  Affordance refers to the perception of possible actions allowed by an object.
Despite its relevance to human-computer interaction, no existing theory
explains the mechanisms that underpin affordance-formation; that is, how
affordances are discovered and adapted via interaction. We propose an
integrative theory of affordance-formation based on the theory of reinforcement
learning in cognitive sciences. The key assumption is that users learn to
associate promising motor actions to percepts via experience when reinforcement
signals (success/failure) are present. They also learn to categorize actions
(e.g., "rotating" a dial), giving them the ability to name and reason about
affordance. Upon encountering novel widgets, their ability to generalize these
actions determines their ability to perceive affordances. We implement this
theory in a virtual robot model, which demonstrates human-like adaptation of
affordance in interactive widgets tasks. While its predictions align with
trends in human data, humans are able to adapt affordances faster, suggesting
the existence of additional mechanisms.
