Bayesian and "anti-Bayesian" biases in sensory integration for action and perception in the size-weight illusion.
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.
Brayanov
Jordan B
JB
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Smith
Maurice A
MA
D000465:Algorithms; D001499:Bayes Theorem; D002138:Calibration; D005260:Female; D006801:Humans; D007088:Illusions / Q000502:physiology*; D008297:Male; D011187:Posture / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology*; D012858:Size Perception / Q000502:physiology*; D014110:Touch / Q000502:physiology; D014892:Weight Perception / Q000502:physiology*; D055815:Young Adult