Modeling body state-dependent multisensory integration.

The brain often integrates multisensory sources of information in a way that is close to the optimal according to Bayesian principles. Since sensory modalities are grounded in different, body-relative frames of reference, multisensory integration requires accurate transformations of information. We have shown experimentally, for example, that a rotating tactile stimulus on the palm of the right hand can influence the judgment of ambiguously rotating visual displays. Most significantly, this influence depended on the palm orientation: when facing upwards, a clockwise rotation on the palm yielded a clockwise visual judgment bias; when facing downwards, the same clockwise rotation yielded a counterclockwise bias. Thus, tactile rotation cues biased visual rotation judgment in a head-centered reference frame. Recently, we have generated a modular, multimodal arm model that is able to mimic aspects of such experiments. The model co-represents the state of an arm in several modalities, including a proprioceptive, joint angle modality as well as head-centered orientation and location modalities. Each modality represents each limb or joint separately. Sensory information from the different modalities is exchanged via local forward and inverse kinematic mappings. Also, re-afferent sensory feedback is anticipated and integrated via Kalman filtering. Information across modalities is integrated probabilistically via Bayesian-based plausibility estimates, continuously maintaining a consistent global arm state estimation. This architecture is thus able to model the described effect of posture-dependent motion cue integration: tactile and proprioceptive sensory information may yield top-down biases on visual processing. Equally, such information may influence top-down visual attention, expecting particular arm-dependent motion patterns. Current research implements such effects on visual processing and attention.