Rediscovering Affordance: A Reinforcement Learning Perspective
- URL: http://arxiv.org/abs/2112.12886v1
- Date: Fri, 24 Dec 2021 00:25:03 GMT
- Title: Rediscovering Affordance: A Reinforcement Learning Perspective
- Authors: Yi-Chi Liao, Kashyap Todi, Aditya Acharya, Antti Keurulainen, Andrew
Howes, Antti Oulasvirta
- Abstract summary: We propose an integrative theory of affordance-formation based on the theory of reinforcement learning in cognitive sciences.
We implement this theory in a virtual robot model, which demonstrates human-like adaptation of affordance in interactive widgets tasks.
- Score: 30.61766085961884
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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