Call for Papers

We encourage the submission of research exploring developmental perspectives on AI, spanning developmental psychology, neuroscience, cognitive science, and machine learning, as well as papers introducing new developmental datasets and benchmarks for AI. 

Topics of interest include, but are not limited to:

• Core Knowledge and Inductive Biases

• Development of Representations and Concepts

• Learning and Generalization from Limited Experience 

• Development of Multimodal Representations 

• Social Learning and Social Cognition in Humans and Machines

• Intuitive Physics, Causal Reasoning, and World Models

• Few-Shot and Data-Efficient Learning Inspired by Development

• Development of Representations in Brains and Machines  

• Visual Representation Learning Inspired by Development

• Language Acquisition and Developmental Language Learning

• Developmental Neuroscience and Computational Accounts of Early Learning

• Developmental Datasets and Naturalistic Learning Environments

• Computational Models of Cognitive Development

• Developmentally Inspired Machine Learning Architectures

• Developmental Benchmarks for AI Systems

• AI as a Tool for Studying Cognitive and Brain Development