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

Published online by Cambridge University Press:  05 February 2024

Tanay Katiyar ,
Jean-François Bonnefon ,
Samuel A. Mehr Open the ORCID record for Samuel A. Mehr [Opens in a new window]  and
Manvir Singh
Tanay Katiyar
Affiliation:
Institut Jean Nicod, Département d'études cognitives, École normale supérieure (ENS-PSL), Paris, France tanay.katiyar20@gmail.com
Jean-François Bonnefon
Affiliation:
Toulouse School of Economics, Centre National de la Recherche Scientifique (TSM-R), Toulouse, France jean-francois.bonnefon@tse-fr.eu; https://jfbonnefon.github.io
Samuel A. Mehr*
Affiliation:
School of Psychology, University of Auckland, Auckland, New Zealand https://mehr.nz/ Yale Child Study Center, Yale University, New Haven, CT, USA sam@yale.edu
Manvir Singh
Affiliation:
Department of Anthropology, University of California-Davis, Davis, CA, USA manvir.manvir@gmail.com; https://manvir.org
*
*Corresponding author.
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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 experiment design.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e50
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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