Dynamics of waiting in pigeons

Two experiments used response-initiated delay schedules to test the idea that when food reinforcement is available at regular intervals, the time an animal waits before its first operant response (waiting time) is proportional to the immediately preceding interfood interval (linear waiting; Wynne & Staddon, 1988). In Experiment 1 the interfood intervals varied from cycle to cycle according to one of four sinusoidal sequences with different amounts of added noise. Waiting times tracked the input cycle in a way which showed that they were affected by interfood intervals earlier than the immediately preceding one. In Experiment 2 different patterns of long and short interfood intervals were presented, and the results implied that waiting times are disproportionately influenced by the shortest of recent interfood intervals. A model based on this idea is shown to account for a wide range of results on the dynamics of timing behavior.     

Observed-score equating as a test assembly problem

A set of linear conditions on item response functions is derived that guarantees identical observed-score distributions on two test forms. The conditions can be added as constraints to a linear programming model for test assembly that assembles a new test form to have an observed-score distribution optimally equated to the distribution on an old form. For a well-designed item pool and items fitting the IRT model, use of the model results into observed-score pre-equating and prevents the necessity ofpost hoc equating by a conventional observed-score equating method. An empirical example illustrates the use of the model for an item pool from the Law School Admission Test.The authors are most indebted to Norman D. Verhelst for suggesting Proposition 4 and its proof, to the Law School Admission Council (LSAC) for making available the data set, and to Wim M. M. Tielen for his computational assistance.     

Tests for linear trend in the smallest eigenvalues of the correlation matrix

A test for linear trend among a set of eigenvalues of a correlation matrix is developed. As a technical implementation of Cattell's scree test, this is a generalization of Anderson's test for the equality of eigenvalues, and extends Bentler and Yuan's work on linear trends in eigenvalues of a covariance matrix. The power of minimumx ² and maximum likelihood ratio tests are compared. Examples show that the linear trend hypothesis is more realistic than the standard hypothesis of equality of eigenvalues, and that the hypothesis is compatible with standard decisions on the number of factors or components to retain in data analysis.This work was supported by National Institute on Drug Abuse Grants DA01070 and DA00017. The assistance of Maia Berkane and several anonymous reviewers is gratefully acknowledged.     

Marginal maximum likelihood estimation for the one-parameter logistic model

Two algorithms are described for marginal maximum likelihood estimation for the one-parameter logistic model. The more efficient of the two algorithms is extended to estimation for the linear logistic model. Numerical examples of both procedures are presented. Portions of this research were presented at the meeting of the Psychometric Society in Chapel Hill, N.C. in May, 1981. Thanks to R. Darrell Bock, Gerhard Fischer, and Paul Holland for helpful comments in the course of this research.     

Long-term efficacy of exercise across diagnostically heterogenous mental disorders and the mediating role of affect regulation skills

BackgroundExercise interventions are efficacious in reducing disorder-specific symptoms in various mental disorders. However, little is known about long-term transdiagnostic efficacy of exercise across heterogenous mental disorders and the potential mechanisms underlying treatment effects.MethodsPhysically inactive outpatients, with depressive disorders, anxiety disorders, insomnia or attention deficit hyperactivity disorder were randomized to a standardized 12-week exercise intervention, combining moderate exercise with behavior change techniques (BCTs) (n = 38), or a passive control group (n = 36). Primary outcome was global symptom severity (Symptom Checklist-90, SCL-90-R) and secondary outcomes were self-reported exercise (Physical Activity, Exercise, and Sport Questionnaire), exercise-specific affect regulation (Physical Activity-related Health Competence Questionnaire) and depression (SCL-90-R) assessed at baseline (T1), post-treatment (T2) and one year after post-treatment (T3). Intention-to-treat analyses were conducted using linear mixed models and structural equations modeling.ResultsFrom T1 to T3, the intervention group significantly improved on global symptom severity (d = −0.43, p = .031), depression among a depressed subsample (d = −0.62, p = .014), exercise (d = 0.45, p = .011) and exercise-specific affect regulation (d = 0.44, p = .028) relative to the control group. The intervention group was more likely to reveal clinically significant changes from T1 to T3 (p = .033). Increases in exercise-specific affect regulation mediated intervention effects on global symptom severity (ß = −0.28, p = .037) and clinically significant changes (ß = −0.24, p = .042).ConclusionsThe exercise intervention showed long-term efficacy among a diagnostically heterogeneous outpatient sample and led to long-lasting exercise behavior change. Long-term increases in exercise-specific affect regulation within exercise interventions seem to be essential for long-lasting symptom reduction.     

