Broken symmetries in a location-invariant word recognition network

Neural Computation

Volumn 23, Issue 1, 2011, Pages 251-283

  Hannagan, Thomas ^a   Dandurand, Frédéric ^a   Grainger, Jonathan ^a  

^a AIX MARSEILLE UNIVERSITÉ   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; HUMAN; LANGUAGE; LETTER; PHYSIOLOGY; READING; STANDARD; STATISTICAL MODEL; VERBAL BEHAVIOR; VISUAL SYSTEM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; LANGUAGE; LINEAR MODELS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; READING; VERBAL BEHAVIOR; VISUAL PATHWAYS;

EID: 78650599493     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00064     Document Type: Letter

References (43)

1. 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.
2. Cohen, L., Dehaene, S., Naccache, L., Lehéricy, S., Dehaene-Lambertz, G., Hénaff, M., et al. (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.
3. Crick, F. (1989). The recent excitement about neural networks. Nature, 337, 129-132.
4. Dandurand, F., Grainger, J., & Dufau, S. (2010). Learning location invariant orthographic representations for printed words. Connection Science, 22, 25-42.
5. Davis, C. J. (2010). The spatial coding model of visual word identification. Psychological Review, 117, 713-758.
6. Davis, C. J., & Bowers, J. S. (2006). Contrasting five theories of letter position coding. Journal of Experimental Psychology: Human Perception and Performance, 32, 535- 557.
7. Dehaene, S., Cohen, L., Sigman, M., & Vinckier, F. (2005). The neural code for written words: A proposal. Trends Cogn. Sci., 9, 335-341.
8. Ellis, A. W., & Lambon Ralph, M. A. (2000). Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: Insights from connectionist networks. Journal of Experimental Psychology: Learning Memory and Cognition, 26, 1103-1123.
9. Gomez, P., Ratcliff, R., & Perea, M. (2008). The overlap model: A model of letter position coding. Psychol. Rev., 115, 577-600.
10. Grainger, J. (2008). Cracking the orthographic code: An introduction. Language and Cognitive Processes, 23, 1-35.
11. 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.
12. 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). Happauge, NY: Nova Science.
13. Guerrera, C., & Forster, K. I. (2008). Masked form priming with extreme transposition. Language and Cognitive Processes, 23, 117-142.
14. Hannagan, T., Dupoux, E., & Christophe, A. (in press). Holographic string encoding. Cognitive Science.
15. Hinton, G. E. (1987). Learning translation invariant recognition in a massively parallel network. In G. Goos & J. Hartmanis (eds.), PA K L E (pp. 1-13). Berlin: Springer-Verlag.
16. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.
17. Kaufman, L., & Rousseeuw, P. (1990). Finding groups in data: An introduction to cluster analysis. New York: Wiley-Interscience.
18. Mayor, J., & Plunkett, K. (2010). A neuro-computational account of taxonomic responding and fast mapping in early word learning. Psychological Review, 117, 1-31.
19. McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights on the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419-457.
20. McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88, 375-407.
21. Minsky, M., & Papert, S. (1969). Perceptrons. Cambridge, MA: MIT Press.
22. Perea, M., & Lupker, S. J. (2003a). Does jugde activate court? Transposed-letter similarity effects in masked associative priming. Memory and Cognition, 31, 829- 841.
23. Perea, M., & Lupker, S. J. (2003b). Transposed-letter confusability effects in masked form priming. In S. Kinoshita and S. J. Lupker (Eds.), Masked priming: The state of the art (pp. 97-120). New York: Psychology Press.
24. Peressotti, F., & Grainger, J. (1999). The role of letter identity and letter position in orthographic priming. Perception and Psychophysics, 61, 691-706.
25. Plate, T. A. (1995). Holographic reduced representations. Transactions on Neural Networks, 6, 623-641.
26. Plate, T., Bert, J., Grace, J., & Band, P. (2000). Visualization of feed-forward neural networks. Neural Computation, 12, 1337-1353.
27. 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.
28. Pollack, J. B. (1990). Recursive distributed representations. Artificial Intelligence, 46, 77-105.
29. Schoonbaert, S., & Grainger, J. (2004). Letter position coding in printed word perception: Effects of repeated and transposed letters. Language and Cognitive Processes, 19, 333-367.
30. Shawe-Taylor, J. (1993). Symmetries and discriminability in feedforward network architectures. IEEE Transactions on Neural Networks, 4, 816-826.
31. Shillcock, R., & Monaghan, P. (2001). The computational exploration of visual word recognition in a split model. Neural Computation, 13, 1171-1198.
32. Shultz, T. R., & Elman, J. L. (1994). Analyzing cross-connected networks. In J. D. Cowan, G. Tesauro, & J. Alspetor (Eds.), Advances in neural information processing systems, 6. San Francisco: Morgan Kaufmann.
33. Simard, P., Victorri, B., Le Cun, Y., & Denker, J. (1992). Tangent prop: A formalism for specifying selected invariances in an adaptive network. In J. E. Moody, S. J. Hanson, & R. P. Lippmann (Eds.), Advances in neural information processing systems, 4 (pp. 895-903). San Francisco: Morgan Kaufmann.
34. Smith, M. A., Cottrell, G. W., & Anderson, K. L. (2001). The early word catches the weights. In S. A. Solla, T. K. Leen, & K.-R. Müller (Eds.), Advances in neural information processing systems, 12 (pp. 52-58). Cambridge, MA: MIT Press.
35. Stevens, M., & Grainger, J. (2003). Letter visibility and the viewing position effect in visual word recognition. Perception and Psychophysics, 65, 133-151.
36. Van Assche, E., & Grainger, J. (2006). A study of relative-position priming with superset primes. Journal of Experimental Psychology: Learning, Memory and Cognition, 32, 399-415.
37. 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.
38. Wallis, G., & Rolls, E. T. (1997). Invariant face and object recognition in the visual system. Progress in Neurobiology, 51, 167-194.
39. 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, 221-243.
40. Whitney, C. (2008). SERIOL reading. Language and Cognitive Processes, 23, 143-164.
41. Whitney, C. (in press). A comparison of the seriol and solar theories of letter-position encoding. Brain and Language.
42. Whitney, C., & Berndt, R. S. (1999). A new model of letter string encoding: Simulating right neglect dyslexia. Progress in Brain Research, 121, 143-163.
43. Xie, X., & Seung, H. S. (2003). Equivalence of backpropagation and contrastive Hebbian learning in a layered network. Neural Computation, 15, 143-163.