Friday, September 17, 2021 from 1pm – 7pm GMT
Alexandra Cohen, New York University
Tobias Hauser, University College London
The “Computational Modeling in Development” workshop will provide a didactic, hands-on introduction to computational modeling in development for researchers with limited prior knowledge in modelling. Following an introduction to principles of computational modelling in the first session, the second session will consist of participants completing practical tutorials in small groups led by trainee facilitators. The workshop will conclude with a panel discussion on the promises and pitfalls of computational modelling in development.
|13:00||Welcome from the Organisers (Ali Cohen & Tobias Hauser)|
|Computational modelling in development: Past, current, and future directions (Cate Hartley)|
|13:30||What is Computational Modelling? Introduction and examples|
a. What is a computational model and why do we use it? (Nadescha Trudel & Alisa Loosen)
b. How to develop a computational model? (Tricia Seow, Sam Hewitt, & Noam Goldway)
c. Principles of modelling and model fitting (Magda Dubois, Naiti Bhatt, Greer Bizzell-Hatcher, & Vasilisa Skvortsova)
d. Model comparison, selection & validation (Kate Nussenbaum, Johanna Habicht, & Vasilisa Skvortsova)
|17:00||Parallel modelling tutorials:|
a. Inferring cognitive models of reinforcement learning from choice data (Maël Lebreton & Stefano Palminteri)
b. Computational modeling of goal-directed and habitual reinforcement-learning strategies (Claire Smid & Wouter Kool)
c. Computational models of human gaze data (Angela Radulescu)
d. Uncovering heterogeneity in preferences and behavior with finite mixture models (Adrian Bruhin)
e. An introduction to drift diffusion modeling (Wenjia Joyce Zhao & Ian Krajbich)
|19:00||Panel discussion: Promises and Pitfalls in Developmental Computational Modelling|
|19:30||virtual drinks / find-a-modeler & find-an-experimentalist session|
Concurrent Practical Tutorials
Tutorial 1: Inferring cognitive models of reinforcement learning from choice data
Led by: Maël Lebreton & Stefano Palminteri
Tutorial Description: In the first part of the tutorial the instructors will briefly first present the behavioural task (two-armed bandit), the computational models and the data structure. In a second step, the instructors will describe the analytical pipeline and the corresponding codes. The attendees will then be asked to perform the analyses and some predefined ‘exercises’ (including calculating correlations and simulation experiments). In the last part the instructors will comment on the results, debrief, answer questions and put the results in a broader perspective.
Programming language: MATLAB/Octave
Tutorial 2: Uncovering heterogeneity in preferences and behavior with finite mixture models
Led by: Adrian Bruhin
Tutorial Description: Finite mixture models enable us to uncover the heterogeneity in preferences and behavior parsimoniously. Unlike most econometric models that postulate a single representative agent, they assume that the population comprises a finite number of distinct types of individuals. By estimating a finite mixture model, we can uncover the relative size and average parameters of each of these types. Furthermore, we also obtain a classification of each individual into the type best fitting her behavior. Thus, finite mixture models allow us to focus on the most relevant part of heterogeneity – namely the distribution of distinct types of individuals – without having to estimate at the individual level. This tutorial provides an introduction to finite mixture models in two parts. The first part introduces the basic concepts and highlights some applications. Subsequently, the second part features a tutorial in the context of voluntary blood donation.
Programming language: R
Tutorial 3: An introduction to drift diffusion modeling
Led by: Wenjia (Joyce) Zhao & Ian Krajbich
Tutorial description: Drift diffusion models are widely applied in psychology and neuroscience to study time-course of decision making. They have been used successfully in a range of perceptual and preferential tasks (for an incomplete list, see https://u.osu.edu/ratcliffmckoon/the-diffusion-model-for-non-specialists/). This tutorial provides a primer on the theoretical framework of the model, as well as example code for model fitting and analyses.
Programming language: Python package (HDDM) and also likely some R
Tutorial 4: Computational models of human gaze data
Led by: Angela Radulescu
Tutorial description: This tutorial will cover the theory and practice of fitting computational models to human gaze data. We will treat gaze data as an observable consequence of a latent selective attention process. We will build generative models of gaze that make real-time predictions about where participants will look, conditional on past choices, observations. and current attentional state. Modeling frameworks we will discuss include reinforcement learning and approximate Bayesian inference (e.g. particle filtering).
Programming language: Python
Tutorial 5: Computational modeling of goal-directed and habitual reinforcement-learning strategies
Led by: Claire Smid & Wouter Kool
Tutorial description: Human behavior is sometimes guided by habit, and sometimes by goal-directed planning. Recent advances in computational cognitive science have formalized this as a distinction between model-free and model-based reinforcement learning. In this tutorial, we will teach you how to use model fitting techniques to distinguish between these forms of decision making in humans across the developmental lifespan.
Programming language: Python (through Google colab)