Effects of Reinforcer Rate and Reinforcer Quality on Time Allocation: Extensions of Matching Theory to Educational Settings

Abstract

We examined how 3 special education students allocated their responding across two concurrently available tasks associated with unequal rates and equal versus unequal qualities of reinforcement. The students completed math problems from two alternative sets on concurrent variable-interval (VI) 30-s VI 120-s schedules of reinforcement. During the equal-quality reinforcer condition, highquality (nickels) and low-quality items ("program money" in the school's token economy) were alternated across sessions as the reinforcer for both sets of problems. During the unequal-quality reinforcer condition, the low-quality reinforcer was used for the set of problems on the VI 30-s schedule, and the high-quality reinforcer was used for the set of problems on the VI 120-s schedule. Equal-and unequal-quality reinforcer conditions were alternated using a reversal design. Results showed that sensitivity to the features of the VI reinforcement schedules developed only after the reinforcement intervals were signaled through countdown timers. Thereafter, when reinforcer quality was equal, the time allocated to concurrent response alternatives was approximately proportional to obtained reinforcement, as predicted by the matching law. However the matching relation was disrupted when, as occurs in most natural choice situations, the quality of the reinforcers differed across the response options.

The Matching Law: A Tutorial for Practitioners

“…In one demonstration of this concept in education, Neef, Mace, Shea, and Shade (1992) offered the choice between two stacks of math problems to students receiving special education services (emotional disturbance and behavior disorders). In particular, the researchers arranged concurrent VI schedules across the two stacks in equal-and unequal-quality reinforcement phases.…”

“…However, if a bias is detected via reinforcer parameter manipulations, the practitioner can isolate the preferred dimension and program reinforcers accordingly; this approach may be useful in contexts that prohibit the ability to arrange all appetitive dimensions of reinforcement (e.g., specific classroom demands associated with the target response do not permit frequent rates of reinforcer delivery, but may permit more immediate or higher quality reinforcers). For example, Reed and Martens (2008) used procedures similar to those described by Neef et al (1992Neef et al ( , 1994 with students receiving standard educational services (i.e., not special education), to demonstrate the utility of matching to academic performance. In Experiment 1 of Reed and Martens' study, the difficulty of the problems in each stack was matched to students' current instructional level (i.e., in a previous assessment, the students demonstrated the ability to complete these problems accurately and fluently).…”

What are we doing when we translate from quantitative models?

“…8 Rich reinforcement is expected to increase persistence (not decrease it), although the size of the effect is left unspecified. Directional predictions also sometimes are derived from the matching law (e.g., Neef, Mace, Shea, & Shade, 1992) and from discounting theory (e.g., Tucker, Vuchinich, & Gladsjo, 1994). Indeed, most of the research that Waltz and Follette cited in support of clinical applications takes this qualitative form.…”

Integrating Basic and Applied Research and the Utility of Lattal and Perone's Handbook of Research Methods in Human Operant Behavior

“…Of special relevance to application are Mazur's discussions on using concurrent reinforcement schedules to examine different response topographies and reinforcers (e.g., Mace, Neef, Shade, & Mauro, 1994;Neef, Mace, Shea, & Shade, 1992) and on the role of rule governance in choice (e.g., Horne & Lowe, 1993). These sections are meaningful because they acknowledge some of the complexities of analyses of choice in everyday situations (Fuqua, 1984;Mace, 1994).…”

“…For example, it would be informative to replicate applied studies of student choice in the context of doing arithmetic problems associated with different rates and qualities of reinforcement (e.g., Neef et al, 1992). Research might examine dimensions of the contingencies operating in the class or home (e.g., rate, quality, response effort) that influence whether or not a student chooses to manipulate materials, engage in activities, and talk to people, all of which would be available to facilitate learning new skills (e.g., Favell & McGimsey, 1993;R.…”