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

Galea, D; Bruza, P; Kitto, K; Nelson, D

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

Galea, D Bruza, P Kitto, K

Nelson, D

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7620 LNCS pp. 172 - 183
Issue Date:: 2012-12-14

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Field	Value	Language
dc.contributor.author	Galea, D	en_US
dc.contributor.author	Bruza, P	en_US
dc.contributor.author	Kitto, K https://orcid.org/0000-0001-7642-7121	en_US
dc.contributor.author	Nelson, D	en_US
dc.date.issued	2012-12-14	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7620 LNCS pp. 172 - 183	en_US
dc.identifier.isbn	9783642356582	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121001
dc.description.abstract	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.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-35659-9_16	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Modelling word activation in semantic networks: Three scaled entanglement models compared	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7620 LNCS	en_US
utslib.for	080203 Computational Logic and Formal Languages	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7620 LNCS	en_US

Abstract:

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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http://hdl.handle.net/10453/121001