Hypothesis for Decision Making

I want to share with you all a page of my notebook.


Previously I came to the conclusion that the purpose of the brain is to control the body. Everything that happens in the brain must eventually impact your actions in some way, or else that brain activity would be quite literally useless. “The brain controls the body” is a conceptual framework for understanding what the brain does and why it does it. To that end, I’ve been studying the principals of closed loop control.

The research question that I will discuss today is: “What is the difference between decision making and closed loop control?”

First let’s write out the equations for closed loop control:

Error = Setpoint - SensoryFeedback
Action = f ( Error )

Where Setpoint and SensoryFeedback are vectors of real numbers, with the same units.
Where Action is a vector of real numbers, but with possibly different units/dimensions than the setpoint and sensory feedback.

Second, let’s look at an example of a decision:

A mouse sees a predator. It could either:
 * Run home to its underground burrow,
 * Climb a tree,
 * Freeze and hope not to be seen.

Third, notice that these actions are all distinct categories, not real-valued numbers. You can not do arithmetic on these actions in the same way that you could for a normal closed loop controller.
Also these actions are all mutually exclusive, the mouse must pick one of these actions.
These are the defining features of decision making.

Note: the setpoint and sensory feedback will never be categorical like this. If you have multiple distinct concerns, each with their own setpoint and sensory feedback, then you can combine all of their errors into one big multidimensional error term. Then you can reason about which action satisfies all of the concerns at once. You can always have multiple desires, even if you can only do one action.

Finally, context is critical for good making decisions. In our example, if the mouse escapes by climbing a tree then the tree needs to be nearby and the predator can’t be a cat because cats can also climb trees. So let’s update our closed loop control equations to:

Error = Setpoint - SensoryFeedback
Action = f ( Error, Context )

Now, the function f() can not be a simple set of synapses. It must at least be a set of cells to combine the inputs from both the context and the error. The context alone does not cause actions, and likewise the error alone does not cause actions. You can think of the error as the motivation to solve problems, which needs to be combined with the contextual ability to perform specific actions, and the result is the actions that you want to perform.

3 Likes

“Climbing a tree” is not a single decision comparable with a closed loop control. To indulge your reasoning, I’d say each of the fibres of a muscle, connected by an individual set of nerves, needs its own controller.

Maybe at a higher level, another controller could steer one limb. And on another level, maybe a combination of limb movements could steer directional motion. But to generate a complex plan to get away from danger, sounds like a lot of processing to be managed by a single controller.

Actually, I do think it’s a great idea for controlling fibres and maybe muscles. I’ve been told that nerves controlling muscle fibres do so in pulses. So you might actually be able to simulate this with binary logic. Well, stochastic binary logic.

1 Like

Lets assume that action in your equation is the final output. The sensory feedback itself gives multiple answers, like seeing a predator that far away it gives feedback as ‘predator was in far-distance, so it takes some time’(of course, it was a complex process idk a mouse could do this). Lets say after knowing there was a sometime the mouse decides to go underground home but suddenly another predator comes Which was close now the feedback system tells the mouse to run faster in a direction that was in no sight of predator.
What im trying to say is decision are sequential, running to north decision was sequence of decisions not a single one. We have to sum the bits of information in our brain to get the actual decision making in case we are trying to understand the brains decision making. And sensory feedback itself gives bits of solution to the problems because of previous exposure and some natural fear emotions which was linked To reflex’s. Reflex are the only fear that childs know fasting running object induce fear like they are already there in our brain even though we don’t have experience of that fear… I may be wrong just saying…

2 Likes

It could be that he’s referring to closed loop control in a not too literal kind of way. Maybe this means the system being envisioned uses reinforcement learning somehow to determine the ‘Setpoint’ and ‘Action’?

Dmac, what is your book called? It’ll be interesting to see an SDR based implementation of it.

2 Likes

This is from my personal notes, not a published book.

2 Likes

take a look at : “Affordance competition hypothesis” and “Phylogenetic refinement” of Paul Cisek Ph.D.

lots of documents and some video files at Paul Cisek

apparently decision making is a serial connected multitude of closed loop controls

6 Likes

You might enjoy this podcast episode:

Henry proposes the brain is one big hierarchical set of control loops, trying to control their output with respect to internally generated reference signals. He was inspired by control theory, but points out that most control theory for biology is flawed by not recognizing that the reference signals are internally generated. Instead, most control theory approaches, and neuroscience research in general, assume the reference signals are what gets externally supplied… by the experimenter.

3 Likes

Yes! I’m familiar with Yin’s work. In his 2014 paper “How the basal ganglia outputs generate behavior” he proposes something called “transition control”, which is a concept that I never fully understood. What I’m proposing here is an alternative explanation for what the basal ganglia does. Under certain circumstances, these two hypotheses are compatible and would yield the same results.

1 Like

I’d refer back to the brainless walking cat experiment from when times were a little different.
The motion control loop in that instance are obviously shown to be dislocated, so the motion instructions may well be near completely void of any context as to why the motion is triggered.
That may change your function which applies (Error,Context)

1 Like