Reinforcer efficacy in a delayed matching-to-sample task.

Five domestic hens were exposed to a delayed matching-to-sample task. Conditions 1, 5, and 8 were variable-delay conditions in which five delays (0.25, 1, 2, 4, and 8 s) from the red or green sample to the presentation of the red and green comparison stimuli were presented a number of times during each session. In the fixed-delay condition (Condition 3), each delay was presented for 15 sessions under a Latin square design across birds. When improvements in accuracy across the variable-delay conditions are taken into account, the data were similar under both the variable and fixed delays. In Conditions 2, 4, 6, and 7 sample-reinforcer intervals were held at 8, 8, 4, and 2 s, respectively, while sample-choice intervals were varied within these during each session. With increasing sample-reinforcer interval, both initial discriminability (i.e., with sample-choice delay = 0) and rate of decrement in discriminability decreased. Although the former would be predicted if accuracy depends of the average sample-reinforcer interval, the latter would not. These data show that increasing the sample-choice interval had less effect on matching accuracy than increasing the sample-reinforcer interval did.     

Covariance structure analysis of ordinal ipsative data

Data are ipsative if they are subject to a constant-sum constraint for each individual. In the present study, ordinal ipsative data (OID) are defined as the ordinal rankings across a vector of variables. It is assumed that OID are the manifestations of their underlying nonipsative vector y, which are difficult to observe directly. A two-stage estimation procedure is suggested for the analysis of structural equation models with OID. In the first stage, the partition maximum likelihood (PML) method and the generalized least squares (GLS) method are proposed for estimating the means and the covariance matrix of A_cy, where A_c is a known contrast matrix. Based on the joint asymptotic distribution of the first stage estimator and an appropriate weight matrix, the generalized least squares method is used to estimate the structural parameters in the second stage. A goodness-of-fit statistic is given for testing the hypothesized covariance structure. Simulation results show that the proposed method works properly when a sufficiently large sample is available.This research was supported by National Institute on Drug Abuse Grants DA01070 and DA10017. The authors are indebted to Dr. Lee Cooper, Dr. Eric Holman, Dr. Thomas Wickens for their valuable suggestions on this study, and Dr. Fanny Cheung for allowing us to use her CPAI data set in this article. The authors would also like to acknowledge the helpful comments from the editor and the two anonymous reviewers.     

An estimating equations approach for the LISCOMP model

Maximum likelihood estimation is computationally infeasible for latent variable models involving multivariate categorical responses, in particular for the LISCOMP model. A three-stage generalized least squares approach introduced by Muthén (1983, 1984) can experience problems of instability, bias, non-convergence, and non-positive definiteness of weight matrices in situations of low prevalence, small sample size and large numbers of observed indicator variables. We propose a quadratic estimating equations approach that only requires specification of the first two moments. By performing simultaneous estimation of parameters, this method does not encounter the problems mentioned above and experiences gains in efficiency. Methods are compared through a numerical study and an application to a study of life-events and neurotic illness.The authors would like to thank Bengt Muthén for many helpful discussions and Scott Henderson for generously providing the Canberra data set. This work was supported in part by grant number GM49909 of the National Institutes of Health.     

Clusteringn objects intok groups under optimal scaling of variables

We propose a method to reduce many categorical variables to one variable withk categories, or stated otherwise, to classifyn objects intok groups. Objects are measured on a set of nominal, ordinal or numerical variables or any mix of these, and they are represented asn points inp-dimensional Euclidean space. Starting from homogeneity analysis, also called multiple correspondence analysis, the essential feature of our approach is that these object points are restricted to lie at only one ofk locations. It follows that thesek locations must be equal to the centroids of all objects belonging to the same group, which corresponds to a sum of squared distances clustering criterion. The problem is not only to estimate the group allocation, but also to obtain an optimal transformation of the data matrix. An alternating least squares algorithm and an example are given.The authors thank Eveline Kroezen and Teije Euverman for their comments on a previous draft of this paper.     

执行功能可分离性及与年龄关系的潜变量分析

应用潜变量分析研究抑制优势反应、记忆刷新和注意转换三种执行功能的可分离性,以及与年龄的关系。被试为18~85岁的健康成年人142名,完成九项执行功能任务。结果显示：本研究设计的九项执行功能任务有较好的信度;验证性因子分析显示,九项任务与对应执行功能间的因子负荷较高（汇聚性效度好）,而不同执行功能间的相关较低（鉴别性效度好）;在此基础上建立了三种执行功能及其与年龄关系的潜变量分析模型。研究结果表明,执行系统不是单一的认知结构,可以分离为若干相对独立的执行加工过程。     

Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random

Methods for the treatment of item non-response in attitudinal scales and in large-scale assessments under the pairwise likelihood (PL) estimation framework and under a missing at random (MAR) mechanism are proposed. Under a full information likelihood estimation framework and MAR, ignorability of the missing data mechanism does not lead to biased estimates. However, this is not the case for pseudo-likelihood approaches such as the PL. We develop and study the performance of three strategies for incorporating missing values into confirmatory factor analysis under the PL framework, the complete-pairs (CP), the available-cases (AC) and the doubly robust (DR) approaches. The CP and AC require only a model for the observed data and standard errors are easy to compute. Doubly-robust versions of the PL estimation require a predictive model for the missing responses given the observed ones and are computationally more demanding than the AC and CP. A simulation study is used to compare the proposed methods. The proposed methods are employed to analyze the UK data on numeracy and literacy collected as part of the OECD Survey of Adult Skills.     

Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang et al. (2015) is applied to minimize the Bayesian information criterion (BIC) for latent variable selection in MIRT models with a known number of latent traits. Under mild assumptions, we prove the numerical convergence of the EMS algorithm for model selection by minimizing the BIC of observed data in the presence of missing data. For the identification of MIRT models, we assume that the variances of all latent traits are unity and each latent trait has an item that is only related to it. Under this identifiability assumption, the convergence of the EMS algorithm for latent variable selection in the multidimensional two-parameter logistic (M2PL) models can be verified. We give an efficient implementation of the EMS for the M2PL models. Simulation studies show that the EMS outperforms the EM-based L₁ regularization in terms of correctly selected latent variables and computation time. The EMS algorithm is applied to a real data set related to the Eysenck Personality Questionnaire.     

Drivers’ and cyclists’ safety perceptions in overtaking maneuvers

Drivers overtaking cyclists on rural roads are a safety concern, as drivers need to handle the interaction with the cyclist and possibly an oncoming vehicle. Improving the maneuver’s outcome requires an understanding of not only the objective, measurable safety metrics, but also the subjective, perceived safety of each road user. Previous research has shown that the perceived safety of the cyclist is most at risk at the passing moment, when driver and cyclist are closest to each other. However, to develop safety measures, it is necessary to know how both road users perceive safety, by understanding the factors that influence their perceptions during the overtaking maneuver. This study measured the perceived safety of drivers in a test-track experiment in Sweden and the perceived safety of cyclists in a field test in Spain. For both drivers and cyclists, we developed Bayesian ordinal logistic regression models of perceived safety scores that take as input objective safety metrics representing the different crash risks at the passing moment. Our results show that while drivers’ perceived safety decreases when there is an oncoming vehicle with a low time-to-collision, cyclists’ perceived safety is reduced by a small lateral clearance and a high overtaking speed. Although our datasets are heterogeneous and limited, our results are in line with previous research. In addition, the Bayesian models presented in this paper are novel and may be improved in future studies once more naturalistic data become available. We discuss how our models may support infrastructure development and regulation, policymaking, driver coaching, the development of active safety systems, and automated driving by providing a possible method for predicting perceived safety.