TY - GEN
T1 - AutoSelect
T2 - International Tutorial and Research Workshop on Perception and Interactive Technologies, PIT 2006
AU - Bee, Nikolaus
AU - Prendinger, Helmut
AU - Nakasone, Arturo
AU - André, Elisabeth
AU - Ishizuka, Mitsuru
PY - 2006
Y1 - 2006
N2 - While objects of our focus of attention ("where we are looking at") and accompanying affective responses to those objects is part of our daily experience, little research exists on investigating the relation between attention and positive affective evaluation. The purpose of our research is to process users' emotion and attention in real-time, with the goal of designing systems that may recognize a user's affective response to a particular visually presented stimulus in the presence of other stimuli, and respond accordingly. In this paper, we introduce the AutoSelect system that automatically detects a user's preference based on eye movement data and physiological signals in a two-alternative forced choice task. In an exploratory study involving the selection of neckties, the system could correctly classify subjects' choice of in 81%. In this instance of AutoSelect, the gaze 'cascade effect' played a dominant role, whereas pupil size could not be shown as a reliable predictor of preference.
AB - While objects of our focus of attention ("where we are looking at") and accompanying affective responses to those objects is part of our daily experience, little research exists on investigating the relation between attention and positive affective evaluation. The purpose of our research is to process users' emotion and attention in real-time, with the goal of designing systems that may recognize a user's affective response to a particular visually presented stimulus in the presence of other stimuli, and respond accordingly. In this paper, we introduce the AutoSelect system that automatically detects a user's preference based on eye movement data and physiological signals in a two-alternative forced choice task. In an exploratory study involving the selection of neckties, the system could correctly classify subjects' choice of in 81%. In this instance of AutoSelect, the gaze 'cascade effect' played a dominant role, whereas pupil size could not be shown as a reliable predictor of preference.
UR - http://www.scopus.com/inward/record.url?scp=33746042547&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33746042547&partnerID=8YFLogxK
U2 - 10.1007/11768029_5
DO - 10.1007/11768029_5
M3 - Conference contribution
AN - SCOPUS:33746042547
SN - 3540347437
SN - 9783540347439
VL - 4021 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 40
EP - 52
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y2 - 19 June 2006 through 21 June 2006
ER -