School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
J Neurophysiol. 2010 Mar;103(3):1518-31. doi: 10.1152/jn.00814.2009. Epub 2010 Jan 20.
Which is heavier: a pound of lead or a pound of feathers? This classic trick question belies a simple but surprising truth: when lifted, the pound of lead feels heavier--a phenomenon known as the size-weight illusion. To estimate the weight of an object, our CNS combines two imperfect sources of information: a prior expectation, based on the object's appearance, and direct sensory information from lifting it. Bayes' theorem (or Bayes' law) defines the statistically optimal way to combine multiple information sources for maximally accurate estimation. Here we asked whether the mechanisms for combining these information sources produce statistically optimal weight estimates for both perceptions and actions. We first studied the ability of subjects to hold one hand steady when the other removed an object from it, under conditions in which sensory information about the object's weight sometimes conflicted with prior expectations based on its size. Since the ability to steady the supporting hand depends on the generation of a motor command that accounts for lift timing and object weight, hand motion can be used to gauge biases in weight estimation by the motor system. We found that these motor system weight estimates reflected the integration of prior expectations with real-time proprioceptive information in a Bayesian, statistically optimal fashion that discounted unexpected sensory information. This produces a motor size-weight illusion that consistently biases weight estimates toward prior expectations. In contrast, when subjects compared the weights of two objects, their perceptions defied Bayes' law, exaggerating the value of unexpected sensory information. This produces a perceptual size-weight illusion that biases weight perceptions away from prior expectations. We term this effect "anti-Bayesian" because the bias is opposite that seen in Bayesian integration. Our findings suggest that two fundamentally different strategies for the integration of prior expectations with sensory information coexist in the nervous system for weight estimation.
一磅铅和一磅羽毛,哪个更重?这个经典的脑筋急转弯背后隐藏着一个简单但令人惊讶的事实:当被提起时,一磅铅会感觉更重——这种现象被称为大小重量错觉。为了估计物体的重量,我们的中枢神经系统结合了两个不完美的信息来源:基于物体外观的先验期望,以及提起它时的直接感官信息。贝叶斯定理(或贝叶斯法则)定义了组合多个信息源以实现最大准确估计的最佳统计方法。在这里,我们询问了组合这些信息源的机制是否会为感知和行动产生统计上最优的重量估计。我们首先研究了在某些情况下,当另一只手从一只手中取出物体时,主体用一只手保持稳定的能力,在这些情况下,有关物体重量的感官信息有时与基于其大小的先验期望相冲突。由于支撑手的稳定性能力取决于生成一个考虑到提升时间和物体重量的运动命令,因此手部运动可以用于通过运动系统来衡量重量估计中的偏差。我们发现,这些运动系统的重量估计以贝叶斯的方式,以统计上最优的方式整合了先验期望和实时本体感觉信息,从而忽略了意外的感官信息。这产生了一种运动大小重量错觉,使重量估计始终偏向先验期望。相比之下,当主体比较两个物体的重量时,他们的感知违反了贝叶斯定律,夸大了意外感官信息的价值。这产生了一种感知大小重量错觉,使重量感知偏离先验期望。我们将这种效果称为“反贝叶斯”,因为偏差与贝叶斯整合中看到的偏差相反。我们的发现表明,在神经系统中,存在两种用于将先验期望与感官信息整合的基本不同策略,用于重量估计。