Rediscovering Affordance: A Reinforcement Learning Perspective

Yi Chi Liao, Kashyap Todi, Aditya Acharya, Antti Keurulainen, Andrew Howes, Antti Oulasvirta

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)


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 languageEnglish
Title of host publicationCHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Number of pages15
ISBN (Electronic)978-1-4503-9157-3
Publication statusPublished - 29 Apr 2022
MoE publication typeA4 Conference publication
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Virtual, Online, New Orleans, United States
Duration: 30 Apr 20225 May 2022


ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
Country/TerritoryUnited States
CityNew Orleans


  • Action
  • Adaptation
  • Affordance
  • Design
  • Interaction
  • Machine Learning
  • Modeling
  • Motion Planning
  • Perception
  • Reinforcement Learning
  • Robotics
  • Theory


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