基于非逻辑机制的条件推理模式：P—Q映射模型及其实证检验

研究结合数学分析方法,提出了基于非逻辑机制的条件推理模型：P-Q映射模型。并根据这个模型,对人们在不同命题类型奈件下的推理行为进行了预测。预测结果显示,当推理前提为LH和HL型命题时,基于P-Q映射模型的预测结果与基于条件概率模型的预测结果完全一致。但当推理前提为LL和HH型命题时,两种模型给出的预测结果存在差异。实验结果表明,当前提命题为LL和HH型命题时,被试的条件推理行为与P-Q映射模型的预言完全一致。     

关系推理中信念偏差效应的研究

采用结论评估范式,考察时向和空间关系推理中信念偏差效应的表现形式,以及心理模型理论对关系推理中信念偏差效应的解释力。研究结果表明,关系推理中信念偏差效应的表现形式与三段论有所不同,不论是时间推理还是空间推理中都存在信念主效应,但没有发现信念与逻辑的交互作用;心理模型理论不能对关系推理中信念偏差效应作出合理解释。     

对归纳推理贝叶斯模型的检验

作者用实验比较检验特征归纳的贝叶斯模型、关联相似性模型、基于特征的归纳推理模型和相似性覆盖模型这四种模型。以大学生为被试的实验结果表明：1）在强关联强度一致时,被试的归纳推理基本符合贝叶斯模型和关联相似性模型的预测,在弱关联强度一致时,不符合这四种模型预测;2）在关联强度不一致时,关联强度效应不符合贝叶斯模型的预测;3）在关联强度一致时,贝叶斯模型和关联相似性模型的预测结果是一致的,不能区分两个模型。实验结果较多地支持贝叶斯模型和关联相似性模型。     

同一性地位理论及其扩展模型

Marcia的同一性地位理论及其模型开辟了同一性研究的实证道路。在此基础上产生了诸多扩展模型,主要有Berzonsky的同一性风格模型;Grotevant的同一性探索过程模型;Kerplman的同一性控制模型;Luyckx的四维度模型;Crocetti的三维度模型。该文对这些模型的基本内容、价值与局限进行了简评,并指出了同一性地位理论模型发展的特点及未来趋势。     

Item difficulty parameter estimation using the idea of the graded response model and computerized adaptive testing

Abstract:  In test operations using IRT (item response theory), items are included in a test before being used to rate subjects and the response data is used to estimate their item parameters. However, this method of test operation may lead to item content leakage and an adequate test operation can become difficult. To address this problem, Ozaki and Toyoda (2005, 2006 ) developed item difficulty parameter estimation methods that use paired comparison data from the perspective of the difficulty of items as judged by raters familiar with the field. In the present paper, an improved method of item difficulty parameter estimation is developed. In this new method, an item for which the difficulty parameter is to be estimated is compared with multiple items simultaneously, from the perspective of their difficulty. This is not a one-to-one comparison but a one-to-many comparison. In the comparisons, raters are informed that items selected from an item pool are ordered according to difficulty. The order will provide insight to improve the accuracy of judgment.     

Identifiability and Equivalence of GLLIRM Models

The generalized logit–linear item response model (GLLIRM) is a linearly constrained nominal categories model (NCM) that computes the scale and intercept parameters for categories as a weighted sum of basic parameters. This paper addresses the problems of the identifiability of the basic parameters and the equivalence between different GLLIRM models. It is shown that the identifiability of the basic parameters depends on the size and rank of the coefficient matrix of the linear functions. Moreover, two models are observationally equivalent if the product of the respective coefficient matrices has full column rank. Finally, the paper also explores the relations between the parameters of nested models. I would like to express my gratitude to the editor and three anonymous reviewers for their helpful suggestions on earlier versions of the paper. This work was supported by the Comunidad de Madrid (Spain) grant: CCG07-UAM/ESP-1615.     

A Bayesian Nonparametric Approach to Test Equating

A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are compared through the analysis of data sets famous in the equating literature. Also, the classical percentile-rank, linear, and mean equating models are each proven to be a special case of a Bayesian model under a highly-informative choice of prior distribution.     

Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable

A method is presented for marginal maximum likelihood estimation of the nonlinear random coefficient model when the response function has some linear parameters. This is done by writing the marginal distribution of the repeated measures as a conditional distribution of the response given the nonlinear random effects. The resulting distribution then requires an integral equation that is of dimension equal to the number of nonlinear terms. For nonlinear functions that have linear coefficients, the improvement in computational speed and accuracy using the new algorithm can be dramatic. An illustration of the method with repeated measures data from a learning experiment is presented.     

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel regression structure which allows the incorporation of covariates to explain the variance in speed and accuracy between individuals and groups of test takers. A Bayesian approach with Markov chain Monte Carlo (MCMC) computation enables straightforward estimation of all model parameters. Model-specific implementations of a Bayes factor (BF) and deviance information criterium (DIC) for model selection are proposed which are easily calculated as byproducts of the MCMC computation. Both results from simulation studies and real-data examples are given to illustrate several novel analyses possible with this modeling framework. The authors thank Steven Wise, James Madison University, and Pere Joan Ferrando, Universitat Rovira i Virgili, for generously making available their data sets for the empirical examples in this paper.     

Repetition blindness: an emergent property of inter-item competition

Repeating an item in a brief or rapid display usually produces faster or more accurate identification of the item (repetition priming), but sometimes produces the opposite effect (repetition blindness). We present a theory of short-term repetition effects, the competition hypothesis, which explains these paradoxical outcomes. The central tenet of the theory is that repetition produces a representation with a higher signal-to-noise ratio but also produces a disadvantage in the representation’s ability to compete with other items for access to awareness. A computational implementation of the competition hypothesis was developed to simulate standard findings in the RB literature and to generate novel predictions which were then tested in three experiments. Results from these experiments suggest that repetition effects emerge from competitive interactions between items and that these influences extend to adjacent, nonrepeated items in the display. The results also present challenges to existing theories of short-term repetition effects.