Abstract
To control targeted movements, such as reaching to grasp an object or hammering a nail, the brain can use divers sources of sensory information, such as vision and proprioception. Although a variety of studies have shown that sensory signals are optimally combined according to principles of maximum likelihood, increasing evidence indicates that the CNS does not compute a single, optimal estimation of the target's position to be compared with a single optimal estimation of the hand. Rather, it employs a more modular approach in which the overall behavior is built by computing multiple concurrent comparisons carried out simultaneously in a number of different reference frames. The results of these individual comparisons are then optimally combined in order to drive the hand. In this article we examine at a computational level two formulations of concurrent models for sensory integration and compare this to the more conventional model of converging multi-sensory signals. Through a review of published studies, both our own and those performed by others, we produce evidence favoring the concurrent formulations. We then examine in detail the effects of additive signal noise as information flows through the sensorimotor system. By taking into account the noise added by sensorimotor transformations, one can explain why the CNS may shift its reliance on one sensory modality toward a greater reliance on another and investigate under what conditions those sensory transformations occur. Careful consideration of how transformed signals will co-vary with the original source also provides insight into how the CNS chooses one sensory modality over another. These concepts can be used to explain why the CNS might, for instance, create a visual representation of a task that is otherwise limited to the kinesthetic domain (e.g., pointing with one hand to a finger on the other) and why the CNS might choose to recode sensory information in an external reference frame.
Highlights
Reaching to grasp an object requires that the CNS compare the position and orientation of the object with the position and orientation of the hand in order to generate a motor command that will bring the hand to the object
According to the principles of maximum likelihood estimation, sensory signals that contain redundant information should be combined based on the expected variability of each so as to maximize the probability of producing a value close to the true value of what is being measured
The position and orientation of the object and of the hand must be effectively subtracted at some level, be it to compute a movement vector during task planning or to apply corrective actions based on real-time feedback during the course of the movement.This aspect of the task immediately brings to mind two additional issues that must be resolved: (1) To compare the position and orientation of two entities, sensory information about each must be expressed in a common coordinate frame
Summary
Reaching to grasp an object requires that the CNS compare the position and orientation of the object with the position and orientation of the hand in order to generate a motor command that will bring the hand to the object. According to the principles of maximum likelihood estimation, sensory signals that contain redundant information should be combined based on the expected variability of each so as to maximize the probability of producing a value close to the true value of what is being measured This concept has been used with success in recent years to explain how humans combine different sources of sensory information to generate robust estimates of the position, size and orientation of external objects (Landy et al, 1995; Ernst and Banks, 2002; Kersten et al, 2004; Kording et al, 2007). Where and how are the principles of maximum likelihood applied? In this article we will contrast two possible models of sensor fusion, which we will call convergent and concurrent, as illustrated in Figure 1 for the task of hitting a nail with a hammer
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