T
Todd M. Gureckis
Researcher at New York University
Publications - 120
Citations - 4986
Todd M. Gureckis is an academic researcher from New York University. The author has contributed to research in topics: Concept learning & Categorization. The author has an hindex of 27, co-authored 119 publications receiving 4210 citations. Previous affiliations of Todd M. Gureckis include Center for Neural Science & Indiana University.
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Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research
TL;DR: This paper replicates a diverse body of tasks from experimental psychology including the Stroop, Switching, Flanker, Simon, Posner Cuing, attentional blink, subliminal priming, and category learning tasks using participants recruited using AMT.
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SUSTAIN: A Network Model of Category Learning.
TL;DR: SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.
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Self-Directed Learning: A Cognitive and Computational Perspective.
Todd M. Gureckis,Douglas Markant +1 more
TL;DR: This review argues that recent advances in these related fields may offer a fresh theoretical perspective on how people gather information to support their own learning.
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psiTurk: An open-source framework for conducting replicable behavioral experiments online.
Todd M. Gureckis,Jay B. Martin,John V. McDonnell,Alexander S. Rich,Doug Markant,Anna Coenen,David Halpern,Jessica B. Hamrick,Patricia Angie Chan +8 more
TL;DR: The basic architecture of the psiTurk system is described and new users are introduced to the overall goals, which aims to reduce the technical hurdles for researchers developing online experiments while improving the transparency and collaborative nature of the behavioral sciences.
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Is it better to select or to receive? Learning via active and passive hypothesis testing.
Douglas Markant,Todd M. Gureckis +1 more
TL;DR: It is suggested that differences between these 2 learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test his or her own individual hypothesis and can lead to the collection of data that facilitates learning compared with reception learning.