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A Computational Model of Semantic Memory Categorization: Identification of a Concept’s Semantic Level from Feature Sharedness

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Abstract

Recent studies have shown that members of superordinate concepts share less features than members of basic-level concepts. An artificial neural network model was implemented to evaluate whether feature sharedness could distinguish between these two types of concepts and whether lesioning the network would particularly affect less shared features and superordinate categorization. The model was successful in the semantic categorization test, supporting the idea that superordinate and basic-level concepts can be distinguished on the basis of feature sharedness. In contrast, lesion results proved that the model structure was not adequate to evaluate the relation between feature sharedness, processing requirements, and patient performance. Limitations and future directions for modeling semantic memory and for semantic computing are discussed.

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References

  1. Rosch E. Principles of categorization. In: Rosch E, Lloyd BB, editors. Cognition and categorization. Hillsdale, NJ: Lawrence Erlbaum; 1978. p. 27–48.

    Google Scholar 

  2. Rosch E, Mervis CB, Gray WD, Johnson DM, Boyes-Braem P. Basic objects in natural categories. Cogn Psychol. 1976;8:382–439.

    Article  Google Scholar 

  3. Lin EL, Murphy GL, Shoben EJ. The effects of prior processing episodes on basic-level superiority. Q J Exp Psychol. 1997;50:25–48.

    Article  CAS  Google Scholar 

  4. Coley JD, Medin DL, Atran S. Does rank have its privilege? inductive inferences within folkbiological taxonomies. Cognition. 1997;63:73–112.

    Article  Google Scholar 

  5. Johnson KE, Mervis CB. Effects of varying levels of expertise on basic level of categorization. J Exp Psychol Gen. 1997;126:248–77.

    Article  CAS  PubMed  Google Scholar 

  6. Medin DL, Ross N, Atran S, Cox D, Coley J, Proffitt J, et al. Folkbiology of freshwater fish. Cognition. 2006;99:237–73.

    Article  PubMed  Google Scholar 

  7. Warrington EK. The selective impairment of semantic memory. Q J Exp Psychol. 1975;27:635–57.

    Article  CAS  PubMed  Google Scholar 

  8. Hodges JR, Graham N, Patterson K. Charting the progression in semantic dementia: implications for the organization of semantic memory. Memory. 1995;3:463–95.

    Article  CAS  PubMed  Google Scholar 

  9. Humphreys GW, Forde EM. Naming a giraffe but not an animal: base-level but not superordinate naming in a patient with impaired semantics. Cogn Neuropsychol. 2005;22(5):539–58.

    Article  PubMed  Google Scholar 

  10. Crutch SJ, Warrington EK. Contrasting patterns of comprehension for superordinate, basic-level, and subordinate names in semantic dementia and aphasic stoke patients. Cogn Neuropsychol. 2008;25(4):582–600.

    Article  PubMed  Google Scholar 

  11. Raposo A, Mendes M, Marques JF. The hierarchical organization of semantic memory: executive function in the processing of superordinate concepts. NeuroImage. 2012;59:1870–8.

    Article  PubMed  Google Scholar 

  12. Marques JF. The general/specific breakdown of semantic memory and the nature of superordinate knowledge: insights from superordinate and basic-level feature norms. Cogn Neuropsychol. 2007;24(8):879–903.

    Article  PubMed  Google Scholar 

  13. Rumelhart DE. Brain style computation: learning and generalization. In: Davis JL, Zornetzer SF, Lau C, editors. An introduction to neural and electronic networks. San Diego: Academic Press; 1990. p. 405–20.

    Google Scholar 

  14. Rumelhart DE, Todd PM. Learning and connectionist representations. In: Attention and Performance XIV: Synergies in experimental psychology, artificial intelligence and Cogn neuroscience. Cambridge, MA: MIT Press; 1993. pp. 3–30.

  15. Power W, Frank R, Done J, Davey N. A modular attractor model of semantic access. In: Mira J, Sánchez-Andrés J, editors. Foundations and tools for neural modeling. Berlin: Springer; 1999. p. 340–7.

    Chapter  Google Scholar 

  16. McClelland JL, Rogers TT. The parallel distributed processing approach to semantic cognition. Nat Rev Neurosci. 2003;4:310–22.

    Article  CAS  PubMed  Google Scholar 

  17. Rogers TT, McClelland JL. Semantic cognition: a parallel distributed approach. Cambridge, MA: MIT Press; 2004.

    Google Scholar 

  18. Rogers TT, McClelland JL. Précis of semantic cognition: a parallel distributed processing approach. Behav Brain Sci. 2008;31:689–749.

