Modelling generalisation gradients as augmented Gaussian functions.
Studying generalisation of associative learning requires analysis of response gradients measured over a continuous stimulus dimension. In human studies, there is often a high degree of individual variation in the gradients, making it difficult to draw conclusions about group-level trends with tradit...
| Publicado en: | Quarterly Journal of Experimental Psychology Vol. 74; no. 1; pp. 106 - 122 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Jan2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=147669355&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 147669355 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 17470218 1US5 jtl: Quarterly Journal of Experimental Psychology issn: 17470218 maglogo: Y pubinfo: dt: Jan2021 vid: 74 iid: 1 pid: 344 pub: Sage Publications Inc. artinfo: ui: 147669355 10.1177/1747021820949470 ppf: 106 ppct: 16 formats: tig: atl: Modelling generalisation gradients as augmented Gaussian functions. aug: au: Lee, Jessica C Mills, Llewellyn Hayes, Brett K Livesey, Evan J affil: School of Psychology, University of New South Wales Sydney, Sydney, NSW, Australia The University of Sydney, Sydney, NSW, Australia su: Trends Gaussian function Generalization Associative learning Parameter estimation sug: subj: Trends Gaussian function Generalization Associative learning Parameter estimation keyword: associative learning Bayesian Gaussian Generalisation gradient parameter estimation peak shift associative learning Bayesian Gaussian Generalisation gradient parameter estimation peak shift ab: Studying generalisation of associative learning requires analysis of response gradients measured over a continuous stimulus dimension. In human studies, there is often a high degree of individual variation in the gradients, making it difficult to draw conclusions about group-level trends with traditional statistical methods. Here, we demonstrate a novel method of analysing generalisation gradients based on hierarchical Bayesian curve-fitting. This method involves fitting an augmented (asymmetrical) Gaussian function to individual gradients and estimating its parameters in a hierarchical Bayesian framework. We show how the posteriors can be used to characterise group differences in generalisation and how classic generalisation phenomena such as peak shift and area shift can be measured and inferred. Estimation of descriptive parameters can provide a detailed and informative way of analysing human generalisation gradients. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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