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Bayesian and Anti-Bayesian Biases in Sensory Integration for Action and Perception in the Size–Weight Illusion

机译:尺寸-重量幻觉中的动作和感知的感觉统合中的贝叶斯和反贝叶斯偏见

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摘要

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.
机译:哪一个较重:一磅铅或一磅羽毛?这个经典的技巧问题掩盖了一个简单但令人惊讶的事实:抬起铅时,磅的磅会感觉更重-这种现象被称为“尺寸-重量幻觉”。为了估计物体的重量,我们的CNS结合了两个不完美的信息源:基于物体的外观的事先期望以及从举起物体中获得的直接感官信息。贝叶斯定理(或贝叶斯定律)定义了统计上最优的方式来组合多个信息源,以实现最大程度的准确估计。在这里,我们询问将这些信息源进行组合的机制是否能为感知和行动产生统计上最优的权重估计。我们首先研究了受试者在一只手从另一只手上取下一只手时能保持稳定的能力,这种条件下,有关该物体重量的感官信息有时会与基于其大小的先前期望相抵触。由于稳定支撑手的能力取决于考虑提升时间和物体重量的电机命令的生成,因此可以使用手运动来衡量电机系统估计重量时的偏差。我们发现,这些运动系统权重估计以贝叶斯统计上的最优方式反映了先前期望与实时本体感受信息的集成,从而消除了意外的感官信息。这会产生一种运动量大小的重量幻觉,使重量估计始终偏向先前的期望。相反,当受试者比较两个物体的重量时,他们的感知违反了贝叶斯定律,夸大了意想不到的感官信息的价值。这会产生一个感知尺寸-重量的错觉,使重量感知偏离先前的预期。我们将这种效应称为“反贝叶斯”,因为这种偏向与贝叶斯积分中的偏向相反。我们的发现表明,在神经系统中共存在两种根本不同的策略,可以将先前的期望与感觉信息整合在一起,以进行体重估计。

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