Item performance in visual word recognition

Psychonomic Bulletin and Review

Volumn 16, Issue 3, 2009, Pages 600-608

  Rey, Arnaud ^a   Courrieu, Pierre ^a   Schmidt Weigand, Florian ^b   Jacobs, Arthur M ^c  

^a AIX MARSEILLE UNIVERSITY   (France)

^b UNIVERSITY OF KASSEL   (Germany)

^c FREIE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER PROGRAM; CONTRAST SENSITIVITY; HUMAN; LINGUISTICS; MATHEMATICAL COMPUTING; PATTERN RECOGNITION; PERCEPTION; PSYCHOLOGICAL MODEL; READING; SEMANTICS;

CONTRAST SENSITIVITY; HUMANS; MATHEMATICAL COMPUTING; MODELS, PSYCHOLOGICAL; PATTERN RECOGNITION, VISUAL; PERCEPTUAL MASKING; PSYCHOLINGUISTICS; READING; SEMANTICS; SOFTWARE;

EID: 67650564836     PISSN: 10699384     EISSN: 15315320     Source Type: Journal    
DOI: 10.3758/PBR.16.3.600     Document Type: Article

References (17)

1. Ans, B., Carbonnel, S., & Valdois, S. (1998). A connectionist multiple-trace memory model for polysyllabic word reading. Psychological Review, 105, 678-723.
2. Baayen, R. H., Piepenbrock, R., & van Rijn, H. (1993). The CELEX lexical database (CD-ROM). Philadelphia: Linguistic Data Consortium, University of Pennsylvania.
3. Balota, D. A., & Spieler, D. H. (1998). The utility of item-level analyses in model evaluation: A reply to Seidenberg and Plaut. Psychological Science, 9, 238-240.
4. Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256.
5. Good, P. (1994). Permutation tests: A practical guide to resampling methods for testing hypotheses. New York: Springer.
6. Grainger, J., & Jacobs, A. M. (1996). Orthographic processing in visual word recognition: A multiple read-out model. Psychological Review, 103, 518-565.
7. Harm, M. W., & Seidenberg, M. S. (2004). Computing the meanings of words in reading: Cooperative division of labor between visual and phonological processes. Psychological Review, 111, 662-720.
8. Opdyke, J. D. (2003). Fast permutation tests that maximize power under conventional Monte Carlo sampling for pairwise and multiple comparisons. Journal of Modern Applied Statistical Methods, 2, 27-49.
9. Perry, C., Ziegler, J. C., & Zorzi, M. (2007). Nested incremental modeling in the development of computational theories: The CDP1 model of reading aloud. Psychological Review, 114, 273-315.
10. Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103, 56-115.
11. Rey, A., Jacobs, A. M., Schmidt-Weigand, F., & Ziegler, J. C. (1998). A phoneme effect in visual word recognition. Cognition, 68, B71-B80. Rey, A., & Schiller, N. O. (2005). Graphemic complexity and multiple print-to-sound associations in visual word recognition. Memory & Cognition, 33, 76-85.
12. Rouder, J. N., & Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin & Review, 12, 573-604.
13. Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523-568.
14. Seidenberg, M. S., & Plaut, D. C. (1998). Evaluating word-reading models at the item level: Matching the grain of theory and data. Psychological Science, 9, 234-237.
15. Seidenberg, M., & Waters, G. S. (1989). Word recognition and naming: A mega study. Bulletin of the Psychonomic Society, 27, 489.
16. Spieler, D. H., & Balota, D. A. (1997). Bringing computational models of word naming down to the item level. Psychological Science, 8, 411-416.
17. Zimmerman, D. W., Zumbo, B. D., & Williams, R. H. (2003). Bias in estimation and hypothesis testing of correlation. Psicolo ́gica, 24, 133-158.