Combinations of Response‐reinforcer Relations in Periodic and Aperiodic Schedules

Matching theory is a general framework for understanding allocation of behavior among activities. It applies to choice in concurrent schedules and was extended to single schedules by assuming that other unrecorded behavior competes with operant behavior. Baum and Davison (2014) found that the competing activities apparently are induced by the “reinforcers” (phylogenetically important events, e.g., food) according to power functions. Combined with power‐function induction, matching theory provides new equations with greater explanatory power. Four pigeons were exposed to conditions in which 7 different schedules of food delivery were presented within each experimental session. We replicated earlier results with variable‐interval schedules: (a) a negatively accelerated increase of peck rate as food rate increased in the low range of food rates; (b) an upturn in pecking at higher rates; and (c) a downturn in pecking at extremely high food rates. When the contingency between pecking and food was removed, the food continued to induce pecking, even after 20 sessions with no contingency. A ratio schedule inserted in place of 1 variable‐interval schedule maintained peck rates comparable to peck rates maintained by short interval schedules. We explained the results by fitting equations that combined matching theory, competition, and induction.

Section: Discussionmentioning

confidence: 99%

Matching theory and induction explain operant performance

Baum

Grace

2020

“…In application, NCR is often used as a control in functional analyses of problem behavior (control or toy play condition; see Beavers, Iwata, &Lerman, 2013, andMcCord, 2003) and as a treatment for problem behavior (e.g., Vollmer et al). Its use as a behavioral treatment stems from basic research demonstrating response-independent presentations of reinforcing events (e.g., food) decrease rates of a target response (e.g., Kuroda et al, 2013;Podlesnik & Shahan, 2008).…”

mentioning

confidence: 99%

Noncontingent reinforcement competes with response performance

Kelley¹,

Nadler

Rey³

et al. 2017

Noncontingent reinforcement is a commonly used procedure to decrease levels of problem behavior. Goals of this intervention are to decrease motivation, responding, and the functional relation between behavior and consequences, but it could also possibly compete with performance of alternative desirable responses. In the current study, we assessed the effects of noncontingent reinforcement arranged from 0% to 100% of sessions on performance of alternative responding across two experiments. Experiment 1 assessed manding (i.e., requests) maintained by attention and tangibles with a child with developmental disabilities and Experiment 2 assessed keypecking maintained by food with six pigeons. We extended previous research by (a) showing that noncontingent reinforcement competes with both the acquisition and maintenance (performance) of an alternative response, (b) extending the generality of the findings across nonhuman and human participants, and (c) eliminating influence of sequence effects through random manipulations of noncontingent value in pigeons. Overall, greater amounts of noncontingent reinforcement competed with both acquisition and maintenance of alternative responding.

“…In the right graphs of this figure, the data from the left graphs are displayed as means across conditions in which the dependency in the alternative component was 10, 50, and 100%. Across conditions, reinforcement rates were similar between components for each rat, and Baseline response rates were directly related to the dependency in each component (Kuroda et al, ; Lattal, ). That is, in conditions in which the dependency in the alternative component was 100%, response rates in both components were similar.…”

Section: Resultsmentioning

confidence: 99%

“…The 10% components differed on how reinforcers were programmed. In one component, responsedependent and -independent reinforcers were programmed as in Experiment 1 (Kuroda et al, 2013;Lattal, 1974); in the other component, those reinforcers were programmed by superimposing a VT schedule on an independent VI schedule (Nevin et al, 1990;Podlesnik & Shahan, 2008).…”

Section: Methodsmentioning

confidence: 99%

Response–reinforcer dependency and resistance to change

Cançado

Abreu‐Rodrigues

Aló

et al. 2017

Self Cite

The effects of the response-reinforcer dependency on resistance to change were studied in three experiments with rats. In Experiment 1, lever pressing produced reinforcers at similar rates after variable interreinforcer intervals in each component of a two-component multiple schedule. Across conditions, in the fixed component, all reinforcers were response-dependent; in the alternative component, the percentage of response-dependent reinforcers was 100, 50 (i.e., 50% response-dependent and 50% response-independent) or 10% (i.e., 10% response-dependent and 90% response-independent). Resistance to extinction was greater in the alternative than in the fixed component when the dependency in the former was 10%, but was similar between components when this dependency was 100 or 50%. In Experiment 2, a three-component multiple schedule was used. The dependency was 100% in one component and 10% in the other two. The 10% components differed on how reinforcers were programmed. In one component, as in Experiment 1, a reinforcer had to be collected before the scheduling of other response-dependent or independent reinforcers. In the other component, response-dependent and -independent reinforcers were programmed by superimposing a variable-time schedule on an independent variable-interval schedule. Regardless of the procedure used to program the dependency, resistance to extinction was greater in the 10% components than in the 100% component. These results were replicated in Experiment 3 in which, instead of extinction, VT schedules replaced the baseline schedules in each multiple-schedule component during the test. We argue that the relative change in dependency from Baseline to Test, which is greater when baseline dependencies are high rather than low, could account for the differential resistance to change in the present experiments. The inconsistencies in results across the present and previous experiments suggest that the effects of dependency on resistance to change are not well understood. Additional systematic analyses are important to further understand the effects of the response-reinforcer relation on resistance to change and to the development of a more comprehensive theory of behavioral persistence.