@inproceedings{d72e7db8d84c48ab9b7a3ad201ab1e17,
title = "U-INVITE: Estimating Individual Semantic Networks from Fluency Data",
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.",
keywords = "fluency, memory retrieval, probabilistic modeling, random walk, semantic networks",
author = "Zemla, {Jeffrey C.} and Kenett, {Yoed N.} and Jun, {Kwang Sung} and Austerweil, {Joseph L.}",
note = "Publisher Copyright: {\textcopyright} 2016 Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. All rights reserved.; 38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 ; Conference date: 10-08-2016 Through 13-08-2016",
year = "2016",
language = "English (US)",
series = "Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016",
publisher = "The Cognitive Science Society",
pages = "1907--1912",
editor = "Anna Papafragou and Daniel Grodner and Daniel Mirman and Trueswell, {John C.}",
booktitle = "Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016",
}