Projects per year
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.
Original language | English |
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Title of host publication | CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems |
Publisher | ACM |
Number of pages | 15 |
ISBN (Electronic) | 978-1-4503-9157-3 |
DOIs | |
Publication status | Published - 29 Apr 2022 |
MoE publication type | A4 Conference publication |
Event | ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Virtual, Online, New Orleans, United States Duration: 30 Apr 2022 → 5 May 2022 |
Conference
Conference | ACM SIGCHI Annual Conference on Human Factors in Computing Systems |
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Abbreviated title | ACM CHI |
Country/Territory | United States |
City | New Orleans |
Period | 30/04/2022 → 05/05/2022 |
Keywords
- Action
- Adaptation
- Affordance
- Design
- Interaction
- Machine Learning
- Modeling
- Motion Planning
- Perception
- Reinforcement Learning
- Robotics
- Theory
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