Concept Learning: Convexity Versus Connectedness.
In the context of the conceptual spaces framework, it has been argued that a natural concept is represented by a convex region in a similarity space. The convexity requirement has been defended on grounds of cognitive economy: among other benefits, concepts represented by convex regions have been sa...
| Publicado en: | Erkenntnis Vol. 91; no. 1; pp. 445 - 463 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Jan2026
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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=190712040&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 190712040 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01650106 5KZ jtl: Erkenntnis issn: 01650106 maglogo: N pubinfo: dt: Jan2026 vid: 91 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 190712040 10.1007/s10670-024-00909-1 ppf: 445 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.6MB tig: atl: Concept Learning: Convexity Versus Connectedness. aug: au: Douven, Igor Verheyen, Steven affil: https://ror.org/02nnpw434 CNRS, IHPST, Paris, France Erasmus School of Social and Behavioural Sciences, Rotterdam, Netherlands su: Concept learning Mathematical connectedness Cognition Curvature Cognitive ability Configuration space Color space Artificial neural networks sug: subj: Concept learning Mathematical connectedness Cognition Curvature Cognitive ability Configuration space Color space Artificial neural networks keyword: Psychology and Cognitive Sciences Psychology ab: In the context of the conceptual spaces framework, it has been argued that a natural concept is represented by a convex region in a similarity space. The convexity requirement has been defended on grounds of cognitive economy: among other benefits, concepts represented by convex regions have been said to be easily learnable, or more easily than concepts represented by nonconvex regions. There is some evidence that concepts in use are represented by regions that are convex, or at least almost so. There is so far no evidence that concepts represented by convex regions are more easily learnable than ones represented by regions that satisfy topological criteria that are somewhat less stringent, most notably that of connectedness. This note presents the outcomes from computational studies carried out on perceptual color space as well as on a shape space for representing various container objects, indicating that convexity indeed facilitates learning more than does connectedness. The studies use the training of neural nets as a model of human learning. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Erkenntnis is a copyright of Springer, 2026. All Rights Reserved. item: Erkenntnis holder: Springer Nature dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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