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J. Lu, L. Itti, Feature-based attention is not object-based, In: Proc. Vision Science Society Annual Meeting (VSS06), May 2006. (Cited by 1)
Abstract: Feature-based attention was revealed as global enhancement of attended visual features throughout the visual cortex. Object-based attention was shown as better performance when concurrently discriminating two features of same object compared to features of different objects. We used fMRI to investigate whether feature-based attention is object-based, i.e, is cortical enhancement of attended features influenced by the objectness the stimulus features appear? The stimuli were two fields of random dots presented bilaterally to central fixation cross. Subjects performed luminance discrimination using two-interval forced-choice paradigm on one side and ignored the stimulus on the other side. The ignored stimulus was always red dots and the attended stimulus was overlapped red and green dots. Subjects performed luminance discrimination on either red dots or on green dots. We compared visual cortical enhancement of the ignored stimulus when subjects attended on the other side to either identical (red) or different (green) stimulus in two conditions: either the dots stimuli on both sides appeared to belong to same object (both fields displayed in the same grey box appearing on top of a textured background, with cast shadows effects around the box), or as two separate objects (each field displayed in a separate box with the same background and shadows). Results showed both in single-object condition and in two-object condition the two subjects consistently had significant enhancement of the ignored stimulus in early visual areas (V1 to V4). Hence it indicated feature-based attentional enhancement exists even between two stimuli which belong to two different objects, suggesting a very early mechanism.
Note: Oral presentation
Themes: Human Psychophysics, Model of Top-Down Attentional Modulation, Functional Neuroimaging
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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