U-INVITE: Estimating Individual Semantic Networks from Fluency Data

Jeffrey C. Zemla, Yoed N. Kenett, Kwang Sung Jun, Joseph L. Austerweil

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

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 languageEnglish
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
EditorsAnna Papafragou, Daniel Grodner, Daniel Mirman, John C. Trueswell
PublisherThe Cognitive Science Society
Pages1907-1912
Number of pages6
ISBN (Electronic)9780991196739
StatePublished - 2016
Externally publishedYes
Event38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 - Philadelphia, United States
Duration: 10 Aug 201613 Aug 2016

Publication series

NameProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016

Conference

Conference38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
Country/TerritoryUnited States
CityPhiladelphia
Period10/08/1613/08/16

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

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