Modeling resurgence with an evolutionary theory of behavior dynamics

Area of Science:

• Behavioral science
• Computational modeling
• Animal behavior

Background:

• The Evolutionary Theory of Behavior Dynamics (ETBD) is a computational framework explaining diverse behavioral phenomena.
• Previous research has validated ETBD in various contexts, but its application to specific laboratory paradigms like resurgence with live animals was limited.

Purpose of the Study:

• To extend the generality of the ETBD by replicating laboratory studies of resurgence using artificial organisms (AOs).
• To validate the ETBD's capability in modeling dynamic behavioral data and provide a computational approach for studying resurgence.

Main Methods:

• Artificial organisms (AOs) animated by the ETBD were subjected to concurrent random-interval random-interval (conc RI RI) reinforcement schedules.
• The target behavior was placed on extinction while an alternative behavior was reinforced, followed by schedule thinning (downshifts) of the alternative response.

Main Results:

• The AOs demonstrated resurgence of the target behavior following schedule thinning of the alternative response.
• The degree of resurgence was directly related to the relative downshift in reinforcement rate and magnitude, mirroring findings from live animal studies.

Conclusions:

• The ETBD successfully replicated behavioral resurgence in a computational model, extending its applicability.
• This study serves as a proof-of-concept for using computational approaches like the ETBD to investigate behavioral resurgence.