Persistence and Relapse
This is a primary line of research examining variables contributing to making behavior persistent and likely to relapse. In addition, this work explores approaches to reducing persistence and likelihood of relapse as potential approaches to make behavioral treatments more effective. These studies use laboratory models with nonhumans (pigeons, rats, zebrafish) and humans and in clinical research with children diagnosed with Autism Spectrum Disorder.
Our work on conditional discrimination focuses on complex control by multiple features of sample stimuli – often called divided attention. More recent work in this area involves the application of quantitative models of conditional discrimination to categorize errors in performance. The goal is to develop this research into methods clinicians can use to target teaching strategies more effectively.
We use online crowdsourcing websites such as Amazon Mechanical Turk and Prolific as a rapid and convenient way to collect data from large numbers of participants. We have used online crowdsourcing in our research examining laboratory models of persistence and relapse, as well as to examine potential differences in learning flexibility in different populations.
We collaborate with Dr. Corina Jimenez-Gomez and others on research evaluating behavioral flexibility, reversal learning, and sensitivity to social consequences.