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Dawn Sarno, Joanna Lewis, Mark Neider; Adding a Dimension to Visual Search. Journal of Vision 2016;16(12):990. doi: https://doi.org/10.1167/16.12.990.
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© ARVO (1962-2015); The Authors (2016-present)
Previous research on the interaction between visual search and depth information has suggested that adding depth information can facilitate search performance (e.g., faster RTs for 3-D vs 2-D displays). In the present study we aimed to replicate these previous findings while controlling for potential confounds (e.g., size). Search arrays were constructed from oriented "T" targets among oriented "L" distractors at two set sizes (18 & 24). To create a percept of depth, 3-D items were rendered with a bevel at three depths and 2-D stimuli were created at three sizes (to match changes in size related to depth in the 3-D condition) without a bevel. Participants were instructed to search for and respond to the presence of a "T" target on each trial – no specific target preview was provided. In half of the trials the search arrays were presented in 2-D and in the other half they were presented in 3-D. The target position was equally distributed across the three depth planes (3-D trials) and three item sizes (2-D trials) over the course of the experiment. Interestingly, the results were inconsistent with previous findings. Overall accuracy did not vary significantly across any manipulations. RTs in the 2-D condition were faster than in the 3-D condition across set size in both target present (~342 ms and ~549 ms, respectively) and target absent trials (~689 ms and ~884 ms). Additionally, search slopes in the 3-D condition (~85 ms) were steeper than in the 2-D condition (~52 ms). Overall, our results contradict previous findings and suggest that adding depth information does not always elicit performance advantages over 2-D displays during visual search. Further studies to systematically explore how various parameters of depth interact with search behavior and attentional deployment are required.
Meeting abstract presented at VSS 2016
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