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

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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.

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Notes

  1. The absence of auto-loops ensures that all entries of the main diagonal (a ii entries) are 0.

  2. Performing a hierarchical clustering directly on the adjacency matrix and setting a threshold in the dendrogram is among the most basic and common approaches used to find modules. Nevertheless, it must be acknowledged that inferred adjacency matrices from empirical data are often noisy or incomplete. This severely affects hierarchical clustering evaluation and misleads the selection of an accurate cutoff value for module detection.

  3. For any m value, GTOM output is a normalized overlap matrix with values between 0 and 1 containing interconnectedness shared information for every pair of nodes.

  4. Every word was converted to its singular and three pure synonyms were unified. Finally, one word that was not an animal was removed.

  5. While methodologies based on co-occurrences have been successfully used to study language networks (Solé et al. 2010), it is important to remark that syntactic constraints severely reduce the possible orderings of items with respect to verbal fluency outputs, where position of concepts is unrestricted.

  6. For instance, l = 1 indicate that they are consecutive words. In general, l = n indicate that there are n − 1 words between the two words under study.

  7. A more individualized approach could be done by assessing individual test sizes instead.

  8. It is assumed that sequences, i.e. tests, do not contain repeated elements. In the unlikely event of finding a word repeated in a test, neighborhoods for all appearances are considered to obtain co-occurrences.

  9. It is straightforward to see that, when \(l=N-1, P_{w_{i},w_{j}}^{(\le l)}= 2\sum_{i=1}^{N-1} {\frac{N-i}{\left[N\atop 2\right]}}=1\).

  10. Setting l = 1 would only consider associations for strictly consecutive words, which are more likely to be related with respect to more distant concepts. The high-order variability naming related concepts requires of a large dataset to capture most relationships. A solution to overcome this issue consists of increasing parameter l. However, large windows provide more candidates for establishing relationships of words but at the same time, they reduce the significance of nearby concepts (method explained below) and are more likely to induce meaningless co-occurrences.

  11. For instance, a word named once would be automatically linked to any word named less than 32 times, considering that N = 31.57 and l = 2 in our dataset.

  12. Removing 39% of distinct words might seem a severe filtering, but they only represented 3.5% of all word occurrences within the tests as they were very low frequent items. Such small reduction of evidence is indeed one step ahead of previous works where semantic distance approaches have been applied to those words either said by a minimum of around 30% of participants or to most named words (threshold set around 12 occurrences) (Henley 1969; Chan et al. 1993; Aloia et al. 1996; Schwartz and Baldo 2001; Prescott et al. 2006).

  13. Those words with no significant interactions were not included in the network (4 words) since they represented isolated words that prevent a network analysis. Additionally, the isolated pair eel-elver was also removed for the same reason, leaving a total of 236 nodes in the network.

  14. Raters had experience at the evaluation of verbal fluency tests in healthy controls and neurological patients. They were asked to judge whether each transition between two words was between animals from the same or different subcategories and had for guidance two articles with rules on how to evaluate clustering and switching (Troyer 2000; Villodre et al. 2006). Raters were blind to the results produced by the in-silico evaluations.

  15. These figures are close to the results of 423 distinct animals, and 175 named only once obtained from 21 participants during 10 min somewhere else (Henley 1969) and might be indicating an average magnitude of the human lexicon size in the category of animals.

  16. The information regarding modularity provided by this matrix is the presence or absence of discrete blocks along the diagonal. When there is no modularity in a network, as it occurs in random graphs, no blocks appear independently of the number of neighborhood expansions until the graph represents itself one module. For those networks where modularity emerges, the selection of a hierarchical clustering cutoff (0.58 in our data) must separate those blocks as well as possible to get a feasible partition of the network in modules.

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Acknowledgments

We would like to acknowledge Ricard V. Solé, Jean Bragard and John F. Wesseling for helpful discussions; Lluis Samaranch for his useful comments and for being rater 2. JG to UTE project CIMA. BCM to James McDonnell Foundation. SAT to project MTM 2009-14409-C02-01. We also thank the referees for their thorough review and highly appreciate their comments and suggestions.