A Mixed Model to Disentangle Variance and Serial Autocorrelation in Affective Instability Using Ecological Momentary Assessment Data

Affective instability, the tendency to experience emotions that fluctuate frequently and intensively over time, is a core feature of several mental disorders including borderline personality disorder. Currently, affect is often measured with Ecological Momentary Assessment protocols, which yield the possibility to quantify the instability of affect over time. A number of linear mixed models are proposed to examine (diagnostic) group differences in affective instability. The models contribute to the existing literature by estimating simultaneously both the variance and serial dependency component of affective instability when observations are unequally spaced in time with the serial autocorrelation (or emotional inertia) declining as a function of the time interval between observations. In addition, the models can eliminate systematic trends, take between subject differences into account and test for (diagnostic) group differences in serial autocorrelation, short-term as well as long-term affective variability. The usefulness of the models is illustrated in a study on diagnostic group differences in affective instability in the domain of eating disorders. Limitations of the model are that they pertain to group (and not individual) differences and do not focus explicitly on circadian rhythms or cycles in affect.     

Estimation Methods for Mixed Logistic Models with Few Clusters

For mixed models generally, it is well known that modeling data with few clusters will result in biased estimates, particularly of the variance components and fixed effect standard errors. In linear mixed models, small sample bias is typically addressed through restricted maximum likelihood estimation (REML) and a Kenward-Roger correction. Yet with binary outcomes, there is no direct analog of either procedure. With a larger number of clusters, estimation methods for binary outcomes that approximate the likelihood to circumvent the lack of a closed form solution such as adaptive Gaussian quadrature and the Laplace approximation have been shown to yield less-biased estimates than linearization estimation methods that instead linearly approximate the model. However, adaptive Gaussian quadrature and the Laplace approximation are approximating the full likelihood rather than the restricted likelihood; the full likelihood is known to yield biased estimates with few clusters. On the other hand, linearization methods linearly approximate the model, which allows for restricted maximum likelihood and the Kenward-Roger correction to be applied. Thus, the following question arises: Which is preferable, a better approximation of a biased function or a worse approximation of an unbiased function? We address this question with a simulation and an illustrative empirical analysis.     

Material handling device selection in cellular manufacturing

This paper presents a new multi‐criteria decision model for the material handling device (MHD) selection problem in cellular manufacturing systems. Given a set of manufacturing cells based on several automatic work‐centres, the technique makes it possible to select a particular MHD for each cell in an integrated way, with different constraints being taken into consideration. The approach is based on two different multi‐attribute analyses executed with analytic hierarchy process (AHP) methodology, and a final integer linear programming including important limitations faced by the designer when making MHD investment decisions. An example using real data is provided to illustrate this methodology. Copyright © 2002 John Wiley & Sons, Ltd.     

Indifference sets of reference points in multi‐objective integer linear programming

Reference point approaches for multi‐objective problems rely on the definition of an achievement scalarizing function that projects reference points onto the non‐dominated solution set. In this paper, we investigate the behaviour of reference points using a Tchebycheff metric‐based scalarizing function in multi‐objective pure integer linear programming (MOILP). Since the non‐dominated solutions are discrete in MOILP, there are multiple reference points that lead to the same solution, i.e. there are indifference sets on the reference point space. We investigate some properties of the reference points in MOILP and also the graphical representation of indifference sets for tri‐objective problems. We further investigate properties of the reference points when additional limitations on the objective function values are introduced. Copyright © 2001 John Wiley & Sons, Ltd.     

儿童数字估计中的心理长度

从儿童对数字属性的认识角度探讨一年级儿童在不同数字范围中采用不同表征形式的根本原因。两个实验均采用数字线估计任务,实验一测量一年级儿童在15cm数字线长度下0～100与0～1000两种范围上的数字估计;实验二测量一年级儿童对0～1000范围10cm和20cm数字线长度的估计。结果显示,无论在不同的数字范围还是在不同的数字线长度下,儿童对低端数字的估计均存在心理长度,即儿童倾向于将低端数字与固定的线段长度对应起来,且这种对应关系不随数字范围与数字线长度的变化而变化。心理长度的存在是儿童在不同数字范围和不同数字线长度中采用不同数字表征形式的根本原因,也是儿童对数概念的认识发展到等距水平时出现的一种独特特点。