Psychiatry has found it difficult to develop a nosology that allows for the targeted treatment of disorders of the mind. The historic inability of the field to agree on a nosology based on clinical experience has led it to retreat to diagnoses based on symptom checklists as laid down in the Diagnostic and Statistical Manual of Mental Disorders (DSM). While this has increased the reliability of diagnoses, hopes that biological findings would lead to the emergence of mechanistically founded diagnostic entities have not been realized despite considerable advances in neurobiology. This article sets out a possible way forward: harnessing systems theory to provide the conceptual constraints needed to link clinical phenomena with neurobiology. This approach builds on the insight that the mind is a system which, to regulate its environment, needs to have a model of that environment and needs to update predictions about it using the rules of inductive logic (i.e., Bayesian inference). The application of the rules of inductive logic is called Bayesian inference because Bayes’s theorem is the most important consequence of these rules, prescribing how beliefs need to be updated in response to new information. Importantly, while Bayesian inference is by definition consistent with the rules of inductive logic, it can still be false (to the point of being pathological), in the sense of leading to false predictions, because the model underlying the inference is inadequate. Further, it can be shown that Bayesian inference can be reduced to updating beliefs based on precision-weighted prediction errors, where a prediction error is the difference between actual and predicted input, and precision is the confidence associated with the input prediction. Precision weighting of prediction errors entails that a given discrepancy between outcome and prediction means more, and leads to greater belief updates, the more confidently the prediction was made. This provides a conceptual framework linking clinical experience with the pathophysiology underlying disorders of the mind. Limitations of this approach are discussed and ways to work around them illustrated with examples. Finally, initial steps and possible future directions toward a nosology based on failures of precision weighting are discussed. Copyright MIT & Frankfurt Institute of Advaced Studies.

How could we get nosology from computation? / Mathys, Christoph Daniel. - 20:(2016), pp. 121-135.

How could we get nosology from computation?

Mathys, Christoph Daniel
2016

Abstract

Psychiatry has found it difficult to develop a nosology that allows for the targeted treatment of disorders of the mind. The historic inability of the field to agree on a nosology based on clinical experience has led it to retreat to diagnoses based on symptom checklists as laid down in the Diagnostic and Statistical Manual of Mental Disorders (DSM). While this has increased the reliability of diagnoses, hopes that biological findings would lead to the emergence of mechanistically founded diagnostic entities have not been realized despite considerable advances in neurobiology. This article sets out a possible way forward: harnessing systems theory to provide the conceptual constraints needed to link clinical phenomena with neurobiology. This approach builds on the insight that the mind is a system which, to regulate its environment, needs to have a model of that environment and needs to update predictions about it using the rules of inductive logic (i.e., Bayesian inference). The application of the rules of inductive logic is called Bayesian inference because Bayes’s theorem is the most important consequence of these rules, prescribing how beliefs need to be updated in response to new information. Importantly, while Bayesian inference is by definition consistent with the rules of inductive logic, it can still be false (to the point of being pathological), in the sense of leading to false predictions, because the model underlying the inference is inadequate. Further, it can be shown that Bayesian inference can be reduced to updating beliefs based on precision-weighted prediction errors, where a prediction error is the difference between actual and predicted input, and precision is the confidence associated with the input prediction. Precision weighting of prediction errors entails that a given discrepancy between outcome and prediction means more, and leads to greater belief updates, the more confidently the prediction was made. This provides a conceptual framework linking clinical experience with the pathophysiology underlying disorders of the mind. Limitations of this approach are discussed and ways to work around them illustrated with examples. Finally, initial steps and possible future directions toward a nosology based on failures of precision weighting are discussed. Copyright MIT & Frankfurt Institute of Advaced Studies.
20
Computational Psychiatry: New Perspectives on Mental Illness
121
135
https://esforum.de/publications/sfr20/Computational%20Psychiatry.html
http://redishlab.neuroscience.umn.edu/Papers/
Mathys, Christoph Daniel
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/48233
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