Statistical extraction affects visually guided action

The visual system summarizes average properties of ensembles of similar objects. We demonstrated an adaptation aftereffect of one such property, mean size, suggesting it is encoded along a single visual dimension (Corbett, et al., 2012), in a similar manner as basic stimulus properties like orientation and direction of motion. To further explore the fundamental nature of ensemble encoding, here we mapped the evolution of mean size adaptation over the course of visually guided grasping. Participants adapted to two sets of dots with different mean sizes. After adaptation, two test dots replaced the adapting sets. Participants first reached to one of these dots, and then judged whether it was larger or smaller than the opposite dot. Grip apertures were inversely dependent on the average dot size of the preceding adapting patch during the early phase of movements, and this aftereffect dissipated as reaches neared the target. Interestingly, perceptual judgements still showed a marked aftereffect, even though they were made after grasping was completed more-or-less veridically. This effect of mean size adaptation on early visually guided kinematics provides novel evidence that mean size is encoded fundamentally in both perception and action domains, and suggests that ensemble statistics not only influence our perceptions of individual objects but can also affect our physical interactions with the external environment.     

Parallel interactive retrieval of item and associative information from event memory

Memory contains information about individual events (items) and combinations of events (associations). Despite the fundamental importance of this distinction, it remains unclear exactly how these two kinds of information are stored and whether different processes are used to retrieve them. We use both model-independent qualitative properties of response dynamics and quantitative modeling of individuals to address these issues. Item and associative information are not independent and they are retrieved concurrently via interacting processes. During retrieval, matching item and associative information mutually facilitate one another to yield an amplified holistic signal. Modeling of individuals suggests that this kind of facilitation between item and associative retrieval is a ubiquitous feature of human memory.     

认知诊断测验中基于信息矩阵的多群组DIF检验

基于改进的Wald统计量,将适用于两群组的DIF检测方法拓展至多群组的项目功能差异（DIF）检验;改进的Wald统计量将分别通过计算观察信息矩阵（Obs）和经验交叉相乘信息矩阵（XPD）而得到。模拟研究探讨了此二者与传统计算方法在多个群组下的DIF检验情况,结果表明：（1）Obs和XPD的一类错误率明显低于传统方法,DINA模型估计下Obs和XPD的一类错误率接近理论水平;（2）样本量和DIF量较大时,Obs和XPD具有与传统Wald统计量大体相同的统计检验力。     

Using MCMC chain outputs to efficiently estimate Bayes factors

One of the most important methodological problems in psychological research is assessing the reasonableness of null models, which typically constrain a parameter to a specific value such as zero. Bayes factor has been recently advocated in the statistical and psychological literature as a principled means of measuring the evidence in data for various models, including those where parameters are set to specific values. Yet, it is rarely adopted in substantive research, perhaps because of the difficulties in computation. Fortunately, for this problem, the Savage–Dickey density ratio (Dickey & Lientz, 1970) provides a conceptually simple approach to computing Bayes factor. Here, we review methods for computing the Savage–Dickey density ratio, and highlight an improved method, originally suggested by Gelfand and Smith (1990) and advocated by Chib (1995), that outperforms those currently discussed in the psychological literature. The improved method is based on conditional quantities, which may be integrated by Markov chain Monte Carlo sampling to estimate Bayes factors. These conditional quantities efficiently utilize all the information in the MCMC chains, leading to accurate estimation of Bayes factors. We demonstrate the method by computing Bayes factors in one-sample and one-way designs, and show how it may be implemented in WinBUGS.     

Understanding Psychopathology: Melding Behavior Genetics, Personality, and Quantitative Psychology to Develop an Empirically Based Model

ABSTRACT— Research on psychopathology is at a historical crossroads. New technologies offer the promise of lasting advances in our understanding of the causes of human psychological suffering. Making the best use of these technologies, however, requires an empirically accurate model of psychopathology. Much current research is framed by the model of psychopathology portrayed in current versions of the Diagnostic and Statistical Manual of Mental Disorders ( DSM ; American Psychiatric Association, 2000 ). Although the modern DSM s have been fundamental in advancing psychopathology research, recent research also challenges some assumptions made in the DSM —for example, the assumption that all forms of psychopathology are well conceived of as discrete categories. Psychological science has a critical role to play in working through the implications of this research and the challenges it presents. In particular, behavior-genetic, personality, and quantitative-psychological research perspectives can be melded to inform the development of an empirically based model of psychopathology that would constitute an evolution of the DSM .     

心理学研究中应用统计方法应注意的几个问题

心理统计是认识心理现象数量特征的重要工具,在心理学研究中或多或少地存在着统计的误用。本文从心理学研究过程的内在逻辑出发,探讨了在心理研究中应用统计应该注意的问题和可能遇到的误用现象:有偏样本与小样本的使用,潜在变量的缺失,欺骗性的统计图表,量表信度与统计显著性检验的考量,事后解释的谬误,统计关系与因果关系等。针对这些问题提出避免统计误用的方法与建议。     

识字能力的单维性检验研究

本研究以义务教育阶段学生识字量测验为工具,综合运用探索性结构方程建模(ESEM)以及非参数项目反应理论中的摩根量表(Mokken量表)和DETECT分析方法,探讨了识字能力的维度。探索性结构方程建模结果显示,识字的单维性模型优于多维模型,多维的结果更多的体现出一个难度维度的特征,即字频的作用。Mokken量表分析结果显示,1~2年级和3~9年级测验更倾向于单维量表的特征。DETECT分析结果显示,两个测验的D值趋近于零,表明识字能力是单维能力。结合三种分析方法,识字能力具有单维性。     

以《易》取名的数理统计与文化透视

古人喜取<周易>命名,这一现象一些学者曾加以注意,但未作深入研究.本文对<中国文学家大辞典>采用穷尽式的数理统计,探求出<坤>、<乾>等16卦为常用卦,其卦彖辞、二五爻辞及<文言>等为常用爻,进而推索卦爻取舍背后所隐藏的价值取向与哲理内涵,提出并考察援<易>取名之宋前少宋后多这一现象.     

The analysis of structural equation models by means of derivative free nonlinear least squares

It is shown that the PAR Derivative-Free Nonlinear Regression program in BMDP can be used to fit structural equation models, producing generalized least squares estimates, standard errors, and goodness-of-fit test statistics. Covariance structure models more general than LISREL can be analyzed. The approach is particularly useful for dealing with new non-standard models and experimenting with alternate methods of estimation. The research of the second author was supported by the NSF grant MCS 83-01587. We wish to thank our referees for some very valuable suggestions.