精神病学数字表型与神经数字复合体的批判性分析

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Big Data & Society Pub Date : 2023-01-01 DOI:10.1177/20539517221149097
Rodrigo De La Fabián, Álvaro Jiménez-Molina, Francisco Pizarro Obaid
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引用次数: 1

摘要

本文批判性地考察了当代精神病学中数字表型的出现和使用。通过对其话语和实践的分析,我们表明数字表型扩散与它解决当代精神病学中所谓的“神经转向”的一些主要僵局的承诺直接相关。然而,数字表现型不仅仅是解决前数字精神病学的旧对象的新工具,我们认为数字表现型参与了一个新的本体-认识论矩阵,即“神经-数字复合体”,它需要重新定义精神病学对象(例如,大脑和精神),诊断类别和程序,主观性(例如,心理健康应用程序的用户),以及一个新的真理制度的出现,它承诺在个体尺度上揭示神经心理学的核心。尽管有这种技术乌托邦,但数字表现型并没有为自我认识产生中性的镜子。我们表明,它诉诸于人口统计,基于数字前神经心理学假设建立的真实数据集,以及人类分类过程。然而,我们建议不要把这种差距看作是一种具有误导性的意识形态事实,而是要强调其生产可能性。从这个角度来看,这个差距成为我们认为自己是谁和我们真正是谁之间的衡量标准,从神经心理学的角度来指导我们的生活。因此,我们得出结论,数字表型不是提供个性化的诊断和治疗,而是产生个性化的正常化和神经心理化途径。
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A critical analysis of digital phenotyping and the neuro-digital complex in psychiatry
This article critically examines the emergence and uses of digital phenotyping in contemporary psychiatry. From an analysis of its discourses and practices, we show that digital phenotyping diffusion is directly related to its promise to solve some of the major impasses of the so-called "neuro-turn" in contemporary psychiatry. However, more than a new tool to address old objects of pre-digital psychiatry, we consider digital phenotyping as participating from a new onto-epistemological matrix, the “neuro-digital complex,” which entails the redefinition of psychiatric objects (e.g., brain and mind), diagnostic categories and procedures, subjectivities (e.g., users of mental health apps), and the emergence of a new regime of truth which promises to reveal the neuropsychological core at the individual scale. Despite this techno-utopia, digital phenotyping does not produce neutral mirrors for self-knowledge. We show that it resorts to population statistics, grounded truth data sets built with pre-digital neuropsychological assumptions, and human categorization processes. Nevertheless, we propose not to approach this gap as a misleading ideological fact but to emphasize its productive possibilities. From this perspective, the gap becomes the measure between whom we think we are and who we really are, working as a guide to conduct our lives in neuropsychological terms. Thus, we conclude that, rather than providing personalized diagnoses and treatments, digital phenotyping produces individualized pathways to normalization and neuropsychologization.
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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