Response Acquisition Under Targeted Percentile Schedules: A Continuing Quandary for Molar Models of Operant Behavior

Abstract

The number of responses rats made in a "run" of consecutive left-lever presses, prior to a trial-ending right-lever press, was differentiated using a targeted percentile procedure. Under the nondifferential baseline, reinforcement was provided with a probability of .33 at the end of a trial, irrespective of the run on that trial. Most of the 30 subjects made short runs under these conditions, with the mean for the group around three. A targeted percentile schedule was next used to differentiate run length around the target value of 12. The current run was reinforced if it was nearer the target than 67% of those runs in the last 24 trials that were on the same side of the target as the current run. Programming reinforcement in this way held overall reinforcement probability per trial constant at .33 while providing reinforcement differentially with respect to runs more closely approximating the target of 12. The mean run for the group under this procedure increased to approximately 10. Runs approaching the target length were acquired even though differentiated responding produced the same probability of reinforcement per trial, decreased the probability of reinforcement per response, did not increase overall reinforcement rate, and generally substantially reduced it (i.e., in only a few instances did response rate increase sufficiently to compensate for the increase in the number of responses per trial). Models of behavior predicated solely on molar reinforcement contingencies all predict that runs should remain short throughout this experiment, because such runs promote both the most frequent reinforcement and the greatest reinforcement per press. To the contrary, 29 of 30 subjects emitted runs in the vicinity of the target, driving down reinforcement rate while greatly increasing the number of presses per pellet. These results illustrate the powerful effects of local reinforcement contingencies in changing behavior, and in doing so underscore a need for more dynamic quantitative formulations of operant behavior to supplement or supplant the currently prevalent static ones.

Shaping Academic Task Engagement With Percentile Schedules

“…For all other participants, we examined m values of 20, 10, and 5 across conditions. We selected these values based on previous investigations of the percentile schedule (Galbicka et al, 1991(Galbicka et al, , 1993. The order of presentation of conditions across participants was semirandom.…”

“…In research with nonhumans, percentile schedules have been used to examine interresponse times (Kutch & Platt, 1976;Platt, 1979), the effects of d-amphetamine on control of response number (Galbicka, Fowler, & Ritch, 1991), response acquisition (Galbicka, Kautz, & Jagers, 1993), and variable response sequences (Machado, 1989). Applications of the percentile schedule with humans have evaluated its efficacy in decreasing cigarette smoking Lamb, Morral, Galbicka, Kirby, & Iguchi, 2005; and increasing variability in computer game playing (Miller & Neuringer, 2000).…”

Seleção Direcional De Numerosidade: Um Estudo Exploratório

“…Using a FCN task and a percentile schedule, Galbicka et al (1993) shaped run length towards the target value of 12. When the current run length was greater than 12, it was reinforced only if it was below a criterion value -shaping proceeded in the downward direction; when the current run length was less than 12, it was reinforced only if it was greater than a criterion value -shaping proceeded in the upward direction.…”

“…Because two of them stress the relation between the run length sampled on each trial, x, and the internal variable, µ, the procedure should allow the experimenter to control that relation. One such procedure is based on the percentile schedule developed by Platt to study shaping (see Platt, 1973) and later adapted by Galbicka to study numerosity differentiation in the rat (Galbicka, Fowler, & Ritch, 1991;Galbicka, Kautz, & Jagers, 1993;see also Galbicka, 1994). Below, we describe how we changed the percentile schedule to examine the assumptions of Machado and Rodrigues' (2007) model of numerosity differentiation and then report some data obtained with the new procedure.…”

In Today's Climate, a Forecast for Change: A Commentary on Donahoe, Palmer, and Burgos

“…Greater difficulties arise, I believe, from the fact that the procedures and methods that were subsequently developed to study operant behavior are not well suited to the development of a science of behavioral dynamics like that promulgated by Donahoe and Palmer (1994), as I and others have argued elsewhere (e.g., Galbicka, 1992, in press;Galbicka, Kautz, & Jagers, 1993). They also differ from those associated with the respondent learning tradition, where, with its emphasis on discrete presentations, response characteristics such as magnitude or latency, or aggregates such as response probability (i.e., responses per trial) are far more common dependent measures.…”