Joseph Balfe

Background

Advancements in computational neuroscience suggest that psychiatric conditions can be accounted for by aberrant predictive processing machinery, whether it be top-down predictions, bottom-up prediction errors, or the precision-weighting of either. In depression, highly precise negative top-down predictions are thought to maintain persistent negative affect, rendering new bottom-up information as largely irrelevant for model revision. In this context, depression can be considered as a disorder of maladaptive belief updating. 

PhD Research

Given the neurobiological nature of depression, it is no surprise that successful antidepressant treatments have one thing in common: neuroplastic induction. By inducing a heightened state of plasticity, a ‘window of opportunity’ presents itself to enable the reorganisation of how information is processed within the brain’s hierarchical architecture. As such, non-invasive brain stimulation methods like transcranial direct current stimulation (tDCS) and transcranial ultrasound stimulation (TUS) hold promise for the targeted modulation of such information processing.

My work focuses on the modulation of two separable but interacting inferential processes: (i) the precision-weighting of prediction error signals, which determines their influence on belief updating, and (ii) the credit assignment of these precision-weighted prediction errors. By using the above brain stimulation methods, I hope to better understand how we can normalise aberrant predictive processing machinery toward more adaptive and accurate modelling of the world.