Models of visual word recognition

Trends in Cognitive Sciences

Volumn 17, Issue 10, 2013, Pages 517-524

  Norris, Dennis ^a  

^a MRC COGNITION AND BRAIN SCIENCES UNIT   (United Kingdom)

Author keywords

Computational modelling; Lexical decision; Reading; Word recognition

Indexed keywords

COGNITIVE PROCESS; COMPUTATIONAL MODEL; COMPUTATIONAL MODELLING; FUNDAMENTAL LIMITATIONS; LEXICAL DECISION; READING; VISUAL WORD RECOGNITION; WORD RECOGNITION;

COMPUTATIONAL METHODS;

VOCABULARY CONTROL;

BOOK; COMPARTMENT MODEL; COMPETITION; COMPUTER MODEL; EYE MOVEMENT; HUMAN; READING; REVIEW; VISUAL MEMORY; WORD RECOGNITION;

COMPUTATIONAL MODELLING; LEXICAL DECISION; READING; WORD RECOGNITION;

BAYES THEOREM; EYE MOVEMENTS; HUMANS; MODELS, NEUROLOGICAL; PATTERN RECOGNITION, VISUAL; READING;

EID: 84884388031     PISSN: 13646613     EISSN: 1879307X     Source Type: Journal    
DOI: 10.1016/j.tics.2013.08.003     Document Type: Review

References (95)

