On the falsifiability of matching theory.

Herrnstein's matching theory requires the parameter, k, which appears in the single-alternative form of the matching equation, to remain invariant with respect to changes in reinforcement parameters like magnitude or immediacy. Recent experiments have disconfirmed matching theory by showing that the invariant-k requirement does not hold. However, the theory can be asserted in a purely algebraic form that does not require an invariant k and that is not disconfirmed by the recent findings. In addition, both the original and the purely algebraic versions of matching theory can be asserted in forms that allow for commonly observed deviations from matching (bias, undermatching, and overmatching). The recent finding of a variable k does not disconfirm these versions of matching theory either. As a consequence, matching remains a viable theory of behavior, the strength of which lies in its general conceptualization of all behavior as choice, and in its unified mathematical treatment of single- and multialternative environments.     

Matching in an undisturbed natural human environment

Data from the Oregon Youth Study, consisting of the verbal behavior of 210 adolescent boys determined to be at risk for delinquency (targets) and 210 of their friends (peers), were analyzed for their conformance to the complete family of matching theory equations in light of recent findings from the basic science, and using recently developed analytic techniques. Equations of the classic and modern theories of matching were fitted as ensembles to rates and time allocations of the boys' rule-break and normative talk obtained from conversations between pairs of boys. The verbal behavior of each boy in a conversation was presumed to be reinforced by positive social responses from the other boy. Consistent with recent findings from the basic science, the boys' verbal behavior was accurately described by the modern but not the classic theory of matching. These findings also add support to the assertion that basic principles and processes that are known to govern behavior in laboratory experiments also govern human social behavior in undisturbed natural environments.     

Toward a contemporary quantitative model of punishment

A direct‐suppression, or subtractive, model of punishment has been supported as the qualitatively and quantitatively superior matching law‐based punishment model (Critchfield, Paletz, MacAleese, & Newland, 2003; de Villiers, 1980; Farley, 1980). However, this conclusion was made without testing the model against its predecessors, including the original (Herrnstein, 1961) and generalized (Baum, 1974) matching laws, which have different numbers of parameters. To rectify this issue, we reanalyzed a set of data collected by Critchfield et al. (2003) using information theoretic model selection criteria. We found that the most advanced version of the direct‐suppression model (Critchfield et al., 2003) does not convincingly outperform the generalized matching law, an account that does not include punishment rates in its prediction of behavior allocation. We hypothesize that this failure to outperform the generalized matching law is due to significant theoretical shortcomings in model development. To address these shortcomings, we present a list of requirements that all punishment models should satisfy. The requirements include formal statements of flexibility, efficiency, and adherence to theory. We compare all past punishment models to the items on this list through algebraic arguments and model selection criteria. None of the models presented in the literature thus far meets all of the requirements.     

Effects of qualitatively different reinforcers on the parameters of the response-strength equation.

This experiment examined the relationship between two qualitatively different reinforcers and the parameters of a quantitative model of reinforced responding, referred to as the response-strength equation or the Herrnstein equation. A group of rats was first food deprived and later water deprived. An 11.5% sucrose solution served as the reinforcer in the food-deprivation condition, and water was the reinforcer in the water-deprivation condition. Each experimental session consisted of a series of seven variable-interval schedules, providing reinforcement rates that varied between 20 and 1,200 reinforcers per hour. The response rates increased in a negatively accelerating function in a manner consistent with the response-strength equation. This equation has two fitted parameters, k and Re. According to one theory, the k parameter is a measure of motor performance, and Re is indicative of the relative reinforcement efficacy of the background uncontrollable sources of reinforcement in relation to the experimentally arranged reinforcer. In this study, k did not change as a result of the different reinforcers, but Re was significantly larger in the sucrose-reinforcement condition. These results are consistent with the interpretation that k and Re measure two independent and experimentally distinguishable parameters and provide further evidence that absolute response rate is a function of relative reinforcement rate, as implied by the derivation of the response-strength equation based on the matching law.     

USING MICROSOFT OFFICE EXCEL® 2007 TO CONDUCT GENERALIZED MATCHING ANALYSES

The generalized matching equation is a robust and empirically supported means of analyzing relations between reinforcement and behavior. Unfortunately, no simple task analysis is available to behavior analysts interested in using the matching equation to evaluate data in clinical or applied settings. This technical article presents a task analysis for the use of Microsoft Excel to analyze and plot the generalized matching equation. Using a data‐based case example and a step‐by‐step guide for completing the analysis, these instructions are intended to promote the use of quantitative analyses by researchers with little to no experience in quantitative analyses or the matching law.     

Contingency and choice: The implications of matching theory for classroom instruction

This paper describes a mathematical account of behavior known as matching theory. Matching theory evolved out of basic operant research and assumes that individuals can engage in a variety of behaviors at any moment, but they choose one to the exclusion of others. According to the matching equation, choices in behavior match the relative amount of reinforcement provided for each alternative. Although the principles of matching theory have proven useful in developing novel treatment strategies, few data exist validating the matching equation in natural human environments. Recent applications of matching theory to children's classroom behavior are described, and the implications of matching theory for classroom management and effective teaching are discussed.     

