Influence of sensory modality and control dynamics on human path integration

Abstract

Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.

Dynamical latent state computation in the male macaque posterior parietal cortex

“…In principle, it is possible to use the learned world model to plan joystick movements ahead of time without any sensory feedback. Although we know from manipulation experiments that monkeys relied on sensory feedback (optic flow), the contribution of autonomous strategies to navigation is likely non-negligible 35 , 66 , 67 . Planning-based computations, which require reasoning about the consequences of action sequences via mental simulation, are thought to be performed in the prefrontal cortex (PFC) 68 , 69 .…”

Predictive steering: Integration of artificial motor signals in self-motion estimation

“…Our study is one among the few recent studies that tested self-motion perception under a direct coupling between the actions of the participants and the sensory feedback (25)(26)(27). These recent experiments imposed fewer artificial constraints than the traditional open-loop psychophysical paradigms on self-motion perception (e.g., [37][38][39][40][41].…”

“…In a recent study by Stavropoulos et al (27), human participants controlled their self-displacement on a motion platform to navigate to a target with and without the presence of concurrent optic flow. They found that steering behavior in darkness was biased (i.e., participants undershot the target location), and therefore concluded that participants could not accurately estimate their self-motion and update their internal model based on the vestibular cues alone.…”

“…In the present study we address this issue in humans, testing whether and how steering-related signals are used in self-motion perception. Recent virtual reality experiments examined how humans (and monkeys) virtually navigated to a memorized location by integrating optic flow generated by their own joystick movements (25)(26)(27). Biases in their steering depended on optic flow density, as a marker of the reliability of sensory evidence, and the control gain of the joystick, as a measure of the internal model prediction of the optic flow, suggesting that the brain combined both signals in the percept of non-vestibular self-motion.…”

“…Biases in their steering depended on optic flow density, as a marker of the reliability of sensory evidence, and the control gain of the joystick, as a measure of the internal model prediction of the optic flow, suggesting that the brain combined both signals in the percept of non-vestibular self-motion. However, the authors mainly focused on the processing of visual information, and the role of the vestibular sense was only studied under continuously changing control dynamics of the joystick (27). How the brain formed an internal model to predict the vestibular self-motion signal thus remains unknown.…”