1. Morton J. The interaction of information in word recognition. Psychol. Rev. 1969, 76:165-178.
2. Davis C.J. The spatial coding model of visual word identification. Psychol. Rev. 2010, 117:713-758.
3. Grainger J., Jacobs A.M. Orthographic processing in visual word recognition: a multiple read-out model. Psychol. Rev. 1996, 103:518-565.
4. Norris D. The Bayesian reader: explaining word recognition as an optimal Bayesian decision process. Psychol. Rev. 2006, 113:327-357.
5. Norris D. Putting it all together: a unified account of word recognition and reaction-time distributions. Psychol. Rev. 2009, 116:207-219.
6. Norris D., Kinoshita S. Reading through a noisy channel: why there's nothing special about the perception of orthography. Psychol. Rev. 2012, 119:517-545.
7. Whitney C., Cornelissen P. SERIOL Reading. Lang. Cogn. Process. 2008, 23:143-164.
8. Adelman J.S. Letters in time and retinotopic space. Psychol. Rev. 2011, 118:570-582.
9. Diependaele K., et al. Fast phonology and the Bimodal Interactive Activation Model. Eur. J. Cogn. Psychol. 2010, 22:764-778.
10. McClelland J.L., Rumelhart D.E. An interactive activation model of context effects in letter perception: part 1. An account of basic findings. Psychol. Rev. 1981, 88:375-407.
11. Rumelhart D.E., McClelland J.L. An interactive activation model of context effects in letter perception: II. The contextual enhancement effect and some tests and extensions of the model. Psychol. Rev. 1982, 89:60-94.
12. Coltheart M., et al. DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychol. Rev. 2001, 108:204-256.
13. Perry C., et al. Beyond single syllables: large-scale modeling of reading aloud with the Connectionist Dual Process (CDP++) model. Cogn. Psychol. 2010, 61:106-151.
14. Perry C., et al. Nested incremental modeling in the development of computational theories: the CDP+ model of reading aloud. Psychol. Rev. 2007, 114:273-315.
15. Sibley D.E., et al. Large-scale modeling of wordform learning and representation. Cogn. Sci. 2008, 32:741-754.
16. Baayen R.H., et al. An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychol. Rev. 2011, 118:438-481.
17. Reichle E.D., et al. Using EZ Reader to model the effects of higher level language processing on eye movements during reading. Psychon. Bull. Rev. 2009, 16:1-21.
18. Reichle E.D., et al. Using E-Z Reader to simulate eye movements in nonreading tasks: a unified framework for understanding the eye-mind link. Psychol. Rev. 2012, 119:155-185.
19. Engbert R., et al. SWIFT: a dynamical model of saccade generation during reading. Psychol. Rev. 2005, 112:777-813.
20. Norris D., McQueen J.M. Shortlist B: a Bayesian model of continuous speech recognition. Psychol. Rev. 2008, 115:357-395.
21. McClelland J.L., Elman J.L. The TRACE model of speech perception. Cogn. Psychol. 1986, 18:1-86.
22. McClelland J., Rumelhart D. An interactive activation model of context effects in letter perception: part 1. An account of basic findings. Psychol. Rev. 1981, 88:375-407.
23. Seidenberg M.S., McClelland J.L. A distributed, developmental model of word recognition and naming. Psychol. Rev. 1989, 96:523-568.
24. Harm M.W., Seidenberg M.S. Computing the meanings of words in reading: cooperative division of labor between visual and phonological processes. Psychol. Rev. 2004, 111:662-720.
25. Plaut D.C., et al. Understanding normal and impaired word reading: computational principles in quasi-regular domains. Psychol. Rev. 1996, 103:56-115.
26. Clark A. Microcognition: Philosophy, Cognitive Science and Parallel Distributed Processing 1989, MIT Press.
27. Rumelhart D. The architecture of mind: a connectionist approach. Foundations of Cognitive Science 1989, 133-159. MIT Press. M. Posner (Ed.).
28. Anderson J.R. Adaptive Character of Thought 1990, Erlbaum.
29. Ratcliff R. A theory of memory retrieval. Psychol. Rev. 1978, 85:59-109.
30. Ratcliff R., et al. A diffusion model account of the lexical decision task. Psychol. Rev. 2004, 111:159-182.
31. Wald A. Sequential Analysis 1947, Wiley.
32. Whitney C. Comparison of the SERIOL and SOLAR theories of letter-position encoding. Brain Lang. 2008, 107:170-178.
33. Davis C.J., Bowers J.S. Contrasting five different theories of letter position coding: evidence from orthographic similarity effects. J. Exp. Psychol. Hum. Percept. Perform. 2006, 32:535-557.
