Evolutionary theory prediction: Response rate as a joint function of reinforcement rate and reinforcer magnitude

Abstract

Artificial organisms (AOs) animated by an evolutionary theory of behavior dynamics (ETBD) worked on concurrent interval schedules with a standard reinforcer magnitude on 1 alternative and a range of reinforcer magnitudes on the other. The reinforcer magnitudes on the second alternative were hedonically scaled using the generalized matching law. The AOs then worked on single interval schedules that arranged various combinations of the scaled reinforcer magnitudes and a range of nominal schedule values. This produced bivariate response rate data to which 5 candidate equations were fitted. One equation was found to provide the best description of the bivariate data in terms of percentage of variance accounted for, information criterion value, and residual profile. This equation consisted of 2 factors, 1 entailing the scaled magnitude, 1 entailing the obtained reinforcement rate, and both expressed in the form of exponentiated hyperbolas. The theory's prediction of the bivariate equation, along with additional predictions of the theory, were tested on data from an experiment in which rats pressed levers for various concentrations of sucrose pellets. The bivariate equation predicted by the theory was confirmed, as were all the additional predictions of the theory that could be tested on this data set.

A test of the evolutionary theory's account of punishment superimposed on single‐alternative schedules

“…The schedules were RI 0.5, 1, 1.5, 2, 3, 5, 7, 15, 30, and 60, where the schedule value indicates the average number of time ticks (i.e., generations) between the delivery of one reinforcer and the set-up of the next. This range of RI values was similar to ranges used in previous research on single schedules (McDowell, 2004;McDowell & Arashanapalli, 2021;McDowell and Klapes, 2020). The smallest RI values were intended to ensure well-defined asymptotes when punishment was superimposed.…”

Applying the evolutionary theory of behavior dynamics to model the subtypes of automatically reinforced self‐injurious behavior

“…We selected these mutation rate and fitness density function means to extend (i.e., higher and lower) beyond the range of values analyzed by Morris and McDowell (2021). Previous research on the ETBD has evaluated mutation rates ranging from 0.5 to 50 (McDowell et al, 2008) and fitness density function means ranging from 10 to 124 (McDowell & Arashanapalli, 2021). Thirty artificial organisms were animated according to each model, and the level of differentiation during the functional analysis and percentage of reduction in treatment was evaluated for each artificial organism, resulting in 750 unique data sets.…”

“…The ETBD generates artificial organism behavior similar to the behavior of live organisms across a variety of experimental arrangements and levels of analysis. Specifically, artificial organisms animated by the ETBD behave similarly to live organisms on single‐interval reinforcement schedules (McDowell & Arashanapalli, 2021; McDowell & Calvin, 2015; McDowell & Caron, 2007; McDowell & Klapes, 2020), concurrent‐interval reinforcement schedules (McDowell et al, 2008; McDowell & Popa, 2010; Popa & McDowell, 2016), concurrent‐ratio reinforcement schedules (McDowell & Klapes, 2018), single‐interval reinforcement schedules with superimposed punishment schedules (McDowell et al, 2022), and concurrent‐interval reinforcement schedules with superimposed punishment schedules (McDowell & Klapes, 2019). The similarity between the behavior of artificial organisms animated by the ETBD and that of live organisms has been observed with steady‐state (e.g., slight undermatching; McDowell et al, 2008) and dynamic (e.g., momentary effect of successive reinforcers; Kulubekova & McDowell, 2013) phenomena.…”

Application of the evolutionary theory of behavior dynamics to severe challenging behavior

“…Research shows emergent outcomes produced by the ETBD mirror steady‐state choice of live organisms in matching arrangements (see McDowell, 2021 for an extensive summary). That is, AOs animated by the ETBD respond comparably to live animals on (a) single‐alternative arrangements using random‐interval (RI) schedules (e.g., McDowell & Arashanapalli, 2021; McDowell & Klapes, 2020; Morris & McDowell, 2021), (b) concurrent RI RI schedules (e.g., McDowell & Calvin, 2015; McDowell & Klapes, 2019), and (c) concurrent random‐ratio (RR) schedules (McDowell & Klapes, 2018). The ETBD has been extended to model responding on concurrent schedules with superimposed punishment contingencies (McDowell & Klapes, 2019).…”