  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.