34. Coltheart M., et al. Access to the internal lexicon. Attention and Performance VI 1977, 535-555. Erlbaum. S. Dornic (Ed.).
35. Bowers J., et al. Automatic semantic activation of embedded words: is there a "hat" in "that"?. J. Mem. Lang. 2005, 52:131-143.
36. Davis C.J., et al. Re(de)fining the orthographic neighborhood: the role of addition and deletion neighbors in lexical decision and reading. J. Exp. Psychol. Hum. Percept. Perform. 2009, 35:1550-1570.
37. Davis C.J., Taft M. More words in the neighborhood: interference in lexical decision due to deletion neighbors. Psychon. Bull. Rev. 2005, 12:904-910.
38. Weingartner K.M., Rayner K. Lexical embeddings produce interference when they are morphologically unrelated to the words in which they are contained: evidence from eye movements. J. Cogn. Psychol. 2013, 24:179-188.
39. Yarkoni T., et al. Moving beyond Coltheart's N: a new measure of orthographic similarity. Psychon. Bull. Rev. 2008, 15:971-979.
40. Kuperman V., Drieghe D. How strongly do word reading times and lexical decision times correlate? Combining data from eye movement corpora and megastudies. Q. J. Exp. Psychol. (Hove) 2013, 66:563-580.
41. White S.J. Eye movement control during reading: effects of word frequency and orthographic familiarity. J. Exp. Psychol. Hum. Percept. Perform. 2008, 34:205-223.
42. Smith N.J., Levy R. Fixation durations in first-pass reading reflect uncertainty about word identity. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society 2010.
43. Paterson K.B., et al. Inhibitory neighbor priming effects in eye movements during reading. Psychon. Bull. Rev. 2009, 16:43-50.
44. Balota D.A., et al. The English Lexicon Project. Behav. Res. Methods 2007, 39:445-459.
45. Keuleers E., et al. The British Lexicon Project: lexical decision data for 28,730 monosyllabic and disyllabic English words. Behav. Res. Methods 2012, 44:287-304.
46. Keuleers E., et al. Practice effects in large-scale visual word recognition studies: a lexical decision study on 14,000 Dutch mono- and disyllabic words and nonwords. Front. Psychol. 2010, 1:174.
47. Ferrand L., et al. The French Lexicon Project: lexical decision data for 38,840 French words and 38,840 pseudowords. Behav. Res. Methods 2010, 42:488-496.
48. Kennedy A., Pynte J. Parafoveal-on-foveal effects in normal reading. Vision Res. 2005, 45:153-168.
49. Yap M.J., Balota D.A. Visual word recognition of multisyllabic words. J. Mem. Lang. 2009, 60:502-529.
50. Kello C.T. Considering the junction model of lexical processing. From Inkmarks to Ideas: Current Issues in Lexical Processing 2006, 50-75. Psychology Press. S. Andrews (Ed.).
51. Sibley D., et al. Learning orthographic and phonological representations in models of monosyllabic and bisyllabic naming. Eur. J. Cogn. Psychol. 2010, 22:650-668.
52. Whitney C. Location, location, location: how it affects the neighborhood (effect). Brain Lang. 2011, 118:90-104.
53. Rey A., et al. Item performance in visual word recognition. Psychon. Bull. Rev. 2009, 16:600-608.
54. Adelman J.S., et al. The unexplained nature of reading. J. Exp. Psychol. Learn. Mem. Cogn. 2013, 39:1037-1053.
55. Diependaele K., et al. How noisy is lexical decision?. Front. Psychol. 2012, 3:348.
56. Murray W.S., Forster K.I. Serial mechanisms in lexical access: the rank hypothesis. Psychol. Rev. 2004, 111:721-756.
57. Adelman J.S., Brown G.D.A. Modeling lexical decision: the form of frequency and diversity effects. Psychol. Rev. 2008, 115:214-229.
58. Balota D.A., et al. Beyond mean response latency: response time distributional analyses of semantic priming. J. Mem. Lang. 2008, 59:495-523.
59. Balota D.A., Spieler D.H. Word frequency, repetition, and lexicality effects in word recognition tasks: beyond measures of central tendency. J. Exp. Psychol. Gen. 1999, 128:32-55.
60. Andrews S., Heathcote A. Distinguishing common and task-specific processes in word identification: a matter of some moment?. J. Exp. Psychol. Learn. Mem. Cogn. 2001, 27:514-544.
61. Wagenmakers E-J., et al. A diffusion model account of criterion shifts in the lexical decision task. J. Mem. Lang. 2008, 58:140-159.
62. Gomez P., et al. A diffusion model account of masked versus unmasked priming: are they qualitatively different?. J. Exp. Psychol. Hum. Percept. Perform. 2013, 10.1037/a0032333.
