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.
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- 1.
Code will be publicly available at https://github.com/thomasroodnl/active-inference-rhi.
- 2.
Note that in Eq. (5), the prediction error with respect to the internal dynamics \(e_f = \mu ' - f(\mu )\) was simplified to \(e_f = -f(\mu )\) under the assumption that \(\mu ' = 0\). In other words, we assume no dynamics on the internal variables.
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Rood, T., van Gerven, M., Lanillos, P. (2020). A Deep Active Inference Model of the Rubber-Hand Illusion. In: Verbelen, T., Lanillos, P., Buckley, C.L., De Boom, C. (eds) Active Inference. IWAI 2020. Communications in Computer and Information Science, vol 1326. Springer, Cham. https://doi.org/10.1007/978-3-030-64919-7_10
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