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Conference Paper

Modeling human word recognition with sequences of artificial neurons

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons216

Wittenburg,  Peter
Technical Group, MPI for Psycholinguistics, Max Planck Society;

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Wittenburg_1996.pdf
(Publisher version), 379KB

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Citation

Wittenburg, P., van Kuijk, D., & Dijkstra, T. (1996). Modeling human word recognition with sequences of artificial neurons. In C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, & B. Sendhoff (Eds.), Artificial Neural Networks — ICANN 96. 1996 International Conference Bochum, Germany, July 16–19, 1996 Proceedings (pp. 347-352). Berlin: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-ECF0-3
Abstract
A new psycholinguistically motivated and neural network based model of human word recognition is presented. In contrast to earlier models it uses real speech as input. At the word layer acoustical and temporal information is stored by sequences of connected sensory neurons which pass on sensor potentials to a word neuron. In experiments with a small lexicon which includes groups of very similar word forms, the model meets high standards with respect to word recognition and simulates a number of wellknown psycholinguistical effects.