@inproceedings{626c08ee95564965b9401b476b93376f,
title = "Sparse category labels obstruct generalization of category membership",
abstract = "Studies of human category learning typically focus on situations where explicit category labels accompany each example (supervised learning) or on situations were people must infer category structure entirely from the distribution of unlabeled examples (unsupervised learning). However, real-world category learning likely involves a mixture of both types of learning (semi-supervised learning). Surprisingly, a number of recent findings suggest that people have difficulty learning in semi-supervised tasks. To further explore this issue, we devised a category learning task in which the distribution of labeled and unlabeled items suggested alternative organizations of a category. This design allowed us to determine whether learners combined information from both types of episodes via their patterns of generalization at test. In contrast with the prediction of many models, we find little evidence that unlabeled items influenced categorization behavior when labeled items were also present.",
keywords = "Semi-supervised category learning, rule induction, unsupervised learning",
author = "McDonnell, {John V.} and Jew, {Carol A.} and Gureckis, {Todd M.}",
note = "Funding Information: We thank Seth Madlon-Kay and Dylan Simon for helpful comments and discussion in the development of this project. TMG was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of the Interior (DOI) contract D10PC20023. e U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. e views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI, or the U.S. Government. Funding Information: We thank Seth Madlon-Kay and Dylan Simon for helpful comments and discussion in the development of this project. TMG was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of the Interior (DOI) contract D10PC20023. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI, or the U.S. Government. Publisher Copyright: {\textcopyright} CogSci 2012.All rights reserved.; 34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012 ; Conference date: 01-08-2012 Through 04-08-2012",
year = "2012",
language = "English (US)",
series = "Building Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012",
publisher = "The Cognitive Science Society",
pages = "749--754",
editor = "Naomi Miyake and David Peebles and Cooper, {Richard P.}",
booktitle = "Building Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012",
}