Causal inference for spatial constancy across whole body-motion

The brain uses self-motion information to internally update egocentric representations of locations of remembered world-fixed visual objects. If a discrepancy is observed between this internal update and reafferent visual feedback, this could be due to either an inaccurate update or because the object has moved during the motion. To optimally infer the object’s location it is therefore critical for the brain to estimate the probabilities of these two causal structures and accordingly integrate and/or segregate the internal and sensory estimates. To test this hypothesis, we designed a spatial updating task involving passive whole-body translation. Participants, seated on a vestibular sled, had to remember the world-fixed position of a visual target. Immediately after the translation, the reafferent visual feedback was provided by flashing a second target around the estimated “updated” target location, and participants had to report the initial target location. We found that the participants’ responses were systematically biased toward the position of the second target position for relatively small but not for large differences between the “updated” and the second target location. This pattern was better captured by a Bayesian causal inference model than by alternative models that would always either integrate or segregate the internally-updated target location and the visual feedback. Our results suggest that the brain implicitly represents the posterior probability that the internally updated estimate and the visual feedback come from a common cause, and use this probability to weigh the two sources of information in mediating spatial constancy across whole-body motion.