The foregoing experiments arranged different reinforcer probabilities correlated with the samples (DO) in one component of a multiple schedule where responding produced DMTS trials according to a VI schedule (the VI DMTS paradigm), and the same reinforcer probabilities (SO) in the alternated component. The major results of these experiments are: 1) DMTS forgetting functions were higher and generally shallower in DO than in SO components even when the latter provided greater total reinforcement; and 2) Between-component differences in the resistance to change of DMTS accuracy depended on differences in total reinforcement and not on differences in accuracy arising from differential versus nondifferential reinforcement. These results are summarized in , which shows that values of log d averaged over retention intervals and subjects for successive baseline determinations were consistently higher in the DO than in the SO component, and that the difference was greatest in Experiment 3 where reinforcer probabilities were the same. also shows that average proportions of baseline during disruption were greater in the SO than in the DO component, although the difference was small and inconsistent in Experiment 3.
Fig. 14 A summary of major results of all three experiments. The filled squares and diamonds give the average baseline values of log d in DO and SO components, respectively, and the unfilled squares and diamonds give the average values of proportions of baseline (more ...)
The baseline results replicate those of Peterson et al. (1980)
in a between-group design, and of Jones et al. (1995)
in a within-subject, signaled-magnitude paradigm for cases with greater total reinforcement in SO than in DO conditions, as well as many other studies that equated total reinforcement between SO and DO conditions (see Urcuioli, 2005
, for review). The results for resistance to change extend and refine those of Odum et al. (2005)
, who found that the resistance to change of DMTS forgetting functions depended directly on total reinforcement in a component. In that study, baseline accuracy also depended directly on total reinforcement, so that resistance to change could have been affected by accuracy levels as well as total reinforcement. Here, we have dissociated those potential determiners of resistance to change by showing that resistance to change depends on total reinforcement and not on differences in baseline accuracy. These results parallel the dissociation of steady-state response rate and resistance to change in research on free-operant performance on multiple schedules of reinforcement (see Nevin & Grace, 2000
Nevin, Davison, Odum, and Shahan (2007)
proposed a quantitative model of DMTS performance that accounts for the differences in baseline levels and resistance to change reported by Odum et al. (2005)
. Briefly, the model assumes that the probabilities of attending to the samples and comparisons in DMTS trials depend directly on reinforcer rates and inversely on the magnitudes of disruptors such as prefeeding, extinction, or distractors within DMTS trials. Thus, attending occurs with higher probability and is less disrupted in a condition with more frequent reinforcement, leading to higher levels and greater resistance to change of DMTS accuracy in that condition as found by Odum et al. Thus, the model accounts for resistance to change in the present experiments. It does not, however, account for the strong and consistent DOE in baseline.
The model can be adapted to predict the DOE in baseline by assuming independent attending to the samples and to the expectancies of reinforcement correlated with those samples. From a behavioral perspective, expectancies are private activities, and the activity of expecting high-probability reinforcement is likely to differ from the activity of expecting low-probability reinforcement. Presumably, those activities provide discriminable cues that are functionally the same as the samples in signaling the correct comparisons, and can form compounds with the samples that might enhance accuracy.1
(Note that when reinforcer probabilities are the same, expecting and its cues are nondifferential with respect to the outcomes, and comparison choice must be based on the samples only.) If attending to the samples and to the expectancy cues are like overt activities such as key pecking, disruptors such as distraction by competing activities during baseline sessions should have a somewhat greater effect in a component with lower overall reinforcer probability. Nevertheless, comparison choice will be correct if attending either to the samples or to expectancy cues persists throughout a given trial because both predict the correct choice. As a result, overall baseline accuracy may be greater with different outcomes despite less frequent reinforcement. During resistance tests when more potent disruptors are deployed, however, this advantage may be eliminated or reversed.
Although a quantitative development of these speculations is beyond the scope of this paper, it is possible that the present Experiments 1 and 2, by chance, have employed reinforcer probabilities and disruptors that allow both greater baseline accuracy and lower resistance to change in the DO component. For example, if the SO reinforcer probability was .9 but the DO probabilities were .09 and .01 instead of .9 and .1, the DOE might be abolished during baseline sessions because the reduction in total reinforcement would lower attending (and hence accuracy) in the DO component so much as to overwhelm any advantage resulting from redundant sample and expectancy cues.
More generally, the Nevin et al. (2007)
model predicts that the magnitude of the DOE should depend directly on the ratio of total DO to total SO reinforcement. Jones et al. (1995)
provide some relevant data. They arranged reinforcer durations of 3.5 and 0.5 s on signaled different-outcome trials, and over three conditions arranged 0.5 s, 3.5s, or 1.5 s durations on signaled same-outcome trials (the second of these was cited above for finding a reliable within-subject DOE despite greater overall reinforcement in same-outcome trials). Jones et al. presented individual parameter values for the intercepts and slopes of exponential functions fitted to their forgetting functions, and reported that there was no reliable effect of SO reinforcer duration on the intercepts or slopes of their forgetting functions. However, they did not examine the differences between pairs of forgetting functions across conditions, which define the magnitude of the DOE as reported here. Accordingly, we estimated the values of log d
at their retention intervals of 0.01, 1, 4, and 8 s from the intercept and slope parameter values in their and averaged them over subjects and retention intervals to give a number directly comparable to our value of log d
averaged over subjects for the same range of retention intervals. We then calculated the difference between means for DO and SO trials – i.e., the magnitude of the DOE. shows that as the log ratio of total DO to total SO reinforcement increased over conditions, the magnitude of the DOE tended to increase, being greater at the highest than at lowest ratio for 4 of their 5 pigeons and greater at the highest than at the intermediate ratio for all 5 pigeons. also presents a comparable treatment of our average DOE values in Experiment 1, 2, and 3, confirming the upward trend. The suggested conclusion is that the magnitude of the DOE depends directly on the total DO/SO reinforcer ratio. Extrapolating back to 0 on the y
-axis, the DOE should be abolished at a log ratio about -1.0. Although it may be difficult to arrange such an extreme ratio experimentally, the DOE should be evaluated over a wider range of DO/SO reinforcer ratios in order to provide parametric data that would test model predictions and evaluate the range of reinforcement conditions over which this truly robust phenomenon is observed.
Fig. 15 The magnitude of the DOE (i.e., the difference between average log d values with differential and nondifferential outcomes) as a function of the log ratio of total differential to nondifferential reinforcement. Filled diamonds present estimated differences (more ...)