Working Memory

I wrote a few things while I was on vacation this summer, but as I was without a computer these thoughts have been sitting on a notepad for a while now. Thus, this post along with the others that I plan to post today/tomorrow were not all from the same sitting, but rather are transcriptions from my notepad.

First, from reading Paul Glimcher's book, I reached the hypothesis (along with him I believe, but I do not speak for him or his book) that computational efficency of the nervous system is the asymptotic solution to evolution. Form follows the environmental function and is constantly getting better. Thus, our evolution is in an asymptotic sense closing in on the limits of computability. "Evolution acts on the algorithmic techniques by which the brain generates behavior" (Glimcher 2004).

Working memory is such that our "changing states of memory, as well as our estimates of the values of any course of action, reflect this dynamic property of our environment" (Glimcher 2004). The dynamic property referred to here is that prior probabilities in the real world that govern decision making are constantly changing. How the brain maps experience through a computation to a prior probability must be understood. Essentially, we can think of this as: sensory event -> Black Box -> p_new = f(p_old, information_new); and in turn: p_old = f(experience). It is the "Black Box" inside the neuronal structure of the brain that will explain how a decision is really made. We can determine some threshold probability, denoted Q, such that once p_event(A),good > Q, then we will be willing to engage in said event as we are willing to take the chance that it will be "good" for our inclusive fitness. Similarly, p_event(B),bad > Q would imply we will ensure decisions are made to prevent engaging in event(B). As we build a complex map of nearly all possible behaviors, we can see how complex, long-term goals are driving by probabilities stored in a working memory. But these, like the world, are dynamic.