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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01v692t9600
Title: Detecting Climate Change in Binary and Continuous Data
Authors: Liu, Grace
Advisors: Griffiths, Tom L
Department: Computer Science
Class Year: 2024
Publisher: Princeton, NJ : Princeton University
Abstract: In this paper, we investigate whether and why people perceive greater change in binary climate data (e.g., lake freeze) compared to continuous climate data (e.g., winter temperature). We conduct a behavioral experiment and find that participants who view binary climate data observed greater change than participants who view continuous climate data. We explore emotional valence and changepoint saliency as potential explanations for this behavior through cognitive experiments and Bayesian changepoint detection.
URI: http://arks.princeton.edu/ark:/88435/dsp01v692t9600
Type of Material: Academic dissertations (M.S.E.)
Language: en
Appears in Collections:Computer Science, 2023

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