Psychiatry, the study and prevention of mental disorders, is currently undergoing a quiet revolution. For decades, even centuries, this discipline has been based largely on subjective observation. Large-scale studies have been hampered by the difficulty of objectively assessing human behavior and comparing it with a well-established norm. Just as tricky, there are few well-founded models of neural circuitry or brain biochemistry, and it is difficult to link this science with real-world behavior.
That has begun to change thanks to the emerging discipline of computational psychiatry, which uses powerful data analysis, machine learning, and artificial intelligence to tease apart the underlying factors behind extreme and unusual behaviors.
Computational psychiatry has suddenly made it possible to mine data from long-standing observations and link it to mathematical theories of cognition. It's also become possible to develop computer-based experiments that carefully control environments so that specific behaviors can be studied in detail.
How is this new-fangled science influencing researchers' understanding of mental illness? Today we get an answer of sorts, thanks to the work of Sarah Fineberg and colleagues at Yale University in New Haven.
From MIT Technology Review
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