对物理概念图填图试题的应用研究

文章详细介绍了物理概念图填图试题的出题样式和评分的具体方法,并对物理概念图填图试题的难度、区分度、信度和效度进行了检验,计算了物理概念图填图试题与传统物理题型共同组成的期末物理测验的信度。研究表明物理概念图填图试题的得分与传统测验题型的得分呈显著正相关,物理概念图填图试题的得分对传统测验题型的得分有显著的预测力。     

线索呈现位置对概率类别学习的影响

当前概率类别学习中主要存在多系统和单系统两种观点之争, 而持不同观点的研究者在其实验中分别采用了不同的线索位置呈现方式, 因此本研究采用经典的天气预测任务通过操纵线索位置的呈现方式来进一步探讨其对概率类别学习的影响。本研究包括2个实验：实验1考察所有线索位置固定和随机对概率类别学习系统的影响; 实验2通过单线索模式下线索位置固定考察概率类别学习的策略。结果发现, 当所有线索的呈现位置固定时, 概率类别学习是外显学习; 而当所有线索的呈现位置随机时, 概率类别学习是内隐学习; 而当只在线索单独出现时固定其位置, 概率类别学习仍是内隐学习。结果表明, 线索位置的不同呈现方式会影响概率类别学习中外显和内隐学习系统的竞争, 研究支持了多系统观点, 且概率类别学习的主要策略可能是多线索策略而不是单模式策略。     

心理测验中的趋中回归与超常分数重现概率

研究了心理测验中的趋中回归及其性质,超常分数重现概率与真分数的分布、测验信度、超常分数界值的关系。结果表明,信度越低,趋中回归越大;超常分数界值越高,趋中回归越大。在正态分布下,超常分数重现概率与信度是指数关系;超常分数重现概率与界值是直线关系。初步讨论了如何在心理学研究中避免和减少趋中回归的误导。     

On psychoanalytic figures as transference objects

One of the tasks that analysts and therapists face at a certain stage in their career is how to develop a way of psychoanalytic thinking and practising of their own. To do this involves modifying or overcoming the transferences established during their training or early career. These transferences are to one's teachers or training analyst, investing them with authority and infallibility, and to received theory, which is treated as though it were dogma. The need to free oneself from such transferences has been discussed in the literature. There is, however, another kind of transference that the developing therapist also needs to resolve, which has received little attention. This is the transference made on to a key figure in the psychoanalytic tradition. Such a psychoanalytic figure will be seen as the originator of or embodiment of those theoretical ideas to which one becomes attached, and/or as standing behind one's training analyst or seminal teachers who become a representative of that figure. The value of an investigation of one's relationship to a psychoanalytic figure is that it is an excellent medium for revealing one's transference, as the figure in question is not a real person but only exists through his/her writings. The body of the paper consists of an extended example of such an analysis, that of my own transference on to the figure of Winnicott. In this example I illustrate how my evaluation of Winnicott's ideas changed from seeing them as providing answers to all my clinical questions to no longer satisfying me in some areas of my work. This change in my relationship to Winnicott's theory went hand in hand with a modification in my transference on to the figure of Winnicott, from seeing him as endowed with authority and goodness to an appreciation of him as a still sustaining figure but now with limits and flaws. In the final part of the paper several questions arising out of my analysis are posed. Can the pull of writing such an account in terms of dramatic rupture rather than gradual and partial change be avoided? Should my account be regarded purely as a form of self‐analysis or does it have anything to say about Winnicott himself and his theory? And do some psychoanalytic figures attract more intense or sticky transferences than others?     

Light Affine Set Theory: A Naive Set Theory of Polynomial Time

In [7], a naive set theory is introduced based on a polynomial time logical system, Light Linear Logic (LLL). Although it is reasonably claimed that the set theory inherits the intrinsically polytime character from the underlying logic LLL, the discussion there is largely informal, and a formal justification of the claim is not provided sufficiently. Moreover, the syntax is quite complicated in that it is based on a non-traditional hybrid sequent calculus which is required for formulating LLL.In this paper, we consider a naive set theory based on Intuitionistic Light Affine Logic (ILAL), a simplification of LLL introduced by [1], and call it Light Affine Set Theory (LAST). The simplicity of LAST allows us to rigorously verify its polytime character. In particular, we prove that a function over {0, 1}* is computable in polynomial time if and only if it is provably total in LAST.     

Testing the significance of the successive components in redundancy analysis

In this paper we study the interrelationships between two sets of data measured on the same subjects via redundancy analysis. We consider redundancy analysis from an inferential point of view. Under the hypothesis of multinormality, tests of significance are obtained for each successive redundancy component so that only the significant factors are retained for prediction purposes. An example illustrates the method. The authors would like to thank the Editor and the referees for their helpful comments. This research has been partly financed by NSERC (Canada).     

Personality factors as predictors of pathological gambling

The aim of this study was to investigate the relationship between different personality variables and pathological gambling (PG). The NEO-FFI and measures of impulsivity and sensation-seeking were administered to a sample of pathological gamblers (n = 90) and to a contrast group of non-pathological gamblers (n = 66) matched on sex and age. Gender, age, education level and the personality variables were entered into crude and adjusted logistic regression analyses with PG-status as the dependent variable. The results showed that educational level and all personality variables were significant predictors of PG in the crude analyses, however only four of the 12 significant predictor variables (Neuroticism, Openness, Impulsivity, and need for Stimulus Intensity) remained significant in the adjusted analysis. All predictor variables accounted for 71% of the variance in PG-status. Clinical implications of the findings are discussed.     

On the geometric approach to multivariate selection

Multivariate selection can be represented as a linear transformation in a geometric framework. This approach has led to considerable simplification in the study of the effects of selection on factor analysis. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation.     

A second generation nonlinear factor analysis

Nonlinear common factor models with polynomial regression functions, including interaction terms, are fitted by simultaneously estimating the factor loadings and common factor scores, using maximum-likelihood-ratio and ordinary-least-squares methods. A Monte Carlo study gives support to a conjecture about the form of the distribution of the likelihood-ratio criterion.The research reported in this paper was partly supported by Natural Sciences and Engineering Research Grant No. A6346.