A Developmental Approach to Machine Learning?

Linda B. Smith, Lauren K. Slone

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
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Abstract

Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order – with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines.
Original languageEnglish
Article number2124
JournalFrontiers in Psychology
Volume8
DOIs
Publication statusPublished - 5 Dec 2017
Externally publishedYes

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