    Article  Google Scholar 

  19. Rogers TT, Patterson K. Object categorization: reversals and explanations of the basic-level advantage. J Exp Psychol Gen. 2007;136(3):451–69.

    Article  PubMed  Google Scholar 

  20. Hinton GE. Implementing semantic networks in parallel hardware. In: Hinton GE, Anderson JA, editors. Parallel models of associative memory. Hillsdale, NL: Lawrence Erlbaum; 1981. p. 161–87.

    Google Scholar 

  21. Rogers TT, Lambon Ralph MA, Garrard P, Bozeat S, McClelland JL, Hodges JR, et al. Structure and deterioration of semantic memory: a neuropsychological and computational investigation. Psychol Rev. 2004;111(1):205–35.

    Article  PubMed  Google Scholar 

  22. Hinton GE, Shallice T. Lesioning an attractor network: investigations of acquired dyslexia. Psychol Rev. 1991;98(1):74–95.

    Article  CAS  PubMed  Google Scholar 

  23. Hinton GE, Plaut DC, Shallice T. Simulation brain damage. Sci Am. 1993;269(4):76–82.

    Article  CAS  PubMed  Google Scholar 

  24. Farah MJ, McClelland JL. A computational model of semantic memory impairment: modality specificity and emergent category specificity. J Exp Psychol Gen. 1991;120(4):339–57.

    Article  CAS  PubMed  Google Scholar 

  25. Lambon Ralph MA, McClelland JL, Patterson K, Galton CJ, Hodges JR. No right to speak? the relationship between object naming and semantic impairment: neuropsychological evidence and a computational model. J Cogn Neurosci. 2001;13(3):341–56.

    Article  CAS  PubMed  Google Scholar 

  26. Cybenko G. Approximation by superpositions of a sigmoidal function. Math Control Signals Syst. 1989;2(4):303–14.

    Article  Google Scholar 

  27. Reed R. Pruning algorithms—a survey. IEEE Trans Neural Netw. 1993;4(5):740–7.

    Article  CAS  PubMed  Google Scholar 

  28. Cantu-Paz E. Pruning neural networks with distribution estimation algorithms. Proceedings of the Genetic and Evolutionary Computation Conference. 2003, 790–800.

  29. Rosch E, Mervis CB. Family resemblances: studies in the internal structural of categories. Cogn Psychol. 1975;7:573–605.

    Article  Google Scholar 

  30. Dry MJ, Storms G. Features of graded category structure: generalizing the family resemblance and polymorphous concept models. Acta Psychol. 2010;133:244–55.

    Article  Google Scholar 

  31. Woollams AM. Apples are not the only fruit: the effects of concept typicality on semantic representation in the anterior temporal lobe. Front Hum Neurosci, 2012; 6: Article 85.

  32. Rendeiro D, Sacramento J, Wichert A. Taxonomical associative memory. Cogn Comput, In press; doi:10.1007/s12559-012-9198-4.

  33. Grassi M, Morbidoni C, Nucci M. A collaborative video annotation system based on semantic web technologies. Cogn Comput. 2012;4:497–514.

    Google Scholar 

  34. Grassi M, Cambria E, Hussain A, Piazza F. Sentic web: a new paradigm for managing social media affective information. Cogn Comput. 2011;3:480–9.

    Article  Google Scholar 

Download references

Acknowledgments

We thank Mel Todd for proofreading and M. Coco for his revision and comments on earlier versions of the manuscript.

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Authors and Affiliations

  1. Faculty of Psychology and Center for Psychological Research, University of Lisbon, Alameda da Universidade, 1649-013, Lisbon, Portugal

    Ana Teresa Santos & J. Frederico Marques

  2. Faculty of Sciences and Laboratory of Agent Modelling, University of Lisbon, Lisbon, Portugal

    Ana Teresa Santos & Luís Correia

Authors
  1. Ana Teresa Santos
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  2. J. Frederico Marques
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  3. Luís Correia
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Corresponding author

Correspondence to J. Frederico Marques.

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Santos, A.T., Marques, J.F. & Correia, L. A Computational Model of Semantic Memory Categorization: Identification of a Concept’s Semantic Level from Feature Sharedness. Cogn Comput 6, 175–181 (2014). https://doi.org/10.1007/s12559-013-9232-1

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  • DOI: https://doi.org/10.1007/s12559-013-9232-1

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