Tactile perception as probabilistic inference

人类认为我们感知外部世界的能力是理所当然的，但神经系统是如何完成这一非凡壮举的呢？我们的感觉器官将物理刺激转化为神经元反应的方式相对来说已经很好理解了，就像大脑中感觉神经元的刺激诱发放电模式一样。更不为人所知的是感知的奥秘：在感官激活之后，大脑如何重建物理世界的图像？我们的研究将解决这个重要的神经科学问题，特别是触觉。大脑中的感觉神经元对重复的相同刺激的反应是高度可变的。因此，感知本质上是不确定的，并且涉及到从其可变效应（神经活动模式）推断原因（物理刺激）的具有挑战性的“逆问题”。这个具有挑战性的问题是通过贝叶斯概率推理最佳接近。** 理想的贝叶斯大脑根据先前的经验评估不确定的感官数据。具体来说，它考虑了两种概率：1）观察到的神经活动的概率，给定可能导致它的每个刺激（“可能性”），以及2）每个刺激发生的概率，基于先前的经验（“先验”）。这两个元素结合起来计算每个刺激的最终概率（“后验”）。贝叶斯模型通常假设具有最高最终概率的刺激被呈现给意识作为感知。贝叶斯模型在视觉感知的研究中被频繁应用，但在触觉感知的研究中却很少被应用。在一系列研究中，我们将比较人类的触觉感知和贝叶斯模型的预测。我们的研究将探讨预期（先前的经验），适应（减少神经反应的持续刺激），多指整合（感知物体的感觉同时与几个手指）和触觉探索（主动触摸物体识别）对人类通过触摸感知的能力的影响。这些发现将表明触觉感知是否在贝叶斯意义上是最佳的。 * 这项工作将具有重要的理论和实践意义。这项工作将加深对感知处理的基本科学理解，也可能为下一代触觉图形显示器，触觉虚拟现实系统和机器人技术的构建提供信息。为了设计一个成功的虚拟现实系统，科学家和工程师必须首先了解最终用户如何处理感官输入以获得感知推理。同样，为了创造出类似人类的机器人系统，科学家和工程师必须首先了解人类的感知过程。我们的研究将使这种理解，从而有利于加拿大的科学和工程。

Humans take for granted our ability to perceive the external world, but how does the nervous system accomplish this remarkable feat? The means by which our sensory organs convert physical stimuli into neuronal responses are relatively well understood, as are the stimulus-evoked firing patterns of sensory neurons in the brain. Much less understood is the mystery of perception: following sensory activation, how does the brain reconstruct an image of the physical world? Our research will address this important neuroscientific question with specific reference to the sense of touch.******The response of a sensory neuron in the brain to a repeated, identical stimulus is highly variable. Perception is thus inherently uncertain, and involves the challenging "inverse problem" of inferring a cause (the physical stimulus) from its variable effects (patterns of neural activity). This challenging problem is optimally approached via Bayesian probabilistic inference. ******The ideal Bayesian brain evaluates uncertain sensory data in light of prior experience. Specifically, it considers two probabilities: 1) the probability of the observed neural activity, given each stimulus that could have caused it (the "likelihood"), and 2) the probability of occurrence of each stimulus, based on previous experience (the "prior"). These two elements are combined to calculate the final probability of each stimulus (the "posterior"). Bayesian models typically assume that the stimulus with highest final probability is presented to consciousness as the percept.******Bayesian models have been applied frequently in studies of visual perception but only rarely in studies of tactile perception. In a series of studies, we will compare human tactile perception to the predictions from Bayesian models. Our studies will probe the effects of expectation (prior experience), adaptation (the reduction in neural response to a sustained stimulus), multi-finger integration (perception of objects felt simultaneously with several fingers), and haptic exploration (active touch for object recognition) on the ability of humans to perceive through touch. The findings will indicate whether or not tactile perception is optimal in the Bayesian sense. ******This work will have important theoretical and practical implications. The work will deepen basic scientific understanding of perceptual processing and may also inform the construction of next-generation tactile graphics displays, haptic virtual reality systems, and robotics. In order to design a successful virtual reality system, scientists and engineers must first understand how the end-user processes sensory input to arrive at a perceptual inference. Similarly, in order to create human-like robotics systems, scientists and engineers must first understand human perceptual processing. Our research will enable such understanding, thereby benefitting Canadian science and engineering.**