2024 Workshop

3rd Workshop on Mental effort

Tutorials

November 21, 2022 (Day 1)

The 3rd Workshop on Mental Effort will host six hands-on modeling tutorials on Day 1 of the workshop (November 21, 2022) covering computational methods such as symbolic architecture modeling, connectionist modeling, non-parametric Bayesian methods, dynamical systems modeling, as well as reinforcement learning. These tutorials will help trainees and senior researchers to expand their knowledge to different computational frameworks, applied to the common concept of mental effort. Participation in each tutorial is limited to a small group of participants, to allow for hands-on exercises.

For more information, please see the workshop booklet [PDF].

All tutorials are listed below (alphabetical order). Click on the tutorial title for a detailed description.

Tutorial 1: An Introduction to Biologically-Inspired Neural Network Modeling By Randall O'Reilly

Tutorial description will follow soon.

Tutorial 2: no longer available

Tutorial 3: Bayesian Models in Brain Science [Matlab] By Andra Geana

This brief tutorial will provide the basic tools for understanding, developing and applying Bayesian models to brain science questions. We will cover methods and challenges of using Bayesian models for hypothesis testing and quantitative fitting of behavioral data and brain-behavior relationships. Topics include basic Bayesian theory, writing a model likelihood function, simulating models, validation and selection, posterior predictive checks, maximum likelihood fitting. All coding will be done via MATLAB, and we will be using learning & decision-making problems as examples.

Tutorial 4: Hierarchical Bayesian Parameter Estimation of Sequential Sampling Models [Python] By Alexander Fengler & Michael J. Frank

Tutorial description will follow soon.

Tutorial 5: Network Dynamic Modeling of Cognitive Control [Python] By Anastasia Bizyaeva

In this tutorial we introduce attendees to the fundamentals of dynamical systems, and demonstrate how they can be used to model processes of effort allocation and decision-making. We introduce a dynamical systems modeling framework to study the evolving behavior of multiple processing units or decision-makers that exchange information over a network. We will illustrate how this framework can be used to derive analytically tractable predictions about underlying mechanisms that can explain real-life decision processes on both the microscopic level (i.e. internal representation of tasks) and the macroscopic level (i.e. interaction of individuals). We will build intuition about the effects of individual differences, network structure, and heterogeneous inputs, through hands-on exercises using Python-language Jupyter notebooks.

Tutorial 6: Reinforcement Learning [Matlab] By Renée S. Koolschijn (on site) & Hanneke den Ouden (virtual)