Personalisation of computational models of attention by simulated annealing parameter tuning

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. © 2010 IEEE

Personalisation of computational models of attention by simulated annealing parameter tuning

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. © 2010 IEEE