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Authors and Affiliations

  1. Department of Neurosciences. Center for Applied Medical Research, University of Navarra, Pamplona, Spain

    Joaquín Goñi, Gonzalo Arrondo, Jorge Sepulcre, Iñigo Martincorena, Nieves Vélez de Mendizábal & Bartolomé Bejarano

  2. Department of Physics and Applied Mathematics, University of Navarra, Pamplona, Spain

    Joaquín Goñi & Sergio Ardanza-Trevijano

  3. ICREA-Complex Systems Lab, Universitat Pompeu Fabra-Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain

    Bernat Corominas-Murtra

  4. Department of Psychology, National University of Distance Education (UNED), Madrid, Spain

    Herminia Peraita

  5. The Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Dennis P. Wall

  6. Department of Neurosciences, Institut d’investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain

    Pablo Villoslada

Authors
  1. Joaquín Goñi
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  2. Gonzalo Arrondo
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  3. Jorge Sepulcre
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  4. Iñigo Martincorena
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  5. Nieves Vélez de Mendizábal
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  6. Bernat Corominas-Murtra
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  7. Bartolomé Bejarano
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  8. Sergio Ardanza-Trevijano
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  9. Herminia Peraita
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  10. Dennis P. Wall
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  11. Pablo Villoslada
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Corresponding author

Correspondence to Pablo Villoslada.

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Appendix

Appendix

A Matlab (The Mathworks Inc., Natick, MA, USA) implementation of the methodology described in this study is available as electronic supplementary material. It starts with a verbal fluency dataset and at the last step obtains an enriched conceptual network. In order to ease the use of the code, all these files contain step-by-step explanations and references to sections and equations of this manuscript where appropriate. The script batch_verbal_fluency.m is also very helpful to comprehend the process in a global manner. The modular implementation of the different functions permits their independent use.

  • batch_verbal_fluency.m: It is the general script that deals with the whole process from the verbal fluency data to the enriched conceptual network. It uses the functions described below.

  • count_words.m: function that counts the number of words of each verbal fluency test contained in the dataset.

  • get_rel_frequencies.m: function that gets the relative frequencies of each word included in the verbal fluency data.

  • getco_occurrences.m: function that counts the number of co-occurrences of every pair words for a given maximum distance (parameter l)

  • get_statistical_co_occurrences.m: function that performs the statistical approach described in the paper for the network inference.

  • get_components.m: function that obtains the components of an undirected graph. This is used to obtain the giant component of the conceptual network.

  • computeGTOM.m: function that performs the modularity analysis using the Generalized Topological Overlap Measure.

  • enrich_newtork.m: function that performs the enrichment process of a network according to its modularity analysis (which is the output of computeGTOM in our case).

  • write_graph_links.m: function that writes pairs of words that are linked in a graph according to a dictionary into a file. Each line consists of a pair word,word.

    The verbal fluency data of the 200 subjects used in this study are available in the file data.mat, which can be loaded typing load data.mat in a Matlab environment. The dictionaries of the 236 words included in the networks are available in dictionaries.mat (first column in Spanish, second column in English). In the case of Spanish, acute accents and dieresis were omitted and letter ñ was substituted by n.

    Finally, both CN and ECN graphs have been included in Spanish (original language of the tests) and English (translation made by the authors). These files include all the pair of words that are connected (i.e. links of the graph) in a comma separated value format (.csv). These files can be easily visualized as graphs with programs such as Pajek (Batagelj and Mrvar 2002) or Cytoscape (Shannon et al. 2003).

  • CN_spa.csv is the conceptual network (CN) with animals written in English (graph with 236 nodes and 611 links).

  • CN_eng.csv is the conceptual network (CN) with animals written in Spanish (graph with 236 nodes and 611 links).

  • ECN_spa.csv is the enriched conceptual network (ECN) with animals written in Spanish (graph with 236 nodes and 2357 links).

  • ECN_eng.csv is the enriched conceptual network (ECN) with animals written in English (graph with 236 nodes and 2357 links).

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Goñi, J., Arrondo, G., Sepulcre, J. et al. The semantic organization of the animal category: evidence from semantic verbal fluency and network theory. Cogn Process 12, 183–196 (2011). https://doi.org/10.1007/s10339-010-0372-x

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