Top-down control of attention by stereoscopic depth

Bochao Zou*, Yue Liu, Jeremy M. Wolfe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Stereoscopic depth has a mixed record as a guiding attribute in visual attention. Visual search can be efficient if the target lies at a unique depth; whereas automatic segmentation of search arrays into different depth planes does not appear to be pre-attentive. These prior findings describe bottom-up, stimulus-driven depth guidance. Here, we ask about the top-down selection of depth information. To assess the ability to direct attention to specific depth planes, Experiment 1 used the centroid judgment paradigm which permits quantitative measures of selective processing of items of different depths or colors. Experiment 1 showed that a subset of observers could deploy specific attention filters for each of eight depth planes, suggesting that at least some observers can direct attention to a specific depth plane quite precisely. Experiment 2 used eight depth planes in a visual search experiment. Observers were encouraged to guide their attention to far or near depth planes with an informative but imperfect cue. The benefits of this probabilistic cue were small. However, this may not be a specific problem with guidance by stereoscopic depth. Equivalently poor results were obtained with color. To check and prove that depth guidance in search is possible, Experiment 3 presented items in only two depth planes. In this case, information about the target depth plane allows observers to search more efficiently, replicating earlier work. We conclude that top-down guidance by stereoscopic depth is possible but that it is hard to apply the full range of our stereoscopic ability in search.

Original languageEnglish
Article number108061
JournalVision Research
Volume198
DOIs
Publication statusPublished - Sept 2022

Keywords

  • Centroid estimation
  • Depth
  • Stereoscopic disparity
  • Visual attention
  • Visual search

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