A fresh look at research strategies in computational cognitive science: The case of enculturated mathematical problem solving.
Marr's seminal distinction between computational, algorithmic, and implementational levels of analysis has inspired research in cognitive science for more than 30 years. According to a widely-used paradigm, the modelling of cognitive processes should mainly operate on the computational level and be...
| Publicado en: | Synthese Vol. 198; no. 4; pp. 3221 - 3264 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Apr2021
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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=149905570&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 149905570 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Apr2021 vid: 198 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 149905570 10.1007/s11229-019-02276-9 ppf: 3221 ppct: 43 formats: fmt: @attributes: type: P size: 956KB tig: atl: A fresh look at research strategies in computational cognitive science: The case of enculturated mathematical problem solving. aug: au: Fabry, Regina E. Pantsar, Markus affil: Department of Philosophy II, Ruhr University Bochum, Room GA 04/142, Universitätsstraße 150, 44780, Bochum, Germany Department of Philosophy, History, and Art Studies, University of Helsinki, P.O. Box 24, Unioninkatu 40, 00014, Helsinki, Finland su: Cognitive science Problem solving Computational complexity Socialization Complexity (Philosophy) sug: subj: Cognitive science Problem solving Computational complexity Socialization Complexity (Philosophy) keyword: Competence Computational modelling Embodied cognition Enculturation Levels of analysis Mathematical problem solving Neural plasticity Performance ab: Marr's seminal distinction between computational, algorithmic, and implementational levels of analysis has inspired research in cognitive science for more than 30 years. According to a widely-used paradigm, the modelling of cognitive processes should mainly operate on the computational level and be targeted at the idealised competence, rather than the actual performance of cognisers in a specific domain. In this paper, we explore how this paradigm can be adopted and revised to understand mathematical problem solving. The computational-level approach applies methods from computational complexity theory and focuses on optimal strategies for completing cognitive tasks. However, human cognitive capacities in mathematical problem solving are essentially characterised by processes that are computationally sub-optimal, because they initially add to the computational complexity of the solutions. Yet, these solutions can be optimal for human cognisers given the acquisition and enactment of mathematical practices. Here we present diagrams and the spatial manipulation of symbols as two examples of problem solving strategies that can be computationally sub-optimal but humanly optimal. These aspects need to be taken into account when analysing competence in mathematical problem solving. Empirically informed considerations on enculturation can help identify, explore, and model the cognitive processes involved in problem solving tasks. The enculturation account of mathematical problem solving strongly suggests that computational-level analyses need to be complemented by considerations on the algorithmic and implementational levels. The emerging research strategy can help develop algorithms that model what we call enculturated cognitive optimality in an empirically plausible and ecologically valid way. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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