Short-term and long-term attentional biases to frequently encountered target features

Li Z. Sha, Roger W. Remington, Yuhong V. Jiang

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


It has long been known that frequently occurring targets are attended better than infrequent ones in visual search. But does this frequency-based attentional prioritization reflect momentary or durable changes in attention? Here we observed both short-term and long-term attentional biases for visual features as a function of different types of statistical associations between the targets, distractors, and features. Participants searched for a target, a line oriented horizontally or vertically among diagonal distractors, and reported its length. In one set of experiments we manipulated the target’s color probability: Targets were more often in Color 1 than in Color 2. The distractors were in other colors. Participants found Color 1 targets more quickly than Color 2 targets, but this preference disappeared immediately when the target’s color became random in the subsequent testing phase. In the other set of experiments, we manipulated the diagnostic values of the two colors: Color 1 was more often a target than a distractor; Color 2 was more often a distractor than a target. Participants found Color 1 targets more quickly than Color 2 targets. Importantly, and in contrast to the first set of experiments, the featural preference was sustained in the testing phase. These results suggest that short-term and long-term attentional biases are products of different statistical information. Finding a target momentarily activates its features, inducing short-term repetition priming. Long-term changes in attention, on the other hand, may rely on learning diagnostic features of the targets.

Original languageEnglish (US)
Pages (from-to)1311-1322
Number of pages12
JournalAttention, Perception, and Psychophysics
Issue number5
StatePublished - Jul 1 2017

Bibliographical note

Publisher Copyright:
© 2017, The Psychonomic Society, Inc.


  • Diagnostic features
  • Selective attention
  • Statistical learning
  • Target’s frequency effect
  • Visual search


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