<p>The signal suppression account of attentional capture was proposed in 2010 to resolve a longstanding debate between bottom-up and top-down theories of capture by proposing that a top-down suppressive mechanism can eliminate bottom-up capture of attention. Since its original proposal, the signal suppression account has garnered much support and has also been challenged in important ways. The current article reviews how the signal suppression account has survived several challenges but has also been updated to account for new findings. The primary updates are that (a) suppression operates on specific feature values and locations rather than squashing a generalized “attend-to-me” signal produced by salient distractors, and (b) suppression reflects implicit learning that is triggered when attention is captured. This revised hypothesis predicts that initial instances of attentional capture are needed to drive the implicit learning processes that lead to distractor suppression. Because high-salience distractors are more likely to capture attention than low-salience distractors prior to this implicit learning process, the revised hypothesis predicts that it will be easier to learn to suppress high-salience distractors than low-salience distractors. It also predicts that explicit attempts to override capture may (ironically) lead to increased rather than decreased distraction.</p>

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Signal suppression 2.0: An updated account of attentional capture and suppression

  • Nicholas Gaspelin,
  • Xiaojin Ma,
  • Steven J. Luck

摘要

The signal suppression account of attentional capture was proposed in 2010 to resolve a longstanding debate between bottom-up and top-down theories of capture by proposing that a top-down suppressive mechanism can eliminate bottom-up capture of attention. Since its original proposal, the signal suppression account has garnered much support and has also been challenged in important ways. The current article reviews how the signal suppression account has survived several challenges but has also been updated to account for new findings. The primary updates are that (a) suppression operates on specific feature values and locations rather than squashing a generalized “attend-to-me” signal produced by salient distractors, and (b) suppression reflects implicit learning that is triggered when attention is captured. This revised hypothesis predicts that initial instances of attentional capture are needed to drive the implicit learning processes that lead to distractor suppression. Because high-salience distractors are more likely to capture attention than low-salience distractors prior to this implicit learning process, the revised hypothesis predicts that it will be easier to learn to suppress high-salience distractors than low-salience distractors. It also predicts that explicit attempts to override capture may (ironically) lead to increased rather than decreased distraction.