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

Scott Alexander argues that a purely biological, apolitical taxonomy of mental disorders is impossible due to ethical and practical considerations that inevitably influence classifications. Longer summary

Scott Alexander discusses the limitations of creating a purely biological, apolitical taxonomy of mental disorders. He argues that such a taxonomy is inherently impossible because the classification of mental disorders is not just a scientific issue, but also a practical and ethical one. Using examples like transgender identity, homosexuality, and pedophilia, he demonstrates how biological similarities can conflict with ethical and practical considerations in classification. The post highlights the tension between scientific accuracy, stigma avoidance, and ensuring access to necessary care. Scott concludes that new taxonomies like HiTOP are still useful, but claims of avoiding political bias in disorder classification are unrealistic. Shorter summary

Scott Alexander presents survey results on people's satisfaction with mental health care, showing varied preferences across different types of therapy and conditions. Longer summary

Scott Alexander analyzes the results of the SSC survey regarding people's experiences with mental health care. The post presents various graphs showing satisfaction ratings for different types of therapy, medication, and mental health issues. Key findings include an average rating of 5.7/10 for both psychotherapy and medication, higher satisfaction with therapy from books compared to in-person or online therapy, and varying preferences for medication vs. therapy depending on the mental health condition. The author emphasizes that the results are exploratory and subject to biases. Shorter summary

The post explores a new model for understanding mental disorders as networks of interconnected symptoms rather than discrete diseases with underlying causes. Longer summary

This post discusses a new way of understanding mental disorders proposed by Nuijten, Deserno, Cramer, and Borsboom (NDCB). Instead of viewing psychiatric conditions as discrete diseases with underlying causes, they suggest viewing them as networks of interconnected symptoms. The author explains how this model can account for various aspects of mental illness that are difficult to explain with traditional models, such as comorbidity, the effects of life stressors, and the polygenic nature of mental illnesses. The post also considers some limitations of this approach, particularly in explaining conditions like bipolar disorder and the rapid effects of treatments like ketamine. Despite these issues, the author finds the network model compelling as a way to understand the complexity of psychiatric disorders. Shorter summary

Scott Alexander examines a study comparing the effectiveness of drugs and therapy for psychiatric disorders, discussing the results and methodological limitations of the research. Longer summary

This post analyzes a study comparing the efficacy of pharmacotherapy and psychotherapy for various psychiatric disorders. The author discusses the graph showing effect sizes for different treatments, noting that most psychiatric treatments have an effect size around 0.5. He expresses some uncertainty about the statistical methods used and highlights three surprising findings: drugs appearing more effective than therapy for borderline personality disorder and insomnia, and drugs being more effective at preventing relapse than stopping acute episodes. The post also discusses the limitations of psychotherapy trials, noting that lower quality trials tend to show much higher effect sizes than high-quality ones, and that psychotherapy research often lacks sufficient blinding and control groups. Shorter summary