A connectionist architecture is developed that can be used for modeling choice probabilities and reaction times in psychophysics and word recognition. The network architecture consists of a feed-forward network and a decoding module. Learning is by mean-variance back-propagation, an extension of the standard back-propagation learning algorithm. The new learning procedure is interpreted as a selective attention mechanism, and leads to a better model of learning in simple identification tasks than the standard back-propagation. Choice probabilities are modeled by the input/output relations of the network, and reaction times are modeled by the time taken for the network, particularly the decoding module, to achieve a stable state. The model is applied to both unidimensional and multidimensional identification tasks in psychophysics and to word recognition.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.74588 |
| Date | January 1990 |
| Creators | Lacouture, Yves |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Psychology.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 001171006, proquestno: AAINN66555, Theses scanned by UMI/ProQuest. |
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