Surfing Uncertainty isn’t pop science and isn’t easy reading. Sometimes it’s on the border of possible-at-all reading. … It’s your book if you want to learn about predictive processing at all, since as far as I know this is the only existing book-length treatment of the subject. And it’s comprehensive, scholarly, and very good at giving a good introduction to the theory and why it’s so important. So let’s be grateful for what we’ve got and take a look.
The key insight: the brain is a multi-layer prediction machine. All neural processing consists of two streams: a bottom-up stream of sense data, and a top-down stream of predictions. These streams interface at each level of processing, comparing themselves to each other and adjusting themselves as necessary. … both streams contain not only data but estimates of the precision of that data. … Each level receives the predictions from the level above it and the sense data from the level below it. Then each level uses Bayes’ Theorem to integrate these two sources of probabilistic evidence as best it can.
there might be some unresolvable conflict between high-precision sense-data and predictions. The Bayesian math will indicate that the predictions are probably wrong. The neurons involved will fire, indicating “surprisal” – a gratuitously-technical neuroscience term for surprise. The higher the degree of mismatch, and the higher the supposed precision of the data that led to the mismatch, the more surprisal – and the louder the alarm sent to the higher levels.
When the higher levels receive the alarms from the lower levels, this is their equivalent of bottom-up sense-data. … All the levels really hate hearing alarms. Their goal is to minimize surprisal – to become so good at predicting the world (conditional on the predictions sent by higher levels) that nothing ever surprises them.