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Do Computational Models of Reading Need a Bit of Semantics?

  • Remo Job
  • Claudio Mulatti
Part of the Studies in Computational Intelligence book series (SCI, volume 64)

Summary. Coltheart, Rastle, Perry, Langdon, and Ziegler [1] claim that “the psychology of reading has been revolutionized by the development of computational models of visual word recognition and reading aloud”. They attribute this to the fact that a computational model is a computer program – an algorithm – “that is capable of performing the cognitive task of interest and does so by using exactly the same information-processing procedures as are specified in a theory of how people carry out this cognitive activity” [1, p. 204]. According to this view, the computational model is the theory, not a simple instantiation of a theory. In this paper we argue that computational models of reading have indeed helped in dealing with such a complex system, in interpreting the phenomena underlying it, and in making sense of the experimental data. However, we also argue that it is crucial for a model of reading to implement a computational semantic system that is as yet a missing component of all computational models. We provide two reasons for such a move. First, this would allow explaining some phenomena arising from the interaction of semantics and lexical variables. We will review the following empirical findings: faster response times to polysemic words [2] and slower response times to synonyms [3]; the leotard [4] and turple effects [5]; and the asymmetry of the neighbourhood density effect in free and conditional reading [6]. Second, such an “enriched” model would be able to account for a richer set of tasks than current computational models do. Specifically, it would simulate tasks that require access to semantic representation to be performed, such as semantic categorization and semantically-based conditional naming. We will present a computational instantiation of a semantic module that accounts for all the described phenomena, and that has helped in generating predictions that guides on-going experimental activity.

Keywords

Visual Word Recognition Ambiguous Word Dense Neighborhood Semantic System Orthographic Neighborhood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Remo Job
    • 1
  • Claudio Mulatti
    • 2
  1. 1.Università degli Studi di TrentoTrentoItaly
  2. 2.Università degli Studi di PadovaPadovaItaly

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