Modelling word activation in semantic networks: Three scaled entanglement models compared

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7620 LNCS pp. 172 - 183
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Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. Previous research assumed a quantum-like model in which the semantic network was modelled as entangled qubits, however the level of activation was clearly being overestimated. This paper explores three variations of this model, each of which are distinguished by a scaling factor designed to compensate the overestimation. © 2012 Springer-Verlag.
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