Abstract
Many measures of human verbal behavior deal primarily with semantics (e.g., associative priming, semantic priming). Other measures are tied more closely to orthography (e.g., lexical decision time, visual word-form priming). Semantics and orthography are thus often studied and modeled separately. However, given that concepts must be built upon a foundation of percepts, it seems desirable that models of the human lexicon should mirror this structure. Using a holographic, distributed representation of visual word-forms in BEAGLE [12], a corpus-trained model of semantics and word order, we show that free association data is better explained with the addition of orthographic information. However, we find that orthography plays a minor role in accounting for cue-target strengths in free association data. Thus, it seems that free association is primarily conceptual, relying more on semantic context and word order than word form information.
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References
Clark, J.J., O’Regan, J.K.: Word ambiguity and the optimal viewing position in reading. Vision Res. 39, 843–857 (1998)
Coltheart, M., Rastle, K., Perry, C., Langdon, R., Ziegler, J.: The DRC model: A model of visual word recognition and reading aloud. Psychol. Rev. 108, 204–258 (2001)
Cox, G.E., Kachergis, G., Recchia, G., Jones, M.N.: Towards a scalable holographic word-form representation. Behav. Res. Methods (in press)
Nelson, D.L., McEvoy, C.L., Schreiber, T.A.: The university of south florida word association, rhyme, and word fragment norms (1998), http://www.usf.edu/FreeAssociation/
Davis, C.J.: The spatial coding model of visual word identification. Psychol. Rev. 117(3), 713–758 (2010)
Davis, C.J.: The self-organising lexical acquisition and recognition (SOLAR) model of visual word recognition. Ph.D. thesis. University of New South Wales, Sydney, Australia (1999)
Davis, C.J., Bowers, J.S.: Contrasting five different theories of letter position coding: Evidence from orthographic similarity effects. J. Exp. Psychol. Human 32(3), 535–557 (2006)
Eliasmith, C.: Learning context sensitive logical inference in a neurobiological simulation. In: Levy, S., Gayler, R. (eds.) Compositional Connectionism in Cognitive Science. AAAI Press, Menlo Park (2004)
Hannagan, T., Dupoux, E., Christophe, A.: Holographic string encoding. Cognitive Sci. 35(1), 79–118 (2011)
Hannagan, T., Dandurand, F., Grainger, J.: Broken symmetries in a location invariant word recognition network. Neural Comput. (in press)
Humphreys, M.S., Bain, J.D., Pike, R.: Different ways to cue a coherent memory system: A Theory for episodic, semantic, and procedural tasks. Psychol. Rev. 96(2), 208–233 (1989)
Jones, M.N., Mewhort, D.J.K.: Representing word meaning and order information in a composite holographic lexicon. Psychol. Rev. 114(1), 1–37 (2007)
Kanerva, P.: The spatter code for encoding concepts at many levels. In: P. Int. Conf. Artif. Neural Networ., vol. 1, pp. 226–229. Springer-Verlag, London (1994)
Murdock, B.B.: A theory for the storage and retrieval of item and associative information. Psychol. Rev. 89(3), 609–626 (1982)
Plate, T.A.: Holographic Reduced Representations. CSLI Publications, Stanford (2003)
Whitney, C.: How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. Psychon. B. Rev. 8(2), 221–243 (2001)
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Kachergis, G., Cox, G.E., Jones, M.N. (2011). OrBEAGLE: Integrating Orthography into a Holographic Model of the Lexicon. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_38
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DOI: https://doi.org/10.1007/978-3-642-21735-7_38
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