Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain

Cognitive Science

Volumn 36, Issue 4, 2012, Pages 575-606

  Hannagan, Thomas ^a   Grainger, Jonathan ^a  

^a AIX MARSEILLE UNIVERSITY   (France)

Author keywords

Open bigrams; Orthographic coding; String kernels; Visual Word Form Area; Visual word recognition

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; HUMAN; METHODOLOGY; PATTERN RECOGNITION; READING; SEQUENCE ANALYSIS;

ARTIFICIAL INTELLIGENCE; HUMANS; PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; READING; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 84860580513     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1551-6709.2012.01236.x     Document Type: Article

References (84)

1. Adelman, J. S., Marquis, S. J., & Sabatos-DeVito, M. G. (2010). Letters in words are read simultaneously, not left-to-right. Psychological Science, 21, 1799-1801.
2. Allison, L., Wallace, C. S., & Yee, C. N. (1992). Finite-state models in the alignment of macro-molecules. Journal of Molecular Evolution, 35, 77-89.
3. Binder, J. R., Medler, D. A., Westbury, C. F., Liebenthal, E., & Buchanan, L. (2006). Tuning of the human left fusiform gyrus to sublexical orthographic structure. Neuroimage, 33, 739-748.
4. Blais, C., Fiset, D., Jolicoeur, P., Arguin, M., Bub, D., & Gosselin, F.(2009). Reading between eye saccades. PLoS ONE, 4(7), e6448. doi:
5. Bouvrie, J., Poggio, T., Rosasco, L., Smale, S., & Wibisono, A. (2010). Generalization and properties of the neural response technical report MIT-CSAIL-TR-2010-051/CBCL-292. Cambridge, MA: Massachusetts Institute of Technology.
6. Bowers, J. (2010). On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience. Psychological Review, 116, 220-251.
7. Burgess, N., & Hitch, G. (1992). Toward a network model of the articulatory loop. Journal of Memory and Language, 31, 429-460.
8. Cohen, L., Dehaene, S., Naccache, L., Lehericy, S., Dehaene-Lambertz, G., Henaff, M., & Michel, F. (2000). The visual word-form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain, 123, 291-307.
9. 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.
10. Conrad, R. (1965). Order error in immediate recall of sequences. Journal of Verbal Learning and Verbal Behavior, 4, 161-169.
11. Cover, T. M. (1965). Geometrical and Statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, EC-14, 326-334.
12. Cox, G. E., Kachergis, G., Recchia, G., & Jones, M. N. (2011). Towards a scalable holographic word-form representation. Behavior Research Methods, 43, 602-615. doi:
13. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge, UK: Cambridge University Press.
14. Davis, C. J. (2010). The spatial coding model of visual word identification. Psychological Review, 117, 713-758.
15. Davis, C. J., & Bowers, J. S. (2006). Contrasting five theories of letter position coding. Journal of Experimental Psychology: Human Perception and Performance, 32(2), 535-557.
16. Dehaene, S., & Cohen, L. (2007) Cultural recycling of cortical maps. Neuron, 56(2), 384-398.
17. Dehaene, S., Jobert, A., Naccache, L., Ciuciu, P., Poline, J.-B., Le Bihan, D., & Cohen, L. (2004) Letter binding and invariant recognition of masked words: Behavioral and neuroimaging evidence. Psychological Science, 15(5), 307-313.
18. Dehaene, S., Cohen, L., Sigman, M., & Vinckier, F. (2005). The neural code for written words: A proposal. Trends in Cognitive Sciences, 9, 335-341.
19. Dennis, S. (2005). A memory-based theory of verbal cognition. Cognitive Science, 29, 145-193.
20. DiCarlo, J.J., & Cox, D. (2007). Untangling invariant object recognition. Trends in Cognitive Sciences, 11, 333-341.
21. Ebbinghaus, H. (1964). Memory: A contribution to experimental psychology. New York: Dover.
22. Fazl, A., Grossberg, S., & Mingolla, E. (2009). View-invariant object category learning, recognition, and search: How spatial and object attention are coordinated using surface-based attentional shrouds. Cognitive Psychology, 58, 1-48.
23. Fischer-Baum, S., McCloskey, M., & Rapp, B. (2010). Representation of letter position in spelling: Evidence from acquired dysgraphia. Cognition, 115, 466-490.
24. Forster, K. I., & Davis, C. (1984). Repetition priming and frequency attenuation in lexical access. Journal of Experimental Psychology: Learning, Memory and Cognition, 10, 680-698.
25. Frost, R. (2006). Becoming literate in Hebrew: The grain-size hypothesis and semitic orthographic systems. Developmental Science, 9(5), 439-440.
26. Gaillard, R., Naccache, L., Pinel, P., Clémenceau, S., Volle, E., Hasboun, D., Dupont, S., Baulac, M., Dehaene, S., Adam, C., & Cohen, L. (2006). Direct intracranial, FMRI, and lesion evidence for the causal role of left inferotemporal cortex in reading. Neuron, 50(2), 191-204.
