视觉搜索中统计学习的情绪调节

Artyom Zinchenko, A. M. Bierlich, M. Conci, Hermann J. Müller, T. Geyer
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引用次数: 0

摘要

当受试者在重复的显示安排中遇到目标时,视觉搜索会得到促进。这种 "情境提示 "效应可归因于空间分心物-目标关系的附带学习,从而更有效地引导视觉搜索指向目标位置。相反,具有行为意义但与任务无关的负面情绪刺激可能会不由自主地吸引注意力,从而影响视觉搜索的表现。为此,我们研究了在搜索任务之前,不同类别的情绪(面部、场景)图片所诱发的情绪警报刺激与记忆相关可塑性的关系。我们对 46 名参与者进行了测试,他们在重复和非重复的搜索布局中,以不同(50、500、1,000 毫秒)的时间间隔出现情绪化与中性的面孔或场景。我们发现,与中性场景相比,情绪化场景增加了情境学习,这导致根本观察不到情境提示,而情绪化(与中性)面孔则观察不到提示效应的调节。我们的结论是,情绪化场景在收回注意力资源方面特别有效,它能使个体参与者以被动(即接受)的方式执行视觉搜索任务,进而改善自动情境学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emotional modulation of statistical learning in visual search
Visual search is facilitated when participants encounter targets in repeated display arrangements. This “contextual-cueing” effect is attributed to incidental learning of spatial distractor-target relations, which subsequently guides visual search more effectively toward the target location. Conversely, behaviorally significant, though task-irrelevant, negative emotional stimuli may involuntarily capture attention and thus hamper performance in visual search. This raises the question of how these two attention-guiding factors connect.To this end, we investigated how an emotionally alerting stimulus induced by different classes of emotional (face, scene) pictures prior to the search task relates to memory-related plasticity. We tested 46 participants who were presented with repeated and non-repeated search layouts, preceded at variable (50, 500, 1,000 ms) intervals by emotional vs. neutral faces or scenes.We found that contextual learning was increased with emotional compared to neutral scenes, which resulted in no contextual cueing was observed at all, while no modulation of the cueing effect was observed for emotional (vs. neutral) faces. This modulation occurred independent of the intervals between the emotional stimulus and the search display.We conclude that emotional scenes are particularly effective in withdrawing attentional resources, biasing individual participants to perform a visual search task in a passive, i.e., receptive, manner, which, in turn, improves automatic contextual learning.
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