Reduced contextual uncertainty facilitates learning what to attend to and what to ignore

TL;DR

The study shows that learning from consistent environments helps people focus on important elements and ignore distractions.

Contribution

The study reveals that reduced attentional capture is due to generic predictions from stable contexts, not just specific cues.

Findings

Participants showed reduced attentional capture in both familiar and novel search displays.

Lower statistical volatility allowed the development of generic predictions about task-relevant features.

Findings suggest attentional capture is influenced by task regularities and learning environment volatility.

Abstract

Variability in the search environment has been shown to affect the capture of attention by salient distractors, as attentional capture is reduced when context variability is low. However, it remains unclear whether this reduction in capture is caused by contextual learning or other mechanisms, grounded in generic context-structure learning. We set out to test this by training participants (n = 200) over two sessions in a visual search task, conducted online, where they gained experience with a small subset of search displays, which significantly reduced capture of attention by colour singletons. In a third session, we then tested participants on a mix of familiar and novel search displays and examined whether this reduction in capture was specific to familiar displays, indicative of contextual cueing effects, or would generalise to novel displays. We found no capture by the singleton in either the familiar or novel condition. Instead, our findings suggested that reduced statistical volatility reduced capture by allowing the development of generic predictions about task-relevant locations and features of the display. These findings add to the current debate about the determinants of capture by salient distractors by showing that capture is also affected by generic task regularities and by the volatility of the learning environment.

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