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1 Integrative Neuroscience Laboratory, Physics Department, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina
2 Computer Science Department, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina
Free-word association has been used as a vehicle to understand the organization of human thoughts. The original studies relied mainly on qualitative assertions, yielding the widely intuitive notion that trajectories of word associations are structured, yet considerably more random than organized linguistic text. Here we set to determine a precise characterization of this space, generating a large number of word association trajectories in a web implemented game. We embedded the trajectories in the graph of word co-occurrences from a linguistic corpus. To constrain possible transport models we measured the memory loss and the cycling probability. These two measures could not be reconciled by a bounded diffusive model since the cycling probability was very high (16 % of order-2 cycles) implying a majority of short-range associations whereas the memory loss was very rapid (converging to the asymptotic value in ∼ 7 steps) which, in turn, forced a high fraction of long-range associations. We show that memory loss and cycling probabilities of free word association trajectories can be simultaneously accounted by a model in which transitions are determined by a scale invariant probability distribution.
Keywords: word association, graph theory, semantics, Markov, networks, simulations
Citation: Elias Costa M, Bonomo F and Sigman M (2009) Scale-invariant transition probabilities in free word association trajectories. Front. Integr. Neurosci. 3:19. doi:10.3389/neuro.07.019.2009
Received: 26 May 2009; paper pending published: 12 June 2009; accepted: 06 August 2009; published online: 11 September 2009.
Edited by:
Sidarta Ribeiro, Edmond and Lily Safra International Institute of Neuroscience of Natal, Brazil; Federal University of Rio Grande do Norte, Brazil
Reviewed by:
Guillermo A. Cecchi, IBM Watson Research Center, USA
Mauro Copelli, Federal University of Pernambuco, Brazil
Osame Kinouchi, Universidade de São Paulo, Brazil
Copyright: © 2009 Elias Costa, Bonomo and Sigman. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
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