  In order to interact with objects in our environment, humans rely on an
understanding of the actions that can be performed on them, as well as their
properties. When considering concrete motor actions, this knowledge has been
called the object affordance. Can this notion be generalized to any type of
interaction that one can have with an object? In this paper we introduce a
method to represent objects in a space where each dimension corresponds to a
broad mode of interaction, based on verb selectional preferences in text
corpora. This object embedding makes it possible to predict human judgments of
verb applicability to objects better than a variety of alternative approaches.
Furthermore, we show that the dimensions in this space can be used to predict
categorical and functional dimensions in a state-of-the-art mental
representation of objects, derived solely from human judgements of object
similarity. These results suggest that interaction knowledge accounts for a
large part of mental representations of objects.
