Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by work in infant psychology, researchers have started evaluating a model's ability to discriminate between expected and surprising scenes as a sign of its reasoning ability. Existing VoE-based 3D datasets in physical reasoning only provide vision data. However, current cognitive models of physical reasoning by psychologists reveal infants create high-level abstract representations of objects and interactions. Capitalizing on this knowledge, we propose AVoE: a synthetic 3D VoE-based dataset that presents stimuli from multiple novel sub-categories for five event categories of physical reasoning. Compared to existing work, AVoE is armed with ground-truth labels of abstract features and rules augmented to vision data, paving the way for high-level symbolic predictions in physical reasoning tasks.
@article{arxiv.2110.05836,
title = {AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition},
author = {Arijit Dasgupta and Jiafei Duan and Marcelo H. Ang and Cheston Tan},
journal= {arXiv preprint arXiv:2110.05836},
year = {2021}
}
Comments
Accepted at the NeurIPS Workshop for Physical Reasoning and Inductive Biases for the Real World