You might have read the Part 1 first before coming to check out this posting. If so, I can comfortably assume that your mind is warmed up for thinking about the limitation of human sensorimotor system #2. If not, here is the summary of the previous postings to bring you up to speed.
Human brain demonstrates its amazing ability to predict and learn while trying to solve the problems related to the limitations of our default motor control system.
One of the limitations that our motor control system has to deal with is that sensory information is very specific to sensory receptors. To get the whole picture of the context we are in, the brain has to integrate information coming from the various sensory receptors. The brain also has to prioritize the information that is most useful to make the most accurate judgement of the situation. This is called the problem of sensory re-weighting.
The sensory integration and re-weighting demands the brain to deal with another limitation that we will go over in this posting.
Let's say that I use my vision to estimate that I am about 70 cm away from my lap top monitor. That is just my cognitive description of visual information that was translated in the form of verbal language. However, the language that is used for neural communication within our sensorimotor system is not that descriptive. Our nervous system literally communicates by changing magnitude and frequency of electrical impulses. If we make a graph of the signal, it would normally look like this.
We can see that the signal contains many random oscillations at each time course. If a sensory receptor sends information like this, the brain has to somehow make sense of this rather chaotic signal. Neuroscientists say that making sense of the signal corrupted by random oscillation is equivalent to transforming a raw signal into smooth curve as shown below.
Image is adapted from Professor Daniel Wolpert's presentation
We can appreciate that there inevitably exists some degree of errors when transforming the raw signal into a smooth curve. The random variability (or oscillation) within the signal is called "signal noise" (not to be confused with the noisy sound). The errors caused by the signal noise is called "signal uncertainty" It is well established that the signal noise and uncertainty occur throughout all of the neural processing within human sensorimotor system.
Faisal and colleagues (2008) described that the sources of the signal noise is deeply embedded even from the molecular level of our body. It is well described by the illustration below in their article. It is speculated many times that this random noise in our system is necessary for healthy metabolism of our cells.
It should be clear now that our nervous system is under a constant demand to deal with random noise and errors that are embedded in all of the neural communication. I also briefly mentioned that the brain has to integrate sensory information from multiple sources to make the most accurate judgement of the situation we are currently in. The problem is that such integration tends to amplify the errors of our neural communication. For example, when perceiving the location of our own hand, the brain combines visual information of the hand location with the propriceptive information (described as somatosensation below) of the hand position. The illustration below shows that these two different sensory receptors use two different reference coordinates for perceiving the hand location. To combine or compare the two different sensory signals, the brain must find a way to deal with such a discrepancy between the two different reference frames. It is known that the signal from one source is often re-calibrated based on the reference coordinate of another source (Cressman & Henriques, 2009). However, the reference coordinate itself is actually not that clearly defined within our nervous system. It is rather another sensory perception of special orientation. Therefore, it is not difficult to appreciate that a re-calibration of a signal would add more error to already existing errors in that signal.
In summary, sensory integration is a very challenging process for the brain as it amplifies the random errors within each sensory signal. The signal error I talked about in this posting is rather specific to the magnitude of the signal, but there is also another error of the signal that is specific to its timing. I will talk about that in the next posting.
Faisal, A. A., Selen, L. P., & Wolpert, D. M. (2008). Noise in the nervous system. Nature reviews neuroscience, 9(4), 292.