Intuitive physics learning in a deep-learning model inspired by developmental psychology.

ABSTRACT: 'Intuitive physics' enables our pragmatic engagement with the physical world and forms a key component of 'common sense' aspects of thought. Current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to even very young children. Here we address this gap between humans and machines by drawing on the field of developmental psychology. First, we introduce and open-source a machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology. Second, we build a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children. We demonstrate that our model can learn a diverse set o

SUBMITTER: Piloto LS 

PROVIDER: S-EPMC9489531 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature