Research Report

Cognitive Processing

, Volume 12, Issue 2, pp 183-196

The semantic organization of the animal category: evidence from semantic verbal fluency and network theory

  • Joaquín GoñiAffiliated withDepartment of Neurosciences. Center for Applied Medical Research, University of NavarraDepartment of Physics and Applied Mathematics, University of Navarra
  • , Gonzalo ArrondoAffiliated withDepartment of Neurosciences. Center for Applied Medical Research, University of Navarra
  • , Jorge SepulcreAffiliated withDepartment of Neurosciences. Center for Applied Medical Research, University of Navarra
  • , Iñigo MartincorenaAffiliated withDepartment of Neurosciences. Center for Applied Medical Research, University of Navarra
  • , Nieves Vélez de MendizábalAffiliated withDepartment of Neurosciences. Center for Applied Medical Research, University of Navarra
  • , Bernat Corominas-MurtraAffiliated withICREA-Complex Systems Lab, Universitat Pompeu Fabra-Parc de Recerca Biomèdica de Barcelona
  • , Bartolomé BejaranoAffiliated withDepartment of Neurosciences. Center for Applied Medical Research, University of Navarra
  • , Sergio Ardanza-TrevijanoAffiliated withDepartment of Physics and Applied Mathematics, University of Navarra
  • , Herminia PeraitaAffiliated withDepartment of Psychology, National University of Distance Education (UNED)
    • , Dennis P. WallAffiliated withThe Center for Biomedical Informatics, Harvard Medical School
    • , Pablo VillosladaAffiliated withDepartment of Neurosciences, Institut d’investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Email author 

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Abstract

Semantic memory is the subsystem of human memory that stores knowledge of concepts or meanings, as opposed to life-specific experiences. How humans organize semantic information remains poorly understood. In an effort to better understand this issue, we conducted a verbal fluency experiment on 200 participants with the aim of inferring and representing the conceptual storage structure of the natural category of animals as a network. This was done by formulating a statistical framework for co-occurring concepts that aims to infer significant concept–concept associations and represent them as a graph. The resulting network was analyzed and enriched by means of a missing links recovery criterion based on modularity. Both network models were compared to a thresholded co-occurrence approach. They were evaluated using a random subset of verbal fluency tests and comparing the network outcomes (linked pairs are clustering transitions and disconnected pairs are switching transitions) to the outcomes of two expert human raters. Results show that the network models proposed in this study overcome a thresholded co-occurrence approach, and their outcomes are in high agreement with human evaluations. Finally, the interplay between conceptual structure and retrieval mechanisms is discussed.

Keywords

Verbal fluency Switching-clustering Semantic memory Network theory