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A universal approach to modeling visual word recognition and reading: Not only possible, but also inevitable

Published online by Cambridge University Press:  29 August 2012

Ram Frost
Ram Frost*
Affiliation:
Department of Psychology, The Hebrew University, Jerusalem 91905, Israel, and Haskins Laboratories, New Haven, CT 06511. frost@mscc.huji.ac.ilhttp://psychology.huji.ac.il/en/?cmd=Faculty.113&letter=f&act=read&id=42~frost/http://www.haskins.yale.edu/staff/ramfrost.html
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Abstract

I have argued that orthographic processing cannot be understood and modeled without considering the manner in which orthographic structure represents phonological, semantic, and morphological information in a given writing system. A reading theory, therefore, must be a theory of the interaction of the reader with his/her linguistic environment. This outlines a novel approach to studying and modeling visual word recognition, an approach that focuses on the common cognitive principles involved in processing printed words across different writing systems. These claims were challenged by several commentaries that contested the merits of my general theoretical agenda, the relevance of the evolution of writing systems, and the plausibility of finding commonalities in reading across orthographies. Other commentaries extended the scope of the debate by bringing into the discussion additional perspectives. My response addresses all these issues. By considering the constraints of neurobiology on modeling reading, developmental data, and a large scope of cross-linguistic evidence, I argue that front-end implementations of orthographic processing that do not stem from a comprehensive theory of the complex information conveyed by writing systems do not present a viable approach for understanding reading. The common principles by which writing systems have evolved to represent orthographic, phonological, and semantic information in a language reveal the critical distributional characteristics of orthographic structure that govern reading behavior. Models of reading should thus be learning models, primarily constrained by cross-linguistic developmental evidence that describes how the statistical properties of writing systems shape the characteristics of orthographic processing. When this approach is adopted, a universal model of reading is possible.

Type
Author's Response
Information
Behavioral and Brain Sciences , Volume 35 , Issue 5 , October 2012 , pp. 310 - 329
Copyright
Copyright © Cambridge University Press 2012 

