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EPISODE · Aug 18, 2021 · 27 MIN

Hindsight

from Cognitive Engineering

Identifying a friend or family member from a baby photo seems like a trivial task. Conversely, showing someone a photo of a child and asking them to determine the corresponding adult can be immensely difficult. Why does matching the faces of friends and family to photos create the illusion that it is a simple and straightforward task? In this episode, we look at hindsight bias. Why do our brains present versions of the past to suit the present and is there a qualitative difference between image recognition and extrapolation? We discuss hypothesis generation, intractable computational problems, and the limits of probability distribution in analysis. Finally, we see what evolutionary insight can be gleaned from matching photos of babies to their adult selves and put our own biases to the test by interpreting family photos. A few things we mentioned in this podcast: 30 celebrity baby photos Unfamiliar face matching with photographs of infants and children Unfamiliar faces are not faces: Evidence from a matching task When age-progressed images are unreliable: The roles of external features and age range Comparative evaluation of automatic age progression methodologies For more information on Aleph Insights visit our website https://alephinsights.com or to get in touch about our podcast email [email protected] This podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrp

Episode metadata supplied by the publisher feed · Published Aug 18, 2021

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Identifying a friend or family member from a baby photo seems like a trivial task. Conversely, showing someone a photo of a child and asking them to determine the corresponding adult can be immensely difficult. Why does matching the faces of friends and family to photos create the illusion that it is a simple and straightforward task? In this episode, we look at hindsight bias. Why do our brains present versions of the past to suit the present and is there a qualitative difference between image recognition and extrapolation? We discuss hypothesis generation, intractable computational problems, and the limits of probability distribution in analysis. Finally, we see what evolutionary insight can be gleaned from matching photos of babies to their adult selves and put our own biases to the test by interpreting family photos. A few things we mentioned in this podcast: - 30 celebrity baby photos https://www.insider.com/27-celebrity-baby-photos-that-show-how-theyve-changed-over-the-years-2017-7#mandy-moore-holds-a-beer-bottle-in-her-baby-photo-2 - Unfamiliar face matching with photographs of infants and children https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6001712/ - Unfamiliar faces are not faces: Evidence from a matching task https://core.ac.uk/download/pdf/204312685.pdf - When age-progressed images are unreliable: The roles of external features and age range https://www.sciencedirect.com/science/article/abs/pii/S1355030616301290 - Comparative evaluation of automatic age progression methodologies https://www.researchgate.net/publication/220057621_Comparative_Evaluation_of_Automatic_Age_Progression_Methodologies For more information on Aleph Insights visit our website https://alephinsights.com or to get in touch about our podcast email [email protected] Image: Jabiz Raisdana via Flickr

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Hindsight

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