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.     

The generalized matching law in elite sport competition: football play calling as operant choice

A mathematical model of operant choice, the generalized matching law was used to analyze play-calling data from the 2004 National Football League season. In all analyses, the relative ratio of passing to rushing plays was examined as a function of the relative ratio of reinforcement, defined as yards gained, from passing versus rushing. Different analyses focused on season-aggregate data for the league as a whole, game-by-game data for the league as a whole, and game-by-game data for individual teams. In all analyses except those for a few individual teams, the generalized matching law accounted for a majority of variance in play calling. The typical play-calling pattern reflected undermatching (suggesting imperfect sensitivity of play calling to yardage-gained reinforcers) and a bias for calling rushing plays. Bias was found to be a function of both the relative risk of turnovers and the relative variability in yards gained associated with passing versus rushing plays. The external validity of the matching analyses was supported by significant correlations between parameters of the generalized matching law and team success on offense and season winning percentage. These results illustrate the broad applicability of the generalized matching law to problems outside of the laboratory.     

The relation between the generalized matching law and signal-detection theory

The generalized matching law can be applied to a signal-detection matrix to give two equations. The first relates responding in the presence of the stimulus to the reinforcements for the responses, and the second relates responding in the absence of the stimulus to the reinforcements for the responses. Evidence for stimulus discrimination is given by biases that are opposite in sign in the two equations. As the logarithmic ratio and z proportion transformations are similar, the combination of the absolute values of the two logarithmic biases gives a measure equivalent to the signal-detection measures d′ and η. The two equations can also be combined to eliminate the biases caused by the signalling stimuli and to produce a generalized matching-law statement relating overall performance to the obtained reinforcements.     

Application of the matching law to pitch selection in professional baseball

This study applied the generalized matching equation (GME) to pitch selection in professional baseball. The GME was fitted to the relation between pitch selection and hitter outcomes for five professional baseball pitchers during the 2014 Major League Baseball season. The GME described pitch selection well. Pitch allocation varied across different game contexts such as inning, count, and number of outs in a manner consistent with the GME. Finally, within games, bias decreased for four of the five pitchers and the sensitivity parameter increased for three of the five pitchers. The results extend the generality of the GME to multialternative natural sporting contexts, and demonstrate the influence of context on behavior in natural environments.     

Concurrent variable-ratio schedules: Implications for the generalized matching law

Rats' responses were reinforced on concurrent variable-ratio variable-ratio schedules in which responses on one lever incremented the ratio counter and responses on a second lever changed the schedule and correlated stimulus. The relative frequency of reinforcement was varied from .10 to .99. In one set of conditions, responding on the main lever incremented both ratio counters, but reinforcement required a response in the presence of the stimulus correlated with the ratio that had been completed. In a second set of conditions, responses on the main lever incremented only the ratio correlated with the stimulus that was currently present. When main-lever responses incremented both ratio counters, subjects distributed responding and time in a manner consistent with the generalized matching law. When responses on the main lever incremented only the schedule currently in effect, the rats responded almost exclusively on the schedule producing the higher frequency of reinforcement. These results extend the applicability of the generalized matching law to dependent ratio schedules.     

Choice in quail neonates: the origins of generalized matching

Although newborns have surprised scientists with their learning skills, proficiency on concurrent schedules of reinforcement requires (in effect) the ability to integrate and compare behavior–consequence relations over time. Can very young animals obey the quantitative relation that applies to such repeated choices, the generalized matching law? The provenance of the skill is not well understood, and this study provides the first investigation of matching in neonates. Northern bobwhite (Colinus virginianus) hatchlings pecked left and right targets on a touchscreen for heat delivery on a concurrent variable-interval reinforcement schedule. Within 5 days after hatching, the chicks showed sensitivity levels significantly greater than zero, but short of typical adult levels. However, stable sequential patterns emerged almost immediately, including a consistent choose-rich tendency after unreinforced responses, one that entails some degree of temporal integration. These exploratory data suggest that the basic ability to match develops quickly in this precocial species, but that more extensive experience may be required to achieve the higher sensitivities typically seen in adults.     

The case against Allen's generalization of the matching law

Allen (1981) claims to have established the formal validity of a power-function generalization of the matching law. This paper argues that Allen's proof is not correct when schedules are presented in pairs and that his initial assumptions are too restrictive when all schedules are simultaneously available.     

The generalized matching law as a description of multiple-schedule responding

The literature was examined to determine how well the generalized matching law (Baum, 1974) describes multiple-schedule responding. In general, it describes the data well, accounting for a median of 91% of the variance. The median size of the undermatching parameter was 0.46; the median bias parameter was 1.00. The size of the undermatching parameter, and the proportion of the variance accounted for by the equation, varied inversely with the number of schedules conducted, with the number of sessions conducted per schedule, and with the time within a component. The undermatching parameter also varied with the operanda used to produce reinforcers and with the reinforcer used. The undermatching parameter did not vary consistently with component duration or with several other variables. Bias was greater when fewer rather than more schedules were conducted, when two rather than one operanda were used, and when White Carneaux rather than homing pigeons served as subjects. These results imply that the generalized matching law may describe both concurrent and multiple-schedule responding, but that the same variables do not always influence the bias and undermatching parameters in the same way for the two types of schedules.     