27. Gales, M. J. F. (2009). Sequence kernels for speaker and speech recognition. Baltimore, MD: Language Technology Workshop at Johns Hopkins University.
28. Gomez, P., Ratcliff, R., & Perea, M. (2008). The overlap model: a model of letter position coding. Psychological Review, 115(3), 577-600.
29. Goswami, U., & Ziegler, J. C. (2006). A developmental perspective on the neural code for written word. Trends in Cognitive Sciences, 10(4), 142-143.
30. Grainger, J. (2008). Cracking the orthographic code: An introduction. Language and Cognitive Processes, 23(1), 1-35.
31. Grainger, J., & van Heuven, W. (2003). Modeling letter position coding in printed word perception. In P. Bonin (Ed.), Mental lexicon: ''Some words to talk about words" (pp. 1-23). New York: Nova Science.
32. Grainger, J., & Ziegler, J. (2011). A dual-route approach to orthographic processing. Frontiers in Language Sciences, 2(54). doi:
33. Grainger, J., Granier, J., Farioli, F., Van Assche, E., & van Heuven, W. (2006). Letter position information and printed word perception: The relative-position priming constraint. Journal of Experimental Psychology: Human Perception and Performance, 32, 865-884.
34. Grünwald, P. (2004). A tutorial introduction to the minimum description length principle. In P. Grünwald, I. J. Myung, & M. Pitt (Eds.), Advances in minimum description length theory and applications. Cambridge, MA: MIT Press.
35. Guerrera, C., & Forster, K. I. (2008). Masked form priming with extreme transposition: Cracking the orthographic code. Language and Cognitive Processes, 23(1), 117-142.
36. Hannagan, T., & Grainger, J. (2011). From learning objects to learning letters: Transfer of invariance in a computational model of the ventral visual system. Fifteenth International Conference on Cognitive and Neural Systems -ICCNS11, Boston, MA.
37. Hannagan, T., Dupoux, E., & Christophe, A. (2011a). Holographic string encoding. Cognitive Science, 35(1), 79-118.
38. Hannagan, T., Dandurand, F., & Grainger, J. (2011b). Broken symmetries in a location invariant word recognition network. Neural Computation, 23(1), 251-283.
39. Hasson, U., Harel, M., Levy, I., & Malach, R. (2003). Large-scale mirror-symmetry organization of human occipito-temporal object areas. Neuron, 37, 1027-1041.
40. Haussler, D. (1999). Convolution kernels on discrete structure. Technical report UCSC-CRL-99-10, UC Santa Cruz.
41. Henson, R. N. A. (1998). Short-term memory for serial order: The Start-End Model of serial recall. Cognitive Psychology, 36, 73-137.
42. Hofmann, T., Schölkopf, B., & Smola, A. J. (2007). Kernel Methods in Machine Learning. Annals of Statistics, 36, 1171-1220.
43. Jacobs, A. M., Rey, A., Ziegler, J. C., & Grainger, J. (1998). MROM-P: An interactive activation, multiple read-out model of orthographic and phonological processes in visual word recognition. In J. Grainger & A. M. Jacobs (Eds.), Localist connectionist approaches to human cognition (pp. 147-188). Mahwah, NJ: Erlbaum.
44. Jäkel, F., Schölkopf, B., & Wichmann, F. A. (2007). A tutorial on kernel methods for categorization. Journal of Mathematical Psychology, 51(6), 381-388.
45. Jäkel, F., Schölkopf, B., & Wichmann, F. A. (2009). Does cognitive science need kernels? Trends in Cognitive Sciences, 13(9), 381-388.
46. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In C. Nédellec & C. Rouveirol (Eds.), Proceedings of the European conference on machine learning (pp. 137-142). Berlin: Springer.
47. Kanwisher, N. (2006). What's in a face. Science, 311, 617.
48. Kobatake, E., & Tanaka, K. (1994). Neuronal selectivities to complex object features in the ventral pathway of the macaque cerebral cortex. Journal of Neurophysiology, 71, 856-857.
49. Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato's problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211-240.
50. Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch W. (Eds.) (2007). Handbook of latent semantic analysis. Mahwah, NJ: Lawrence Erlbaum Associates.
51. Leslie, C., & Kuang, R. (2004). Fast string kernels using inexact matching for protein sequences. Journal of Machine Learning Research, 5, 1435-1455.
52. Lewis, D. (2004). The Reuters-21578 text categorization test collection, distribution 1.0. Available at: (accessed on February 28, 2008).
53. Li, M., & Vitanyi, P. (1997). An introduction to Kolmogorov complexity and its applications. New York: Springer-Verlag.
54. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., & Watkins, C. (2002). Text classification using string kernels. Journal of Machine Learning Research, 2, 419-444.
