If you’ve been anywhere around YouTube or any TED talks recently, you’ll probably have heard the idea that your brain hallucinates your conscious reality. Simply put, your senses aren’t good enough to give you a perfect picture of reality as it is now, so your brain fills in the gaps by creating a model of the world in your mind and it uses your senses to direct that model, to keep it grounded in reality (whatever that is). This internal model of the world is what you use to make decisions.
Probably the most famous example of the fallibility of our senses is the blind spot we all have in our eyes, where the optic nerve enters the eye. Yet we don’t experience this blind spot. What we experience is an internal model of the world, and the lack of visual information at the eye’s blind spot simply has no effect on our internal model. Certainly, something happening in our environment can be obscured by the blind spot, but even with one eye closed we don’t perceive a big hole in our visual field.
Have you ever seen something out of the corner of your eye, or at a distance, and thought it was something else? I routinely experience this when I’m driving to work in the dark and passing a forested area beside the road. A weirdly shaped bush or a branch can be recognized as a deer about to jump out on the road. I instinctively tap the brakes, only to realize it’s not a deer, but a bush. When this happens I’m inclined to say I “thought it was a deer, but I was mistaken.” In truth, I literally saw a deer, and in my internal model of the world it was a deer, fully formed, ready to leap onto the road, and I reacted accordingly. Occasionally this happens, and as I get closer, I confirm it really is a deer.
A while ago, I wrote an article on this blog about part tracking, and if you don’t remember reading it, I suggest you go back and take a look (it’s a rather short read). No, honestly, go back and read it.
For the most part, in the world of industrial automation we’re blessed with very reliable sensors. Thru-beam and proximity sensors are remarkably reliable. But as I discussed in the part tracking post, even our best sensors can sometimes send us erroneous data. There are blind spots in our industrial sensors.
Novice PLC programmers are obsessed with inputs and outputs. One of the earliest exercises I like to give to a new PLC programmer is to program a light to flash continuously. More than half will start their program with an input (usually a pushbutton). Clearly this is incorrect… it’s a flashing light, so the only input is time. The solution requires an internal model of time (in our case the on-delay timer). But to someone fresh out of their first PLC programming course, inputs and outputs are king.
In my part tracking post, I literally ask you to “focus on part tracking”. Your first job, as a PLC programmer, is to create a simplified model of the world in the PLC’s internal state and maintain the accuracy of this model using the inputs. I call this “part tracking”, but it’s literally the PLC’s hallucination of its world. The internal model of the world stored in the PLC is the proper foundation of the decisions the PLC will make. This is the core idea behind of the patterns of ladder logic programming. The mission pattern, step pattern, and five rung patterns are all on the output side of the program. They are the actuators of a decision that was made in the mission controller based on the model (a.k.a. part tracking).
Now here’s a bonus thought… is the PLC’s internal model an accurate representation of reality? Well, yes, your goal is to make it more accurate than our sensors. But is it a complete representation of reality? Clearly not. We pare down the model to the simplest, most fundamental level. We only include the bare minimum of what we need for the machine to do its job.
So… what could that mean about our own internal model of reality? If we evolved this consciousness (or I guess if it was designed like we designed the PLC program), there’s a good point to be made that accuracy has a lot of value. Our internal model of reality is likely more accurate than our senses can perceive. But is it complete? Almost certainly not. Modeling a part of reality that we don’t need in order to survive is just wasteful.