Pre-conference Events

The ManyBabies Project:
How it works, what it contributes to developmental cognitive science, and how to get involved


Thursday, January 4th, 8:30am - 12:30pm

Overview and motivation:
The ManyBabies Project is a collaborative research initiative that studies core theoretical and methodological questions in developmental cognitive science. By conducting large-scale, multi-lab replications of fundamental findings in infant cognition, the project addresses issues of replicability and generalizability in developmental cognitive science, while quantitatively exploring factors that contribute to cross-lab variation. These efforts generate large, diverse datasets that can be used to investigate novel questions via spin-off projects. Researchers at all career stages from around the world work together developing novel methodological approaches, harmonizing research practices and promoting transparent research practices.

We start this workshop with an introduction to the ManyBabies framework, explaining its main goals, workflows, and measures taken in an effort to create and facilitate an open, positive, and inclusive research environment. Subsequently, we provide updates from the ManyBabies subprojects. In part three, we synthesize developments from each project to shed light on emerging project-overarching theoretical and methodological advances. We will discuss not only what we have learned so far about the specific phenomenon under study, but also across different phenomena, for example about the strength of different paradigms. Further, we will discuss pros and cons of doing science as a big team effort. Philosopher Suilin Lavelle will enrich this part as a discussant with an interdisciplinary perspective on the current state of developmental cognitive science. Finally, we present ways of getting involved with the ManyBabies framework, especially as early career researchers. We will explain how researchers with different backgrounds, expertises and resources can join the project.

Speakers will include: Judit Gervain (University of Padua, Italy), Martin Zettersten (Princeton University, USA), Francis Yuen (University of British Columbia, Canada), Ingmar Visser (University of Amsterdam, the Netherlands), Claartje Levelt (Leiden University, the Netherlands), Tobias Schuwerk (LMU Munich, Germany), Dora Kampis (University of Copenhagen), Suilin Lavelle (University of Edinburgh)

Advances in infant neuroscience:
What state-of-the-art imaging can reveal about the developing mind?


Thursday, January 4th, 8:30am - 12:30pm

Barbara Pomiechowska, University of Birmingham
Moritz Köster, Universität Regensburg

Check detailed program

Bayesian models of science learning in Python

Tutorial; limited to 12 participants

Thursday, January 4th, 8:30am - 12:30pm

Overview and motivation:
Science learning is a highly relevant topic for the investigation of cognitive development. First, misconceptions about scientific phenomena follow a developmental trajectory. Second, science learning depends on metacognitive skills such as inhibition or cognitive reflection, that develop during childhood. Therefore, methods for investigating science learning can provide novel insights into cognitive development. The present tutorial introduces such a method, namely Bayesian modeling of science learning.

Bayesian inference is a method of statistical inference, i.e., a method of drawing conclusions from data. The central notion of this method is Bayes' theorem, which states that the probability that a hypothesis is true given some evidence (P(H|E), called posterior) is proportional to the product of the probability of the hypothesis (P(H), called prior) and the probability that the evidence would occur if the hypothesis were true (P(E|H), called likelihood). While Bayesian inference is a common method in empirical research, it can also be seen as a formalized model of science learning. The learner's prior belief (say, that heavier objects displace more water) is updated after seeing some evidence (say, that a lighter but larger object displaces more water). This approach is supported by previous research (Ullman & Tenenbaum, 2020; Colantonio et al., 2022).

The newly developed Python package "Bayesian Science Learning" (BaSciL) provides an easy-to-use and flexible tool for Bayesian modeling of science learning. It can be used in cases where children learn about scientific phenomena from experimental comparisons. Researchers upload a spreadsheet with information about experimental trials and participants' responses. The program automatically calculates likelihoods and learners' priors, and performs Bayesian updating. Because the functions are pre-built and run automatically, BaSciL makes Bayesian modeling accessible even to researchers unfamiliar with the Python programming language.

Learning goals:
The aim is that participants…

  • understand why Bayesian inference provides a formal model of science learning
  • are able to create and upload an input table for the modeling in Python
  • know how to execute the package functions to perform the modeling
  • can inspect and interpret the modeling output

Python fundamentals for eye-tracking research

Tutorial; limited to 40 participants

Thursday, January 4th, 8:30am - 12:30pm

Overview and motivation:
Eye-tracking is a technique of fundamental importance for investigating cognitive abilities in infants and children. In the last 20 years, it has revolutionized what we know about the developing mind through unique and clever experimental designs. Yet, a comprehensive guide on how to implement experimental designs and collect eye-tracking data with developmental populations is lacking. This Python tutorial will deliver this knowledge in three ways. First, Python affords unparalleled flexibility, allowing researchers to surpass the limitations of proprietary software. Second, the tutorial aims to establish a standardized, open-access pipeline for eye-tracking data collection and analysis in developmental populations. Lastly, it provides an array of options for deriving eye-tracking measures from raw data, thereby enhancing the adaptability of the research method to the research question at hand.

Learning goals:
Participants will learn: How to implement eye-tracking designs in Python; How to interact with an eye-tracker via Python to calibrate participants' eyes and collect eye-tracking data; How to extract and visualize meaningful eye-tracking measures from the raw data.