Matching and conditioned reinforcement rate

Attempts to examine the effects of variations in relative conditioned reinforcement rate on choice have been confounded by changes in rates of primary reinforcement or changes in the value of the conditioned reinforcer. To avoid these problems, this experiment used concurrent observing responses to examine sensitivity of choice to relative conditioned reinforcement rate. In the absence of observing responses, unsignaled periods of food delivery on a variable-interval 90-s schedule alternated with extinction on a center key (i.e., a mixed schedule was in effect). Two concurrently available observing responses produced 15-s access to a stimulus differentially associated with the schedule of food delivery (S+). The relative rate of S+ deliveries arranged by independent variable-interval schedules for the two observing responses varied across conditions. The relation between the ratio of observing responses and the ratio of S+ deliveries was well described by the generalized matching law, despite the absence of changes in the rate of food delivery. In addition, the value of the S+ deliveries likely remained constant across conditions because the ratio of S+ to mixed schedule food deliveries remained constant. Assuming that S+ deliveries serve as conditioned reinforcers, these findings are consistent with the functional similarity between primary and conditioned reinforcers suggested by general choice theories based on the concatenated matching law (e.g., contextual choice and hyperbolic value-added models). These findings are inconsistent with delay reduction theory, which has no terms for the effects of rate of conditioned reinforcement in the absence of changes in rate of primary reinforcement.     

The assumptions underlying the generalized matching law

Allen (1981) derived the power-function generalization of the matching law from a functional equation involving relative response rates on three concurrently available schedules of reinforcement. This paper defines the conditions (relative homogeneity and independence) under which a more general class of behavioral laws reduces to the power law. The proof also removes two deficiencies of Allen's result (discussed by Houston, 1982), which are, first, that his derivation produces a power law without a bias coefficient, and second, that it holds only for experiments with three or more concurrent schedules.     

An analytic comparison of Herrnstein's equations and a multivariate rate equation

Herrnstein's equations are approximations of the multivariate rate equation at ordinary rates of reinforcement and responding. The rate equation is the result of a linear system analysis of variable-interval performance. Rate equation matching is more comprehensive than ordinary matching because it predicts and specifies the nature of concurrent bias, and predicts a tendency toward undermatching, which is sometimes observed in concurrent situations. The rate equation contradicts one feature of Herrnstein's hyperbola, viz., the theoretically required constancy of k. According to the rate equation, Herrnstein's k should vary directly with parameters of reinforcement such as amount or immediacy. Because of this prediction, the rate equation asserts that the conceptual framework of matching does not apply to single alternative responding. The issue of the constancy of k provides empirical grounds for distinguishing between Herrnstein's account and a linear system analysis of single alternative variable-interval responding.     

A COMPUTATIONAL THEORY OF SELECTION BY CONSEQUENCES APPLIED TO CONCURRENT SCHEDULES

Virtual organisms animated by a computational theory of selection by consequences responded on symmetrical and asymmetrical concurrent schedules of reinforcement. The theory instantiated Darwinian principles of selection, reproduction, and mutation such that a population of potential behaviors evolved under the selection pressure exerted by reinforcement from the environment. The virtual organisms' steady‐state behavior was well described by the power function matching equation, and the parameters of the equation behaved in ways that were consistent with findings from experiments with live organisms. Together with previous research on single‐alternative schedules (McDowell, 2004; McDowell & Caron, 2007) these results indicate that the equations of matching theory are emergent properties of the evolutionary dynamics of selection by consequences.     

Reanalysis of the equation for simple action

De Villiers and Herrnstein (1976) have shown that the equation for simple action, derived from the matching law, predicts change in behavioral output for some 40 experiments in which the value of a single source of reinforcement has been varied. Using only the positive-reinforcement studies they cite that used five or more different reinforcement values, we found the high percentage of variance they report accommodated by this equation (94%) is predicated on instances of averaging rates of behavioral output before making a least-squares fit of the equation. In our reanalysis, which minimizes rate averaging, only 78% of the data variance is accommodated. This diminished data-variance accommodation can be improved by adding parameters that permit the equation's scaling constant to change as a function of reinforcement. Although these parameters permit acceptable levels of accommodation of data variance, they correspond to no obvious psychological processes. These findings support the view that the equation for simple action is an inadequate model for behavioral output.     

When less is not always more: stereotype knowledge and reasoning development

Developmental studies on heuristics and biases have reported controversial findings suggesting that children sometimes reason more logically than do adults. We addressed the controversy by testing the impact of children's knowledge of the heuristic stereotypes that are typically cued in these studies. Five-year-old preschoolers and 8-year-old children were tested with a card game version of the classic base-rate task. Problems were based on stereotypes that were familiar or unfamiliar for preschoolers. We also manipulated whether the cued stereotypical response was consistent (no-conflict problems) or inconsistent (conflict problems) with the correct analytic response that was cued in the problem. Results showed that an age-related performance decrease on the conflict problems was accompanied by an age-related performance increase on the no-conflict problems. These age effects were most pronounced for problems that adopted stereotypes that were unfamiliar for the 5-year-old preschoolers. When preschoolers were familiar with the stereotypes, their performance also started being affected. Findings support the claim that previously reported age-related performance decreases on classic reasoning tasks need to be attributed to the increased need to deal with tempting heuristics and not to a decrease in analytic thinking skills per se.     

Experimental evaluation of matching via a commercially available basketball video game

Many recent nonlaboratory-based quantitative analyses of behavior have relied on archival competitive sporting data. However, the ratio-based reinforcement schedules in most athletic competitions and the correlational nature of archival data analyses raise concern over the contributions of such findings to the behavior analytic literature. The current experiment evaluated whether matching in a human operant paradigm would approximate matching observed in nonlaboratory-based sports data. To this end, we used in-game attributes to parametrically manipulate 2- and 3-point shooting in a commercially available basketball video game. The behavior of 6 of 9 participants conformed to the generalized matching equation. These results suggest matching in sporting contexts may be a product of restricted nonindependent concurrent random-ratio schedules. Implications of this experiment, including those in applied behavior analysis and potential influence on gamification, are discussed.