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Censor Detection: When and how do people generalize from censored evidence?

Authors
Prof. Brett Hayes
University of New South Wales ~ Psychology
Dr. Saoirse Connor Desai
University of New South Wales ~ Psychology
Charles Kemp
University of Melbourne ~ Psychology
Abstract

When do people that data has been “censored” from an evidence sample and how do they respond? The present work examines 1) how people generalize from a smaller sample that may have been subject to censoring, to a larger sample, 2) compares inferences based on different sample distributions, and 3) inferences with and without a censoring prompt. Participants sampled on-line quality ratings of a novel restaurant that followed several different distributions (e.g., bimodal, left-skewed), summarized in a frequency distribution figure. They then constructed their own frequency distribution of a larger “population” of ratings and answered questions about the trustworthiness/believability of the initial sample. Participants were more likely to “fill in” missing data when the sample distribution observations were sparse (e.g., one-star ratings), or were inconsistent with priors about distribution shape. Human responses were compared with predictions of a computational model that reproduced the initial sample, a Bayesian model that assumed no censoring, a Bayesian “censoring” model, and a model that averages the empirical priors and initial observations. The averaging model performed best but did not capture responses in the sparse observation conditions. Results suggest people factor in both their prior distributional beliefs and observed sample data when generalizing from censored data.

Tags

Keywords

Censoring
Sample Bias
Reasoning
Generalization

Topics

Mathematical Psychology
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Cite this as:

Hayes, B. K., Connor Desai, S., & Kemp, C. (2021, February). Censor Detection: When and how do people generalize from censored evidence? Paper presented at Australasian Mathematical Psychology Conference 2021. Via mathpsych.org/presentation/338.