Editorial: Probabilistic Perspectives on Brain (Dys)function Parr et al. Editorial: Probabilistic Perspectives

Abstract

'Ryan Smith' 'Casper Hesp' 'Karl Friston'] While observations in neurobiology provide inspiration for methods in artificial intelligence and machine learning-most famously, in the development of artificial neural networks ([12]; [18]; [19]) -the reciprocal relationship has also proved fruitful. Put simply, many of the problems that machine learning is designed to solve have already been solved by the brain. When we have a good understanding of how the brain deals with a problem, we can draw inspiration from this solution in other domains. When we have a poor understanding of aspects of brain function, we can look to how these functions are performed in machine learning. If natural selection has arrived at the same optimum, we hypothesize that brain architectures support analogous procedures. Perhaps the most obvious example of this translation is the Bayesian brain hypothesis ([11]; [4]), and recent extensions of this idea ([15]). This perspective treats the brain as a statistician who makes use of a probabilistic model of the world to make sense of sensory input. It has been central to the development of theories of brain function-like predictive coding ([20]; [16]; [7]; [2]). This research topic was designed to showcase the application of contemporary probabilistic methods to understanding how the brain works, and how it can go awry in psychiatric disorders. Broadly, the applications of probabilistic methods to the brain fall into two camps. The first applies these methods to neurobiological or psychophysical data to draw better inferences about the brain. The second assumes the brain itself makes use of these methods and engages in inference about the data it gathers from receptors in the eyes, ears, and other sensory organs. Both approaches are usefully illustrated by [Feltgen and Daunizeau](). Their focus is on refinement of the estimation procedure for drift-diffusion models ([17]). While drift-diffusion dynamics may be seen as a metaphor for evidence accumulation in the brain, the estimation procedure advocated by the authors represents a means of drawing inferences about cognition from psychophysical measurements.