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.
摘要：
路径积分是一种感觉运动计算，可用于通过积分自运动线索来推断潜在的动态状态。我们使用新颖的运动提示算法研究了感官观察（视觉/前庭）和潜在控制动力学（速度/加速度）对人体路径集成的影响。感觉形态和控制动力学在试验中都是随机变化的，当参与者控制操纵杆引导到虚拟现实中记忆的目标位置时。只有当参与者控制他们的加速度时，视觉和前庭转向提示才允许相当的准确性，表明前庭信号，靠自己,在没有持续加速的情况下，无法支持准确的路径整合。然而，在所有条件下的性能反映了未能完全适应基础控制动态的变化，这一结果很好地解释了动力学估计中的偏差。这项工作证明了尽管有连续的感官反馈，但不正确的控制动力学内部模型如何影响易变环境中的导航。
