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Experience-Guided Search: A Theory of Attentional Control

机译：经验导游搜索：注意力控制理论

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People perform a remarkable range of tasks that require search of the visual environment for a target item among distractors. The Guided Search model (Wolfe, 1994, 2007), or GS, is perhaps the best developed psychological account of human visual search. To prioritize search, GS assigns saliency to locations in the visual field. Saliency is a linear combination of activations from retinotopic maps representing primitive visual features. GS includes heuristics for setting the gain coefficient associated with each map. Variants of GS have formalized the notion of optimization as a principle of attentional control (e.g., Baldwin & Mozer, 2006; Cave, 1999; Navalpakkam & Itti, 2006; Rao et al., 2002), but every GS-like model must be 'dumbed down' to match human data, e.g., by corrupting the saliency map with noise and by imposing arbitrary restrictions on gain modulation. We propose a principled probabilistic formulation of GS, called Experience-Guided Search (EGS), based on a generative model of the environment that makes three claims: (1) Feature detectors produce Poisson spike trains whose rates are conditioned on feature type and whether the feature belongs to a target or distractor; (2) the environment and/or task is nonstationary and can change over a sequence of trials; and (3) a prior specifies that features are more likely to be present for target than for distractors. Through experience, EGS infers latent environment variables that determine the gains for guiding search. Control is thus cast as probabilistic inference, not optimization. We show that EGS can replicate a range of human data from visual search, including data that GS does not address.
机译：人们执行令人瞩目的任务范围，需要搜索患者的目标项目的视觉环境。引导的搜索模式（Wolfe，1994,2007）或GS，也许是人类视觉搜索的最佳心理帐户。要优先搜索搜索，GS为Visual字段中的位置分配显着性。显着性是来自视网膜映射图的激活的线性组合，代表原始视觉特征。 GS包括设置与每个映射相关联的增益系数的启发式。 GS的变体正式化了优化的概念作为注意力控制原则（例如，Baldwin＆Mozer，2006;洞穴，1999; Navalpakkam＆Itti，2006; Rao等，2002），但是每个GS样式都必须是“愚蠢”以匹配人类数据，例如，通过损坏具有噪声的显着图，并通过对增益调制施加任意限制。我们提出了一个原则性的概率制定GS，称为经验引导的搜索（EGS），基于制作三个权利要求的环境的生成模型：（1）特征探测器产生泊松峰列车，其速率在特征类型上有条件，以及是否特征属于目标或分散注意力; （2）环境和/或任务是非间平，可以通过一系列试验来改变; （3）先前指定的特征更有可能出现针对目标而不是令人满意的人。通过经验，EGS Infers确定用于指导搜索的增益的潜在环境变量。因此，控制作为概率推断，而不是优化。我们表明EGS可以从视觉搜索复制一系列人类数据，包括GS没有地址的数据。

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