Estimating Semantic Networks of Groups and Individuals from Fluency Data

Estimating Semantic Networks of Groups and Individuals from Fluency Data One popular and classic theory of how the mind encodes knowledge is an associative semantic network, where concepts and associations between concepts correspond to nodes and edges, respectively. A major issue in semantic network research is that there is no consensus among researchers as to the best method for estimating the network of an individual or group. We propose a novel method (U-INVITE) for estimating semantic networks from semantic fluency data (listing items from a category) based on a censored random walk model of memory retrieval. We compare this method to several other methods in the literature for estimating networks from semantic fluency data. In simulations, we find that U-INVITE can recover semantic networks with low error rates given only a moderate amount of data. U-INVITE is the only known method derived from a psychologically plausible process model of memory retrieval and one of two known methods that we found to be consistent estimators of this process: if semantic memory retrieval is consistent with this process, the procedure will eventually estimate the true network (given enough data). We conduct the first exploration of different methods for estimating psychologically valid semantic networks by comparing people’s similarity judgments of edges estimated http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Brain & Behavior Springer Journals

Estimating Semantic Networks of Groups and Individuals from Fluency Data

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Publisher
Springer International Publishing
Copyright
Copyright © 2018 by Springer International Publishing
Subject
Psychology; Cognitive Psychology; Mathematical Models of Cognitive Processes and Neural Networks; Psychological Methods/Evaluation
ISSN
2522-0861
eISSN
2522-087X
D.O.I.
10.1007/s42113-018-0003-7
Publisher site
See Article on Publisher Site

Abstract

One popular and classic theory of how the mind encodes knowledge is an associative semantic network, where concepts and associations between concepts correspond to nodes and edges, respectively. A major issue in semantic network research is that there is no consensus among researchers as to the best method for estimating the network of an individual or group. We propose a novel method (U-INVITE) for estimating semantic networks from semantic fluency data (listing items from a category) based on a censored random walk model of memory retrieval. We compare this method to several other methods in the literature for estimating networks from semantic fluency data. In simulations, we find that U-INVITE can recover semantic networks with low error rates given only a moderate amount of data. U-INVITE is the only known method derived from a psychologically plausible process model of memory retrieval and one of two known methods that we found to be consistent estimators of this process: if semantic memory retrieval is consistent with this process, the procedure will eventually estimate the true network (given enough data). We conduct the first exploration of different methods for estimating psychologically valid semantic networks by comparing people’s similarity judgments of edges estimated

Journal

Computational Brain & BehaviorSpringer Journals

Published: Jun 6, 2018

References

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