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.

中文翻译：

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橡胶手错觉的扩展贝叶斯模型。

人体感觉非常灵活——在橡胶手错觉 (RHI) 中，精确执行的视觉触觉刺激会引发对假手的所有权感。普遍的共识是，对于可以以这种方式合并的对象存在某些自上而下的语义限制：特别是，要体现的对象应该在结构上类似于存储在内部身体模型中的视觉表示。然而，经验证据表明，所有权意识可能会扩展到在形态和结构上截然不同的物体（例如，机械臂），而关于外观相关性的假设缺乏直接的经验支持。概率多感官整合方法构成了一个有前途的替代方案。然而，最近的 RHI 贝叶斯模型限制了影响可能性和先验概率分布的可能因素。在本文中，我分析了如何扩展 RHI 的贝叶斯模型。引入基于皮肤的空间信息可以解释引起 RHI 的感觉信号的交叉补偿。此外，根据（1）感觉线索的相关性（耦合强度）的内部学习模型，（2）时间绑定窗口的范围和（3）个人空间的扩展，添加贝叶斯耦合先验将允许量化个人倾向整合发散的视觉和体感信号。