Rediscovering affordance: a reinforcement learning perspective

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationCHI '22
Subtitle of host publicationCHI Conference on Human Factors in Computing Systems
EditorsSimone Barbosa, Cliff Lampe, Caroline Appert, David A. Shamma, Steven Drucker, Julie Williamson, Koji Yatani
PublisherAssociation for Computing Machinery (ACM)
Number of pages15
ISBN (Print)9781450391573
DOIs
Publication statusPublished - 29 Apr 2022
EventCHI '22: CHI Conference on Human Factors in Computing Systems - New Orleans, United States
Duration: 29 Apr 20225 May 2022

Publication series

NameHuman factors in computing systems
ISSN (Print)1062-9432

Conference

ConferenceCHI '22
Country/TerritoryUnited States
CityNew Orleans
Period29/04/225/05/22

Bibliographical note

Funding Information:
This project is funded by the Department of Communications and Networking (Aalto University), Finnish Center for Artifcial Intelligence (FCAI), Academy of Finland projects Human Automata (Project ID: 328813) and BAD (Project ID: 318559), and HumaneAI. We thank John Dudley for his support with data visualization and all study participants for their time commitment and valuable insights.

Publisher Copyright:
© 2022 ACM.

Keywords

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

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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