Modeling resurgence with an evolutionary theory of behavior dynamics

McDowell's (2004) Evolutionary Theory of Behavior Dynamics (ETBD) is a computational theory that has reproduced a wide variety of behavioral phenomena observed in material reality. Here, we extended the generality of the ETBD by successfully replicating laboratory studies of resurgence with live animals using artificial organisms (AOs) animated by the theory. We ran AOs on concurrent random-interval random-interval (conc RI RI) schedules of reinforcement wherein one alternative (i.e., a target behavior) was reinforced while the other alternative (i.e., an alternative behavior) was not reinforced. Then, we placed the target behavior on extinction and reinforced the alternative response, producing a shift in allocation of responding from the target behavior to the alternative response. Finally, schedule thinning of the alternative response (i.e., downshifts) resulted in resurgence of target behavior. Our findings indicated that resurgence increased as a function of the relative downshift in reinforcement rate and magnitude, replicating findings from previous studies with live animals. These results further illustrate the utility of the ETBD for generating dynamic behavioral data and serve as a proof-of-concept for a novel computational approach for studying and understanding resurgence in future studies.