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References

Ahissar, M. (2007) Dyslexia and the anchoring-deficit hypothesis. Trends in Cognitive Sciences 11:458–65.Google Scholar
Andrews, S. (2006) All about words: A lexicalist perspective on reading. In: From inkmarks to ideas: Current issues in lexical processing, ed. Andrews, S., pp. 314–48. Psychology Press.Google Scholar
Baayen, R. H. (under review). Learning from the Bible: Computational modeling of the costs of letter transpositions and letter exchanges in reading Classical Hebrew and Modern English.Google Scholar
Baayen, R. H., Milin, P., Durdevic, D. F., Hendrix, P. & Marelli, M. (2011) An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review 118:428–81.CrossRefGoogle ScholarPubMed
Banai, K. & Ahissar, M. (2009) Perceptual learning as a tool for boosting working memory among individuals with reading and learning disability. Learning and Perception 1:115–34.CrossRefGoogle Scholar
Bentin, S. & Frost, R. (1987) Processing lexical ambiguity and visual word recognition in a deep orthography. Memory and Cognition 15:13–23.Google Scholar
Bertram, R., Kuperman, V., Baayen, R. H. & Hyönä, J. (2011) The hyphen as a segmentation cue in triconstituent compound processing: It's getting better all the time. Scandinavian Journal of Psychology 52:530–44.Google Scholar
Bialystok, E., Luk, G. & Kwan, E. (2005) Bilingualism, biliteracy, and learning to read: Interaction among languages and writing systems. Scientific Studies of Reading 9:43–61.Google Scholar
Chomsky, N. (1965) Aspects of the theory of syntax. MIT Press.Google Scholar
Chomsky, N. (1995) The minimalist program. MIT Press.Google Scholar
Chomsky, N. (2006) Language and mind, 3rd edition. Cambridge University Press.Google Scholar
Davis, C. J. (2010) The spatial coding model of visual word identification. Psychological Review 117:713–58.Google Scholar
Evans, N. & Levinson, S. C. (2009) The myth of language universals: Language diversity and its importance for cognitive science. Behavioural and Brain Sciences 32:429–92.Google Scholar
Evans, J., Saffran, J. & Robe-Torres, K. (2009) Statistical learning in children with specific language impairment. Journal of Speech, Language, and Hearing Research 52:321–36.Google Scholar
Friedmann, N. & Haddad-Hanna, M. (in press a) Letter position dyslexia in Arabic: From form to position. Behavioural Neurology 25. doi: 10.3233/BEN-2012-119004.Google Scholar
Frost, R. (1998) Toward a strong phonological theory of visual word recognition: True issues and false trails. Psychological Bulletin 123(1):71–99.Google Scholar
Frost, R. (2003) The robustness of phonological effects in fast priming. In: Masked priming the state of the art, ed. Kinoshita, S. & Lupker, S. J., pp. 173–92. [The Macquarie Monographs in Cognitive Science.] Psychology Press.Google Scholar
Frost, R., Ahissar, M., Gottesman, R. & Tayeb, S. (2003) Are phonological effects fragile? The effect of luminance and exposure duration on form priming and phonological priming. Journal of Memory and Language 48:346–78.Google Scholar
Frost, R. & Yogev, O. (2001) Orthographic and phonological computation in visual word recognition: Evidence from backward masking in Hebrew. Psychonomic Bulletin and Review 8:524–30.CrossRefGoogle ScholarPubMed
Gebhart, A. L., Newport, E. L. & Aslin, R. N. (2009) Statistical learning of adjacent and nonadjacent dependencies among nonlinguistic sounds. Psychonomic Bulletin & Review 14:486–90.Google Scholar
Gilbert, C. D., Sigman, M. & Crist, R. E. (2001) The neural basis of perceptual learning. Neuron 13:681–97.Google Scholar
Gomez, R. (2007) Statistical learning in infant language development. In: Oxford handbook of psycholinguistics, ed. Gaskell, M. G.. Oxford University Press.Google Scholar
Grainger, J., Dufau, S., Montant, M., Ziegler, J. C. & Fagot, J. (2012) Orthographic processing in baboons (Papio papio). Science 336:245–48.Google Scholar
Gronau, N. & Frost, R. (1997) Prelexical phonologic computation in a deep orthography: Evidence from backward masking in Hebrew. Psychonomic Bulletin and Review 4:107–112.Google Scholar
Halliday, M. A. K. (1977) Ideas about language. In: Aims and perspectives in linguistics. Occasional Papers, No. I, pp. 32–55. Applied Linguistics Association of Australia.Google Scholar
Hannagan, T. & Grainger, J. (in press) Protein analysis meets visual word recognition: A case for string kernels in the brain. Cognitive Science. doi:10.1111/j.1551-6709.2012.01236.x Google Scholar
Kasisopa, B., Reilly, R. & Burnham, D. (2010) Orthographic factors in reading Thai: An eye tracking study. In: Proceedings of the Fourth China International Conference on Eye Movements (CICEM), Tianjin, China, May 24–26, 2010, ed. Shen, D., Bai, X., Yan, G. & Rayner, K.. p. 8. Tianjin Normal University.Google Scholar
