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<p>The visual system can encode multiple objects in the form of ensemble representations. Such representations can be accessed with either explicit or implicit reports, but depending on the type of report, the observed properties of the ensemble representation can differ in detail. Previous studies have suggested that the saliency of individual items biases the perceived mean of ensembles (the so-called amplification effect). It is unclear, however, whether saliency affects implicit representations of the whole feature distribution (beyond mean and variance). Our observers were presented with sets of lines varying in orientation and size where size was a task-irrelevant salient feature. To estimate explicit representations, observers adjusted the mean orientation. To access the implicit representation of the feature distribution, we used a visual search task (Feature Distribution Learning) for an oddly oriented line among heterogeneous distractors and measured the search times. The results revealed a strong saliency-induced bias in the explicit report task, with mean orientation estimates biased toward the more salient items. However, no such amplification effect was observed for the implicit report. Our results support the hypothesis that distinct mechanisms may underlie the implicit and explicit ensemble representations.</p>

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Amplification from saliency affects explicit but not implicit ensemble representations

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • Aleksei U. Iakovlev,
  • Vladislav A. Khvostov,
  • Árni Gunnar Ásgeirsson,
  • Igor S. Utochkin,
  • Árni Kristjánsson

摘要

The visual system can encode multiple objects in the form of ensemble representations. Such representations can be accessed with either explicit or implicit reports, but depending on the type of report, the observed properties of the ensemble representation can differ in detail. Previous studies have suggested that the saliency of individual items biases the perceived mean of ensembles (the so-called amplification effect). It is unclear, however, whether saliency affects implicit representations of the whole feature distribution (beyond mean and variance). Our observers were presented with sets of lines varying in orientation and size where size was a task-irrelevant salient feature. To estimate explicit representations, observers adjusted the mean orientation. To access the implicit representation of the feature distribution, we used a visual search task (Feature Distribution Learning) for an oddly oriented line among heterogeneous distractors and measured the search times. The results revealed a strong saliency-induced bias in the explicit report task, with mean orientation estimates biased toward the more salient items. However, no such amplification effect was observed for the implicit report. Our results support the hypothesis that distinct mechanisms may underlie the implicit and explicit ensemble representations.