Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/83781
Type: | Conference paper |
Title: | Finding hidden types: inductive inference in long-tailed environments |
Author: | Navarro, D. |
Citation: | Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013 / pp.1061-1066 |
Publisher: | Cognitive Science Society |
Publisher Place: | Germany |
Issue Date: | 2013 |
ISBN: | 9780976831891 |
Conference Name: | Annual Meeting of the Cognitive Science Society (35th : 2013 : Berlin, Germany) |
Statement of Responsibility: | Daniel Navarro |
Abstract: | Making inference in everyday life often requires people to make inferences about low frequency events. In the most extreme case, some types of object or event may have never been previously observed. An experiment is presented in which participants needed to infer the existence and number of unobserved event types, based solely on the frequency distribution of a set of observed events. Results indicate people’s inferences are sensitive to the shape of the distribution over the observed events, even when the number of observed events and event types is held constant, and that people are able to infer abstract rules that describe entire classes of event distributions. Human inferences are shown to be similar to those made by a hierarchical Bayesian model. |
Keywords: | inductive inference Bayesian cognition frequency effects concept learning |
Rights: | ©Authors |
Description (link): | http://cognitivesciencesociety.org/conference2013/index.html |
Published version: | https://mindmodeling.org/cogsci2013/papers/0208/index.html |
Appears in Collections: | Aurora harvest Psychology publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_83781.pdf | Published version | 255.68 kB | Adobe PDF | View/Open |
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