Rediscovering Affordance: A Reinforcement Learning Perspective
This work addresses the lack of a mechanistic theory for affordance-formation in human-computer interaction, though it is incremental as it builds on existing reinforcement learning concepts.
The paper tackles the problem of explaining how affordances are discovered and adapted through interaction by proposing a reinforcement learning-based theory, implemented in a virtual robot model that demonstrates human-like adaptation but is slower than humans.
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.