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Filip Děchtěrenko, Jiří Lukavský, Christina J. Howard; Tracking objects in 1/f noise and plain backgrounds. Journal of Vision 2020;20(11):479. doi: https://doi.org/10.1167/jov.20.11.479.
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Past research has shown that people can reliably track several moving objects among distractors. Although laboratory studies typically use clearly visible objects and a uniform background, this is rarely the case for tracking in real life. Therefore, the contribution of visibility and attentional enhancement of visibility to tracking performance is currently underexplored. In this study we explored performance when tracking 4 Gabor patches amongst 4 distractor patches in 1/f noise (noise tracking) and plain backgrounds (traditional tracking).
In the first experiment (n=25), we explored noisetracking performance when object detectability is reduced. Gabor patches were presented at four contrast levels. In the second experiment (n=38), we tested whether any reduction in performance is caused by lower detectability either during tracking or in the response phase after objects have stopped moving. Additionally, we tested how tracking performance in noise correlated with performance in traditional MOT. First, we presented participants with Gabor patches of three different contrast levels based on results from Experiment 1. In half of trials, we highlighted all objects after the motion phase with a red circle, while in the other half, objects were not highlighted. After completing the noise tracking task, participants were tested on traditional tracking with white circles on a uniform gray background (60 trials in total).
Tracking performance was impaired with decreases in detectability (both Experiments) and highlighting the targets during the response phase increased performance (Experiment 2). Moreover, performance in noise tracking was highly correlated with performance in traditional tracking (r > .61). We report two main findings: First, general tracking performance appears to be partly determined by a combination of detectability both during and after the tracking phase. Second, performance in noise tracking shares individual variability with performance in traditional MOT.
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