Machines Learning Depth Erases Human Proof of Spatial Understanding

Early Childhood Maths Group

Working with and living in spatially complex environments may help delay cognitive decline, though more research is needed.

Spatial world models, representations that support flexible reasoning about spatial relations, are central to developing computational models that could operate in the physical world, but their precise mechanistic underpinnings are nuanced by the borrowing of underspecified or misguided accounts of human cognition. This paper revisits the simulation versus rendering dichotomy and draws on evidence from aphantasia to argue that fine-grained perceptual content is critical for model-based spatial reasoning. Drawing on recent research into the neural basis of visual awareness, we propose that spatial simulation and perceptual experience depend on shared representational geometries captured by higher-order indices of perceptual relations. We argue that recent developments in embodied AI support this claim, where rich perceptual details improve performance on physics-based world engagements. To this end, we call for the development of architectures capable of maintaining structured perceptual representations as a step toward spatial world modelling in AI.

Sticks and stones can make two and three-dimensional shapes that students can use to explore geometry in an outdoor setting.