55. Mozer, M. C. (1987). Early parallel processing in reading: A connectionist approach. In M. Coltheart (Ed.), Attention and performance XII: The psychology of reading (pp. 83-104). London: Erlbaum.
56. Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995). SCOP: A structural classification of proteins database for the investigation of sequences and structures. Journal of Molecular Biology, 247, 536-540.
57. Page, M. P. A., & Norris, D. G. (1998). The primacy model: A new model of immediate serial recall. Psychological Review, 104, 761-781.
58. Papadimitriou, C. (1993). Computational complexity (1st ed.). Reading, MA: Addison Wesley. ISBN 0-201-53082-1.
59. Perea, M., & Lupker, S. J. (2004). Can CANISO activate CASINO? Transposed-letter similarity effects with nonadjacent letter positions. Journal of Memory and Language, 51, 231-246.
60. Perry, C., Ziegler, J. C., & Zorzi, M. (2007). Nested incremental modeling in the development of computational theories: The CDP+ model of reading aloud. Psychological Review, 114, 273-315.
61. Perry, C., Ziegler, J. C., & Zorzi, M. (2010). Beyond single syllables: Large-scale modeling of reading aloud with the Connectionist Dual Process (CDP++) model. Cognitive Psychology, 61(2), 106-151.
62. Pitt, M. A., & Myung, I. J. (2002). When a good fit can be bad. Trends in Cognitive Sciences, 6(10), 421-425.
63. Plate, T. A. (1995). Holographic reduced representations. Transactions on Neural Networks, 6(3), 623-641.
64. Plaut, D. C., & McClelland, J. L. (2010). Locating object knowledge in the brain: A critique of Bowers' (2009) attempt to revive the grandmother cell hypothesis. Psychological Review, 117, 284-288.
65. 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.
66. Rastle, K., & Davis, M. H. (2008). Morphological decomposition based on the analysis of orthography. Language and Cognitive Processes, 23(7-8), 942-971.
67. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019-1025.
68. Schoonbaert, S., & Grainger, J. (2004). Letter position coding in printed word perception: Effects of repeated and transposed letters. Language and Cognitive Processes, 19(3), 333-367.
69. Serre, T., Kouh, M., Cadieu, C., Knoblich, U., Kreiman, G., & Poggio, T. (2005). A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex. MIT-CSAIL Technical Report. Cambridge, MA: Massachusetts Institute of Technology.
70. Shawe-Taylor, J., Anthony, M., & Kern, W. (1992). Classes of feedforward neural networks and their circuit complexity. Neural Networks, 5(6), 971-977.
71. Sibley, D. E., Kello, C. T., Plaut, D. C., & Elman, J. L. (2008). Large-scale modeling of wordform learning and representation. Cognitive Science, 32, 741-754.
72. Slamecka, N. (1985). Ebbinghaus: Some associations. Journal of Experimental Psychology: Learning, Memory and Cognition, 11, 414-435.
73. Tsapkini, K., & Rapp, B. (2010). The orthography-specific functions of the left fusiform gyrus: Evidence of modality and category specificity. Cortex, 46(2), 185-205.
74. Tsunoda, K. (2001). Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns. Nature Neuroscience, 4, 832-838.
75. Tydgat, I., & Grainger, J. (2009). Serial position effects in the identification of letters, digits and symbols. Journal of Experimental Psychology: Human Perception and Performance, 35(2), 480-498.
76. Valiant, L. (1984). A theory of the learnable. Communications of the ACM, 27(11), 1134-1142.
77. Van Assche, E., & Grainger, J. (2006). A study of relative-position priming with superset primes. Journal of Experimental Psychology: Learning, Memory and Cognition, 32(2), 399-415.
78. van Rooij, I., & Wareham, T. (2008). Parameterized complexity in cognitive modeling: Foundations, applications and opportunities. Computer Journal, 51(3), 385-404.
79. Velan, H., & Frost, R. (2011). Words with and without internal structure: What determines the nature of orthographic and morphological processing? Cognition, 118(2), 141-156.
80. Vinckier, F., Dehaene, S., Jobert, A., Dubus, J., Sigman, M., & Cohen, L. (2007). Hierarchical coding of letter strings in the ventral stream: Dissecting the inner organization of the visual word-form system. Neuron, 55, 143-156.
81. Wallis, G., & Rolls, E. T. (1997). Invariant face and object recognition in the visual system. Progress in Neurobiology, 51, 167-194.
82. Whitney, C. (2001). How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. Psychonomic Bulletin & Review, 8, 221-243.
83. Wickelgren, W. A. (1969). Auditory or articulatory coding in verbal short-term memory. Psychological Review, 76, 232-235.
84. Yarkoni, T., Balota, D. A., & Yap, M. J. (2008). Moving beyond Coltheart's N: A new measure of orthographic similarity. Psychonomic Bulletin & Review, 15, 971-979.