Abstract
We introduce a particular Attractor Neural Network (ANN) with a learning rule able to store sets of patterns with a two-level ultrametric structure, in order to model human semantic memory operation. Our simulations show that this model is able to reproduce a particular quantitative feature of this operation observed in experiments with human subjects, i.e. the correlation between high values of the prototypicity of exemplars of a given concept and low values of recognition reaction times for phrases asserting that the same exemplars belong to this concept. This shows that ANNs can be considered as good candidates for modelling some features of human semantic memory.
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© 1998 Springer-Verlag London Limited
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Pessa, E., Penna, M.P. (1998). Attractor Neural Networks as Models of Semantic Memory. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN VIETRI-97. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1520-5_12
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DOI: https://doi.org/10.1007/978-1-4471-1520-5_12
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1522-9
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