Body Representation as a Predictive Multisensory System: From Disruption to Adaptive Refinement

Body representation is a dynamic model that emerges from the integration of sensory signals, motor processes, and self-related representations. Far from a static map, it constitutes a continuously updated representation that allows the brain to anticipate, monitor, and adapt bodily states during environmental interactions. This model, shaped by ongoing interchange among sensory inputs, motor intentions, and internal bodily signals, supports perception, action, and bodily self-awareness. This thesis examines variations in body representation across different contexts, ranging from neurological impairments to everyday variability and expertise-related refinements. The first study analyzes anosognosia for hemiplegia following stroke. Lesion mapping and associated deficits reveal that unawareness of paralysis stems from disruptions in distributed networks involved in monitoring action and internal bodily states, rather than from basic motor or sensory deficits. These findings point to the involvement of higher-order mechanisms in monitoring and updating bodily states. The second study systematically reviews evidence on body representation in extreme weight conditions, focusing on anorexia nervosa and obesity. It emphasizes how altered multisensory integration, signal weighting, and interoceptive processing contribute to the persistent distortions of bodily self-awareness observed in these conditions. The third and fourth studies explore body representation in healthy individuals, showing that subtle distortions in body size perception are common and that motor expertise, such as professional piano training, can refine predictive body representations through repeated sensorimotor experience. Collectively, the thesis advances a multisensory, predictive account of bodily self-awareness, suggesting that disruptions, distortions, and refinements of body representation reflect different manifestations of shared underlying mechanisms.