Discrimination learning in a foraging situation

Rats were allowed to forage in a simulated natural environment made up of eight food sources (patches) each containing a fixed number of pellets. Two of the eight contained an extra supply of peanuts. The peanut patches were signaled by an olfactory/visual cue located at the bottom of the ladder leading to the patch. In successive phases the number of sessions per day, height of the patches, and availability of peanuts were manipulated. Subjects showed evidence of discrimination learning under these conditions, although the degree of discriminatory behavior varied as a function of environmental manipulations. Assessment of behavior within foraging sessions showed that subjects systematically changed their patterns of utilization of patches across time. Sampling or exploration, as well as food reinforcement, seem implicated in these results.     

A post hoc correction procedure for systematic errors in time-sampling duration estimates

Numerous previous studies have shown that partial-interval sampling in direct observation systematically overestimates duration and underestimates frequency. Whole-interval sampling systematically underestimates both duration and frequency. This paper presents a post hoc method through which the systematic errors in duration estimates in partial-interval sampling and whole-interval sampling can be minimized.     

Effect of Daily Life Reward Loop Functioning on the Course of Depression

Engagement in activities increases positive affect (Reward Path 1), which subsequently reinforces motivation (Reward Path 2), and hence future engagement in activities (Reward Path 3). Strong connections between these three reward loop components are considered adaptive, and might be disturbed in depression. Although some ecological nomentary assessment (EMA) studies have investigated the cross-sectional association between separate reward paths and individuals’ level of depression, no EMA study has looked into the association between individuals’ reward loop strength and depressive symptom course. The present EMA study assessed reward loop functioning (5x/day, 28 days) of 46 outpatients starting depression treatment at secondary mental health services and monitored with the Inventory of Depressive Symptomatology—Self-Report (IDS-SR) during a 7-month period. Results of multilevel regression analyses showed significant within-person associations for Reward Path 1 (b = 0.21, p < .001), Reward Path 2 (b = 0.43, p < .001), and Reward Path 3 (b = 0.20, p < .001). Stronger average reward loops (i.e., within-person mean of all reward paths) did not relate to participants’ improvement in depressive symptoms over time. Path-specific results revealed that Reward Paths 1 and 2 may have partly opposite effects on depressive symptom course. Together, our findings suggest that reward processes in daily life might be best studied separately and that further investigation is warranted to explore under what circumstances strong paths are adaptive or not.     

Inferential procedures for multifaceted coefficients of generalizability

A two-facet measurement model with broad application in the behavioral sciences is identified, and its coefficient of generalizability (CG) is examined. A normalizing transformation is proposed, and an asymptotic variance expression is derived. Three other multifaceted measurement models and CGs are identified, and variance expressions are presented. Next, an empirical investigation of the procedures follows, and it is shown that, in most cases, Type I error control in inferential applications is precise, and that the estimates are relatively efficient compared with the correlation coefficient. Implications for further research and for practice are noted. In an Appendix, four additional models, CGs, and variance expressions are presented.The research reported herein formed part of a doctoral dissertation conducted by Marsha Schroeder (Schroeder, 1986), under the direction of Ralph Hakstian, at the University of British Columbia. We acknowledge with thanks the contributions to this research of Todd Rogers and James Steiger. We are also very indebted to an mous reviewer who provided some important clarifications in connection with two of the models considered. Some support for this research was provided by a grant to Ralph Hakstian from the Natural Sciences and Engineering Research Council of Canada.     

Exploring the set-theoretical structure of objects by additive trees

Arrangements of feature sets that have been proposed to represent qualitative and quantitative variation among objects are shown to generate identical sets of set-symmetric distances. The set-symmetric distances for these feature arrangements can be represented by path lengths in an additive linear tree. Imperfect versions of these feature arrangements are proposed, which also are indistinguishable by the set-symmetric distance model. The distances for the imperfect versions can be represented by path lengths in an additive imperfectly linear tree. When dissimilarities are defined by the more general contrast model and a constant may be added to proximity data, then for both the perfect and imperfect arrangements an additive tree analysis obtains a perfect fit with an imperfectly linear tree. However, in the case of the contrast model also the distinction between the perfect and imperfect arrangements disappears in that also for the perfect arrangements the resulting tree need no longer be linear.The author is grateful to Mathieu Koppen for detailed comments on an earlier version of this article.     

Assessing the stability of principal components using regression

This paper presents an analysis, based on simulation, of the stability of principal components. Stability is measured by the expectation of the absolute inner product of the sample principal component with the corresponding population component. A multiple regression model to predict stability is devised, calibrated, and tested using simulated Normal data. Results show that the model can provide useful predictions of individual principal component stability when working with correlation matrices. Further, the predictive validity of the model is tested against data simulated from three non-Normal distributions. The model predicted very well even when the data departed from normality, thus giving robustness to the proposed measure. Used in conjunction with other existing rules this measure will help the user in determining interpretability of principal components.The authors would like to thank the four anonymous reviewers and the two editors for their valuable comments. Atanu R. Sinha gratefully acknowledges the research support received from the Marketing Studies Center, AGSM, UCLA. Send requests for reprints to Atanu R. Sinha, B418 Gold Hall, 110 Westwood Plaza, Los Angeles, CA 90095.     

THE BEHAVIOR OBSERVATION INSTRUMENT: A METHOD OF DIRECT OBSERVATION FOR PROGRAM EVALUATION1

The background and development of a multicategory direct observation system, the Behavior Observation Instrument (BOI), is described. This time-sampling procedure for recording the behavior of persons is demonstrated in several treatment settings and the results applied to issues of program evaluation. Elements that have prevented direct observation from being widely adopted, such as costs, manpower, and training requirements, are systematically analyzed. A basic psychometric analysis of the instrument is used to determine optimum frequency and duration of observation intervals as well as observer agreement. The results imply that direct observation methods, once assumed by some to belong to the special province of the single-subject design, can be used to assess the effects of programs on groups of psychiatric clients in an efficient and economic manner.     

On various causes of improper solutions in maximum likelihood factor analysis

In the applications of maximum likelihood factor analysis the occurrence of boundary minima instead of proper minima is no exception at all. In the past the causes of such improper solutions could not be detected. This was impossible because the matrices containing the parameters of the factor analysis model were kept positive definite. By dropping these constraints, it becomes possible to distinguish between the different causes of improper solutions. In this paper some of the most important causes are discussed and illustrated by means of artificial and empirical data.The author is indebted to H. J. Prins for stimulating and encouraging discussions.