Precision not prediction: Body-ownership illusion as a consequence of online precision adaptation under Bayesian inference
Humans can experience body-ownership of new (external) body parts, for instance, via visuotactile stimulation. While there are models that capture the influence of such body illusions in body localization and recalibration, the computational mechanism that drives the experience of body-ownership of external limbs is still not well understood and under discussion. Here, we describe a mathematical model of this phenomenon via uncertainty minimization. Using the Rubber Hand Illusion (RHI) as a proxy, we show that to properly estimate one's arm position, an agent infers the least uncertain world model that explains the observed reality through online adaptation of the signals' relevance, i.e., the precision parameters (inverse variance of the prediction error signal). Our computational model describes that the illusion is triggered when the sensory precision quickly adapts to account for the increase of sensory noise during the physical stimulation of the rubber hand due to the real hand occlusion. This adaptation produces a change in the uncertainty of the body position estimates yielding to a switch of the perceived reality: the 'rubber hand is the agent's hand' becomes the most plausible model (i.e., it has the least posterior uncertainty). Overall, our theoretical account, along with the numerical simulations provided, suggests that while the perceptual drifts in body localization may be driven by prediction error minimization, body-ownership illusions are a consequence of estimating the signals precision, i.e. the uncertainty associated with the prediction error.