A deep active inference model of the rubber-hand illusion

Abstract

Understanding how perception and action deal with sensorimotor conflicts, such as the rubber-hand illusion (RHI), is essential to understand how the body adapts to uncertain situations. Recent results in humans have shown that the RHI not only produces a change in the perceived arm location, but also causes involuntary forces. Here, we describe a deep active inference agent in a virtual environment, which we subjected to the RHI, that is able to account for these results. We show that our model, which deals with visual high-dimensional inputs, produces similar perceptual and force patterns to those found in humans.

arXiv comment8 pages, 3 figures, Accepted in 1st International Workshop on Active Inference, in Conjunction with European Conference of Machine Learning 2020. The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-64919-7_10

References in corpus (4)

• Optimal control as a graphical model inference problem
• Adaptive robot body learning and estimation through predictive coding
• End-to-End Pixel-Based Deep Active Inference for Body Perception and Action
• Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot

Cited by in corpus (2)

• Robot in the mirror: toward an embodied computational model of mirror self-recognition
• Deep Active Inference for Partially Observable MDPs