Unlimited Associative Learning as a Null Hypothesis.
A common strategy in comparative cognition is to require that one reject associative learning as an explanation for behavior before concluding that an organism is capable of causal reasoning. In this paper, I argue that standard causal-reasoning tasks can be explained by a powerful form of associati...
| Publicado en: | Philosophy of Science Vol. 89; no. 5; pp. 1186 - 1196 |
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| Formato: | Artículo |
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Cambridge University Press
Dec2022
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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=161723333&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 161723333 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00318248 PSC jtl: Philosophy of Science issn: 00318248 maglogo: N pubinfo: dt: Dec2022 vid: 89 iid: 5 pid: 15979 pub: Cambridge University Press artinfo: ui: 161723333 10.1017/psa.2022.66 ppf: 1186 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P size: 171KB tig: atl: Unlimited Associative Learning as a Null Hypothesis. aug: au: Halina, Marta affil: Department of History and Philosophy of Science, University of Cambridge, Cambridge, UK su: Associative learning Null hypothesis Problem solving Information processing Problem solving in children sug: subj: Associative learning Null hypothesis Problem solving Information processing Problem solving in children ab: A common strategy in comparative cognition is to require that one reject associative learning as an explanation for behavior before concluding that an organism is capable of causal reasoning. In this paper, I argue that standard causal-reasoning tasks can be explained by a powerful form of associative learning: unlimited associative learning (UAL). The lesson, however, is not that researchers should conduct more studies to reject UAL, but that they should instead focus on 1) enriching the cognitive hypothesis space and 2) testing a broader range of information processing patterns—errors, biases and limits, rather than successful problem solving alone. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Philosophy of Science is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Philosophy of Science holder: Cambridge University Press dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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