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一种统一特征搜索和关联搜索的项目间相似性模型。

An inter-item similarity model unifying feature and conjunction search.

作者信息

Phillips Steven, Takeda Yuji, Kumada Takatsune

机构信息

Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan.

出版信息

Vision Res. 2006 Oct;46(22):3867-80. doi: 10.1016/j.visres.2006.06.016. Epub 2006 Aug 22.

Abstract

We propose a model of visual search to address the hitherto unresolved issue of reconciling serial deployment of attention accounts with inter-item similarity effects. Target-distractor and distractor-distractor similarity were systematically varied in 85 (17x5) set type-size conditions over seven experiments, including univariate feature and bivariate conjunction search. The model, a power (square root) function of dimension-specific target-distractor and distractor-distractor similarity in linear combination with set size, accounted for 98% of the variance on type-size means. It suggests that much of efficient and inefficient search can be unified under a single theory involving item similarity.

摘要

我们提出了一种视觉搜索模型,以解决迄今为止尚未解决的问题,即如何协调注意力分配的串行部署与项目间相似性效应。在七项实验中的85种(17×5)集合类型-大小条件下,系统地改变了目标-干扰项和干扰项-干扰项的相似性,包括单变量特征搜索和双变量联合搜索。该模型是特定维度的目标-干扰项和干扰项-干扰项相似性的幂(平方根)函数,与集合大小进行线性组合,解释了类型-大小均值方差的98%。这表明,高效和低效搜索的大部分情况都可以在一个涉及项目相似性的单一理论下得到统一。

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