Towards a Mechanistic Account of Embodied Implicit Bias: Heterarchical Control Network and Its Implications for Intervention.
Implicit bias perpetuates social injustice, serving as a significant barrier to equality and fairness. Despite extensive efforts, most debiasing interventions have failed to produce lasting, robust, and generalizable changes. In this paper, we introduce a new framework—the Heterarchical Control Netw...
| Publicado en: | Topoi: An International Review of Philosophy Vol. 44; no. 4; pp. 899 - 915 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=188515357&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 188515357 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01677411 NM2 jtl: Topoi: An International Review of Philosophy issn: 01677411 maglogo: N pubinfo: dt: Oct2025 vid: 44 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 188515357 10.1007/s11245-025-10176-6 ppf: 899 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: Towards a Mechanistic Account of Embodied Implicit Bias: Heterarchical Control Network and Its Implications for Intervention. aug: au: Sechman, Michael Scott Huang, Linus Ta-Lun affil: https://ror.org/02ttsq026 Department of Philosophy, University of Colorado, Boulder, USA https://ror.org/00t33hh48 The Department of Cultural and Religious Studies, The Chinese University of Hong Kong, Hong Kong, Hong Kong S.A.R. su: Implicit bias Social justice Psychological techniques Theorists Causal inference sug: subj: Implicit bias Social justice Psychological techniques Theorists Causal inference keyword: Debiasing intervention Embedded cognition Embodied cognition Intergroup contact theory ab: Implicit bias perpetuates social injustice, serving as a significant barrier to equality and fairness. Despite extensive efforts, most debiasing interventions have failed to produce lasting, robust, and generalizable changes. In this paper, we introduce a new framework—the Heterarchical Control Network model—that goes beyond the limitations of current embodied approaches, which often lack mechanistic details or remain closely tied to classical architecture. Our model conceptualizes implicit bias as emerging from the dynamic interactions of multiple semi-autonomous mechanisms, many of which are embodied and embedded. By providing a more detailed mechanistic account, it explains both the failure and the relative successes of existing interventions. This framework not only deepens our theoretical understanding of implicit bias but also offers practical implications for designing more effective, durable, and generalizable interventions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Topoi: An International Review of Philosophy is a copyright of Springer, 2025. All Rights Reserved. item: Topoi: An International Review of Philosophy holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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