Sensitivity and Bias Under Conditions of Equal and Unequal Academic Task Difficulty

Abstract

We conducted an experimental analysis of children's relative problem-completion rates across two workstations under conditions of equal (Experiment 1) and unequal (Experiment 2) problem difficulty. Results were described using the generalized matching equation and were evaluated for degree of schedule versus stimulus control. Experiment 1 involved a symmetrical choice arrangement in which the children could earn points exchangeable for rewards contingent on correct math problem completion. Points were delivered according to signaled variable-interval schedules at each workstation. For 2 children, relative rates of problem completion appeared to have been controlled by the schedule requirements in effect and matched relative rates of reinforcement, with sensitivity values near 1 and bias values near 0. Experiment 2 involved increasing the difficulty of math problems at one of the workstations. Sensitivity values for all 3 participants were near 1, but a substantial increase in bias toward the easier math problems was observed. This bias was possibly associated with responding at the more difficult workstation coming under stimulus control rather than schedule control.

Food Preference, Keeper Ratings, and Reinforcer Effectiveness in Exotic Animals: The Value of Systematic Testing

“…Additionally, these choice patterns stabilized quickly, with 4 of the 5 animals reaching criterion within the minimum three sessions required. Although changes less than 20% were used as criteria, the majority of subjects had fluctuations of behavior of 10% or less, similar to those seen in other applied studies (Reed & Martens, 2008). These results coincide with reports with humans, as food preference is generally predictive of reinforcer effectiveness (Penrod, Wallace, & Dyer, 2008).…”

The Matching Law: A Tutorial for Practitioners

“…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?

“…Bias was linked to situational factors in a study by Reed and Martens (2008), in which children could complete math problems to earn tokens at either of two work stations. In general, more problems were completed at the station with the richer reinforcement schedule.…”

“…For instance, Reed and Martens (2008) devised their study of matching in academic behavior ( Figure 6C) explicitly to reflect research showing that response effort makes a behavior option less appealing; this research provided good reason to expect that the difficulty of academic problems would create bias. Vollmer and Bourret's (2000) application of the generalized matching law to basketball shot selection (two-point vs. three-point shots) was guided, in part, by laboratory research showing that differential reinforcer magnitude causes bias.…”