Local Rates of Responding and Reinforcement During Concurrent Schedules

Transition-state choice behavior of pigeons was examined in two experiments designed to test predictions of melioration and the kinetic model. Both experiments began with an initial training condition during which subjects were maintained on concurrent variable-interval schedules. In Experiment 1, subjects were then exposed to concurrent variable-ratio schedules, whereas in Experiment 2, subjects were then exposed to concurrent extinction. Contrary to the predictions of melioration, but consistent with the kinetic model, acquisition of preference on concurrent variable-ratio schedules followed a negatively accelerated logistic trajectory, and preference remained stable in concurrent extinction. Predictions made by the kinetic model concerning rates of switching between alternatives were also supported.Key words: kinetic model, melioration, matching law, choice, transition-state behavior, concurrent schedules, key peck, pigeonsThe behavior of organisms on probabilistic concurrent schedules of reinforcement is well described by the matching law (Herrnstein, 1970; for a review see de Villiers, 1977). For time allocation, the matching law may be written as(1) (Baum & Rachlin, 1969), where T1 and T2 are the cumulative durations of two behaviors and R1 and R2 are the obtained rates of reinforcement for those behaviors. The purpose of the present endeavor is to compare two theories of the process that gives rise to matching. One theory, melioration, describes a process whereby the organism adjusts its preference so as to equalize local rates of reinforcement (Herrnstein, 1982;Herrnstein & Vaughan, 1980;Vaughan, 1981;Vaughan & Herrnstein, 1987). The other theory, the kinetic model, describes an equilibration process formally similar to the kinetics of chemical reactions (Myerson & Miezin, 1980;Staddon, 1977 According to the melioration theory, organisms allocate behavior based on the difference, D, between local reinforcement rates: (2) where P is preference for the alternative whose current local reinforcement rate is N1/T1, and f(D) is any increasing function such that f(0) = 0. At equilibrium, NI/T -N2/T2 = 0, and rearranging yields T1/T2 = N1/N2. Dividing both the numerator and denominator of the right-hand fraction by T1 + T2 converts this fraction to the ratio of the overall reinforcement rates which equals (matches) the time ratio (Equation 1).According to the kinetic model, each reinforcement on one schedule decreases the rate of switching to the alternative by some proportion, k, and the sum of the local rates of switching back and forth is a constant, c. This may be expressed as dX/dt = k*R2*(c -X) -k*R1*X, (3) where X is the local rate of switching from the alternative reinforced at rate R, to the alternative reinforced at rate R2, and c -X is the local rate of switching back again. Local switching rate is defined as the number of switches from a schedule divided by the time spent on that schedule. Thus the local switching rates X and c -X equal n/Tj and n/T2, respectively, where n is the number of switches 2...

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Choice in Transition: A Comparison of Melioration and the Kinetic Model

Myerson

Hale

1988

“…It is possible, however, that the basic procedure could have yielded matching if other variables were altered. For example, although no COD was used in the Bradshaw studies, it has been argued that a COD should generally be included in concurrent schedule procedures (Catania, 1966;McSweeney et al, 1983). This experiment investigated the addition of a COD to the procedure employed in Experiment 1.…”

Section: Verbal Reportsmentioning

confidence: 99%

“…Many studies of concurrent schedule performance have employed a changeover delay (COD); this is a time period during which no scheduled reinforcement is delivered for a specified time following a change from responding on one alternative to responding on the other, and it is designed to prevent reinforcement of changeover responses. A number of authors have argued that the inclusion of a COD is an important feature of any effective concurrent schedule procedure (see Catania, 1966;McSweeney, Melville, Buck, & Whipple, 1983). Several reviews of the literature have shown that performance of nonhumans conforms closely to the generalized matching law, with values of a falling between 0.5 and 1.3 in the great majority of cases (Baum, 1979(Baum, , 1983de Villiers, 1977;Horne, 1986;Wearden & Burgess, 1982), though procedural variables have been found to affect sensitivity (Taylor & Davison, 1983;Todorov, Oliveira Castro, Hanna, Bittencourt de Sa, & Barreto, 1983).…”

mentioning

confidence: 99%

Determinants of Human Performance on Concurrent Schedules

Horne¹,

Lowe²

1993

100

Six experiments, each with 5 human adults, were conducted to investigate the determinants of human performance on multiple concurrent variable-interval schedules. A two-key procedure was employed in which subjects' key presses produced points exchangeable for money. Variables manipulated across experiments were (a) changeover delay (Experiments 2, 4, and 6), (b) ordinal cues related to scheduled reinforcement frequencies (Experiments 3 and 4), and (c) instructions describing the ordinal relations between schedule-correlated stimuli and scheduled reinforcement frequency (Experiments 5 and 6). The performances of only 13 of the 30 subjects could be described by the generalized matching equation and were within a range of values typical of those reported in the animal literature. Eight subjects showed indifference, 9 undermatched, 7 approximated matching, 3 overmatched, and a further 3 responded exclusively to the richer component of the concurrent schedules. These differing modes of responding were closely related to the different types of performance rules reported by subjects in postexperimental questionnaires. The results are in good agreement with those from studies of human performance on single schedules, suggesting that rule-governed behavior, in interaction with contingencies, may be an important determinant of human choice.

“…Alternatively, it may be that the differences between the measures used for concurrent and multiple schedules are symptomatic of fundamental differences between responding on the two schedules. For example, McSweeney, Melville, Buck, and Whipple (1983) found that the local rates of reinforcement obtained from the components of concurrent schedules are approximately equal for most schedules and that the local rates of responding are equal for many others. If this is so, then Equation 1 does not have an interesting application to the local rates of responding and reinforcement during concurrent schedules.…”

Section: Limitations Of the Present Comparisonsmentioning

confidence: 99%

The Generalized Matching Law as a Description of Multiple‐schedule Responding

McSweeney

Farmer

Dougan

et al. 1986

Self Cite

The literature was examined to determine how well the generalized matching law (Baum, 1974) describes multiple-schedule responding. In general, it describes the data well, accounting for a median of 91% of the variance. The median size of the undermatching parameter was 0.46; the median bias parameter was 1.00. The size of the undermatching parameter, and the proportion of the variance accounted for by the equation, varied inversely with the number of schedules conducted, with the number of sessions conducted per schedule, and with the time within a component. The undermatching parameter also varied with the operanda used to produce reinforcers and with the reinforcer used. The undermatching parameter did not vary consistently with component duration or with several other variables. Bias was greater when fewer rather than more schedules were conducted, when two rather than one operanda were used, and when White Carneaux rather than homing pigeons served as subjects. These results imply that the generalized matching law may describe both concurrent and multiple-schedule responding, but that the same variables do not always influence the bias and undermatching parameters in the same way for the two types of schedules.