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Author van Lambalgen, R. ♦ van Maanen, P.-P.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Parameter Tuning ♦ Attention Model ♦ Computational modeling ♦ Adaptation model ♦ Humans ♦ Simulated annealing ♦ Data models ♦ Mathematical model ♦ Tuning ♦ Personalisation
Abstract In this paper it is explored whether personalisation of an existing computational model of attention can increase the model’s validity. Computational models of attention are for instance applied in attention allocation support systems and can benefit from this increased validity. Personalisation is done by tuning the model’s parameters during a training phase, using Simulated Annealing (SA). The adapted attention model is validated using a task, varying in difficulty and attentional demand. Results show that the attention model with personalisation results in a more accurate estimation of an individual’s attention as compared to the model without personalisation.
ISBN 9781424484829
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-08-31
Publisher Place Canada
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 272.73 kB
Page Count 6
Starting Page 376
Ending Page 381


Source: IEEE Xplore Digital Library