Scott Alexander examines the 'canalization' theory in computational psychiatry and its refinement through deep learning concepts in the Deep CANAL model. Longer summary

Scott Alexander discusses a new paradigm in computational psychiatry called 'canalization', which models mental processes as an energy landscape with valleys representing attractors or persistent beliefs/behaviors. He then explores a follow-up paper that applies concepts from deep learning to refine this theory, introducing the Deep CANAL model. This model attempts to explain various mental disorders by mapping them onto different types of computational issues in artificial neural networks, such as overfitting/underfitting and the stability/plasticity dilemma. Scott expresses both interest and skepticism about this approach, noting its potential insights but also its limitations and potential contradictions with other theories. Shorter summary