Rational representations of uncertainty: a pluralistic approach to bounded rationality.
An increasingly prevalent approach to studying human cognition is to construe the mind as optimally allocating limited cognitive resources among cognitive processes. Under this bounded rationality approach (Icard in Philos Sci 85(1):79–101, 2018; Simon in Utility and probability, Palgrave Macmillan,...
| Published in: | Synthese Vol. 203; no. 5; pp. 1 - 31 |
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| Format: | Article |
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Springer Nature
May2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=176915007&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 176915007 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: May2024 vid: 203 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 176915007 10.1007/s11229-024-04586-z ppf: 1 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P size: 698KB tig: atl: Rational representations of uncertainty: a pluralistic approach to bounded rationality. aug: au: Davis, Isaac affil: https://ror.org/03v76x132 Department of Psychology, Yale University, 100 College St, 06510, New Haven, CT, USA sug: keyword: Bounded rationality Computational cognitive science Philosophy of cognitive science Rational analysis ab: An increasingly prevalent approach to studying human cognition is to construe the mind as optimally allocating limited cognitive resources among cognitive processes. Under this bounded rationality approach (Icard in Philos Sci 85(1):79–101, 2018; Simon in Utility and probability, Palgrave Macmillan, 1980), it is common to assume that resource-bounded cognitive agents approximate normative solutions to statistical inference problems, and that much of the bias and variability in human performance can be explained in terms of the approximation strategies we employ. In this paper, we argue that this approach restricts itself to an unnecessarily narrow scope of cognitive models, which limits its ability to explain how humans flexibly adapt their representations to novel environments. We argue that more attention should be paid to how we form our cognitive representations in the first place, and advocate for pluralistic framework which jointly optimizes over both representations and algorithms for manipulating them. We identify several fundamental trade-offs that manifest in this joint optimization, and draw on recent work to motivate a unified formal framework for this analysis. We illustrate a simplified version of this analysis with a case study in social cognition, and outline several new directions for research that this approach suggests. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2024. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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