4.2 Article

Decomposing experience-driven attention: Opposite attentional effects of previously predictive cues

Journal

ATTENTION PERCEPTION & PSYCHOPHYSICS
Volume 78, Issue 7, Pages 2185-2198

Publisher

SPRINGER
DOI: 10.3758/s13414-016-1101-z

Keywords

Attention learning; Experience-driven attention; Attentional capture; Inhibition; Task context

Funding

  1. NEI NIH HHS [R01 EY017491] Funding Source: Medline
  2. NIBIB NIH HHS [T32 EB008389] Funding Source: Medline

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A central function of the brain is to track the dynamic statistical regularities in the environment - such as what predicts what over time. How does this statistical learning process alter sensory and attentional processes? Drawing upon animal conditioning and predictive coding, we developed a learning procedure that revealed two distinct components through which prior learning-experience controls attention. During learning, a visual search task was used in which the target randomly appeared at one of several locations but always inside an encloser of a particular color - the learned color served to direct attention to the target location. During test, the color no longer predicted the target location. When the same search task was used in the subsequent test, we found that the learned color continued to attract attention despite the behavior being counterproductive for the task and despite the presence of a completely predictive cue. However, when tested with a flanker task that had minimal location uncertainty - the target was at the fixation surrounded by a distractor - participants were better at ignoring distractors in the learned color than other colors. Evidently, previously predictive cues capture attention in the same search task but can be better suppressed in a flanker task. These results demonstrate opposing components - capture and inhibition - in experience-driven attention, with their manifestations crucially dependent on task context. We conclude that associative learning enhances context-sensitive top-down modulation while it reduces bottom-up sensory drive and facilitates suppression, supporting a learning-based predictive coding account.

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