
Top-down attention selection is fine grained
Author(s) -
Vidhya Navalpakkam,
Laurent Itti
Publication year - 2006
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/6.11.4
Subject(s) - dimension (graph theory) , granularity , top down and bottom up design , computer science , interval (graph theory) , feature (linguistics) , psychophysics , control (management) , attentional control , saccade , artificial intelligence , pattern recognition (psychology) , psychology , mathematics , perception , eye movement , cognition , neuroscience , combinatorics , pure mathematics , operating system , linguistics , philosophy , software engineering
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.