Cognitive Models of Limb Embodiment in Structurally Varying Bodies: A Theoretical Perspective

Abstract

Using the seminal rubber hand illusion and related paradigms, the last two decades unveiled the multisensory mechanisms underlying the sense of limb embodiment, that is, the cognitive integration of an artificial limb into one's body representation. Since also individuals with amputations can be induced to embody an artificial limb by multimodal sensory stimulation, it can be assumed that the involved computational mechanisms are universal and independent of the perceiver's physical integrity. This is anything but trivial, since experimentally induced embodiment has been related to the embodiment of prostheses in limb amputees, representing a crucial rehabilitative goal with clinical implications. However, until now there is no unified theoretical framework to explain limb embodiment in structurally varying bodies. In the present work, we suggest extensions of the existing Bayesian models on limb embodiment in normally-limbed persons in order to apply them to the specific situation in limb amputees lacking the limb as physical effector. We propose that adjusted weighting of included parameters of a unified modeling framework, rather than qualitatively different model structures for normally-limbed and amputated individuals, is capable of explaining embodiment in structurally varying bodies. Differences in the spatial representation of the close environment (peripersonal space) and the limb (phantom limb awareness) as well as sensorimotor learning processes associated with limb loss and the use of prostheses might be crucial modulators for embodiment of artificial limbs in individuals with limb amputation. We will discuss implications of our extended Bayesian model for basic research and clinical contexts.

Human-Robot Body Experience: An Artificial Intelligence Perspective

“…1. As suggested by Bliek et al [4], a top-down modulation of the (sub)models' prior knowledge through learning methods seems reasonable.…”

“…Thinking of assistive robots, e.g., prostheses, we could use cognitive models to probe whether the device is embodied or not and how we could adapt to improve the users' body experiences via control [22]. Still, a theoretical framework to explain limb embodiment is lacking, particularly when considering structurally varying bodies, e.g., in case of amputation [4]. Modular modeling frameworks combining bottom-up multisensory integration with cognitive reasoning and top-down adaptation, e.g., learning a person's predispositions and experiences, could be an approach to represent body experience flexibly [4,13].…”

“…Still, a theoretical framework to explain limb embodiment is lacking, particularly when considering structurally varying bodies, e.g., in case of amputation [4]. Modular modeling frameworks combining bottom-up multisensory integration with cognitive reasoning and top-down adaptation, e.g., learning a person's predispositions and experiences, could be an approach to represent body experience flexibly [4,13]. Beyond applications in assistive robotics, this might also help to endow humanoid robots with human-like body representations, which could improve their interaction capabilities and provide them with more human-like action-perception versatility [22].…”

“…So far, Bayesian cognitive models of multimodal sensory processing in rubber limb illusion paradigms have been shown to roughly predict experimental outcomes [21,23]. However, key issues of cognitive body experience models, i.e., limitations of accuracy, individualizability, and online capabilities, remain despite promising suggestions for methodical extension [4,13,22].…”

“…Fig. 1 presents a potential structure for artificial body intelligence based on the considerations of Bliek et al [4]. A bottom-up path describes the processes of sensation and perception, multisensory integration, and cognition: multimodal sensory data is gathered and integrated to a common sensory representation [16] to update the situation-specific body experience.…”