title: Exploring Human Cognition Using Large Image Databases.
abstract: 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.
authors.affiliation: Department of Psychology, University of California, Berkeley.
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.affiliation: Department of Psychology, University of California, Berkeley.
authors.forename: Joshua T
authors.initials: JT
authors.lastname: Abbott
authors.affiliation: School of Electronic Engineering and Computer Science, Queen Mary, University of London.
authors.forename: Anne S
authors.initials: AS
authors.lastname: Hsu
mesh_terms: D000465:Algorithms; D003071:Cognition; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008960:Models, Psychological; D011340:Problem Solving
keywords: Big data; Categorization; Computer vision; Natural images; Randomness; Representativeness; Word learning