Disentangling Reafferent Effects by Doing Nothing

An agent's ability to distinguish between sensory effects that are self-caused, and those that are not, is instrumental in the achievement of its goals. This ability is thought to be central to a variety of functions in biological organisms, from perceptual stabilisation and accurate motor control, to higher level cognitive functions such as planning, mirroring and the sense of agency. Although many of these functions are well studied in AI, this important distinction is rarely made explicit and the focus tends to be on the associational relationship between action and sensory effect or success. Toward the development of more general agents, we develop a framework that enables agents to disentangle self-caused and externally-caused sensory effects. Informed by relevant models and experiments in robotics, and in the biological and cognitive sciences, we demonstrate the general applicability of this framework through an extensive experimental evaluation over three different environments.

Authors

• Benedict Wilkins Royal Holloway University of London
• Kostas Stathis Royal Holloway University of London

Keywords

• CMS: Other Foundations of Cognitive Modeling & Systems
• MAS: Agent/AI Theories and Architectures
• MAS: Modeling other Agents
• ML: Causal Learning
• ML: Representation Learning