A multivariate assessment of the rapidly changing procedure with McDowell's Evolutionary Theory of Behavior Dynamics

Abstract

A multivariate analysis is concerned with more than one dependent variable simultaneously. Models that generate event records have a privileged status in a multivariate analysis. From a model that generates event records, we may compute predictions for any dependent variable associated with those event records. However, because of the generality that is afforded to us by these kinds of models, we must carefully consider the selection of dependent variables. Thus, we present a conditional compromise heuristic for the selection of dependent variables from a large group of variables. The heuristic is applied to McDowell's Evolutionary Theory of Behavior Dynamics (ETBD) for fitting to a concurrent variable‐interval schedule in‐transition dataset. From the parameters obtained from fitting ETBD, we generated predictions for a wide range of dependent variables. Overall, we found that our ETBD implementation accounted well for various flavors of the log response ratio, but had difficulty accounting for the overall response rates and cumulative reinforcer effects. Based on these results, we argue that the predictions of our ETBD implementation could be improved by decreasing the base response probabilities, either by increasing the response latencies or by decreasing the sizes of the operant classes.

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

“…Popa and McDowell (2016) studied the effect of wide ranges of mutation rates and reinforcer magnitudes, and a larger phenotype range and therefore longer bit-string (i.e., genotype) length. Li et al (2018) experimented with smaller target class sizes. This work on the boundaries of the theory has not called into question the standard settings used in the present, or in any previous, research.…”

On the current status of the evolutionary theory of behavior dynamics

“…Li, Hautus, and Elliffe () described a novel method of fitting computational theories to data from individual organisms. Li, Elliffe, and Hautus (in press) used a version of this method to fit the ETBD to data from Davison and Baum's () experiment (just described), in which rapidly changing within‐session reinforcer ratios were arranged. They concluded that the theory accounted relatively well for the distribution of responding between operanda in individual birds.…”

“…They concluded that the theory accounted relatively well for the distribution of responding between operanda in individual birds. Important questions have been raised about this method and the interpretation of its results (e.g., Li, Elliffe, and Hautus, in press; McDowell & Klapes, ), but if these questions can be answered satisfactorily, then Li et al's method will be a useful addition to the tools used to evaluate computational theories.…”

Model‐based estimates for operant selection

“…Although the conceptual framework of selection by consequences has a long tradition in behavior analysis and remains a popular narrative (e.g., Baum, 2023; Becker, 2019; Donahoe, 2011; Donahoe et al, 1993; Hull et al, 2001; McDowell, 2013, 2023; Simon, 2020; Simon & Hessen, 2019), it seems to have had few effects on the actual practices of many behavior analysts (but see McDowell, 2019, and Li et al, 2018). One possible reason for this gap between theory and practice may be that operant selection is sometimes used as a mere synonym for what is traditionally called “reinforcement.” Of course, adopting the language and vocabulary of evolutionary biology alone does not add much to the theoretical foundations of the experimental analysis of behavior.…”