(Mis)computation in Computational Psychiatry

Colombo, Matteo

doi:10.1007/978-3-030-54092-0_18

Cited by 5 publications

(3 citation statements)

References 69 publications

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“…On the one hand, any computational model will be shaped by the type of data selected for its training, and by the context of their acquisition, as repeatedly stressed by our interviewees. In addition, insofar as computational psychiatry draws on a concept of miscomputation, it employs a value-laden and perspectival notion of normalcy for its explanations (Colombo, 2021). Also with the support of ML, it will therefore remain crucial to be mindful of the epistemic perspectives informing classificatory systems in psychiatry.…”

Section: Discussionmentioning

confidence: 99%

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

2023

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The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Much recent literature examines the ethical consequences of employing ML within medical and psychiatric practice but the potential impact on psychiatric diagnostic systems has so far not been well-developed. In this article, we aim to explore the challenges that arise from the recent use of ANNs for the old problems of psychiatric nosology. To enable an empirically supported critical reflection on the topic, we conducted semi-structured qualitative interviews with Swiss and German experts in computational psychiatry. Here, we report our findings structured around two themes, namely (1) the possibility of using ML for defining or refining of psychiatric classification, and (2) the desirability of employing ML for psychiatric nosology. We discuss these themes by relating them to recent debates about network theory for psychiatric nosology and show why empirical research in the field should critically reflect on its contribution to psychopathology research. In sum, we argue that beyond technical, regulatory, and ethical challenges, philosophical reflection is crucial to harness the potential of ML in psychiatry.

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Section: Discussionmentioning

confidence: 99%

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

2023

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“…The miscomputation desideratum allows us to explain this failure. Piccinini provides one account of miscomputation; others have provided somewhat different ones (Fresco and Primiero 2013;Dewhurst 2014;Tucker 2018;Colombo 2021). Although I agree that computation is normative, I will not address this (important) desideratum here.…”

Section: Featuresmentioning

confidence: 97%

The Nature of Physical Computation

Shagrir¹

2022

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Computing systems are everywhere today. Even the brain is thought to be a sort of computing system. But what does it mean to say that a given organ or system computes? What is it about laptops, smartphones, and nervous systems that they are deemed to compute, and why does it seldom occur to us to describe stomachs, hurricanes, rocks, or chairs that way? The book provides an extended argument for the semantic view of computation, which states that semantic properties are involved in the nature of computing systems. Laptops, smartphones, and nervous systems compute because they are accompanied by representations. Stomachs, hurricanes, and rocks, for instance, which do not have semantic properties, do not compute. The first part of the book argues that the linkage between the mathematical theory of computability and the notion of physical computation is weak. Theoretical notions such as algorithms, effective procedure, program, and automaton play only a minor role in identifying physical computation. The second part of the book reviews three influential accounts of physical computation and argues that while none of these accounts is satisfactory, each of them highlights certain key features of physical computation. The final part of the book develops and argues for a semantic account of physical computation and offers a characterization of computational explanations.

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“…More recently, in an attempt to overcome some of the challenges faced by existing diagnostic schemes, including their limited specificity and stability (see e.g. Rehm et al 2013), 2 to improve knowledge of the causal structure of psychiatric illnesses and to find effective treatments, psychiatrists have been increasingly relying on computational modelling (Huys et al 2021;Maia & Frank 2011;Montague et al 2012;Moutoussis et al 2018;Seriés 2020;Colombo 2021).…”

Section: Introductionmentioning

confidence: 99%

Computational Modelling for Alcohol Use Disorder

Colombo

2022

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In this paper, I examine Reinforcement Learning (RL) modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang’s (2004) account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.

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(Mis)computation in Computational Psychiatry

Cited by 5 publications

References 69 publications

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

The Nature of Physical Computation

Computational Modelling for Alcohol Use Disorder

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