Abstract
Predictions based on learned statistical regularities in the visual world have been shown to facilitate attention and goal-directed behavior by sharpening the sensory representation of goal-relevant stimuli in advance. Yet, how the brain learns to ignore predictable goal-irrelevant or distracting information is unclear. Here, we used EEG and a visual search task in which the predictability of a distractor's location and/or spatial frequency was manipulated to determine how spatial and feature distractor expectations are neurally implemented and reduce distractor interference. We find that expected distractor features could not only be decoded pre-stimulus, but their representation differed from the representation of that same feature when part of the target. Spatial distractor expectations did not induce changes in preparatory neural activity, but a strongly reduced Pd, an ERP index of inhibition. These results demonstrate that neural effects of statistical learning critically depend on the task relevance and dimension (spatial, feature) of predictions.
Highlights
The ability to ignore distracting information is a key component of selective attention, and critical to goal-directed behavior
We aimed to extend our recent finding that distractor location expectations reduced post-distractor inhibition, as indicated by a greatly reduced or even eliminated Pd ERP, by determining if this effect is dependent on featurebased distractor expectations or not
Having established that distractors were generally more efficiently ignored at high-probability distractor locations, we investigated whether as in Experiment 1, target processing was impaired at high-probability distractor locations and if this was dependent on the extent to which targets and/or distractors were predictable at the feature level
Summary
The ability to ignore distracting information is a key component of selective attention, and critical to goal-directed behavior. This work shows that distractor suppression strongly depends on distractor-based learning, for example, about its likely location in the visual environment (Failing et al, 2019a; Ferrante et al, 2018; Goschy et al, 2014; Reder et al, 2003; Sauter et al, 2018; Wang and Theeuwes, 2018a) or its non-spatial features, for example its color or shape (Cunningham and Egeth, 2016; Stilwell et al, 2019; Vatterott and Vecera, 2012) Based on these observations, it has been proposed that distractor inhibition may be dependent on expectations derived from past experience about the likelihood of events (Noonan et al, 2018; Moorselaar and Slagter, 2020).
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