Causal Inference of Body Ownership in the Posterior Parietal Cortex

How do we come to sense that a hand in view belongs to our own body or not? Previous studies have suggested that the integration of vision and somatosensation in the frontoparietal areas plays a critical role in the sense of body ownership (i.e., the multisensory perception of limbs and body parts as our own). However, little is known about how these areas implement the multisensory integra-tion process at the computational level and whether activity predicts illusion elicitation in individual participants on a trial-by-trial ba-sis. To address these questions, we used functional magnetic resonance imaging and a rubber hand illusion-detection task and fitted the registered neural responses to a Bayesian causal inference model of body ownership. Thirty healthy human participants (male and female) performed 12 s trials with varying degrees of asynchronously delivered visual and tactile stimuli of a rubber hand (in view) and a (hidden) real hand. After the 12 s period, participants had to judge whether the rubber hand felt like their own. As hypothe-sized, activity in the premotor and posterior parietal cortices was related to illusion elicitation at the level of individual participants and trials. Importantly, activity in the posterior parietal cortex fit the predicted probability of illusion emergence of the Bayesian causal inference model based on each participant's behavioral response profile. Our findings suggest an important role for the poste-rior parietal cortex in implementing Bayesian causal inference of body ownership and reveal how trial-by-trial variations in neural sig-natures of multisensory integration relate to the elicitation of the rubber hand illusion.

Automated summary

The study found that activity in the premotor and posterior parietal cortices was related to individual participants and trials in illusion elicitation. Additionally, activity in the posterior parietal cortex matched the predicted probability of illusion emergence of the Bayesian causal inference model based on each participant's behavioral response profile.