The Finite-Dimensional Freeman Thesis

I suggest a modification—and mathematization—of Freeman’s thesis on the relations among “perception”, “the finite brain”, and “the world”, based on my recent proposal that the theory of finite topological spaces is both an adequate and a natural mathematical foundation for human psychology.

Re-conceptualizing the past: Historical data in vocational interest research

Noteworthy progress has been made in the development of statistical models for evaluating the structure of vocational interests over the past three decades. It is proposed that historically significant interest datasets, when combined with modern structural methods of data analysis, provide an opportunity to re-examine the underlying assumptions of J.L. Holland’s [Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 35–45; Holland, J. L. (1997). Making vocational choices (3rd ed.). Odessa, FL: Psychological Assessment Resources] RIASEC model. To illustrate this potential, data obtained from J. P. Guilford’s study of interest structure were re-analyzed using modern circumplex and hierarchical clustering techniques to evaluate Holland’s and I. Gati’s [Gati, I. (1979). A hierarchical model for the structure of interests. Journal of Vocational Behavior, 15, 90–106; Gati, I. (1991). The structure of vocational interests. Psychological Bulletin, 109, 309–324] interest structures. Obtained results indicate that a circumplex model can be used to effectively represent the structure underlying Guilford’s interest measures. However, hierarchical clustering results suggest that Holland’s RIASEC types may not be the most effective categories for grouping specific interest measures into broader interest areas. The current findings provide support for the continued investigation of alternatives to Holland’s interest categories using modern measures of basic interests.     

Developing symbolic capacity one step at a time

The present research examines the ability of children as young as 4 years to use models in tasks that require scaling of distance along a single dimension. In Experiment 1, we found that tasks involving models are similar in difficulty to those involving maps that we studied earlier (Huttenlocher, J., Newcombe, N., & Vasilyeva, M. (1999). Spatial scaling in young children. Psychological Science, 10, 393-398). In Experiment 2, we found that retrieval tasks, where children indicate the location of a hidden object in an actual space are substantially more difficult than placement tasks, where children put a visible object in a particular location in an actual space. We discuss possible implications of the differential difficulty of retrieval and placement tasks for the understanding of symbolic development.     

基于网络与演化视角的集体球类项目团队共享心智模型

团队既是当今组织中的重要工作方式, 又是集体竞技的组织形式, 故成为众多学科关注的焦点。共享心智模型是团队成员共享的团队相关情境关键要素的有组织理解、知识心理表征或信念。近年来, 中国足、篮、排三大球集体陷入低谷, 输球又输人的状况屡见取端, 引发举国关注。振兴“三大球”成为建设体育强国必然要迈过的坎。研究提出从网络与演化视角出发, 以“三大球”为主要研究对象, 通过对主题专家的深度访谈与“三大球”标杆球队的进球(或得分)录像分析, 寻找“三大球”共享心智模型演化的共性与殊性, 并为后续研究提供素材与参数; 接着, 采用多重个案设计与追踪研究范式, 交叉检定与复现共享心智模型的演化理论与作用机制理论; 再次, 实证检验成员社会网络类型、结构与演化对共享心智模型与团队效能的影响; 最后, 运用Repast 软件对共享心智模型的形成与演化进行模拟, 以为预测与实时干预提供参考。     

恋爱受挫对自我—他人工作模型的影响

摘 要 该研究使用内隐联想测验（IAT）考察了成人依恋的主要表现形式恋爱关系中,恋爱受挫对被试自我—他人工作模型的影响。结果表明：（1）虽然实验组与控制组在依恋焦虑、依恋回避得分上均无显著差异,但IAT结果发现,组别、依恋类型在IAT效应（自我积极—他人消极模型指标）上的主效应显著,其交互作用也显著。除了回避型被试,安全型和焦虑型实验组的IAT效应显著高于控制组。（2）控制组中,焦虑型被试相容部分反应时显著大于不相容部分,回避型被试则相容部分反应时显著小于不相容部分,安全型被试无显著差异。     

A Bayesian analysis of finite mixtures in the LISREL model

In this paper, we propose a Bayesian framework for estimating finite mixtures of the LISREL model. The basic idea in our analysis is to augment the observed data of the manifest variables with the latent variables and the allocation variables. The Gibbs sampler is implemented to obtain the Bayesian solution. Other associated statistical inferences, such as the direct estimation of the latent variables, establishment of a goodness-of-fit assessment for a posited model, Bayesian classification, residual and outlier analyses, are discussed. The methodology is illustrated with a simulation study and a real example.This research was supported by a Hong Kong UGC Earmarked grant CUHK 4026/97H. The authors are indebted to the Editor, the Associate Editor, and three anonymous reviewers for constructive comments in improving the paper, and also to ICPSR and the relevant funding agency for allowing the use of the data. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

Bayesian inference for graphical factor analysis models

We generalize factor analysis models by allowing the concentration matrix of the residuals to have nonzero off-diagonal elements. The resulting model is named graphical factor analysis model. Allowing a structure of associations gives information about the correlation left unexplained by the unobserved variables, which can be used both in the confirmatory and exploratory context. We first present a sufficient condition for global identifiability of this class of models with a generic number of factors, thereby extending the results in Stanghellini (1997) and Vicard (2000). We then consider the issue of model comparison and show that fast local computations are possible for this purpose, if the conditional independence graphs on the residuals are restricted to be decomposable and a Bayesian approach is adopted. To achieve this aim, we propose a new reversible jump MCMC method to approximate the posterior probabilities of the considered models. We then study the evolution of political democracy in 75 developing countries based on eight measures of democracy in two different years. We acknowledge support from M.U.R.S.T. of Italy and from the European Science Foundation H.S.S.S. Network. We are grateful to the referees and the Editor for many useful suggestions and comments which led to a substantial improvement of the paper. We also thank Nanny Wermuth for stimulating discussions and Kenneth A. Bollen for kindly providing us with the data-set.     

Spatial reconstruction of human motion by means of a single camera and a biomechanical model

The value of the results of the inverse dynamic analysis procedures used in the study of human tasks is dependent on the quality of the kinematic and kinetic data supplied to the biomechanical model that supports it. The kinematic data, containing the position, velocity and acceleration of all anatomical segments of the biomechanical model, result from the reconstruction of human spatial motion by means of the evaluation of the anatomic points positions that enable to uniquely define the position of all anatomical segments. Furthermore, the motion data must be kinematically consistent with the structure of the biomechanical model used in the analysis. The traditional photogrammetric methodologies used for the spatial reconstruction of the human motion require images of two or more calibrated and synchronized cameras. This is due to the fact that the projection of each anatomical point is described by two linear equations relating its three spatial coordinates with the two coordinates of the projected point. The need for the image of another camera arises from the fact that a third equation is necessary to find the original spatial position of the anatomical point. The methodology proposed here substitutes the projection equations of the second camera with the kinematic constraint equations associated with a biomechanical model in the motion reconstruction process. In the formulation the system of equations arising from the point projections and biomechanical model kinematic constraints, representing the constant length of the anatomical segments, are solved simultaneously. Because the system of equations has multiple solutions for each image, a strategy based on the minimization of a cost function associated to the smoothness of the reconstructed motion is devised. It is shown how the process is implemented computationally avoiding any operator intervention during the motion reconstruction for a given time period. This leads to an automated computer procedure that ensures the uniqueness of the reconstructed motion. The result of the reconstruction process is a set of data that is kinematically consistent with the biomechanical model used. Through applications of the proposed methodology to several sports exercises its benefits and shortcomings are discussed.