Welcome to "A Light In The Darkness" - a realm that explores the mysterious and the occult; the paranormal and the supernatural; the unexplained and the controversial; and, not forgetting, of course, the conspiracy theories; including Artificial Intelligence; Chemtrails and Geo-engineering; 5G and EMR Hazards; The Global Warming Debate; Trans-Humanism and Trans-Genderism; The Covid-19 and mRNA vaccine issues; The Ukraine Deception ... and a whole lot more.
Search A Light In The Darkness
Friday, 5 April 2019
Consciousness: A battle between your beliefs and perceptions?
S.O.T.T: Imagine you're at a magic show, in which the performer suddenly vanishes. Of course, you ultimately know that the person is probably just hiding somewhere. Yet it continues to look as if the person has disappeared. We can't reason away that appearance, no matter what logic dictates. Why are our conscious experiences so stubborn?
The fact that our perception of the world appears to be so intransigent, however much we might reflect on it, tells us something unique about how our brains are wired. Compare the magician scenario with how we usually process information. Say you have five friends who tell you it's raining outside, and one weather website indicating that it isn't. You'd probably just consider the website to be wrong and write it off. But when it comes to conscious perception, there seems to be something strangely persistent about what we see, hear and feel. Even when a perceptual experience is clearly 'wrong', we can't just mute it.
Why is that so? Recent advances in artificial intelligence (AI) shed new light on this puzzle. In computer science, we know that neural networks for pattern-recognition - so-called deep learning models - can benefit from a process known as predictive coding. Instead of just taking in information passively, from the bottom up, networks can make top-down hypotheses about the world, to be tested against observations. They generally work better this way. When a neural network identifies a cat, for example, it first develops a model that allows it to predict or imagine what a cat looks like. It can then examine any incoming data that arrives to see whether or not it fits that expectation...read more>>>...