This paper describes Guided Search 6.0 (GS6), a revised model of visual search. When we encounter a scene, we can see something everywhere. However, we cannot recognize more than a few items at a time. Attention is used to select items so that their features can be “bound” into recognizable objects. Attention is “guided” so that items can be processed in an intelligent order. In GS6, this guidance comes from five sources of preattentive information: (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g., priming), (4) reward, and (5) scene syntax and semantics. These sources are combined into a spatial “priority map,” a dynamic attentional landscape that evolves over the course of search. Selective attention is guided to the most active location in the priority map approximately 20 times per second. Guidance will not be uniform across the visual field. It will favor items near the point of fixation. Three types of functional visual field (FVFs) describe the nature of these foveal biases. There is a resolution FVF, an FVF governing exploratory eye movements, and an FVF governing covert deployments of attention. To be identified as targets or rejected as distractors, items must be compared to target templates held in memory. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 ms/item. Since selection occurs more frequently than that, it follows that multiple items are undergoing recognition at the same time, though asynchronously, making GS6 a hybrid of serial and parallel processes. In GS6, if a target is not found, search terminates when an accumulating quitting signal reaches a threshold. Setting of that threshold is adaptive, allowing feedback about performance to shape subsequent searches. Simulation shows that the combination of asynchronous diffusion and a quitting signal can produce the basic patterns of response time and error data from a range of search experiments.

本文介绍了引导搜索 6.0 (GS6)，这是视觉搜索的修订模型。当我们遇到一个场景时，我们到处都能看到一些东西。然而，我们一次只能识别几个项目。注意力用于选择项目，以便将其特征“绑定”到可识别的对象中。注意力被“引导”，以便可以按照智能顺序处理物品。在 GS6 中，该指导来自前注意信息的五个来源：（1）自上而下和（2）自下而上的特征指导，（3）先前的历史（例如启动），（4）奖励和（5）场景语法和语义。这些来源被组合成一个空间“优先级地图”，这是一个在搜索过程中不断演变的动态注意力景观。选择性注意力每秒大约 20 次被引导至优先级图中最活跃的位置。整个视野的引导不会是统一的。它会偏爱靠近固定点的物品。三种类型的功能视野（FVF）描述了这些中心凹偏差的性质。有一个决议FVF，一个控制探索性眼球运动的FVF，以及一个控制注意力的隐蔽部署的FVF。要被识别为目标或被拒绝为干扰项，必须将项目与内存中保存的目标模板进行比较。受关注对象的绑定和识别被建模为扩散过程，耗时 > 150 ms/item。由于选择发生的频率比这更高，因此多个项目会同时进行识别（尽管是异步的），从而使 GS6 成为串行和并行过程的混合体。在GS6中，如果没有找到目标，则当累积的退出信号达到阈值时，搜索终止。该阈值的设置是自适应的，允许有关性能的反馈来影响后续搜索。仿真表明，异步扩散和退出信号的组合可以从一系列搜索实验中产生响应时间和误差数据的基本模式。