Human-Like Geometric Abstraction in Large Pre-trained Neural Networks

Specifically, we apply that can capture regularities in the external world. By neural network models to behavioral tasks from recent empirical forming abstractions that can generalize to future experience, work (Sablé-Meyer et al., 2021, 2022; Hsu, Wu, & humans are able to exhibit efficient learning and strong generalization Goodman, 2022) that catalogue three effects indicative of abstraction across domains (Lake, Salakhutdinov, & Tenenbaum, in human geometric reasoning. First, humans are 2015; Hull, 1920). One domain in which this has sensitive to geometric complexity, such that they are slower been observed by cognitive scientists is geometric reasoning to recall complex images as compared to simpler ones (Sablé- (Dehaene, Al Roumi, Lakretz, Planton, & Sablé-Meyer, Meyer et al., 2022). Second, humans are sensitive to geometric 2022), where people consistently extract abstract concepts, regularity (based on features such as right angles, parallel such as parallelism, symmetry, and convexity, that generalize sides, and symmetry) such that they are able to classify regular across many visual instances.