Application of the matching law to pitch selection in professional baseball

The matching law provides a quantitative description of social time allocation in children with autism

Recent research has developed and evaluated assessments of sociability in which time allocation near or away from an adult who initiates social interactions is used to characterize the participant as social, indifferent, or avoidant of social interaction. Though these qualitative outcomes have been useful, no studies have evaluated methods of obtaining more quantitative measures of sociability. The matching law has been demonstrated to describe a wide range of human behavior and may also be useful in describing social time allocation. We adapted the matching law and assessment of sociability procedures with the aim of providing a more precise, quantitative measure of sociability. We fitted the matching equation to the social time allocation data of 8 children with autism spectrum disorder. The equation was effective in quantifying sociability, accounted for a large proportion of variance in participants' behavior, did so equally well for participants who were social and avoidant, and provided a more sensitive measure relative to those used in previous research. The implications of this methodology, its potential utility, and directions for future research are discussed.

How individuals make choices explains addiction's distinctive, non-eliminable features

In keeping with the goals of this Special Issue, this paper poses the following questions: What are addiction's non-eliminable features and can they be explained by one or more general principles? I have added the qualifier "distinctive" to these goals, as in "distinctive non-eliminable features." The result is a highly heterogeneous list, which includes features of addiction's natural history, such as its high remission rates, its unique idioms (e.g., "kicking the habit"), and its patented interventions, such as Alcoholics Anonymous. I show that each of these distinctive features reflects how individuals make choices. In particular, they reflect the competing claims of two basic choice processes: global maximizing of the sort assumed in introductory economics textbooks and Herrnstein's matching law, which has empirical rather than theoretical roots. These are basic choice processes, which apply to all decision making, not just drugs and not just addicts. Nevertheless, they can result in addiction when one of the options has the capacity to undermine the value of competing interests and undermine global maximizing. Conversely, the analyses also show that the two basic choice processes combine so as to predict that addiction is a semi-stable state that is biased to resolve in favor of remission. These predictions are supported by the high rates of addiction, by the high rates of remission from addiction, and by the fact that remission is often unassisted or "spontaneous." The analyses fail to support the idea that pathological psychological processes lead to addiction. Rather they show that addiction emerges from the interactions of normal choice processes and the behaviorally toxic effects of drugs.

Further application of the generalized matching law to multialternative sports contexts

Cox et al. (2017) successfully applied the multialternative version of the generalized matching law (GML) to pitch selection among a sample of Major League Baseball (MLB) pitchers. The purpose of the present study was to replicate and extend these findings by fitting the multialternative GML to pitch data among a sample of MLB pitchers with varying levels of success in the major leagues. We also examined how matching parameters changed as a function of novel antecedent game contexts such as the infield shift, game location, and number of times the pitcher faced the batters in the batting order. These results replicate the findings from Cox et al. and suggest the multialternative GML is a robust descriptor of pitch selection among MLB pitchers. Together, these findings further extend the generality of the multialternative GML to naturalistic, nonlaboratory environments.

Application of the matching law to Mixed Martial Arts

In the contemporary behavior-analytic literature, athletic performance (e.g.,choice of shot or movement) across multiple team sports has been found to correspond with predictions of the generalized matching equation. However, the research in this area has focused primarily on team sports. In the current study the Generalized Matching Equation (GME) was applied to Mixed Martial Arts (MMA) performance by examining strike selection as a function of landing significant strikes among fighters from various weight classes in the Ultimate Fighting Championship. The results suggest that the GME is a good descriptor of strike selection in MMA, an individual sport that is dynamic and fast paced where responding results in immediate feedback from an opponent.

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.