Timothy Shahan Lab

My lab was the first to develop a formal quantitative theory of resurgence. Resurgence is typically defined as an increase (i.e., relapse) of a previously extinguished target behavior when a more recently reinforced alternative behavior is later extinguished. Our original theory of resurgence was an extension of Behavioral Momentum theory. We and others have noted a number of shortcomings of this theory. As a result, we have developed a novel choice-based quantitative theory of resurgence (i.e., Resurgence as Choice). Much current research in my lab is directed at testing the predictions and implications of this new theory.

Shahan, T. A., Browning, K. O., & Nall, R. W. (2020). Resurgence as choice in context: Treatment duration and on/off alternative reinforcement. Journal of the Experimental Analysis of Behavior, 113(1), 57–76. https://doi.org/10.1002/jeab.563

Shahan, T. A., & Craig, A. R. (2017). Resurgence as choice. Behavioural Processes, 141, 100–127. https://doi.org/10.1016/j.beproc.2016.10.006

We recently developed the first quantitative theory of the extinction burst. An extinction burst is an initial increase in an operant behavior following the removal of reinforcement for that behavior. Extinction bursts are often seen when the reinforcement (e.g., attention from caregivers) that has been maintaining a problem behavior is eliminated during clinical interventions. A large number of experiments in the lab are examining basic predictions and implications of the theory.

Shahan, T. A., & Avellaneda, M. (2025). The extinction burst: Effects of alternative reinforcement magnitude. Journal of the Experimental Analysis of Behavior, 124(2), e70045. https://doi.org/10.1002/jeab.70045

Shahan, T. A. (2022). A theory of the extinction burst. Perspectives on Behavior Science, 45(3), 495–519. https://doi.org/10.1007/s40614-022-00340-3

For nearly 20 years, we have been involved in making use of basic quantitative theories of behavior to aid in the development of improved Applied Behavior Analysis treatments for problem behavior of children with developmental disabilities. We conduct basic research with animals to develop and evaluate potential novel treatments involving differential reinforcement of alternative behavior. We also collaborate directly with applied behavior analysts to test the predictions of quantitative theories directly in clinical settings. Current NIH-funded translational grants in the lab evaluate the potential utility of our quantitative theories of resurgence and the extinction burst for improving treatment in applied settings.

Shahan, T. A., & Greer, B. D. (2021). Destructive behavior increases as a function of reductions in alternative reinforcement during schedule thinning: A retrospective quantitative analysis. Journal of the Experimental Analysis of Behavior, 116(2), 243–248. https://doi.org/10.1002/jeab.708

Greer, B. D., & Shahan, T. A. (2019). Resurgence as Choice: Implications for promoting durable behavior change. Journal of Applied Behavior Analysis, 52(3), 816–846. https://doi.org/10.1002/jaba.573

Nevin, J. A., & Shahan, T. A. (2011). Behavioral momentum theory: Equations and applications. Journal of Applied Behavior Analysis, 44(4), 877–895. https://doi.org/10.1901/jaba.2011.44-877

Over approximately the last 15 years, we have worked to reconceptualize our understanding of operant learning by generating novel non-associative accounts based on insights provided by information theory. We have described a novel approach that re-conceptualizes conditioned (i.e., secondary) reinforcement in terms of the signaling function of reward-predictive cues. We have formalized this approach and provided a means to quantify this signaling function by using an established application of information theory to Pavlovian conditioning. This approach provides a formal integration of Pavlovian conditioning and operant conditioned reinforcement—two phenomena recognized for generations as being related, but that had eluded formal integration.  We have subsequently extended the approach to account for how informative reward-predictive cues can contribute to suboptimal decision making and have worked to quantitatively understand how the relative age of information about decision options impacts adaptive decision making under changing circumstances. Finally, we continue to extend such an information-theoretical approach to provide a formal quantitative measure and definition of response-reinforcer contingencies. This approach seeks to extend previous information theory-based accounts of Pavlovian conditioning to re-conceptualize operant learning in terms of how organisms derive the information required to learn what responses produce what outcomes (i.e., the assignment of credit problem).

Gallistel, C. R., & Shahan, T. A. (2024). Time-scale invariant contingency yields one-shot reinforcement learning despite extremely long delays to reinforcement. Proceedings of the National Academy of Sciences, 121(30), e2405451121. https://doi.org/10.1073/pnas.2405451121

Gallistel, C. R., Craig, A. R., & Shahan, T. A. (2019). Contingency, contiguity, and causality in conditioning: Applying information theory and Weber’s Law to the assignment of credit problem. Psychological Review, 126(5), 761–773. https://doi.org/10.1037/rev0000163

Shahan, T. A. (2017). Moving Beyond Reinforcement and Response Strength. The Behavior Analyst, 40(1), 107–121. https://doi.org/10.1007/s40614-017-0092-y

Shahan, T. A., & Cunningham, P. (2015). Conditioned reinforcement and information theory reconsidered. Journal of the Experimental Analysis of Behavior, 103(2), 405–418. https://doi.org/10.1002/jeab.142

Shahan, T. A. (2010). Conditioned reinforcement and response strength. Journal of the Experimental Analysis of Behavior, 93(2), 269–289. https://doi.org/10.1901/jeab.2010.93-269