Experimental evaluation of matching via a commercially available basketball video game

Abstract

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.

Assisted Training Model for Improving the Three-Point Shootout in Basketball

“…At the same time, this paper compares their scores. 5 This article uses statistical data to explore the effect of functional strength training on shooting hits.…”

Training Methods in Basketball Players to Increase Their Shooting Percentage

“…This makes the players' movements proficient. 8 Then, in the case of proficient unarmed movements, strengthen the training of holding the ball and shooting the basket. In this way, after the shooting angle is determined, the players have a certain direction instead of randomly shooting.…”

Further application of the generalized matching law to multialternative sports contexts

“…First examined in basic laboratory contexts, researchers have increasingly applied the GML to naturalistic, socially significant human behavior (Borrero & Vollmer, 2002). The generality of the GML has also been assessed in describing complex operant behavior in competitive contexts such as basketball (e.g., Alferink et al, 2009), baseball (e.g., Poling et al, 2011), football (e.g., Reed et al, 2006), hockey (Seniuk et al, 2015), martial arts (Seniuk et al, 2019), chess (Cero & Falligant, 2019), volleyball (Rotta et al, 2020), and videogames (Schenk & Reed, 2019). However, in many naturally occurring contexts, there are more than two concurrently available response alternatives/schedules of reinforcement.…”