Precision and the Bayesian brain

I've been studying and trying to understand the new prevailing model of how our brains work that is emerging - the brain as a Baysean predictive processing machine that compares its prior knowledge with incoming evidence of its correctness. If a mis-match occurs that might suggest alterning a prior expectation, the precision of the incoming evidence is very important. In a recent issue of Current Biology Yon and Frith offer a very simple and lucid primer (open source) on what precision is how it influences adrenergic and dopaminergic neuromodulatory systems to alter the synaptic gain afforded to top-down predictions and bottom-up evidence.:

Scientific thinking about the minds of humans and other animals has been transformed by the idea that the brain is Bayesian. A cornerstone of this idea is that agents set the balance between prior knowledge and incoming evidence based on how reliable or ‘precise’ these different sources of information are — lending the most weight to that which is most reliable. This concept of precision has crept into several branches of cognitive science and is a lynchpin of emerging ideas in computational psychiatry — where unusual beliefs or experiences are explained as abnormalities in how the brain estimates precision. But what precisely is precision? In this Primer we explain how precision has found its way into classic and contemporary models of perception, learning, self-awareness, and social interaction. We also chart how ideas around precision are beginning to change in radical ways, meaning we must get more precise about how precision works.