Extending unified-theory-of-reinforcement neural networks to steady-state operant behavior

Behavioral Research with Planaria

This article serves as a brief primer on planaria for behavior scientists. In the 1950s and 1960s, McConnell's planarian laboratory posited that conditioned behavior could transfer after regeneration, and through cannibalization of trained planaria. These studies, the responses, and replications have been collectively referred to as the "planarian controversy." Successful behavioral assays still require refinement with this organism, but they could add valuable insight into our conceptualization of memory and learning. We discuss how the planarian's distinctive biology enables an examination of biobehavioral interaction models, and what behavior scientists must consider if they are to advance behavioral research with this organism. Suggestions for academics interested in building planaria learning laboratories are offered.

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

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.