Lee, C. H. & Taft, M. (2009) Are onsets and codas important in processing letter position? A comparison of TL effects in English and Korean. Journal of Memory and Language 60(4):530–42. doi:10.1016/j.jml.2009.01.002.Google Scholar
Lee, C. H. & Taft, M. (2011) Subsyllabic structure reflected in letter confusability effects in Korean word recognition. Psychonomic Bulletin and Review 18(1):129–34. doi:10.3758/s13423-010-0028-y.Google Scholar
Lerner, I. & Frost, R. (in press) Letter statistics in a learning network model accounts for cross-linguistic differences in letter transposition effects.Google Scholar
Liu, Y., Dunlap, S., Fiez, J. & Perfetti, C. (2007) Evidence for neural accommodation to a writing system following learning. Human Brain Mapping 28:1223–34.Google Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.Google Scholar
Misyak, J. B. & Christiansen, M. H. (2012) Statistical learning and language: An individual differences study. Language Learning 62(1):302–31.Google Scholar
Nazir, T., ben-Boutayab, N., Decoppet, N., Deutsch, A. & Frost, R. (2004) Reading habits, perceptual learning, and the recognition of printed words. Brain and Language 88:294–311.Google Scholar
Pacton, S., Perruchet, P., Fayol, M. & Cleeremans, A. (2001) Implicit learning out of the lab: The case of orthographic regularities. Journal of Experimental Psychology: General 130:401–26.Google Scholar
Perfetti, C. A. (2011) Reading processes and reading problems: Progress toward a universal reading science. In: Dyslexia across languages: Orthography and the brain-gene-behavior link, ed. McCardle, P., Miller, B., Lee, J. R. & Tzeng, O. J. L., pp. 18–32. Brookes.Google Scholar
Perfetti, C. A., Liu, Y., Fiez, J., Nelson, J., Bolger, D. J. & Tan, L-H. (2007) Reading in two writing systems: Accommodation and assimilation in the brain's reading network. Bilingualism: Language and Cognition 10(2):131–46. [Special issue on “Neurocognitive approaches to bilingualism: Asian languages,” ed. P. Li.]Google Scholar
Rastle, K. & Davis, M. H. (2008) Morphological decomposition based on the analysis of orthography. Language and Cognitive Processes 23:942–71.Google Scholar
Rastle, K., Davis, M. H. & New, B. (2004) The broth in my brother's brothel: Morpho-orthographic segmentation in visual word recognition. Psychonomic Bulletin and Review 11:1090–98.Google Scholar
Ravid, D. (2012) Spelling morphology: The psycholinguistics of Hebrew spelling. Springer.Google Scholar
Rayner, K. (1975) The perceptual span and peripheral cues in reading. Cognitive Psychology 7:65–81.CrossRefGoogle Scholar
Seidenberg, M. S. (2011) Reading in different writing systems: One architecture, multiple solutions. In: Dyslexia across languages: Orthography and the brain-gene-behavior link, ed. McCardle, P., Miller, B., Lee, J. R. & Tzeng, O. J. L., pp. 146–68. Paul H. Brookes.Google Scholar
Share, D. L. (2008a) On the Anglocentricities of current reading research and practice: The perils of overreliance on an “outlier” orthography. Psychological Bulletin 134(4):584–615.Google Scholar
Sigman, M. & Gilbert, C. D. (2000) Learning to find a shape. Nature Neuroscience 3:264–69.Google Scholar
Simos, P. G., Breier, J. I., Fletcher, J. M., Foorman, B. R., Castillo, E. M. & Papanicolaou, A. C. (2002) Brain mechanisms for reading words and pseudowords: An integrated approach. Cerebral Cortex 12(3):297–305.Google Scholar
Solomyak, O. & Marantz, A. (2010) Evidence for early morphological decomposition in visual word recognition. Journal of Cognitive Neuroscience 22(9):2042–57.Google Scholar
Taft, M. & Nillsen, C. (in press) Morphological decomposition and the transposed letter-effect. Language and Cognitive Processes.Google Scholar
Tan, L. H., Spinks, J. A., Feng, C. M., Siok, W. T., Perfetti, C. A., Xiong, J., Fox, P. T. & Gao, J. H. (2003) Neural systems of second language reading are shaped by native language. Human Brain Mapping 18:155–66.Google Scholar
Velan, H., Deutsch, A. & Frost, R. (in press) The flexibility of letter-position flexibility: evidence from eye-movements in reading Hebrew.Google Scholar
Velan, H. & Frost, R. (2011) Words with and without internal structure: What determines the nature of orthographic and morphological processing? Cognition 118:141–56.Google Scholar
Whitney, C. (2001) How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. Psychonomic Bulletin and Review 8(2):221–43.Google Scholar
Whitney, C. & Cornelissen, P. (2008) SERIOL reading. Language and Cognitive Processes 23(1):143–64. doi:10.1080/01690960701579771.Google Scholar
Winkler, I., Denham, S. L. & Nelken, I. (2009) Modeling the auditory scene: Predictive regularity representations and perceptual objects. Trends in Cognitive Science 13:532–40.Google Scholar
Wittgenstein, L. (1953) Philosophical Investigations, trans. Anscombe, G. E. M.. Basil Blackwell.Google Scholar