SciFades🇺🇸AF

When we frame the world around us there are three important dimensions to how we process the specificity and context of our environments: aspectuality, centrality and temporality. Aspectuality is concerned with what features are relevant to understanding or recognizing the essence of something (What color, shape, size, etc). This can happen in a graded way, for example, we might recognize the metal gold as dense, shiny, golden object, but if we use the available technologies of the mind we might also be able to perform a spectroscopy or other analysis and “see” its atomic number. Centrality is how that something or its essence is important to you. Does a piece of art or music vibe with you? Do you care about or love another person? How important is money to you? Temporality is concerned with the relevance to that something in space and time. If it snowed in Japan yesterday does that matter right now, and to what extent does it matter? Does the stock price of a company being low right now matter? Procedurality is the way in which we go about framing our environments. It is the system for which we algorithmically recognize the salience of things (what features matter most in the moment), recognize the relevance of them to our situations (foregrounding and figuration), and frame them in these contexts (categorization and problem solving). We are ourselves participants within this framing due to the centrality of our realizations. Abusing some language from mathematics, we will say complexification is the interplay between how we integrate and differentiate our understanding of the relevance of a situation within our minds in order to build our world view. As we integrate information we are compressing a set of information into an approximation of its essence to make our neural networks more efficient. As we differentiate information we determine the appropriate level of connectedness between two ideas, objects, or essences in order to make our neural networks more resilient. This is part of the interplay between the firing and wiring of nodes within our neural networks. We are constantly attempting to find an optimal point between efficiency and resiliency on the network level, so the network topology is forever evolving based on our iterative framing process. This is the same idea behind how we need sentience to notice what is happening before we can decide how to respond to it; We need intelligence to be able to come up with solutions to how to respond given the size of a solution space; But we can only ever pursue the understanding of how relevant our solutions are. We can pursue wisdom, yet we can never be truly wise. This is because at the level of neurons as we go through the process of learning and pursuing wisdom, the topology of our neural networks is in a state of flux. We might connect two previously disparate ideas to us as we learn they are analogous in their own domains which can increase the efficiency of information traversing our neural network, or we might study something repeatedly over time and strengthen the resiliency of the corresponding connections. We might even get way too stoned and forget something we learned recently, or have an epiphany that connects two ideas. There is no sustainable state of pure enlightenment - a perfectly optimized neural network - because our consciousness is an ever-changing dynamical system in an agent-arena relationship with our environment attempting to optimize for both efficiency and resiliency in an autopoietic manner subject to its available bioeconomics.

Bear trap? 22140 bears last shot, above we gonna squeeze for days.