Abstract
Semantic networks have been used extensively in psychology to describe how humans organize facts and knowledge in memory. Numerous methods have been proposed to construct semantic networks using data from memory retrieval tasks, such as the semantic fluency task (listing items in a category). However these methods typically generate group-level networks, and sometimes require a very large amount of participant data. We present a novel computational method for estimating an individual's semantic network using semantic fluency data that requires very little data. We establish its efficacy by examining the semantic relatedness of associations estimated by the model.
Original language | English |
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Title of host publication | Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016 |
Editors | Anna Papafragou, Daniel Grodner, Daniel Mirman, John C. Trueswell |
Publisher | The Cognitive Science Society |
Pages | 1907-1912 |
Number of pages | 6 |
ISBN (Electronic) | 9780991196739 |
State | Published - 2016 |
Externally published | Yes |
Event | 38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 - Philadelphia, United States Duration: 10 Aug 2016 → 13 Aug 2016 |
Publication series
Name | Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016 |
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Conference
Conference | 38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 |
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Country/Territory | United States |
City | Philadelphia |
Period | 10/08/16 → 13/08/16 |
Bibliographical note
Publisher Copyright:© 2016 Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. All rights reserved.
Keywords
- fluency
- memory retrieval
- probabilistic modeling
- random walk
- semantic networks