September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Learning to calibrate age estimates
Author Affiliations & Notes
  • Jordan W Suchow
    Department of Psychology, University of California, Berkeley
  • Thomas L Griffiths
    Department of Psychology, Princeton University
Journal of Vision September 2019, Vol.19, 188b. doi:
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      Jordan W Suchow, Thomas L Griffiths; Learning to calibrate age estimates. Journal of Vision 2019;19(10):188b.

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      © ARVO (1962-2015); The Authors (2016-present)

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Age is a primary social category and, with little effort, we can quickly approximate it from photographs. Here, we analyze 1.5 million age judgments derived from a popular online website where participants estimate the age of a person depicted in a photograph, with feedback. We find that median age judgments across participants are linear in the actual age, with little bias. However, the slope is considerably less than one, such that the aggregate overestimates the age of younger people and underestimates the age of older people. Age estimates are found to be unbiased at 37.5 years, which coincides with the median age across all the depicted persons. These results are consistent with an account in which, over time, participants learn to calibrate an analogue magnitude to the learned distribution of encountered ages, combining photographic evidence with distributional information to arrive at an estimate that balances the two.


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