Making Sense of Time-Series Data: How Language Can Help Identify Long-Term Trends

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 long-term 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.


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