1887
Volume 13, Issue 3
  • ISSN 1871-1340
  • E-ISSN: 1871-1375
USD
Buy:$35.00 + Taxes

Abstract

Abstract

An oft-cited shortcoming of Interactive Activation as a psychological model of word reading is that it lacks the ability to simultaneously represent words of different lengths.

We present an implementation of the Interactive Activation model, which we call Metameric, that can simulate words of different lengths, and show that there is nothing inherent to Interactive Activation which prevents it from simultaneously representing multiple word lengths. We provide an in-depth analysis of which specific factors need to be present, and show that the inclusion of three specific adjustments, all of which have been published in various models before, lead to an Interactive Activation model which is fully capable of representing words of different lengths. Finally, we show that our implementation is fully capable of representing all words between 2 and 11 letters in length from the English Lexicon Project (31, 416 words) in a single model. Our implementation is completely open source, heavily optimized, and includes both command line and graphical user interfaces, but is also agnostic to specific input data or problems. It can therefore be used to simulate a myriad of other models, e.g., models of spoken word recognition. The implementation can be accessed at www.github.com/clips/metameric.

Loading

Article metrics loading...

/content/journals/10.1075/ml.18017.tul
2019-05-14
2019-10-15
Loading full text...

Full text loading...

References

  1. Balota, D. A. , Yap, M. J. , Hutchison, K. A. , Cortese, M. J. , Kessler, B. , Loftis, B. , Neely, J. H. , Nelson, D. L. , Simpson, G. B. , and Treiman, R.
    (2007) The English lexicon project. Behavior research methods, 39(3), 445–459. 10.3758/BF03193014
    https://doi.org/10.3758/BF03193014 [Google Scholar]
  2. Carpenter, G. A. and Grossberg, S.
    (1987a) Art 2: Self-organization of stable category recognition codes for analog input patterns. Applied optics, 26(23), 4919–4930. 10.1364/AO.26.004919
    https://doi.org/10.1364/AO.26.004919 [Google Scholar]
  3. (1987b) A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer vision, graphics, and image processing, 37(1), 54–115. 10.1016/S0734‑189X(87)80014‑2
    https://doi.org/10.1016/S0734-189X(87)80014-2 [Google Scholar]
  4. Carpenter, G. A. , Grossberg, S. , and Reynolds, J. H.
    (1991a) Artmap: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural networks, 4(5), 565–588. 10.1016/0893‑6080(91)90012‑T
    https://doi.org/10.1016/0893-6080(91)90012-T [Google Scholar]
  5. Carpenter, G. A. , Grossberg, S. , and Rosen, D. B.
    (1991b) Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural networks, 4(6), 759–771. 10.1016/0893‑6080(91)90056‑B
    https://doi.org/10.1016/0893-6080(91)90056-B [Google Scholar]
  6. Coltheart, M. , Rastle, K. , Perry, C. , Langdon, R. , and Ziegler, J.
    (2001) DRC: a dual route cascaded model of visual word recognition and reading aloud. Psychological review, 108(1), 204. 10.1037/0033‑295X.108.1.204
    https://doi.org/10.1037/0033-295X.108.1.204 [Google Scholar]
  7. Davis, C. J.
    (2003) Factors underlying masked priming effects in competitive network models of visual word recognition. Masked priming: The state of the art, 121–170.
    [Google Scholar]
  8. (2010) The spatial coding model of visual word identification. Psychological review, 117(3), 713. 10.1037/a0019738
    https://doi.org/10.1037/a0019738 [Google Scholar]
  9. Davis, C. J. and Lupker, S. J.
    (2006) Masked inhibitory priming in english: Evidence for lexical inhibition. Journal of Experimental Psychology: Human Perception and Performance, 32(3), 668.
    [Google Scholar]
  10. Dijkstra, T. and Rekké, S.
    (2010) Towards a localist-connectionist model of word translation. The Mental Lexicon, 5(3), 401–420. 10.1075/ml.5.3.08dij
    https://doi.org/10.1075/ml.5.3.08dij [Google Scholar]
  11. Dijkstra, T. and Van Heuven, W. J.
    (2002) The architecture of the bilingual word recognition system: From identification to decision. Bilingualism: Language and cognition, 5(3), 175–197. 10.1017/S1366728902003012
    https://doi.org/10.1017/S1366728902003012 [Google Scholar]
