Intuitive physics understanding emerges from self-supervised pretraining on natural videos
This challenges the idea that innate hardwiring is necessary for intuitive physics, potentially impacting developmental psychology and AI by showing it can be learned from data.
The study tackled the problem of whether intuitive physics understanding can emerge in neural networks from self-supervised pretraining on natural videos, finding that models predicting masked regions in a learned representation space achieved above-chance performance on tasks like object permanence, unlike pixel-space or text-based models.
We investigate the emergence of intuitive physics understanding in general-purpose deep neural network models trained to predict masked regions in natural videos. Leveraging the violation-of-expectation framework, we find that video prediction models trained to predict outcomes in a learned representation space demonstrate an understanding of various intuitive physics properties, such as object permanence and shape consistency. In contrast, video prediction in pixel space and multimodal large language models, which reason through text, achieve performance closer to chance. Our comparisons of these architectures reveal that jointly learning an abstract representation space while predicting missing parts of sensory input, akin to predictive coding, is sufficient to acquire an understanding of intuitive physics, and that even models trained on one week of unique video achieve above chance performance. This challenges the idea that core knowledge -- a set of innate systems to help understand the world -- needs to be hardwired to develop an understanding of intuitive physics.