Temporal patterns of responding in small fixed-ratio schedules

Pigeons were exposed to an ascending series of small fixed-ratio schedules from fixed-ratio 1 to 7. Two of those pigeons were later placed on a fixed-ratio 30 schedule. The two primary dependent variables were the postreinforcement pause and the interresponse time. Changes in these variables under small fixed ratios were sometimes opposite to changes reported with large fixed ratios. For example, postreinforcement pauses decreased in length as the fixed-ratio requirement increased from fixed-ratio 1 to fixed-ratio 3. Also, the interresponse times early in the small fixed-ratio schedule were shorter than those immediately preceding reinforcement. These findings question the role of interresponse-time reinforcement in determining temporal patterns of responding under small fixed-ratio schedules. They also suggest that there may be a limited region in which the independent variable, fixed-ratio size, does not operate as previously described.     

Behavioral economics and behavioral momentum

Some relations between elasticity of demand and the conditions of reinforcement are reanalyzed in terms of resistance to change, in ways suggested by the metaphor of behavioral momentum; some relations between resistance to change and the conditions of reinforcement are reanalyzed in terms of elasticity of demand, in ways suggested by behavioral economics. In addition, some data on labor supply in relation to variable-ratio schedules and alternative reinforcement are reanalyzed in terms of resistance to change and compared with steady-state resistance data for performance on multiple and concurrent interval schedules. The results of these studies can be summarized by two functions based on the behavioral momentum approach, relating relative behavioral mass to relative reinforcement per response or per unit time. The former is a relation between relative unit price and relative behavioral mass, suggesting the possibility of convergent measurement of a theoretical construct common to both approaches. However, the momentum and economic approaches differ fundamentally on whether it is preferable to construe discriminated operant behavior as selected and strengthened by its consequences or as part of a behavior–consequence bundle that maximizes utility.     

Thurstonian modeling of ranking data via mean and covariance structure analysis

Although Thurstonian models provide an attractive representation of choice behavior, they have not been extensively used in ranking applications since only recently efficient estimation methods for these models have been developed. These, however, require the use of special-purpose estimation programs, which limits their applicability. Here we introduce a formulation of Thurstonian ranking models that turns an idiosyncratic estimation problem into an estimation problem involving mean and covariance structures with dichotomous indicators. Well-known standard solutions for the latter can be readily applied to this specific problem, and as a result any Thurstonian model for ranking data can be fitted using existing general purpose software for mean and covariance structure analysis. Although the most popular programs for covariance structure analysis (e.g., LISREL and EQS) cannot be presently used to estimate Thurstonian ranking models, other programs such as MECOSA already exist that can be straightforwardly used to estimate these models.This paper is based on the author's doctoral dissertation. Ulf Böckenholt was my advisor. The author is indebted to Ulf Böckenholt for his comments on a previous version of this paper and to Gerhard Arminger for his extensive support on the use of MECOSA. The final stages of this research took place while the author was at the Department of Statistics and Econometrics, Universidad Carlos III de Madrid. Conversations with my colleague there, Adolfo Hernández, helped to greatly improve this paper.     

Error is proportional to distance measured by honeybees: Weber’s law in the odometer

Honeybees were trained to fly a specific distance, the same over trials, down a tunnel for a reward. After training, they were tested occasionally with the reward absent. On tests, bees fly to or just past the expected place of reward, then turn around and fly back. After some distance, they turn back again, and may continue turning back and forth a number of times. Past research has shown that distance estimation in this task is based on the retinal flow of visual texture on the walls of the tunnel. Here we measured the errors in distance estimation as a function of training distance. Errors were measured as the standard deviation across trials of the positions of the first two turns (Turn1 and Turn2), and of the Middle (average of Turn1 and Turn2) and Spread (difference between Turn1 and Turn2). All errors were proportional to the training distance, thus obeying Weber’s law. Models of possible mechanisms underlying this phenomenon are discussed. The mean Spread matches the errors in Turn1 and Middle, suggesting that the bee chooses the spread of its search to match the expected odometric error. Received: 28 April 1998 / Accepted after revision: 19 November 1998     

Nonunique solutions to the likelihood equation for the three-parameter logistic model

Samejima identified the possibility of multiple solutions to the likelihood equation (multiple maxima in the likelihood function) for estimating an examinee's trait value for the three-parameter logistic model. In the practical applications that Lord studied, he found that multiple solutions did not occur when the number of items was 20. In the present paper, fourteen multiple-choice achievement tests with from 20 to 50 items were examined to see if it was possible for them to produce item response vectors with multiple maxima; such vectors were found for all the tests. Examination of response vectors for large groups of real examinees found that from 0 to 3.1% of them had response vectors with multiple maxima. The implications of these results for multiple-choice tests are discussed.     

A three-stage estimation procedure for structural equation models with polytomous variables

This paper is concerned with the analysis of structural equation models with polytomous variables. A computationally efficient three-stage estimator of the thresholds and the covariance structure parameters, based on partition maximum likelihood and generalized least squares estimation, is proposed. An example is presented to illustrate the method.This research was supported in part by a research grant DA01070 from the U.S. Public Health Service. The production assistance of Julie Speckart is gratefully acknowledged.     

Multidimensional scaling models for reaction times and same-different judgments

A method for joint analysis of reaction times and same-different judgments is discussed. A set of stimuli is assumed to have some parametric representation which uniquely defines dissimilarities between the stimuli. Those dissimilarities are then related to the observed reaction times and same-different judgments through a model of psychological processes. Three representation models of dissimilarities are considered, the Minkowski power distance model, the linear model, and Tversky's feature matching model. Maximum likelihood estimation procedures are developed and implemented in the form of a FORTRAN program. An example is given to illustrate the kind of analyses that can be performed by the proposed method.The work reported in this paper is supported by Grant A6394 to the first author from the Natural Sciences and Engineering Research Council of Canada. Portions of this study have been presented at the Psychometric Society meeting in Chapel Hill, N.C., in May, 1981. We thank Tony Marley, Jim Ramsay and anonymous reviewers for their helpful comments. MAXRT, a computer program which performs the computations described in this paper may be obtained by writing to the first author.     

Taking a closer look: time sampling and measurement error

A person manufactured his in-seat behavior for 15, 30-min sessions so that there were three blocks of five sessions where the behavior occurred 20%, 50%, and 80% of the time. Whole interval, partial interval, and momentary time-sample measures of the behavior were taken and compared to the continuous measure of the behavior i.e., per cent of time the behavior occurred. For interval time sampling, the difference between the continuous and sample measures i.e., measurement error, was: (1) extensive, (2) unidirectional, (3) a function of the time per response, and (4) inconsistent across changes in the continuous measure. A procedural analysis demonstrated that the frequency and duration of behavior are confounded in interval time sampling. Momentary time sampling was found to be superior to interval time sampling in estimating the duration a behavior occurs.     

Species differences in temporal control of behavior II: human performance

Human subjects responded on two panels. A differential-reinforcement-of-low-rate schedule with a limited-hold contingency operated on Panel A. In Condition 1, responses on Panel B produced a stimulus on the panel that signalled whether reinforcement was available on Panel A. In Condition 2, responses on Panel B briefly illuminated a digital clock. In both conditions, performance on Panel A was very efficient; with few exceptions, Panel A was pressed only when reinforcement was available. Thus, in effect, a fixed-interval schedule operated on Panel B. In Condition 1, a “break-and-run” response pattern occurred on Panel B; with increasing temporal parameters, the duration of the postreinforcement pause on Panel B increased linearly while overall response rate and running rate (calculated by excluding the postreinforcement pauses) remained approximately constant. In Condition 2, the response pattern on Panel B was scalloped; the postreinforcement pause was a negatively accelerated increasing function of schedule value, while overall response rate and running rate were negatively accelerated decreasing functions of schedule value. The performance of subjects in Condition 2, but not in Condition 1, was highly sensitive to the contingencies in operation, and resembled that of other species on the fixed-interval schedule.     

Goodness of fit tests for paired comparison models

A goodness of fit test presented by Andersen is shown to be incorrect. The correct test is described and a re-analysis of Andersen's data is provided.