Estimating Semantic Networks of Groups and Individuals from Fluency Data

Jeffrey C. Zemla, Joseph L. Austerweil

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

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 by each network estimation method. To encourage best practices, we discuss the merits of each network estimation technique, provide a flow chart that assists with choosing an appropriate method, and supply code for others to employ these techniques on their own data.

Original languageEnglish (US)
Pages (from-to)36-58
Number of pages23
JournalComputational Brain and Behavior
Volume1
Issue number1
DOIs
StatePublished - Mar 2018
Externally publishedYes

Keywords

  • Bayesian modeling
  • Fluency
  • Knowledge representation
  • Methodology
  • Semantic networks

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Neuropsychology and Physiological Psychology

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