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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 37th Annual Meeting of the Cognitive Science Society
Place of PublicationAustin, TX
PublisherCognitive Science Society
Pages872-877
Number of pages6
ISBN (Electronic)978-0-9911967-2-2
ISBN (Print)9781510809550
Publication statusPublished - 2015

Cite this