@inproceedings{32474a80e7564665bd5fb78b0736eb55,
title = "Categorization, Information Selection and Stimulus Uncertainty",
abstract = "Although a common assumption in models of perceptual discrimination, most models of categorization do not explicitly account for uncertainty in stimulus measurement. Such uncertainty may arise from inherent perceptual noise or external measurement noise (e.g., a medical test that gives variable results). In this paper we explore how people decide to gather information from various stimulus properties when each sample or measurement is noisy. The participant's goal is to correctly classify the given item. Across two experiments we find support for the idea that people take category structure into account when selecting information for a classification decision. In addition, we find some evidence that people are also sensitive to their own perceptual uncertainty when selecting information.",
keywords = "attention, categorization, information sampling",
author = "Halpern, {David J.} and Gureckis, {Todd M.}",
note = "Funding Information: Acknowledgments This research was supported by NSF grant BCS-1255538 and a John S. McDonnell Foundation Scholar Award to TMG. Publisher Copyright: {\textcopyright} CogSci 2017.; 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 ; Conference date: 26-07-2017 Through 29-07-2017",
year = "2017",
language = "English (US)",
series = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition",
publisher = "The Cognitive Science Society",
pages = "464--469",
booktitle = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society",
}