The paper brought to mind a famous study by Libet: tried to measure when decision-making happens and its relationship to conscious awareness. Libet claimed to have shown that most decisions happen prior to conscious awareness of them. One thing he argued was that this showed that we lack the kind of control required for free will. The picture seemed to suggest that conscious awareness is epiphenomenal.
Many people in action theory think that the Libet study did not show what Libet said it did.
Different theories of attention:
Bottom-up: what we pay attention to is out of our conscious control, things we have developed (evolutionarily) mechanisms to pay attention to.
But must admit that memory, not just evolution, plays a role (e.g., if you remember that something is dangerous, it will grab your attention).
Top-down: sure there are some things that can grab your attention, but there are things you can do to block out things that grab your attention (e.g., remembering that you are looking for someone in a red sweater in a crowded room); and there must be some top-down control that alters the mechanism that determines what you are paying attention to.
If the leech had top-down control, it could alter the way that the stimulus responded.
Some people claim that it is purely top-down (e.g., a kid with no experience with fire will be drawn to it, not repelled from it, until she learns it is dangerous).
The main issue is how they relate to each other in terms of working memory.
Does this issue relate to David Marr’s analysis, which we talked about a while ago?
Perhaps. Proponents of bottom-up approach skips a role for working-memory. Top-down approach claims a big role for working memory—i.e., the algorithm goes through your working memory.
The sticking point is whether you have any conscious control over the relevant processes. (e.g., conscious awareness affecting decisions).
How does this connect with what we are talking about?
The issues raised in this article and the previous one point to the conclusion that the problem of reverse engineering the human brain is not simply a matter of big data. There are enormous complexities—plasticity, figuring out the role of single and groups of neurons in behavioral outputs, etc.
The upshot is this: there are many complex issues to be worked out about the role of neurons and groups of neurons in the functional outputs of our brain, even given a complete map of the neuronal structure of the brain.
In this article: we have a central pattern generator, such that given a simple stimulus, we have a response. Once the choice is made, it goes on its own. But what choice it makes cannot be predicted from the central pattern generator. So it is unclear what the choice depends on. Once the mechanism is kick-started, we can tell what will happen. But what kick-starts the mechanism?
This generalizes: what accounts for our decision to begin walking with our right foot or left foot?
The paper seems to support a top-down approach: there is some control over when the mechanism becomes engaged, even thought the behavior unfolds without need for conscious control after the mechanism has been engaged (e.g., chewing is like this. So is walking—once started, you’ll go until your brain tells you to stop).
In the leech case: it seems form this study that what choice is made, which mechanism gets selected, swimming or crawling, is not determined by neurons internal to the mechanism that produces these behaviors. There is something else that determines which choice gets made (perhaps rest state prior to stimulus).
But remember: the neurons internal to the mechanism could very well overlap with other systems, involved in multiple mechanisms.
What we have here is a very simple brain, a well-defined, simple mechanism, a choice between two behaviors given a single stimulus, and yet we still cannot predict with accuracy what will result.
This makes it look very doubtful that we will be able to predict human behavior from a good understanding of the structure of the human brain anytime soon. Those predicting uploading in the near future seem to be way too optimistic.
The conclusion of this paper: either (i) choice depends on rest state prior to stimulus or (ii) the system is reset each time and then behaves non-deterministically after stimulus.
If the hypothesis is correct hat the behavioral output depends on the rest state prior to the stimulus, then it seems in principle possible to acquire the required information for predictive success.
But how do you define rest state? Of the whole system? Of the mechanism?
What about plasticity and changes in connective patterns? When does one neuron inhibit another?
But, given enough trials, shouldn’t we be able to rule out different possibilities and fine-tune our predictive models?