Visual similarity effects in categorical search.

Visual similarity effects in categorical search.
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DOI:
10.1167/11.8.9
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发表时间:
2011-07-14
期刊:
影响因子:
1.8
通讯作者:
Zelinsky GJ
Zelinsky GJ
中科院分区:
医学4区
文献类型:
--
作者:
Alexander RG;Zelinsky GJ

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我们询问视觉相似性关系如何影响对分类定义目标的搜索指导(没有视觉预览)。实验1使用基于网络的任务,收集两个目标类别,泰迪熊和蝴蝶,和随机类别的对象,从我们创建的搜索显示在实验2中具有高相似性干扰,低相似性干扰,或“混合”显示与高,中,低相似性干扰之间的视觉相似性排名。目标缺席试验的分析显示,更快的手动响应和更少的固定分心低相似性显示相比,高。在混合显示器上,第一注视更频繁的高相似性干扰(熊=49%;蝴蝶=58%)比低相似性干扰(熊=9%;蝴蝶=12%)。实验3使用相同的高/低/混合条件,但现在这些条件是使用计算机视觉模型的相似性估计创建的,该模型根据颜色,纹理和形状相似性对对象进行排名。发现了相同的模式,这表明分类搜索确实可以由纯粹的视觉相似性指导。实验4比较了模型和人类排名不同的情况下,当他们同意。我们发现,相似性效应最好预测的情况下,两组排名一致,这表明人类视觉相似性排名和计算机视觉模型捕捉的功能,引导搜索到分类目标的重要性。
We asked how visual similarity relationships affect search guidance to categorically-defined targets (no visual preview). Experiment 1 used a web-based task to collect visual similarity rankings between two target categories, teddy bears and butterflies, and random-category objects, from which we created search displays in Experiment 2 having either high-similarity distractors, low-similarity distractors, or “mixed” displays with high, medium, and low-similarity distractors. Analysis of target-absent trials revealed faster manual responses and fewer fixated distractors on low-similarity displays compared to high. On mixed displays, first fixations were more frequent on high-similarity distractors (bear=49%; butterfly=58%) than on low-similarity distractors (bear=9%; butterfly=12%). Experiment 3 used the same high/low/mixed conditions, but now these conditions were created using similarity estimates from a computer vision model that ranked objects in terms of color, texture, and shape similarity. The same patterns were found, suggesting that categorical search can indeed be guided by purely visual similarity. Experiment 4 compared cases where the model and human rankings differed and when they agreed. We found that similarity effects were best predicted by cases where the two sets of rankings agreed, suggesting that both human visual similarity rankings and the computer vision model captured features important for guiding search to categorical targets.
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