Consciousness: Signals in the Noise
In February’s blog post, How your Brain is Like an Ant Colony, we discussed how neural networks follow the concept of emergence: when it comes to connections between neurons, much of the order arises by neurons organizing themselves, without top-down direction.
Arne Dietrich, the author of How Creativity Happens in the Brain, writes that some of those networks are hardwired and some are flexible and built in the moment. What determines the strength and intensity of a neural network include “a person’s unique past experience, opinions, preferences, and expertise." He explains that, in the same way "lightning follows the path of least resistance," the strongest connections send the fastest signals, taking over brain regions in a phenomenon called "spreading activation."
The lack of an overall leader makes ant colonies fascinating. But if our own thoughts (activated neural networks) are all just a matter of signal strength, what is the self? How does self-awareness arise?
Oliver Selfridge, a pioneer of artificial intelligence, also known as the "Father of Machine Perception,” posited the seminal idea back in 1959 that it’s basically pandemonium among neural networks until one dominates consciousness, albeit temporarily. This is known, not surprisingly, as the Selfridge Pandemonium model.
Imagine a diverse group of neural networks. Each network is competing to be heard, to send the strongest signal and thus show up in your working memory and achieve conscious awareness. When you find yourself suddenly thinking about baseball, then your baseball neural circuit won out over say, a thought about the long-term effects of climate change.
All the while, the brain's executive control, or EC, exerts pressure from above. This may sound like top-down leadership, but keep in mind that in this case, executive control is not “control” in the classic sense: no part of your EC is consciously weighing the merits of these networks and making informed decisions.
The only metric this process runs on is whether or not a network has been strong enough to broadcast its signal in the past. If there’s a signal that’s succeeded many times before, the EC adds to the effect by suppressing and quieting other competing neural networks. So if you’ve been thinking a lot about the upcoming World Series, your brain is likely to stay with that theme and Executive Control is tamping down thoughts about climate change, politics and what’s on the menu for dinner tonight.
The more often a neural network shows up in your working memory, the more powerful the network becomes, and the more likely it will be called back for an encore. This kind of encore biasing is important because only one neural network is going to rule your conscious airwaves.
This is known as the all-important “frequency of occurrence,” a concept at the heart of Hebb’s rule, “neurons that fire together wire together.” When these neuron networks reach critical mass and show up in working memory, the signal is distributed to all areas of the brain.
Daniel C. Dennett says, “Consciousness is accomplished by a distributed society of specialists that is equipped with a working memory, called a global workspace, whose contents can be broadcast to the system as a whole.”
You’ve been intensely focused on baseball. The time has finally arrived: you flip on the TV and now your entire brain is tuned into the start of the World Series. Every aspect of your sensory and motor system knows it’s game time! The excitement in the crowd is palpable, and you can practically smell the beer and hotdogs up in the stands. But there is no guarantee the neural network dominating your working memory will be able to hold your awareness for long.
The band of neurons might break up under its own weight of individualism, meaning that for a fleeting second you start thinking about work on Monday.
It might be ousted by another more aggressive network of neurons with more powerful connections. Now you’re not only thinking about work, but those emails from your boss piling up in your inbox.
Executive control might also dump a network because something else grabs its attention, like when a speeding fire truck whizzes by your house and suddenly a new thought (‘I wonder what’s on fire?') steals the show away from work or the baseball game.
Your brain’s insula can be understood as the remote control that toggles back and forth between what will be spotlighted in working memory: either focused thoughts sponsored by executive control (and suddenly the roar of the crowd pulls you back into the heat of the game), or less focused daydreaming content (as your thoughts slowly drift away to a childhood memory of a cat basking itself in the warn sun on a porch somewhere).
Dietrich says that in this type of scenario, the idea of ‘Self’ as an entity is not the “continuous integrated flow we experience but the outcome of a series of discrete representations, sequenced together on the fly from different, endlessly competing, parallel streams of computations, each consisting of continuously shifting coalitions of neurons."
You don’t feel the shift as your thoughts battle it out, because the neural editing is so smooth each thought seems to blend seamlessly into the next one, belying the underlying pandemonium as a whole host of thoughts jockey for the dominant position of awareness.
‘Self’ is therefore a construct: an interface device providing the same effect that running Microsoft Office has on your computer. It gives you the sense of a nice tidy controlled mechanism, as if you’re watching a perfectly edited highlight reel. In actuality, there’s a cacophony of neural networks fighting to grab the center stage of consciousness at that moment. The grand prize? That neural network is large and in charge—of your thinking.
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