Scott reviews a paper proposing a computational model of mood and emotions based on predictive processing, discussing its implications for understanding mood disorders. Longer summary

This post discusses a paper by Clark, Watson, and Friston that proposes a computational perspective on mood and emotions. The authors argue that emotions reflect changes in the uncertainty about the somatic consequences of action, while mood corresponds to hyperpriors about emotional states. The theory suggests that depression is a prediction of bad outcomes with high confidence, mania is a prediction of good outcomes with high confidence, and anxiety is a prediction of bad outcomes with low confidence. The post explores how this theory explains various aspects of mood disorders and their symptoms, including learned helplessness and the role of serotonin. The author finds the theory intriguing but notes some inconsistencies, particularly in unifying the concepts of 'prior on bad outcomes' and 'low precision of predictions'. Shorter summary

Scott proposes a speculative theory of depression as pathologically low confidence in neural predictions within the predictive processing framework, explaining how this could account for various depressive symptoms. Longer summary

This post explores a potential theory of depression within the predictive processing (PP) framework. Scott starts by noting the lack of a compelling PP account for depression, then proposes that depression might be a state of pathologically low confidence in neural predictions. He explains how this could account for various symptoms of depression, including perceptual changes, psychomotor retardation, and lack of motivation. The post then speculates on why low confidence might cause sadness, suggesting that emotions could be a way of globally adjusting confidence levels based on past success or failure. Scott acknowledges the speculative nature of these ideas and some potential problems with the theory. Shorter summary