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. 2014 Apr 16;9(4):e94291.
doi: 10.1371/journal.pone.0094291. eCollection 2014.

CDP++.Italian: modelling sublexical and supralexical inconsistency in a shallow orthography

Affiliations

CDP++.Italian: modelling sublexical and supralexical inconsistency in a shallow orthography

Conrad Perry et al. PLoS One. .

Abstract

Most models of reading aloud have been constructed to explain data in relatively complex orthographies like English and French. Here, we created an Italian version of the Connectionist Dual Process Model of Reading Aloud (CDP++) to examine the extent to which the model could predict data in a language which has relatively simple orthography-phonology relationships but is relatively complex at a suprasegmental (word stress) level. We show that the model exhibits good quantitative performance and accounts for key phenomena observed in naming studies, including some apparently contradictory findings. These effects include stress regularity and stress consistency, both of which have been especially important in studies of word recognition and reading aloud in Italian. Overall, the results of the model compare favourably to an alternative connectionist model that can learn non-linear spelling-to-sound mappings. This suggests that CDP++ is currently the leading computational model of reading aloud in Italian, and that its simple linear learning mechanism adequately captures the statistical regularities of the spelling-to-sound mapping both at the segmental and supra-segmental levels.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. CDP++.Italian.
Note f = feature, l = letter, S = Stress, o = onset, v = vowel, c = coda. Numbers correspond to the overall slot number with the letter and feature nodes or the particular slot within an onset, vowel, or coda grouping for the rest of the representations. The thick divisors in the Phoneme Output Buffer represent syllable boundaries. The grapheme and phoneme nodes in the TLA network are simply used as an example, and do not correspond to the actual set of graphemes used in the network.
Figure 2
Figure 2. The graphemic parser.
Note: t = time; L = Letter.
Figure 3
Figure 3. Percentage of graphemes selected incorrectly with networks trained on different numbers of exemplars over 15 cycles of training.
Figure 4
Figure 4. Overall results from the stress regularity/consistency studies.
Note: Pen = Penultimate; Ante = Antepenultimate.

References

    1. Venezky RL (1970) The structure of English orthography. Netherlands, The Hague: Mouton & Co.
    1. Yang JF, McCandliss BD, Shu H, Zevin J (2009) Simulating language-specific and language-general effects in a statistical learning model of Chinese reading. Journal of Memory and Language 61: 238–257. - PMC - PubMed
    1. Ziegler JC, Perry C, Coltheart M (2000) The DRC model of visual word recognition and reading aloud: An extension to German. European Journal of Cognitive Psychology 12: 413–430.
    1. Perry C, Ziegler JC, Zorzi M (2007) Nested modeling and strong inference resting in the development of computational theories: The CDP+ model of reading aloud. Psychological Review 27: 301–333. - PubMed
    1. Perry C, Ziegler JC, Zorzi M (2010) Beyond single syllables: Large-scale modeling of reading aloud with the Connectionist Dual Process (CDP++) model Cognitive Psychology. 61: 106–151. - PubMed

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