Revealing Neuro-computational Mechanisms of Reinforcement Learning and Decision-making With the hBayesDM Package
Posted by Institutional Development Scheme for HKSYU
Event Type: Research Workshop
Event Theme: Decision Making
Speaker: Dr. Woo-young Ahn (Assistant Professor, Department of Psychology, Ohio State University)
Date: 17 June 2016 (Friday)
Time: 10:00am to 1:00pm
Venue: LG 303, Main Building, HKSYU
Language: English
Remarks:
1) Free Admission
2) We recommend registration in advance for seat-reservation and news update.
Introduction
Reinforcement learning and decision-making (RLDM) provide a quantitative framework, which allows us to specify normative and aberrant conditions with basic dimensions of neurocognitive functioning. Such a framework can also provide insights into the brain substrates of particular RLDM processes as exemplified by model-based functional magnetic resonance imaging (fMRI). I will introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers cutting-edge computational modeling (hierarchical Bayesian modeling) on an array of RLDM tasks. Importantly, it is extremely user-friendly: users can perform computational modeling, output visualization, and model comparisons–each with a single line of coding. Users can also extract trial-by-trial latent variables required for model-based fMRI/EEG. In this workshop, I will first present some theoretical background in RLDM and Bayesian data analysis. Next, I will provide step-by-step tutorials how to use the hBayesDM package.