63. Yap M.J., et al. Individual differences in visual word recognition: insights from the English lexicon project. J. Exp. Psychol. Hum. Percept. Perform. 2011, 38:53-79.
64. Ratcliff R., et al. A diffusion model analysis of the effects of aging in the lexical-decision task. Psychol. Aging 2004, 19:278-289.
65. Dufau S., et al. How to say "no" to a nonword: a leaky competing accumulator model of lexical decision. J. Exp. Psychol. Learn. Mem. Cogn. 2012, 38:1117-1128.
66. Gomez P., et al. The overlap model: a model of letter position coding. Psychol. Rev. 2008, 115:577-600.
67. Norris D., Kinoshita S. Perception as evidence accumulation and Bayesian inference: insights from masked priming. J. Exp. Psychol. Gen. 2008, 137:434-455.
68. Norris D., et al. A stimulus sampling theory of letter identity and order. J. Mem. Lang. 2010, 62:254-271.
69. Ratcliff R., McKoon G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 2008, 20:873-922.
70. Rayner K., et al. Raeding wrods with jubmled lettres: there is a cost. Psychol. Sci. 2006, 17:192-193.
71. Velan H., Frost R. Cambridge University versus Hebrew University: the impact of letter transposition on reading English and Hebrew. Psychon. Bull. Rev. 2007, 14:913-918.
72. Perea M., Lupker S.J. Does jugde activate COURT? Transposed-letter similarity effects in masked associative priming. Mem. Cognit. 2003, 31:829-841.
73. Kinoshita S., Norris D. Transposed-letter priming of prelexical orthographic representations. J. Exp. Psychol. Learn. Mem. Cogn. 2009, 35:1-18.
74. Perea M., Lupker S. Can CANISO activate CASINO? Transposed-letter similarity effects with nonadjacent letter positions. J. Mem. Lang. 2004, 51:231-246.
75. Schoonbaert S., Grainger J. Letter position coding in printed word perception: effects of repeated and transposed letters. Lang. Cogn. Process. 2004, 19:333-367.
76. Dehaene S., et al. The neural code for written words: a proposal. Trends Cogn. Sci. 2005, 9:335-341.
77. Dehaene S. Reading in the Brain 2009, Viking.
78. Grainger J., et al. Letter position information and printed word perception: the relative-position priming constraint. J. Exp. Psychol. Hum. Percept. Perform. 2006, 32:865-884.
79. Davis C. SOLAR versus SERIOL revisited. Eur. J. Cogn. Psychol. 2010, 22:695-724.
80. Chung S., Legge G. Precision of position signals for letters. Vision Res. 2009, 49:1948-1960.
81. Peressotti F., Grainger J. The role of letter identity and letter position in orthographic priming. Percept. Psychophys. 1999, 61:691-706.
82. Van Assche E., Grainger J. A study of relative-position priming with superset primes. J. Exp. Psychol. Learn. Mem. Cogn. 2006, 32:399-415.
83. Welvaert M., et al. Graded effects of number of inserted letters in superset priming. Exp. Psychol. 2008, 55:54-63.
84. Kinoshita S., Norris D. Letter order is not coded by open bigrams. J. Mem. Lang. 2013, 69:135-150.
85. Roberts S., Pashler H. How persuasive is a good fit? A comment on theory testing. Psychol. Rev. 2000, 107:358-367.
86. Lewandowsky S., Farrell S. Computational Modeling in Cognition: Principles and Practice 2010, Sage.
87. Pitt M.A., et al. Measuring model flexibility with parameter space partitioning: an introduction and application example. Cogn. Sci. 2008, 32:1285-1303.
88. Pitt M.A., et al. Global model analysis by parameter space partitioning. Psychol. Rev. 2006, 113:57-83.
89. Jacobs A.M., Grainger J. Models of visual word recognition: sampling the state of the art. J. Exp. Psychol. Hum. Percept. Perform. 1994, 20:1311-1334.
90. Roelofs A. From Popper to Lakatos: a case for cumulative computational modeling. Twenty-first Century Psycholinguistics: Four Cornerstones 2005, 313-330. Erlbaum. A. Cutler (Ed.).
91. Forster K.I. Computational modeling and elementary process analysis in visual word recognition. J. Exp. Psychol. Hum. Percept. Perform. 1994, 20:1292-1310.
92. Norris D. How do computational models help us develop better theories?. Twenty-first Century Psycholinguistics: Four Cornerstones 2005, 331-346. Erlbaum. A. Cutler (Ed.).
93. Griffiths T.L., et al. Probabilistic models of cognition: exploring representations and inductive biases. Trends Cogn. Sci. 2010, 14:357-364.
94. Griffiths T.L., et al. How the Bayesians got their beliefs (and what those beliefs actually are): comment on Bowers and Davis (2012). Psychol. Bull. 2012, 138:415-422.
95. Box G.E.P., Draper N.R. Empirical Model Building and Response Surfaces 1987, John Wiley & Sons.