Making sense of time-series data: How language can help identify long-term trends

Harold, Jordan, Coventry, Kenny, Lorenzoni, Irene and Shipley, Thomas (2015) Making sense of time-series data: How language can help identify long-term trends. In: Proceedings of the 37th Annual Meeting of the Cognitive Science Society. Cognitive Science Society, Austin, TX, pp. 872-877. ISBN 9781510809550

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Abstract

Real-world time-series data can show substantial short-term variability as well as underlying long-term trends. Verbal descriptions from a pilot study, in which participants interpreted a real-world line graph about climate change, revealed that trend interpretation might be problematic (Experiment 1). The effect of providing a graph interpretation strategy, via a linguistic warning, on the encoding of longterm trends was then tested using eye tracking (Experiment 2). The linguistic warning was found to direct visual attention to task-relevant information thus enabling more detailed internal representations of the data to be formed. Language may therefore be an effective tool to support users in making appropriate spatial inferences about data.

Item Type: Book Section
Uncontrolled Keywords: sdg 13 - climate action ,/dk/atira/pure/sustainabledevelopmentgoals/climate_action
Faculty \ School: Faculty of Social Sciences > School of Psychology
University of East Anglia > Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research
Faculty of Science > School of Environmental Sciences
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Depositing User: LivePure Connector
Date Deposited: 24 Dec 2019 04:09
Last Modified: 30 Sep 2021 17:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/73441
DOI:

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