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The feasibility of ideography as an empirical question for a science representational systems design

Published online by Cambridge University Press:  02 October 2023

Peter C.-H. Cheng Open the ORCID record for Peter C.-H. Cheng [Opens in a new window]
Peter C.-H. Cheng*
Affiliation:
Department of Informatics, University of Sussex, Brighton, UK p.c.h.cheng@sussex.ac.uk; http://users.sussex.ac.uk/~peterch/
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Abstract

The possibility of ideography is an empirical question. Prior examples of graphic codes do not provide compelling evidence for the infeasibility of ideography, because they fail to satisfy essential cognitive requirements that have only recently been revealed by studies of representational systems in cognitive science. Design criteria derived from cognitive principles suggest how effective graphic codes may be engineered.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e237
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Bradshaw, G. (1992). The airplane and the logic of invention. In Giere, R. N. (Ed.), Cognitive models of science (pp. 239–250). University of Minnesota Press.Google Scholar
Cheng, P. C.-H. (2002). Electrifying diagrams for learning: Principles for effective representational systems. Cognitive Science, 26(6), 685–736.CrossRefGoogle Scholar
Cheng, P. C.-H. (2011). Probably good diagrams for learning: Representational epistemic re-codification of probability theory. Topics in Cognitive Science 3(3), 475–498.CrossRefGoogle Scholar
Cheng, P. C.-H. (2012). Algebra diagrams: A HANDi introduction. In Cox, P., Plimmer, B., & Rodgers, P. (Eds.), Diagrammatic representation and inference: 7th International conference on diagrams 2012 (pp. 178–192). Springer Verlag.CrossRefGoogle Scholar
Cheng, P. C.-H. (2020). Truth diagrams versus extant notations for propositional logic. Journal of Language, Logic and Information, 29, 121–161.CrossRefGoogle Scholar
Gardenfors, P. (2014). The geometry of meaning: Semantics based on conceptual spaces. MIT Press.CrossRefGoogle Scholar
Gurr, C. A. (1998). On the isomorphism, or lack of it, of representations. In Marriott, K. & Meyer, B. (Eds.), Visual language theory (pp. 293–306). Springer Verlag.CrossRefGoogle Scholar
Lakoff, G., & Johnson, M. (2008). Metaphors we live by. University of Chicago Press.Google Scholar
Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.CrossRefGoogle Scholar
Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8(3), 382–439.CrossRefGoogle Scholar
Shimojima, A. (2015). Semantic properties of diagrams and their cognitive potentials. CSLI Press.Google Scholar
Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10(1), 89–96.CrossRefGoogle ScholarPubMed
Stenning, K., & Oberlander, J. (1995). A cognitive theory of graphical and linguistic reasoning: Logic and implementation. Cognitive Science, 19(1), 97–140.CrossRefGoogle Scholar
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680.CrossRefGoogle ScholarPubMed
Zhang, J. (1996). A representational analysis of relational information displays. International Journal of Human Computer Studies, 45, 59–74.CrossRefGoogle Scholar