使用似然比D2统计量的题目属性定义方法

题目属性的定义是实施认知诊断评价的关键步骤, 通过有丰富经验的领域专家对题目的属性进行定义是当前的主要方法, 然而该方法受到许多主观经验因素的影响。寻找客观的题目属性定义或验证方法可以为主观定义过程提供策略支持或对结果进行改进, 因此已经引起研究者们的关注。本研究构建了一种简单高效的题目属性定义方法, 研究使用似然比D2统计量从作答数据中估计题目属性的方法, 实现属性掌握模式、题目参数和题目属性向量的联合估计。模拟研究结果表明, 使用似然比D2统计量可以有效地识别题目的属性向量, 该方法一方面可以实现新编制题目属性向量的在线估计, 另一方面可以验证已经定义的题目属性向量的准确性。     

儿童数字线表征的发展——心理长度的影响

探讨儿童在数字线估计任务中心理长度的发展及其对数量表征模式的影响.选取7~9岁儿童共109名进行数字线估计任务测试,设置了10cm和20cm两种长度条件,要求儿童完成根据位置判断数字任务(position to number,PN任务).结果表明儿童在数字线PN任务中存在心理长度,且7岁儿童心理长度的范围超过10,随着儿童年龄的增长,他们的心理长度范围不断缩小;心理长度范围影响儿童的表征模式,随着心理长度范围的缩小,儿童的数字线表征出现从指数模式到线性模式的变化趋势;与表征模式的发展趋势一致,儿童估计的精确性随年龄增长逐渐提高.     

Reducing Measurement Error in Student Achievement Estimation

The achievement level is a variable measured with error, that can be estimated by means of the Rasch model. Teacher grades also measure the achievement level but they are expressed on a different scale. This paper proposes a method for combining these two scores to obtain a synthetic measure of the achievement level based on the theory developed for regression with covariate measurement error. In particular, the focus is on ordinal scaled grades, using the SIMEX method for measurement error correction. The result is a measure comparable across subjects with smaller measurement error variance. An empirical application illustrates the method.     

Majority merging by adaptive counting

The present paper introduces a belief merging procedure by majority using the standard format of Adaptive Logics. The core structure of the logic ADM ^c (Adaptive Doxastic Merging by Counting) consists in the formulation of the conflicts arising from the belief bases of the agents involved in the procedure. A strategy is then defined both semantically and proof-theoretically which selects the consistent contents answering to a majority principle. The results obtained are proven to be equivalent to a standard majority operator for bases with partial support.     

Variability in Adaptive Behavior in Autism: Evidence for the Importance of Family History

Adaptive behavior in autism is highly variable and strongly related to prognosis. This study explored family history as a potential source of variability in adaptive behavior in autism. Participants included 77 individuals (mean age = 18) with average or better intellectual ability and autism. Parents completed the Family History Interview about the presence of broader autism phenotype symptoms and major psychiatric disorders in first degree relatives. Adaptive behavior was assessed via the Vineland Adaptive Behavior Scales (VABS). Based on family history variables, age, and intelligence quotient (IQ), 87% of participants were correctly classified as having impaired or average VABS scores. Family history of depression and shyness accounted for the most variance in VABS scores, and they had the greatest influence on VABS Socialization scores in particular. Possible underlying mechanisms include genetics, psychosocial factors, and social resources. This study provides initial evidence of the importance of family history to adaptive behavior in autism and has implications for genetics and treatment.     

A fast numerical algorithm for the estimation of diffusion model parameters

In this paper, we describe a new algorithmic approach for parameter estimation in Ratcliff's [(1978). A theory of memory retrieval. Psychological Review, 85 (2), 59-108] diffusion model. This problem, especially if inter-trial variabilities of parameters are included in the model, is computationally very expensive; the parameter estimation procedure often takes a long time even with today's high-speed computers. The algorithm described here makes the calculation of the cumulative distribution functions for predicted process durations computationally much less expensive. This improvement is achieved by solving the Kolmogorov backward equation numerically instead of employing the previously used closed form solution. Additionally, the algorithm can determine the optimum fit for one of the model parameters (the starting point z) directly, thereby reducing the dimension of the parameter search space by one. The resulting method is shown to be notably faster than the standard (closed-form solution) method for parameter estimation.     

Strategy generalization across orientation tasks: testing a computational cognitive model

Humans use their spatial information processing abilities flexibly to facilitate problem solving and decision making in a variety of tasks. This article explores the question of whether a general strategy can be adapted for performing two different spatial orientation tasks by testing the predictions of a computational cognitive model. Human performance was measured on an orientation task requiring participants to identify the location of a target either on a map (find-on-map) or within an egocentric view of a space (find-in-scene). A general strategy instantiated in a computational cognitive model of the find-on-map task, based on the results from Gunzelmann and Anderson (2006) , was adapted to perform both tasks and used to generate performance predictions for a new study. The qualitative fit of the model to the human data supports the view that participants were able to tailor a general strategy to the requirements of particular spatial tasks. The quantitative differences between the predictions of the model and the performance of human participants in the new experiment expose individual differences in sample populations. The model provides a means of accounting for those differences and a framework for understanding how human spatial abilities are applied to naturalistic spatial tasks that involve reasoning with maps.     

Limited information estimation and testing of discretized multivariate normal structural models

Discretized multivariate normal structural models are often estimated using multistage estimation procedures. The asymptotic properties of parameter estimates, standard errors, and tests of structural restrictions on thresholds and polychoric correlations are well known. It was not clear how to assess the overall discrepancy between the contingency table and the model for these estimators. It is shown that the overall discrepancy can be decomposed into a distributional discrepancy and a structural discrepancy. A test of the overall model specification is proposed, as well as a test of the distributional specification (i.e., discretized multivariate normality). Also, the small sample performance of overall, distributional, and structural tests, as well as of parameter estimates and standard errors is investigated under conditions of correct model specification and also under mild structural and/or distributional misspecification. It is found that relatively small samples are needed for parameter estimates, standard errors, and structural tests. Larger samples are needed for the distributional and overall tests. Furthermore, parameter estimates, standard errors, and structural tests are surprisingly robust to distributional misspecification. This research was supported by the Department of Universities, Research and Information Society (DURSI) of the Catalan Government, and by grants BSO2000-0661 and BSO2003-08507 of the Spanish Ministry of Science and Technology.     

How to, and how not to, bridge computational cognitive neuroscience and Husserlian phenomenology of time consciousness

A number of recent attempts to bridge Husserlian phenomenology of time consciousness and contemporary tools and results from cognitive science or computational neuroscience are described and critiqued. An alternate proposal is outlined that lacks the weaknesses of existing accounts.