Vuyk | M1.03

Plantage Muidergracht 12, 1018 TV Amsterdam

Jul 18 @ 09:00 UTC - Jul 18 @ 12:30 UTC

Public session

Recent years have witnessed a dramatic increase in the use of reinforcement learning (RL) models in decision neuroscience and affective neuroscience. This approach, in combination with neuroimaging techniques such as functional magnetic resonance imaging, enables quantitative investigations into latent mechanistic processes underlying social decision-making. Additionally, there is a growing popularity of hierarchical Bayesian approaches for performing model estimation, which provides the granularity of population-level regulation meanwhile retains individual differences. However, cognitive and social neuroscientists do not necessarily have formal training in computational modeling, which involves multiple steps that require programming as well as quantitative skills. To bridge this gap, this tutorial will first present a comprehensive framework for the examination of (social) decision-making with the simple Rescorla-Wagner RL model. I will then provide a principled interpretation of the functional role of the learning rate parameter. I will also discuss potential misconceptions of RL models and provide an applicable workflow for applying RL models. Finally, I will showcase a few studies that applied RL modeling frameworks in decision neuroscience, including an emerging field of Computational Psychiatry. In the practical session, I will focus on a newly developed probabilistic programming language Stan (mc-stan.org), and an associated R package hBayesDM (github.com/CCS-Lab/hBayesDM) to perform hierarchical Bayesian analyses of a simple RL task. In sum, this tutorial aims to provide simple and scalable explanations and practical guidelines for employing RL models in order to assist both beginners and advanced users in better implementing and interpreting their model-based analyses. Workshop materials can be found here: https://github.com/lei-zhang/talks_and_workshops/tree/main/20230718_MathPsy_ICCM_EMPG

Jul 18 @ 14:00 UTC - Jul 18 @ 17:30 UTC

Public session

The topic for this year's symposium is responsible supervision and managing lab dynamics. We want to discuss what good supervision looks like, mentoring styles, how group dynamics in the lab play out, and possible interactions between mentoring and cultural differences. In the first part of the symposium, we receive input from a researcher in the field of responsible supervision, Dr. Tamarinde Haven. Based on her expert input, we will continue with a panel discussion on lab dynamics and supervision with researchers from mathematical psychology from different (academic) cultural backgrounds and career stages. We will then open the discussion to include all symposium participants in smaller groups. We hope the session will lead to an exchange of experiences and advice. The WoMP Professional Development Symposium is open to all registered conference attendees, regardless of career stage. Plan: Talk on responsible supervision by Dr. Tamarinde Haven (14:00 - 14:45); Discussion (14:45 - 15:00); Break (15:00 - 15:20); Panel discussion on managing lab dynamics with a special focus on supervision and cultural differences (15:20 - 15:50); Group discussions on responsible supervision and lab dynamics (15:50 - 17:30)

Jul 19 @ 09:00 UTC - Jul 19 @ 11:00 UTC

In-person session

Jul 19 @ 11:20 UTC - Jul 19 @ 12:40 UTC

In-person session

Jul 19 @ 15:20 UTC - Jul 19 @ 17:00 UTC

In-person session

Jul 20 @ 09:00 UTC - Jul 20 @ 10:40 UTC

In-person session

Jul 20 @ 11:00 UTC - Jul 20 @ 12:40 UTC

In-person session

Jul 20 @ 15:20 UTC - Jul 20 @ 17:00 UTC

In-person session

Jul 21 @ 09:00 UTC - Jul 21 @ 10:40 UTC

In-person session

Jul 21 @ 11:00 UTC - Jul 21 @ 12:40 UTC

In-person session

Jul 21 @ 15:20 UTC - Jul 21 @ 17:00 UTC

In-person session