The present study sought to identify the effects that derived relational training had on simulated gambling behavior with slot machines. Prior to conducting any relational training procedures, participants' relational responses in each contextual condition was probed. Each of the four backgrounds/conditions (red, blue, green, yellow) were presented with various stimuli from differing stimulus classes. Twenty trials were presented with no feedback for correct responding. Upon completion of the probes, pretest preference assessments were administered with blue and red slot machines being concurrently available for twenty trials then green and yellow slot machines doing the same. Following the pretest preference assessment, relational training began, here stimuli from each of the stimulus classes were presented on screen for three of the contextual conditions with the blue contextual condition left out to assess for possible derived relational responses. After completion, the second set of relational probes and posttest preference assessments began to assess if relational training altered the response allocation of slot machines and accuracy of responding during probes. After relational training, all participants showed an increase in response allocation to the red slot machine and a decrease to the blue. The yellow slot machine was selected increasingly with three participants while green was selected more often with one individual, one staying at the same number, and two selecting it less than in the pretest.

The study of choice and allocation has often focused on certain parameters of reinforcement, but rarely on historical variables. The goal of this study was to investigate the potential effects of gradual vs. abrupt teaching methods on future response allocation. A secondary goal was to see if the results of teaching-method manipulations might be correlated with the parameters that the teaching method produced, specifically unit costs, rate of reinforcement, or error rates. Results indicate that these teaching procedures can produce transient shifts in allocation, but not in a consistent direction. Neither unit cost nor rate or reinforcement alone can account for observed response allocation shifts after training. Researchers saw that subjects reliably shifted towards a manipulanda that produced higher rates of errors, therefore investigating the influence that error rate (or types of errors) may have on response allocation may aid in general teaching method preferences. Future research could focus on the combination of historical procedural variables and current variables that could determine response allocation.

The Generalized Matching Equation (GME) has been utilized by many behavioral researchers as a model of operant choice, especially in elite sports. In this instance, the GME was used to analyze the penalty-kick selections (e.g., shoot right or shoot left) of nine elite international soccer players. The analysis of archival data from in-game decisions demonstrated the presence of matching across participants and within a naturalistic setting. Undermatching consistent with previous laboratory and non-laboratory research on the matching law was also observed. The GME accounted for a majority of variance in penalty-kick selection, with minimal bias in responding. The players’ choices did not demonstrate clear preferences for shooting at either side of the goal, but did expose trends in individual responding worth noting. While the results extend the research literature regarding the applicability of GME to professional sports, future research should investigate matching relationships through analyzing larger sample sizes, team-wide responses, and team outcomes. / Applied Behavioral Analysis

Many children are exposed to excessively technology. Such use of technology may lead to health issues including obesity, attention deficits, and sleep disorders. Research has shown that parameters of reinforcement, such as quality and delay, may influence how children allocate their preferences. One way to drive preference away from high-tech toys may be to arrange delays to reinforcement following such selections and immediate reinforcement for an alternative response. In Experiment 1, four subjects who preferred high-tech leisure items over low-tech leisure items were identified through the pair-stimulus preference assessments. The results of Experiment 2 indicated that all subjects were sensitive to delay to reinforcement. When delays were implemented following selection of high-tech items, preference shifted from high-tech to low-tech leisure items at different delays.

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