Perceptual precision of passive body tilt is consistent with statistically optimal cue integration
Perceptual precision of passive body tilt is consistent with statistically optimal cue integration
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DOI：
10.1152/jn.00073.2016
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发表时间：
2017-05-01
影响因子：
2.5
通讯作者：
Merfeld, Daniel M.
中科院分区：
文献类型：
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作者：
Lim, Koeun;Karmali, Faisal;Merfeld, Daniel M.
在做出知觉决策时，人类已被证明能够最优地整合独立的、有噪音的多感官信息，匹配最大似然(ML)限制。这种最大似然估计器为感知精度(即最小阈值)提供了理论上的限制。然而，大脑如何结合两个相互作用(即不独立)的感觉线索仍然是一个悬而未决的问题。为了研究组合相互作用的感觉信号时达到的精确度，我们测量了6名正常人在0至5赫兹之间的知觉侧滚倾斜和侧滚旋转阈值。初步结果显示，0.2至0.5赫兹之间的翻滚倾斜阈值显著低于ML估计器的预测，该估计器仅包括不相互作用的前庭贡献。在这篇文章中，我们展示了其他线索(例如，躯体感觉)以及感觉和身体动力学的内部表征如何独立地有助于观察到的性能提高。简而言之，卡尔曼滤波和最大似然估计器相结合以匹配人类的表现，而非前庭提示的潜在贡献是使用已公布的双侧丢失患者数据来评估的。我们的结果表明，卡尔曼滤波模型仅包括先前证实的管-耳石相互作用(没有前庭提示)可以解释观察到的性能改善，就像包括非前庭贡献的模型一样。新和值得注意的是，我们发现在动态侧滚期间测量的人体全身运动方向识别阈值显着低于通过传统的最大似然加权的侧滚角速度和准静态侧滚倾斜提示预测的阈值。在这里，我们展示了两个模型可以各自匹配这种明显的优于最佳的性能：1)包括体感贡献和2)通过卡尔曼滤波模型包括耳道和耳石线索之间的动态感觉交互作用。
When making perceptual decisions, humans have been shown to optimally integrate independent noisy multisensory information, matching maximum-likelihood (ML) limits. Such ML estimators provide a theoretic limit to perceptual precision (i.e., minimal thresholds). However, how the brain combines two interacting (i.e., not independent) sensory cues remains an open question. To study the precision achieved when combining interacting sensory signals, we measured perceptual roll tilt and roll rotation thresholds between 0 and 5 Hz in six normal human subjects. Primary results show that roll tilt thresholds between 0.2 and 0.5 Hz were significantly lower than predicted by a ML estimator that includes only vestibular contributions that do not interact. In this paper, we show how other cues (e.g., somatosensation) and an internal representation of sensory and body dynamics might independently contribute to the observed performance enhancement. In short, a Kalman filter was combined with an ML estimator to match human performance, whereas the potential contribution of nonvestibular cues was assessed using published bilateral loss patient data. Our results show that a Kalman filter model including previously proven canal-otolith interactions alone (without nonvestibular cues) can explain the observed performance enhancements as can a model that includes nonvestibular contributions.NEW & NOTEWORTHY We found that human whole body selfmotion direction-recognition thresholds measured during dynamic roll tilts were significantly lower than those predicted by a conventional maximum-likelihood weighting of the roll angular velocity and quasistatic roll tilt cues. Here, we show that two models can each match this "apparent" better-than-optimal performance: 1) inclusion of a somatosensory contribution and 2) inclusion of a dynamic sensory interaction between canal and otolith cues via a Kalman filter model.