= PDF Reprint,     = BibTeX entry,     = Online Abstract

Click to download PDF version Click to download BibTeX data Clik to view abstract V. Navalpakkam, L. Itti, Top-down attention selection is fine-grained, Journal of Vision, Vol. 6, No. 11, pp. 1180-1193, Oct 2006. [2005 impact factor: 3.469] (Cited by 89)

Abstract: Although much is known about the sources and modulatory effects of top-down attentional signals, the information capacity of these signals is less known. Here, we investigate the granularity of top-down attentional signals. Previous theories in psychophysics have provided conflicting evidence on whether top-down guidance is coarse grained (i.e., one gain control term per feature dimension) or fine grained (i.e., multiple gain control terms per dimension). We resolve the conflict by designing new experiments that disentangle top-down from bottom-up contributions, thereby avoiding confounds existing in previous studies. The results of our eye-tracking experiments show that subjects can selectively saccade to items belonging to the relevant feature interval compared with irrelevant intervals within a dimension. This suggests that top-down signals can specify not only the relevant feature dimension but also the relevant feature interval within a dimension. We conclude that top-down signals are fine grained and can specify multiple gain control terms per dimension.

Themes: Model of Top-Down Attentional Modulation, Model of Bottom-Up Saliency-Based Visual Attention, Computational Modeling, Human Psychophysics


Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
This page generated by bibTOhtml on Fri Jan 26 09:25:23 PST 2018