Short-term memory in the pigeon: the previously reinforced response

Eighteen pigeons served in a discrete-trials short-term memory experiment in which the reinforcement probability for a peck on one of two keys depended on the response reinforced on the previous trial: either the probability of reinforcement on a trial was 0.8 for the same response reinforced on the previous trial and was 0.2 for the other response (Group A), or, it was 0 or 0.2 for the same response and 1.0 or 0.8 for the other response (Group B). A correction procedure ensured that over all trials reinforcement was distributed equally across the left and right keys. The optimal strategy was either a winstay, lose-shift strategy (Group A) or a win-shift, lose-stay strategy (Group B). The retention interval, that is the intertrial interval, was varied. The average probability of choosing the optimal alternative reinforced 80% of the time was 0.96, 0.84, and 0.74 after delays of 2.5, 4.0, and 6.0 sec, respectively for Group A, and was 0.87, 0.81, and 0.55 after delays of 2.5, 4.0, and 6.0 sec, respectively, for Group B. This outcome is consistent with the view that behavior approximated the optimal response strategy but only to an extent permitted by a subject's short-term memory for the cue correlated with reinforcement, that is, its own most-recently reinforced response. More generally, this result is consistent with “molecular” analyses of operant behavior, but is inconsistent with traditional “molar” analyses holding that fundamental controlling relations may be discovered by routinely averaging over different local reinforcement contingencies. In the present experiment, the molar results were byproducts of local reinforcement contingencies involving an organism's own recent behavior.     

Effects on indscal of non-orthogonal perceptions of object space dimensions

A general question is raised concerning the possible consequences of employing the very popular INDSCAL multidimensional scaling model in cases where the assumptions of that model may be violated. Simulated data are generated which violate the INDSCAL assumption that all individuals perceive the dimensions of the common object space to be orthogonal. INDSCAL solutions for these various sets of data are found to exhibit extremely high goodness of fit, but systematically distorted object spaces and negative subject weights. The author advises use of Tucker's three-mode model for multidimensional scaling, which can account for non-orthogonal perceptions of the object space dimensions. It is shown that the INDSCAL model is a special case of the three-mode model.     

A method of estimating item characteristic functions using the maximum likelihood estimate of ability

A method of estimating item characteristic functions is proposed, in which a set of test items, whose operating characteristics are known and which give a constant test information function for a substantially wide range of ability, are used. The method is based on the maximum likelihood estimates of ability for a group of several hundred examinees. Throughout the present study the Monte Carlo method is used.     

Multivariate analysis with linearizable regressions

We study the class of multivariate distributions in which all bivariate regressions can be linearized by separate transformation of each of the variables. This class seems more realistic than the multivariate normal or the elliptical distributions, and at the same time its study allows us to combine the results from multivariate analysis with optimal scaling and classical multivariate analysis. In particular a two-stage procedure which first scales the variables optimally, and then fits a simultaneous equations model, is studied in detail and is shown to have some desirable properties.     

Items bundles

An item bundle is a small group of multiple choice items that share a common reading passage or graph, or a small group of matching items that share distractors. Item bundles are easily identified by paging through a copy of a test. Bundled items may violate the latent conditional independence assumption of unidimensional item response theory (IRT), but such a violation would not typically suggest the existence of a new fundamental human ability to read one specific reading passage or to interpret one specific graph. It is important, therefore, to have theoretical concepts and empirical checks that distinguish between, on the one hand, anticipated violations of latent conditional independence within item bundles, and, on the other hand, violations that cannot be attributed to idiosyncratic features of test format and instead suggest departures from unidimensionalty. To this end, two theorems on unidimensional IRT are extended to describe observable item response distributions when there is conditional independencebetween but not necessarilywithin item bundles.The author is grateful to Ivo Molenaar and the referees for many helpful suggestions, and to D. Thayer for assistance with computing.     

Rating scale analysis with latent class models

A general approach for analyzing rating data with latent class models is described, which parallels rating models in the framework of latent trait theory. A general rating model as well as a two-parameter model with location and dispersion parameters, analogous to Andrich's Dislocmodel are derived, including parameter estimation via the EM-algorithm. Two examples illustrate the application of the models and their statisticalcontrol. Model restrictions through equality constrains are discussed and multiparameter generalizations are outlined.     

A note to Bollen's alternative fit measure

The problem of dependence of the nonnormed fit index on sample size in covariance structure analysis is discussed. Contrary to Bollen (1986) we show that the mean of the nonnormed fit index is independent of sample size for true and almost true models whereas Bollen's alternative index does depend on sample size.     

Reliability and factorial structure of the Chinese version of the State-Trait Anxiety Inventory

The Chinese version of the State-Trait Anxiety Inventory (STAI) was administered to 2150 Chinese secondary-school students. Reliability data revealed that the A-State and A-Trait scales had a high internal consistency, and high item-total correlations were found for most of the items under each scale. Factor analytic data showed that while two factors were abstracted from the A-Trait scale (Anxiety Present and Anxiety Absent), two (Anxiety Present and Anxiety Absent) or three (Anxiety Present, Calmness, and Happiness) factors were abstracted from the A-State scale. By randomly splitting the total sample into two subsamples, factors extracted from the first two factor-solutions could be reproduced reliably and high coefficients of congruence were found. These findings generally suggest that the Chinese A-State and A-Trait scales possess acceptable psychometric properties and the factor analytic data tend to support Spielberger's conception of the multidimensional nature of the A-State and A-Trait scales.This work was supported by UPGC Grant CPAS/714.     

Causal models and the assessment-treatment relationship in behavior therapy

The relationship between causal models in behavior therapy and strategies for designing intervention programs on the basis of assessment data is considered. The evolution away from simple univariate causal models is noted and complex causal models for behavior disorders are stressed. However, complex causal models currently preclude empirically based functional analytic and keystone target behavior assessment strategies for intervention design. Future directions for intervention design involving a combination of diagnostic, functional, analytic, and keystone strategies and markers for causal paths are discussed.