Categorization, Information Selection and Stimulus Uncertainty

Bibtex entry:

@inproceedings{halpern2017categorization,
	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 noise in stimulus measurement (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. 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.},
	address = {Austin, TX},
	author = {Halpern, D. and Gureckis, T.M.},
	booktitle = {Proceedings of the 39th Annual Conference of the Cognitive Science Society},
	editor = {Gunzelmann, G. and Howes, A. and Tenbrink, T. and Davelaar, E. J.},
	publisher = {Cognitive Science Society},
	title = {Categorization, Information Selection and Stimulus Uncertainty},
	year = {2017}}

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