  12. Dijkstra, T. , Van Heuven, W. J. , and Grainger, J.
    (1998) Simulating cross-language competition with the bilingual interactive activation model. Psychologica Belgica.
    [Google Scholar]
  13. Dijkstra, T. , Wahl, A. , Buytenhuijs, F. , van Halem, N. , Al-jibouri, Z. , de Korte, M. , and Rekké, S.
    (2018) Multilink: A computational model for bilingual word recognition and word translation. Bilingualism: Language and Cognition, 1–23.
    [Google Scholar]
  14. Grainger, J.
    (2008) Cracking the orthographic code: An introduction. Language and cognitive processes, 23(1), 1–35. 10.1080/01690960701578013
    https://doi.org/10.1080/01690960701578013 [Google Scholar]
  15. Grainger, J. and Jacobs, A. M.
    (1993) Masked partial-word priming in visual word recognition: Effects of positional letter frequency. Journal of experimental psychology: human perception and performance, 19(5), 951.
    [Google Scholar]
  16. (1996) Orthographic processing in visual word recognition: A multiple read-out model. Psychological review, 103(3), 518. 10.1037/0033‑295X.103.3.518
    https://doi.org/10.1037/0033-295X.103.3.518 [Google Scholar]
  17. Grainger, J. and Van Heuven, W. J.
    (2004) Modeling letter position coding in printed word perception.
    [Google Scholar]
  18. Grossberg, S.
    (1978) A theory of visual coding, memory, and development. Formal theories of visual perception, 7–26.
    [Google Scholar]
  19. (1987) Competitive learning: From interactive activation to adaptive resonance. Cognitive science, 11(1), 23–63. 10.1111/j.1551‑6708.1987.tb00862.x
    https://doi.org/10.1111/j.1551-6708.1987.tb00862.x [Google Scholar]
  20. Jacobs, A. M. , Rey, A. , Ziegler, J. C. , and Grainger, J.
    (1998) Mrom-p: An interactive activation, multiple readout model of orthographic and phonological processes in visual word recognition. In Grainger, J. and Jacobs, A. , editors, Localist connectionist approaches to human cognition, 147–188. Lawrence Erlbaum Associates Publishers.
    [Google Scholar]
  21. Loncke, M. , Martensen, H. , van Heuven, W. J. , and Sandra, D.
    (2009) Who is dominating the dutch neighbourhood? On the role of subsyllabic units in dutch nonword reading. The Quarterly Journal of Experimental Psychology, 62(1), 140–154. 10.1080/17470210701851206
    https://doi.org/10.1080/17470210701851206 [Google Scholar]
  22. McClelland, J. L. and Rumelhart, D. E.
    (1981) An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological review, 88(5), 375. 10.1037/0033‑295X.88.5.375
    https://doi.org/10.1037/0033-295X.88.5.375 [Google Scholar]
  23. Page, M.
    (2000) Connectionist modelling in psychology: A localist manifesto. Behavioral and Brain Sciences, 23(4), 443–467. 10.1017/S0140525X00003356
    https://doi.org/10.1017/S0140525X00003356 [Google Scholar]
  24. Reicher, G. M.
    (1969) Perceptual recognition as a function of meaningfulness of stimulus material. Journal of experimental psychology, 81(2), 275. 10.1037/h0027768
    https://doi.org/10.1037/h0027768 [Google Scholar]
  25. Rumelhart, D. E. and McClelland, J. L.
    (1982) An interactive activation model of context effects in letter perception: II. The contextual enhancement effect and some tests and extensions of the model. Psychological review, 89(1), 60. 10.1037/0033‑295X.89.1.60
    https://doi.org/10.1037/0033-295X.89.1.60 [Google Scholar]
  26. Rumelhart, D. E. and Siple, P.
    (1974) Process of recognizing tachistoscopically presented words. Psychological review, 81(2), 99. 10.1037/h0036117
    https://doi.org/10.1037/h0036117 [Google Scholar]
  27. Snell, J., van Leipsig, S., Grainger, J., & Meeter, M.
    (2018) OB1-reader: A model of word recognition and eye movements in text reading. Psychological review, 125(6), 969. 10.1037/rev0000119
    https://doi.org/10.1037/rev0000119 [Google Scholar]
  28. 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(2), 221–243. 10.3758/BF03196158
    https://doi.org/10.3758/BF03196158 [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1075/ml.18017.tul
Loading
/content/journals/10.1075/ml.18017.tul
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): computational modeling , interactive activation and lexical decision
This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error