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Experience-Guided Search: A Theory of Attentional Control

机译：经验引导的搜索：注意控制理论

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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：人们执行大量任务，需要在视觉环境中搜索干扰项中的目标项目。引导搜索模型（Wolfe，1994，2007）或GS可能是人类视觉搜索的最完善的心理学解释。为了优先搜索，GS将显着性分配给视野中的位置。显着性是来自代表原始视觉特征的视网膜局部位点图激活的线性组合。 GS包括用于设置与每个图相关联的增益系数的试探法。 GS的变体已将优化的概念正式化为注意力控制的原则（例如Baldwin和Mozer，2006; Cave，1999; Navalpakkam和Itti，2006; Rao等，2002），但是每个类似于GS的模型都必须“沉没”以匹配人类数据，例如，通过用噪声破坏显着图并通过对增益调制施加任意限制。我们提出了一种有原则的GS概率公式，称为“经验指导搜索（EGS）”，它基于环境的生成模型，该模型提出了以下三点主张：（1）特征检测器产生泊松峰值序列，其速率取决于特征类型以及是否取决于特征类型。特征属于目标或干扰因素; （2）环境和/或任务不稳定，并且可以在一系列试验中改变; （3）先验条件指定目标比干扰项更可能出现特征。通过经验，EGS推断潜在的环境变量，这些变量确定了指导搜索的收益。因此，控制被转换为概率推断，而不是优化。我们证明了EGS可以从视觉搜索中复制一系列人类数据，包括GS无法处理的数据。

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