Download e-book for iPad: Connectionist Models in Cognitive Neuroscience: The 5th by Richard Shillcock, Padraic Monaghan, T. Mark Ellison

By Richard Shillcock, Padraic Monaghan, T. Mark Ellison (auth.), Dietmar Heinke PhD, Glynn W. Humphreys PhD, Andrew Olson PhD (eds.)

ISBN-10: 1447108132

ISBN-13: 9781447108139

ISBN-10: 185233052X

ISBN-13: 9781852330521

1. Introdudion This quantity collects jointly the refereed types of 25 papers offered on the fifth Neural Computation and Psychology Workshop (NCPW5), held on the college of Birmingham from the eighth till the lOth of September 1998. The NCPW is a well-established, vigorous discussion board, which brings jointly researchers from more than a few disciplines (artificial intelligence, arithmetic, cognitive technology, desktop technological know-how, neurobiology, philosophy and psychology), all of whom have an interest within the software of neurally-inspired (connectionist) types to issues in psychology. The subject matter of the fifth workshop within the sequence used to be Connectionist versions in cognitive neuroscience', and the workshop aimed to compile papers all for the inter-relations among practical (psychological) bills of cognition and neural debts of underlying mind methods, associated via connectionist versions. From the very beginnings of contemporary psychology, with the paintings of William James and his contemporaries, researchers have believed it vital to narrate behavioural analyses to neurological underpinnings. besides the fact that, with the arrival of connectionist modelling, the place types are no less than encouraged by means of neuronal procedures, this firm has acquired a brand new enhance. With this quantity, we are hoping that this quantity provides one additional mosaic stone to this formidable aim, of unifying practical and neuronal bills of performance.

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Extra info for Connectionist Models in Cognitive Neuroscience: The 5th Neural Computation and Psychology Workshop, Birmingham, 8–10 September 1998

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3 Learning Learning consisted of two phases: one-shot Hebbian learning followed by a period of supervised training (the practice phase) as described in [12]. 0 rather than the node that actually won (Glasspool, Personal Communication). If this is not done, an error early in the word could result in errors later. e. no further adjustments to weights were made after correct recall in the practice phase). The model failed to learn (after 3000 epochs) 21 of the words with geminates (16%), 25 of the words with repeated letters (13%) and 8 (38%) of the words containing both repeated letters and geminates.

The first letter of a word always occupied positions 1-15, and the last letter 15-29. The other letters were equally spaced between these (see Figure 2). This simulated the presentation of words in a "word centred frame" with the end letters having privileged location codes. The model was trained on 160 "words" of four different lengths (3-6 letters). The stimuli were not real words but were pseudo-randomly chosen strings from the eight letters known to the model. positio n a b c d A B OOOOOOOOOC) 00008 • • • @ 00000000000 00000000000 00000000000 00000000000 Figure l.

046 vs. 01; 4 and 5-letter words were not compared as they were generally not learned as well due to increased similarity caused by constraints for other tests). Effects of case mixing x length on RTs have not yet been tested experimentally, however, we predict that there will be a greater effect on longer words. 078 vs. 17). A small effect of buried words has been found on word naming latencies [ 19]. 4 Letter Spacing The effect of increasing the spacing between letters was simulated in the model by reducing the spread of activations across letters in the input.

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Connectionist Models in Cognitive Neuroscience: The 5th Neural Computation and Psychology Workshop, Birmingham, 8–10 September 1998 by Richard Shillcock, Padraic Monaghan, T. Mark Ellison (auth.), Dietmar Heinke PhD, Glynn W. Humphreys PhD, Andrew Olson PhD (eds.)

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