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◆ Journal of vision2026-08-03

Attentional tuning to color in efficient visual search: Evidence for optimal peak shifts and relational asymmetry.

Dirk Kerzel, Martin Constant

原始摘要(英文原文)· Original abstract
This study investigated attentional templates and color tuning during efficient visual search. To evaluate optimal tuning and relational accounts, we used a paradigm where participants searched for a color singleton target while ignoring a cross-dimensional shape distractor. Crucially, the distractor's color varied along a precise continuum within the same dimension, spanning values toward and away from the nontarget background. We modeled response times (RTs) and oculomotor capture by fitting asymmetric Gaussian functions to the response curves. Supporting optimal tuning, oculomotor capture (Experiment 1) and manual responses under fixed fixation (Experiment 2) revealed peak interference systematically shifted away from the nontarget color to maximize target-nontarget contrast. Intriguingly, this peak shift was significantly more pronounced in early oculomotor capture than manual RTs, suggesting that optimal tuning biases peripheral attentional guidance (guiding template) more strongly than foveated target verification (target template). Furthermore, response functions exhibited profound asymmetry, displaying shallower slopes for colors extending along the target-defining vector. Whereas Experiment 3 confirmed highly efficient search slopes, Experiment 4 demonstrated that a feature-dissimilar distractor produced negligible capture, validating that selection was governed by a feature-based template. Together, these findings indicate that although attentional templates center on a specifically tuned, contrast-maximizing peak, their overall distribution remains bound to a relative, directional feature rule, consistent with the relational account. Data and scripts are available at https://doi.org/q9f4.
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Attentional tuning to color in efficient visual search: Evidence for optimal peak shifts and relational asymmetry. — 科研速览 Science Skim