The applied importance of research on the matching law

In this essay, we evaluate the applied implications of two articles related to the matching law and published in the Journal of the Experimental Analysis of Behavior, May 1994. Building on Mace's (1994) criteria for increasing the applied relevance of basic research, we evaluate the applied implications of basic research studies. Research by Elsmore and McBride (1994) and Savastano and Fantino (1994) involve an extension of the behavioral model of choice. Elsmore and McBride used rats as subjects, but arranged a multioperant environment that resembles some of the complex contingencies of human behavior. Savastino and Fantino used human subjects and extended the matching law to ratio and interval contingencies. These experiments contribute to a growing body of knowledge on the matching law and its relevance for human behavior.     

An application of the matching law to social dynamics

Using a procedure similar to the one described by Conger and Killeen (1974), we evaluated levels of attending for 25 college students who participated in either a 20-min (n = 12) or 30-min (n = 13) discussion on juvenile delinquency. Confederates delivered statements of agreement (e.g., "I agree with that point") according to independent variable-interval schedules. Pooled results were evaluated using three generalized formulations of the matching law, and showed that matching was more likely during the first 5 min of the discussion than during the last 5 min. Individual data for 7 of 9 participants were better described by the generalized response-rate matching equation than by the generalized time-allocation matching equation when response allocation was characterized in terms of frequency rather than duration.     

Application of the generalized matching law to point-after-touchdown conversions and kicker selection in college football

Coaches make a variety of complex decisions in American-rules college football, especially related to point-after-touchdown (PAT) conversions and, historically, kicker selection. However, little research has characterized the pattern of these choices and whether they are sensitive to environmental manipulations, such as increased effort to score. In the present study, the generalized matching law (GML)—a model that predicts a linear relation between log-transformed choices for two alternatives and the amount of reinforcement garnered from them—was applied to PAT-conversion (1 point vs. 2 point) and kicker (soccer-style vs. conventional-style) selection using archived data from the National Collegiate Athletic Association. Overall, PAT-conversion and kicker selection exhibited matching. Further, narrowing the goal-post width was associated with decreased preference for one-point (relative to two-point) PAT attempts and enhanced sensitivity to increases in points scored from one-point (relative to two-point) PAT attempts. This investigation provides support for the ecological validity of the GML and demonstrates that complex decisions in football conform to an orderly pattern that can be described using the GML.     

Concurrent reinforcement schedules for problem behavior and appropriate behavior: experimental applications of the matching law

This study evaluated how children who exhibited functionally equivalent problem and appropriate behavior allocate responding to experimentally arranged reinforcer rates. Relative reinforcer rates were arranged on concurrent variable-interval schedules and effects on relative response rates were interpreted using the generalized matching equation. Results showed that relative rates of responding approximated relative rates of reinforcement. Finally, interventions for problem behavior were evaluated and differential reinforcement of alternative behavior and extinction procedures were implemented to increase appropriate behavior and decrease problem behavior. Practical considerations for the application of the generalized matching equation specific to severe problem behavior are discussed, including difficulties associated with defining a reinforced response, and obtaining steady state responding in clinical settings.     

Multilevel analysis of matching behavior

Multilevel modeling has been considered a promising statistical tool in the field of the experimental analysis of behavior and may serve as a convenient statistical analysis for matching behavior because it structures data in groups (or levels) to account simultaneously for the within‐subject and between‐subject variances. Heretofore, researchers have sometimes pooled data erroneously from different subjects in a single analysis by using average ratios, average response and reinforcer rates, aggregation of subjects, etc. Unfortunately, this leads to loss of information and biased estimations, which can severely undermine generalization of the results. Instead, a multilevel approach is advocated to combine several subjects' matching behavior. A reanalysis of previous data on matching behavior is provided to illustrate the method and point out its advantages. It illustrates that multilevel regression leads to better estimations, is more convenient, and offers more behavioral information. We hope this paper will encourage the use of multilevel modeling in the statistical practices of behavior analysts.     

Further analysis of the matching law to describe two- and three-point shot allocation by professional basketball players

The matching law was used to analyze whether the proportion of shots taken from two- or three-point range would match the proportional reinforcement rates produced by those shots when the reinforcement rate of three-point shooting was changed. Rule changes in 1994 and 1997 altered the distance of the three-point line in the National Basketball Association, which created a quasiexperimental reversal design, thereby naturally changing three-point reinforcement rates. The present data partially confirmed predictions made by the matching law, in that increases in the relative rate of three-point shots attempted corresponded to increases in the relative rate of three-point shots made.     

Choice, experience, and the generalized matching law

Five pigeons were exposed to different pairs of concurrent variable-interval, variable-interval schedules on nine experimental conditions of 30 sessions each. For every session, the parameters of the generalized matching equation were computed for the first five, six, seven, eight, and nine experimental conditions. The exponent a, both for response and time distribution, tended to decrease with increases in number of experimental conditions and to increase with number of sessions per condition, but values of k (bias) varied unsystematically. When the subjects were exposed to five new pairs of schedules, with 55 sessions per condition, the findings were confirmed. Data from the literature on the generalized matching law suggest that the variability of exponent values may be explained in part by the use of naive or experienced subjects in different investigations and by the variability in number of experimental conditions and in number of sessions per condition.