Variability leads to overestimation of mean summaries
Name
13414_2021_2269_ReferencePDF.pdf
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1.22 MB
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Checksum (MD5)
03b44fafb23dbbeaff75090a9b541de4
Author(s)
Semizer, Yelda
Boduroglu, Aysecan
Date Issued
March 26, 2021
Publisher
Springer US
Version
Author's final manuscript
Abstract
Abstract
Research on ensemble perception has shown that people can extract both mean and variance information, but much less is understand how these two different types of summaries interact with one another. Some research has argued that people are more erroneous in extracting the mean of displays that have greater variability. In all three experiments, we manipulated the variability in the displays. Participants reported the mean size of a set of circles (Experiment 1) and mean length of horizontally placed (Experiment 2a) and randomly oriented lines (Experiment 2b). In all experiments, we found that mean size estimations were more erroneous for higher than smaller variance displays. More critically, there was a tendency to overestimate the mean, driven by variance in both task-relevant and task-irrelevant features. We discuss these findings in relation to limitations in concurrent summarization ability and outlier discounting in ensemble perception.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.3758/s13414-021-02269-2
