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{
    "pk": 29293,
    "title": "Learning to calibrate age estimates",
    "subtitle": null,
    "abstract": "Age is a primary social category and, with little effort, we can quickly approximate it from photographs. Here, we analyze1.5 million age judgments derived from a popular online website where participants estimate the age of a person depicted ina photograph, with feedback. We find that median age judgments across participants are linear in the actual age, with littlebias. However, the slope is considerably less than one, such that the aggregate overestimates the age of younger peopleand underestimates the age of older people. Age estimates are found to be unbiased at 37.5 years, which coincides with themedian age across all the depicted persons. These results are consistent with an account in which, over time, participantslearn to calibrate an analogue magnitude to the learned distribution of encountered ages, combining photographic evidencewith distributional information to arrive at an estimate that balances the two.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Member Abstracts",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/6tn8g7mn",
    "frozenauthors": [
        {
            "first_name": "Jordan",
            "middle_name": "",
            "last_name": "Suchow",
            "name_suffix": "",
            "institution": "Stevens Institute of Technology",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2019-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/29293/galley/19164/download/"
        }
    ]
}