Target templates and the time course of distractor location learning.

When searching for a shape target, colour distractors typically capture our attention. Capture is smaller when observers search for a fixed target that allows for a feature-specific target template compared to a varying shape singleton target. Capture is also reduced when observers learn to predict the likely distractor location. We investigated how the precision of the target template modulates distractor location learning in an additional singleton search task. As observers are less prone to capture with a feature-specific target, we assumed that distractor location learning is less beneficial and therefore less pronounced than with a mixed-feature target. Hierarchical Bayesian parameter estimation was used to fit fine-grained distractor location learning curves. A model-based analysis of the time course of distractor location learning revealed an effect on the asymptotic performance level: when searching for a fixed-feature target, the asymptotic distractor cost indicated smaller distractor interference than with a mixed-feature target. Although interference was reduced for distractors at the high-probability location in both tasks, asymptotic distractor suppression was less pronounced with fixed-feature compared to mixed-feature targets. We conclude that with a more precise target template less distractor location learning is required, likely because the distractor dimension is down-weighted and its salience signal reduced.