Extending Bayesian Models of the Rubber Hand Illusion

Human body sense is surprisingly flexible — in the Rubber Hand Illusion (RHI), precisely administered visuo-tactile stimulation elicits a sense of ownership over a fake hand. The general consensus is that there are certain semantic top-down constraints on which objects may be incorporated in this way: in particular, to-be-embodied objects should be structurally similar to a visual representation stored in an internal body model. However, empirical evidence shows that the sense of ownership may extend to objects strikingly distinct in morphology and structure (e.g., robotic arms) and the hypothesis about the relevance of appearance lacks direct empirical support. Probabilistic multisensory integration approaches constitute a promising alternative. However, the recent Bayesian models of RHI limit too strictly the possible factors influencing likelihood and prior probability distributions. In this paper, I analyse how Bayesian models of RHI could be extended. The introduction of skin-based spatial information can account for the cross-compensation of sensory signals giving rise to RHI. Furthermore, addition of Bayesian Coupling Priors, depending on (1) internal learned models of relatedness (coupling strength) of sensory cues, (2) scope of temporal binding windows, and (3) extension of peripersonal space, would allow quantification of individual tendencies to integrate divergent visual and somatosensory signals. The extension of Bayesian models would yield an empirically testable proposition accounting comprehensively for a wide spectrum of RHI-related phenomena and rendering appearance-oriented internal body models explanatorily redundant.