Abstract
The standard finding of frequency effect is a high frequency advantage in recall and low frequency advantage in recognition. However, there are exception from these findings in from of a high frequency advantage of cues in recognition, an advantage for recognition of words over non-words, and a lack of frequency effects in mixed list of recall. A distributed connectionist memory model consisting of two mechanism sensitive to frequency is suggested. The error correcting learning rule controls encoding of items keeping the system from catastrophic interference with correlated patterns. This mechanism is found essential to simulate low frequency advantage in recognition. The familiarity is measured as the absolute net input. This mechanism accounts for the advantage of high frequency in recall, words over non-words in recognition, and high frequency cues. The model is implemented in a modified Hopfield network and analyzed analytically.
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Sikström, S. (1999). A Connectionist Model for Frequency Effects in Recall and Recognition. In: Heinke, D., Humphreys, G.W., Olson, A. (eds) Connectionist Models in Cognitive Neuroscience. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0813-9_10
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DOI: https://doi.org/10.1007/978-1-4471-0813-9_10
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