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Cognitive Analysis for Representation Change

Published version
Peer-reviewed

Repository URI

https://www.repository.cam.ac.uk/handle/1810/347649

Repository DOI

https://doi.org/10.17863/CAM.95064
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Published version (569.49 KB)

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Conference Object

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Authors

Stockdill, A  ORCID logo  https://orcid.org/0000-0003-3312-5267
Garcia, GG  ORCID logo  https://orcid.org/0000-0002-7327-7225
Cheng, PCH  ORCID logo  https://orcid.org/0000-0002-0355-5955
Raggi, D  ORCID logo  https://orcid.org/0000-0002-9207-6621
Jamnik, M  ORCID logo  https://orcid.org/0000-0003-2772-2532

Abstract

The rep2rep project is developing an AI tool to automatically select an appropriate representation to solve a particular problem for a particular person. A prerequisite of this tool is to understand (i.e., model) how a reader interprets a representation. But interpretations can vary wildly between novices and experts, readers of similar ability, or even the same reader in different tasks. We present a theory and notation (RIST and RISN) for analysing the cognitive features of a representation's interpretation, and introduce a web app to construct RISN models. These models provide information about cognitive properties of representations to guide automated representation selection to support human problem solving.

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Journal Title

CEUR Workshop Proceedings

Conference Name

HLC 2022: 3rd International Workshop on Human-Like Computing

Journal ISSN

1613-0073

Volume Title

3227

Publisher

CEUR-WS.org

Publisher DOI

Publisher URL

https://ceur-ws.org/Vol-3227/Stockdill-et-al.PP5.pdf

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Attribution 4.0 International
Except where otherwised noted, this item's license is described as Attribution 4.0 International

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University of Cambridge Research Outputs (Articles and Conferences)