  Lexical semantics and cognitive science point to affordances (i.e. the
actions that objects support) as critical for understanding and representing
nouns and verbs. However, study of these semantic features has not yet been
integrated with the "foundation" models that currently dominate language
representation research. We hypothesize that predictive modeling of object
state over time will result in representations that encode object affordance
information "for free". We train a neural network to predict objects'
trajectories in a simulated interaction and show that our network's latent
representations differentiate between both observed and unobserved affordances.
We find that models trained using 3D simulations from our SPATIAL dataset
outperform conventional 2D computer vision models trained on a similar task,
and, on initial inspection, that differences between concepts correspond to
expected features (e.g., roll entails rotation). Our results suggest a way in
which modern deep learning approaches to grounded language learning can be
integrated with traditional formal semantic notions of lexical representations.
