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Updated: Nov 11, 2025

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
Published on: October 11, 2018
Variability leads to overestimation of mean summaries
Yelda Semizer1,2, Aysecan Boduroglu3
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. yelda.semizer@njit.edu.
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.
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