments on the paper.     

Function invariant and parameter scale-free transformation methods

The parameter matrices of factor analysis and principal component analysis are arbitrary with respect to the scale of the factors or components; typically, the scale is fixed so that the factors have unit variance. Oblique transformations to optimize an objective statement of a principle such as simple structure or factor simplicity yield arbitrary solutions, unless the criterion function is invariant with respect to the scale of the factors, or the parameter matrix is scale free with respect to the factors. Criterion functions that are factor scale-free have a number of invariance characteristics, such as being equally applicable to primary pattern or reference structure matrices. A scale-invariant simple structure function of previously studied function components is defined. First and second partial derivatives are obtained, and Newton-Raphson iterations are utilized. The resulting solutions are locally optimal and subjectively pleasing.Aspects of this paper were presented at the 1970 and 1974 annual meetings, Society of Multivariate Experimental Psychology, and the 1975 annual meeting, Psychometric Society. This investigation was supported in part by a Research Scientist Development Award (K02-DA00017) and research grants (MH24149 and DA01070) from the U. S. Public Health Service. The assistance of Bonnie Barron, Sik-Yum Lee, and several extremely helpful reviewers is gratefully acknowledged.     

Univariate repeated measures techniques applied to multivariate data

Repeated measures designs in psychology have traditionally been analyzed by the univariate mixed model approach, in which the repeated measures effect is tested against an error term based on the subject by treatment interaction. This paper considers the extension of this analysis to designs in which the individual repeated measures are multivariate. Sufficient conditions for a valid multivariate mixed model analysis are given, and a test is described to determine whether or not given data satisfy these conditions.     

Tool use by chimpanzees (Pan troglodytes) of the Arnhem Zoo community

Abstract: Chimpanzees have a large repertoire of tool-use behaviors. This study reports on the variety and the extent of tool use exhibited by the chimpanzees of the Arnhem Zoo community in The Netherlands, living in an enriched captive setting since 1971. The use of tools by 29 chimpanzees aged from 0 to 37 years was observed. We identified 13 types of tool use comparable to those found in the wild. Some of these types of tool use seem to be specific to this community, and can be explained by the ecological characteristics of this captive setting. Chimpanzees started to use tools from the age of 2 years. Young chimpanzees, from 5 to 9 years old, showed a greater repertoire of tool use than infants and adults. All types of tool use in the community have appeared by the age of 10, the age of puberty for chimpanzees. Multivariate analysis was applied for the 29 individuals by 13 types of tool use in a one-zero matrix. The results show two major categories of tool use, one in a practical or substantial context and the other in a nonpractical or play context. The subjects clustered into groups reflecting developmental stages, although there are great individual differences. In conclusion, this captive community provides a unique opportunity to clarify the details of tool use by chimpanzees.     

Fitting multivariage normal finite mixtures subject to structural equation modeling

This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises common factor and structural regression models. The introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidence intervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.