The human visual system is equipped to rapidly and implicitly learn and exploit the statistical regularities in our environment. Within visual search, contextual cueing demonstrates how implicit knowledge of scenes can improve search performance. This is commonly interpreted as spatial context in the scenes becoming predictive of the target location, which leads to a more efficient guidance of attention during search. However, what drives this enhanced guidance is unknown. First, it is under debate whether the entire scene (global context) or more local context drives this phenomenon. Second, it is unclear how exactly improved attentional guidance is enabled by target enhancement and distractor suppression. In the present MEG experiment, we leveraged Rapid Invisible Frequency Tagging (RIFT) to answer these two outstanding questions. We found that the improved performance when searching implicitly familiar scenes was accompanied by a stronger neural representation of the target stimulus, at the cost specifically of those distractors directly surrounding the target. Crucially, this biasing of local attentional competition was behaviorally relevant when searching familiar scenes, indicating that it is the local, and not global, spatial context that is modulated, culminating in a search advantage for familiar scenes. Taken together, we conclude that implicitly learned spatial predictive context improves how we search our environment by sharpening the attentional field.

中文翻译：

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空间预测上下文通过偏向局部注意力竞争来加速视觉搜索

人类视觉系统能够快速、隐式地学习和利用我们环境中的统计规律。在视觉搜索中，上下文提示展示了场景的隐式知识如何提高搜索性能。这通常被解释为场景中的空间上下文可以预测目标位置，从而在搜索过程中更有效地引导注意力。然而，是什么推动了这种增强的指导意见尚不清楚。首先，人们争论是整个场景（全球背景）还是更局部的背景驱动了这种现象。其次，目前还不清楚如何通过目标增强和干扰抑制来改善注意力引导。在目前的 MEG 实验中，我们利用快速不可见频率标记 (RIFT) 来回答这两个问题。我们发现，在搜索隐式熟悉的场景时，性能的提高伴随着目标刺激的更强的神经表征，但代价是直接围绕目标的干扰因素。至关重要的是，这种局部注意力竞争的偏差在搜索熟悉场景时与行为相关，这表明调节的是局部而非全局空间上下文，最终导致对熟悉场景的搜索优势。综上所述，我们得出的结论是，隐式学习的空间预测上下文通过增强注意力领域来改善我们搜索环境的方式。