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Exploring Human Cognition Using Large Image Databases
Author(s) -
Griffiths Thomas L.,
Abbott Joshua T.,
Hsu Anne S.
Publication year - 2016
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12209
Subject(s) - representativeness heuristic , cognition , computer science , artificial intelligence , set (abstract data type) , perception , randomness , natural (archaeology) , contrast (vision) , data science , cognitive science , psychology , social psychology , statistics , mathematics , archaeology , neuroscience , history , programming language
Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well‐controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications. We discuss how some of the challenges of using natural images as stimuli in experiments can be addressed through increased sample sizes, using representations from computer vision, and developing new experimental methods. Finally, we illustrate these points by summarizing recent work using large image databases to explore questions about human cognition in four different domains: modeling subjective randomness, defining a quantitative measure of representativeness, identifying prior knowledge used in word learning, and determining the structure of natural categories.