Pre-Conference Workshops

Computational Modelling in Development
Friday, September 17, 2021 from 1pm – 7pm GMT


Workshop Organizers:

Alexandra Cohen, New York University
Tobias Hauser,
University College London

Workshop Description:

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.


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 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)


FIT’NG Full Day Workshop
Friday, September 17, 2021 

FIT’NG All Ages: Advantages and Challenges of Longitudinal Fetal, Infant, and Toddler Neuroimaging


Time: Friday Sept 17; 9:00 am – 3:00 pm EST

Please note: The $20 registration fee is to support FIT’NG and not Flux Society.


Meeting Organizers:

FIT’NG (Fetal, Infant, Toddler Neuroimaging Group)

Sarah Shultz, PhD, Emory University/Marcus Autism Center (co-chair)

Dustin Scheinost, PhD, Yale University School of Medicine (co-chair)

Zeena Ammar, Emory University/Marcus Autism Center

Cat Camacho, Washington University in St. Louis

Aiden Ford, Emory University/Marcus Autism Center

Roxane Licandro, Vienna University of Technology

Kelly Vaughn, University of Texas Health Science Center at Houston



Meeting Description:

Longitudinal MRI is essential for quantifying trajectories of brain change in typical development and in neurodevelopmental disorders. Rapid changes in brain anatomy and physiology during the prenatal, infant and toddler period necessitate longitudinal measurement but also present unique challenges for data acquisition, processing, and analysis. This satellite meeting will provide a forum for discussing these challenges and identifying possible solutions. Session 1 will focus on challenges relating to data collection (choice of sequence parameters and equipment, data acquisition procedures, and participant recruitment and retention) and data analysis (approaches to segmentation and parcellation, registration, and curve fitting). In Session 2, expert panelists will provide a ‘behind the scenes’ look at important decision points and strategies adopted in their own research designs, stimulating a live discussion of solutions to challenges inherent in longitudinal neuroimaging. Finally,  Session 3 will showcase new and exciting work utilizing longitudinal approaches discussed in preceding sessions.