  Our ability to interact with the world around us relies on being able to
infer what actions objects afford -- often referred to as affordances. The
neural mechanisms of object-action associations are realized in the visuomotor
pathway where information about both visual properties and actions is
integrated into common representations. However, explicating these mechanisms
is particularly challenging in the case of affordances because there is hardly
any one-to-one mapping between visual features and inferred actions. To better
understand the nature of affordances, we trained a deep convolutional neural
network (CNN) to recognize affordances from images and to learn the underlying
features or the dimensionality of affordances. Such features form an underlying
compositional structure for the general representation of affordances which can
then be tested against human neural data. We view this representational
analysis as the first step towards a more formal account of how humans perceive
and interact with the environment.
