Conci Markus, Zhao Feifei
Department of Psychology, Ludwig-Maximilians-Universität München, München, Germany; Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, München, Germany.
Department of Psychology, Ludwig-Maximilians-Universität München, München, Germany; Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, München, Germany.
Vision Res. 2025 May;230:108591. doi: 10.1016/j.visres.2025.108591. Epub 2025 Mar 26.
Attentional orienting in complex visual environments is supported by statistical learning of regularities. For instance, visual search for a target is faster when a distractor layout is repeatedly encountered, illustrating that learned contextual invariances improve attentional guidance (contextual cueing). Although contextual learning is usually relatively efficient, relocating the target (within an otherwise unchanged layout) typically abolishes contextual cueing, while revealing only a slow recovery of learning. However, such a "lack-of-adaptation" was usually only shown with artificial displays with target/distractor letters. The current study in turn used more realistic natural scene images to determine whether a comparable cost would also be evident in real-life contexts. Two experiments compared initial contextual cueing and the subsequent updating after a change in displays that either presented artificial letters, or natural scenes as contexts. With letter displays, an initial cueing effect was found that was associated with non-explicit, incidental learning, which vanished after the change. Natural scene displays either revealed a rather large cueing effect that was related to explicit memory (Experiment 1), or cueing was less strong and based on incidental learning (Experiment 2), with the size of cueing and the explicitness of the memory representation depending on the variability of the presented scene images. However, these variable initial benefits in scene displays always led to a substantial reduction after the change, comparable to the pattern in letter displays. Together, these findings show that the "richness" of natural scene contexts does not facilitate flexible contextual updating.
复杂视觉环境中的注意定向受规律的统计学习支持。例如,当反复遇到干扰项布局时,对目标的视觉搜索会更快,这说明习得的上下文不变性会改善注意引导(上下文线索效应)。尽管上下文学习通常相对高效,但重新放置目标(在其他方面不变的布局内)通常会消除上下文线索效应,同时仅显示学习的缓慢恢复。然而,这种“缺乏适应性”通常仅在带有目标/干扰项字母的人工显示中得到体现。本研究转而使用更逼真的自然场景图像来确定在现实生活情境中是否也会出现类似的代价。两项实验比较了初始上下文线索效应以及在显示变化后(显示人工字母或自然场景作为上下文)的后续更新情况。对于字母显示,发现了一种与非显性的偶然学习相关的初始线索效应,在变化后消失了。自然场景显示要么揭示了一种与显性记忆相关的相当大的线索效应(实验1),要么线索效应较弱且基于偶然学习(实验2),线索效应的大小和记忆表征的显性程度取决于所呈现场景图像的可变性。然而,场景显示中这些可变的初始益处总是在变化后大幅减少,这与字母显示中的模式相当。总之,这些发现表明自然场景上下文的“丰富性”并不能促进灵活的上下文更新。