Discovering the unknown unknowns of research cartography with high-throughput natural description

Tanay Katiyar, Jean-François Bonnefon, Samuel Mehr () and Manvir Singh ()
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Tanay Katiyar: IJN - Institut Jean-Nicod - DEC - Département d'Etudes Cognitives - ENS-PSL - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - CdF (institution) - Collège de France - CNRS - Centre National de la Recherche Scientifique - Département de Philosophie - ENS-PSL - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres
Samuel Mehr: University of Auckland [Auckland], Yale University [New Haven]
Manvir Singh: IAST - Institute for Advanced Study in Toulouse

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Abstract: To succeed, we posit that research cartography will require high-throughput natural description to identify unknown unknowns in a particular design space. High-throughput natural description, the systematic collection and annotation of representative corpora of real-world stimuli, faces logistical challenges, but these can be overcome by solutions that are deployed in the later stages of integrative experimental design.

Date: 2024
Note: View the original document on HAL open archive server: https://hal.science/hal-04551319v1
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Published in Behavioral and Brain Sciences, 2024, 47, pp.e50. ⟨10.1017/S0140525X23002170⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04551319

DOI: 10.1017/S0140525X23002170

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