Sensory superstition on multiple interval schedules.

Pigeons were exposed to multiple schedules in which an irregular repeating sequence of five stimulus components was correlated with the same reinforcement schedule throughout. Stable, idiosyncratic, response-rate differences developed across components. Components were rank-ordered by response rate; an approximately linear relation was found between rank order and the deviation of mean response rate from the overall mean rate. Nonzero slopes of this line were found for multiple fixed-interval and variable-time schedules and for multiple variable-interval schedules both when number of reinforcements was the same in all components and when it varied. The steepest function slopes were found in the variable schedules with relatively long interfood intervals and relatively short component durations. When just one stimulus was correlated with all components of a multiple variable-interval schedule, the slope of the line was close to zero. The results suggest that food-rate differences may be induced initially by different reactions to the stimuli and subsequently maintained by food.     

Choice and multiple reinforcers

Pigeons chose between equivalent two-component mixed and multiple terminal-link schedules of reinforcement in the concurrent-chains procedure. The pigeons preferred the multiple schedule over the mixed when the components of the compound schedules were differentiated in terms of density of reinforcement, but the pigeons were indifferent when the components were differentiated in terms of number of reinforcers per cycle. Taken together, these results indicate that a local variable, the interval to the first reinforcer, but not a molar variable, the number of reinforcers, was sufficient to differentiate the components and thereby evoke preference.     

Problems with bootstrapping pearson correlations in very small bivariate samples

Efron'sMonte Carlo bootstrap algorithm is shown to cause degeneracies in Pearson'sr for sufficiently small samples. Two ways of preventing this problem when programming the bootstrap ofr are considered.     

On the optimality of the simultaneous redundancy transformations

The objective of this paper is to introduce and motivate additional properties and interpretations for the redundancy variables. It is shown that these variables can be derived by application of certain invariance arguments and without reference to the index of redundancy. In addition, an optimality property for the variables is presented which is important whenever one restricts attention in a study to a subset of the redundancy variables. This optimality property pertains to the subset rather than to the individual variables.This paper is based in part on the author's doctoral dissertation, Department of Statistics, Princeton, University. Research was conducted under the supervision of Lawrence S. Mayer.     

The polyserial correlation coefficient

The polyserial and point polyserial correlations are discussed as generalizations of the biserial and point biserial correlations. The relationship between the polyserial and point polyserial correlation is derived. The maximum likelihood estimator of the polyserial correlation is compared with a two-step estimator and with a computationally convenient ad hoc estimator. All three estimators perform reasonably well in a Monte Carlo simulation. Some practical applications of the polyserial correlation are described.By coincidence, the first author and the second and third authors learned that they were working independently on closely related problems and, consequently, decided to write a jointly authored paper.     

Canonical analysis of longitudinal and repeated measures data with stationary weights

When measuring the same variables on different occasions, two procedures for canonical analysis with stationary compositing weights are developed. The first, SUMCOV, maximizes the sum of the covariances of the canonical variates subject to norming constraints. The second, COLLIN, maximizes the largest root of the covariances of the canonical variates subject to norming constraints. A characterization theorem establishes a model building approach. Both methods are extended to allow for Cohort Sequential Designs. Finally a numerical illustration utilizing Nesselroade and Baltes data is presented.The authors wish to thank John Nesselroade for permitting us to use the data whose analysis we present.     

Changes in a multiple-response repertoire during response-contingent punishment and response restriction: Sequential relationships

A multiple-response baseline of four activities was established using gerbils as subjects. When one of the baseline responses was punished (Experiment 1) or restricted (Experiments 2 and 3), only the most probable of the alternative baseline responses increased. The response most likely to follow the punished or restricted responses during baseline sessions was also suppressed during subsequent punishment or response-restriction treatment.     

An experimental analysis of the effects of d-amphetamine and cocaine on the acquisition and performance of response chains in monkeys.

In one component of a multiple schedule of food presentation, monkeys acquired a different four-response chain each session by responding sequentially on three keys in the presence of four geometric forms (learning). In the other component, the four-response chain was the same each session (performance). Both d-amphetamine and cocaine, at the higher doses, disrupted the behavior in the learning component; the overall response rate decreased, the overall accuracy was impaired (i.e., percent errors increased), and there was less within-session error reduction. The performance component was generally less sensitive than the learning component to the disruptive effects of both drugs on rate and accuracy. After pre-feeding or during an extended session, the response rate decreased in both components, but accuracy was generally unaffected. When the four discriminative stimuli in both components were removed, the behavior was disrupted to a greater extent in the performance component. The disruptive effects of both drugs on behavior in the learning component were attenuated when the drugs were administered during the session after the response chain had been acquired. It was concluded that the greater sensitivity of the learning component to disruptive drug effects is related to the relatively weak stimulus control and/or the lower rate of reinforcement associated with that component.     

Reducing residential electrical energy use: payments, information, and feedback

Monetary payments, energy information, and daily feedback on consumption were employed to reduce electricity use in four units of a university student housing complex. A combined multiple-baseline and withdrawal design permitted both within- and between-unit comparisons. Payments produced immediate and substantial reductions in consumption in all units, even when the magnitude of the payments was reduced considerably. Feedback also produced reductions, but information about ways to conserve and about the cost of using various appliances did not. It was also found that, in general, payments combined with either information or feedback produced no greater effect than payments alone.     

Time-series analysis in operant research

A time-series method is presented, nontechnically, for analysis of data generated in individual-subject operant studies, and is recommended as a supplement to visual analysis of behavior change in reversal or multiple-baseline experiments. The method can be used to identify three kinds of statistically significant behavior change: (a) changes in score levels from one experimental phase to another, (b) reliable upward or downward trends in scores, and (c) changes in trends between phases. The detection of, and reliance on, serial dependency (autocorrelation among temporally adjacent scores) in individual-subject behavioral scores is emphasized. Examples of published data from the operant literature are used to illustrate the time-series method.