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Martinez JE. Facecraft: Race Reification in Psychological Research With Faces. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2023:17456916231194953. [PMID: 37819250 DOI: 10.1177/17456916231194953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Faces are socially important surfaces of the body on which various meanings are attached. The widespread physiognomic belief that faces inherently contain socially predictive value is why they make a generative stimulus for perception research. However, critical problems arise in studies that simultaneously investigate faces and race. Researchers studying race and racism inadvertently engage in various research practices that transform faces with specific phenotypes into straightforward representatives of their presumed race category, thereby taking race and its phenotypic associations for granted. I argue that research practices that map race categories onto faces using bioessentialist ideas of racial phenotypes constitute a form of racecraft ideology, the dubious reasoning of which presupposes the reality of race and mystifies the causal relation between race and racism. In considering how to study racism without reifying race in face studies, this article places these practices in context, describes how they reproduce racecraft ideology and impair theoretical inferences, and then suggests counterpractices for minimizing this problem.
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Affiliation(s)
- Joel E Martinez
- Data Science Initiative, Harvard University
- Department of Psychology, Harvard University
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Greene AS, Shen X, Noble S, Horien C, Hahn CA, Arora J, Tokoglu F, Spann MN, Carrión CI, Barron DS, Sanacora G, Srihari VH, Woods SW, Scheinost D, Constable RT. Brain-phenotype models fail for individuals who defy sample stereotypes. Nature 2022; 609:109-118. [PMID: 36002572 PMCID: PMC9433326 DOI: 10.1038/s41586-022-05118-w] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 07/15/2022] [Indexed: 01/19/2023]
Abstract
Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.
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Affiliation(s)
- Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
- MD-PhD program, Yale School of Medicine, New Haven, CT, USA.
| | - Xilin Shen
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- MD-PhD program, Yale School of Medicine, New Haven, CT, USA
| | - C Alice Hahn
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jagriti Arora
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Fuyuze Tokoglu
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Carmen I Carrión
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Daniel S Barron
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Vinod H Srihari
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Scott W Woods
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA.
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
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Edge MD, Ramachandran S, Rosenberg NA. Celebrating 50 years since Lewontin's apportionment of human diversity. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200405. [PMID: 35430889 PMCID: PMC9014183 DOI: 10.1098/rstb.2020.0405] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Michael D. Edge
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Sohini Ramachandran
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI 02912, USA
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Roseman CC. Lewontin did not commit Lewontin's fallacy, his critics do: Why racial taxonomy is not useful for the scientific study of human variation. Bioessays 2021; 43:e2100204. [PMID: 34738661 DOI: 10.1002/bies.202100204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/11/2021] [Accepted: 10/12/2021] [Indexed: 11/10/2022]
Abstract
In 1972, R.C. Lewontin concluded that it follows from the fact that the large majority of human genetic variation (≈ 85%) is among individuals within local populations that racial taxonomy is unjustified. Three decades later, Edwards demonstrated that while the accuracy with which individuals may be assigned to groups is poor for a single locus, consideration of multi-locus data allows for highly accurate assignments. Edwards concluded that Lewontin's dismissal of racial taxonomy was unwarranted. Edwards misidentified the aim of Lewontin's critique, which was directed at the utility of racial classification and not at assigning individuals to groups using genetic data. Moreover, Edwards conflated distinct kinds of correlation when sketching out his argument. If we follow Edwards' argument to its natural terminus, it becomes clear that it is consideration of all of the correlation structure among local groups in human genetic data that renders racial taxonomy scientifically useless. Lewontin considers the correlation structure relevant to his analysis of racial taxonomy and does not make his eponymous misstep. Rather, critics of Lewontin who use racial taxonomies in their work are the primary offenders when it comes to committing Lewontin's fallacy.
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Affiliation(s)
- Charles C Roseman
- Department of Evolution, Ecology, and Behavior, School of Integrative Biology, University of Illinois, Urbana, Illinois, USA
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Schouler-Ocak M, Bhugra D, Kastrup MC, Dom G, Heinz A, Küey L, Gorwood P. Racism and mental health and the role of mental health professionals. Eur Psychiatry 2021; 64:e42. [PMID: 34134809 PMCID: PMC8278246 DOI: 10.1192/j.eurpsy.2021.2216] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/18/2021] [Accepted: 05/31/2021] [Indexed: 11/23/2022] Open
Abstract
The concept of "race" and consequently of racism is not a recent phenomenon, although it had profound effects on the lives of populations over the last several hundred years. Using slaves and indentured labor from racial groups designated to be "the others," who was seen as inferior and thus did not deserve privileges, and who were often deprived of the right to life and basic needs as well as freedoms. Thus, creation of "the other" on the basis of physical characteristics and dehumanizing them became more prominent. Racism is significantly related to poor health, including mental health. The impact of racism in psychiatric research and clinical practice is not sufficiently investigated. Findings clearly show that the concept of "race" is genetically incorrect. Therefore, the implicit racism that underlies many established "scientific" paradigms need be changed. Furthermore, to overcome the internalized, interpersonal, and institutional racism, the impact of racism on health and on mental health must be an integral part of educational curricula, from undergraduate levels through continuing professional development, clinical work, and research. In awareness of the consequences of racism at all levels (micro, meso, and macro), recommendations for clinicians, policymakers, and researchers are worked out.
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Affiliation(s)
- M. Schouler-Ocak
- Psychiatric University Clinic of Charité at St. Hedwig Hospital, Berlin, Germany
| | - D. Bhugra
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - M. C. Kastrup
- Specialist in Psychiatryformer Treasurer World Association Social Psychiatryformer Secretary General European Psychiatric Associationformer Member Executive Committee World Psychiatric Association Copenhagen, Denmark
| | - G. Dom
- CAPRI, University of Antwerp (UAntwerp), Antwerp, Belgium
| | - A. Heinz
- Department for Psychiatry and Psychotherapy, CCM, Charité—University Medicine, Berlin, Germany
| | - L. Küey
- Istanbul Bilgi University, Istanbul, Turkey
| | - P. Gorwood
- CMME, Hopital Sainte-Anne GHU Paris Psychiatrie et Neurosciences Université de Paris, INSERM U894, Paris, France
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