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ABSTRACT
In this paper, we firstly present what is
Interactive Evolutionary Computation (IEC)
and rapidly how we have combined this
artificial intelligence technique with an eyetracker
for visual optimization. Next, in order
to correctly parameterize our application, we
present results from applying data mining
techniques on gaze information coming from
experiments conducted on about 80 human
individuals.

France 2008 Pallez behavior model IEC Data Mining gaze Eye Tracking Tobii 1750

in list: Cognitive & Behavioural Psychology

ABSTRACT
Interactive Evolutionary Computation (IEC) community
aims at reducing user's fatigue during an optimization task
involving subjective criteria: a set of graphic potential
solutions are simultaneously shown to a user which task is
to identify most interesting solutions to the problem he had
to solve. Evolutionary operators are applied to user choices
expecting to produce better solutions. As traditional IEC
ask the user to give a mark to each solution or to explicitly
choose bests solutions with a mouse, we propose a new
framework that uses in real time gaze information to predict
which parts of a screen is more significant for a user. We
can therefore avoid the user to explicitly choose which
solutions are interesting for him. In this paper, we mainly
focus on automatically ordering solutions shown on a
screen given a gaze path obtained by an eye-tracker. We
applied several supervised learning methods (SVM, neural
networks…) on two different experiments. We obtain a
formula that predict with 85% user choices. We
demonstrate that decisive criterion is time spent on one
solution and we show the independency between this
formula and the experiment.

Interactive Evolutionary Computation Eye Tracking Pallez Tobii 1750 France 2010

in list: HCI & Usability