Attractor
Chapter 1 - Worldview
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Welcome to the Attractor page
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Self-organisation
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'Attractors' describe stable states or patterns of activity that your brain system tends to settle into over time and represent various cognitive states or behaviours (e.g. beliefs, habits, etc.)
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Key take-aways from the deep dive
- Attractors forces systems in your brain to fall into predictable states, thereby reinforcing the model you already generated of the world
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Deep dive
Self-organising
As complex systems, we are self-organising because we have attractors. These cycles of mutually reinforcing states allow processes to reach a point of stability, not by losing energy until they stop, but by what is known as dynamic equilibrium. An attractor is simply a stable state in a complex (dynamic) system.
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Dynamic equilibrium
Seeds grow into trees and then stabilise to an attractor: the tree acquires a shape. Birds fly in sync witheach other and form a V-shaped (or other-shaped) flock. Ecosystems go through periods of massive change (eg, speciation and species death) and then stabilise. Cities stabilise. Cultures stabilise. Even family dynamics stabilise.
A more personal example is homeostasis. If you are startled by a predator, your heart rate and breathing will accelerate. You automatically do something to calm your cardiovascular system (according to the so-called 'fight or flight response'). Every time there is a deviation from the attractor, it causes streams of thoughts, feelings and movements that eventually return you to your cycle of attracting, familiar states.
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Attractor
In humans, our body and brain excitations can be described as moving towards our attractors, that is, towards our most likely circumstances. All our thoughts and behaviours push us towards more and more probable states. How do we do that? By using two functions in doing so:
- on the one hand, surprise – that is, the improbability of being in a specific state
- on the other hand, evidence – that is, the probability that a given explanation or model for that condition is correct.
If we exist, we need to increase our model proof or self-evidence to minimise surprises.
A rebounding state has a low surprise and high proof. Therefore, complex systems fall into known, reliable cycles because these processes are necessarily concerned with validating the principle underlying their existence.
Attractors force systems to fall into predictable states, thereby reinforcing the model the system has generated of its world.
If this surprise-minimizing, self-evident, inferential behaviour fails, the system will fall into surprising, unknown states – until it no longer exists in any meaningful way. Attractors are the product of processes that deal with inferences to bring themselves into existence. In other words, attractors are the foundation of what it means to be alive.
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Attractors are essential because they provide a framework for understanding how neural networks transition between different activity states. They maintain stability or exhibit dynamic behaviours responding to various inputs or stimuli. They also play a crucial role in neural computation and learning models, as they influence the trajectory of neural activity and the formation of memories and associations within the brain.
- Neural networks exhibit point attractors, where they converge to a single stable state of activity. For example, in memory retrieval tasks, certain patterns of neural activity may represent specific memories, and the network may converge to these patterns when retrieving those memories.
- Limit cycle attractors appear when the system settles into a repeating pattern of activity rather than a single stable state. We observe this in rhythmic behaviours such as walking or breathing, where neural activity oscillates in a coordinated manner.
- Strange attractors may occur in more complex systems, such as those involved in decision-making or cognition. Strange attractors are characterized by non-repeating, chaotic behaviour within a bounded region of phase space. This can represent the complex dynamics of cognitive processes where multiple stable states or activity patterns compete.
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| Link to more information |
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| Attractor network - Scholarpedia |
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