Attenuated sensory precision and contextual prediction error in persistent depression

Chronic and recurrent forms of clinical depression can persist for a lifetime and often respond poorly to intervention. Psychological formulations implicate rigid, negative expectations of self and world which are resistant to updating with new information, a phenomenology consistent with a Bayesian account of brain function. Bayesian predictive processing models suggest that sensory data which is represented with low precision (high uncertainty) in the brain cannot exert much influence on existing beliefs, giving rise to the hypothesis that persistent forms of depression may be characterised by disturbances in sensory precision optimization. We optimized a computational model with data from a cross-modal (visual, auditory, somatic) covert attention task to estimate sensory precision in persistently depressed participants relative to healthy controls. Results suggested that both sensory precision and the salience of attentional targets were attenuated in depressed participants across sensory modalities, contributing to a suppression of contextual prediction error in this group. These outcomes provide support for a novel theoretical account of depression chronicity and suggest avenues for enhancing the effectiveness of psychological interventions for this population.