Academic

Abstract

Learning to make categorization decisions is important in almost all aspects of our lives. Understanding how we learn novel categories can shed novel insights into the mechanisms underlying experience-dependent brain plasticity. Scientists have traditionally studied these questions through longitudinal category learning experiments, analyzing data on response categories and associated response times under a multi-alternative decision-making paradigm. Drift-diffusion processes are popular in such contexts for their ability to mimic the underlying neural mechanisms in providing joint models for these variables. Motivated by these problems, we develop a novel Bayesian semiparametric inverse Gaussian drift-diffusion mixed model for multi-category decision-making in longitudinal settings. We designed Markov chain Monte Carlo algorithms for posterior computation. We evaluate the method’s empirical performance through synthetic experiments. Applied to our motivating longitudinal tone learning study, the method provides novel insights into how the biologically interpretable model parameters evolve with learning, differ between input-response tone combinations, and differ between well and poorly-performing adults. Related models for scenarios when data on only response categories are available, the challenges of ‘local inference’ in such experiments, etc. will also be discussed.

Biography

Dr Abhra Sarkar is an assistant professor in the Department of Statistics and Data Sciences at The University of Texas at Austin. He was a postdoctoral fellow in the Department of Statistical Science at Duke University from 2014 to 2017. He received his PhD in Statistics from Texas A&M University in 2014.  His research interests center around the development of novel statistical approaches in diverse application areas, including nutritional epidemiology, auditory, and vocal communication neuroscience. He is a recipient of the Mitchell Prize from the International Society of Bayesian Analysis (ISBA) in 2018 and 2020 for his applied Bayesian papers.