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聚焦视觉叙事的认知神经科学。

Zooming in on the cognitive neuroscience of visual narrative.

机构信息

Department of Communication and Cognition, Tilburg School of Humanities and Digital Sciences, Tilburg University, Netherlands.

Department of Psychology, University of Essex, UK.

出版信息

Brain Cogn. 2020 Dec;146:105634. doi: 10.1016/j.bandc.2020.105634. Epub 2020 Nov 3.

DOI:10.1016/j.bandc.2020.105634
PMID:33157490
Abstract

Visual narratives like comics and films often shift between showing full scenes and close, zoomed-in viewpoints. These zooms are similar to the "spotlight of attention" cast across a visual scene in perception. We here measured ERPs to visual narratives (comic strips) that used zoomed-in and full-scene panels either throughout the whole sequence context or at specific critical panels. Zoomed-in panels were automatically generated on the basis of fixations from prior participants' eye movements to the crucial content of panels (Foulsham & Cohn, 2020). We found that these fixation panels evoked a smaller N300 than full-scenes, indicative of reduced cost for object identification, but that they also evoked a slightly larger amplitude N400 response, suggesting a greater cost for accessing semantic memory with constrained content. Panels in sequences where fixation panels persisted across all positions of the sequence also evoked larger posterior P600s, implying that constrained views required more updating or revision processes throughout the sequence. Altogether, these findings suggest that constraining a visual scene to its crucial parts triggers various processes related not only to the density of its information but also to its integration into a sequential context.

摘要

视觉叙事,如漫画和电影,经常在展示全景和特写、放大的视角之间切换。这些缩放类似于感知中在视觉场景上投射的“注意力焦点”。我们在这里测量了对视觉叙事(漫画)的 ERP,这些叙事在整个序列上下文中或在特定关键面板中使用了放大和全景面板。放大面板是根据先前参与者的眼球运动对面板关键内容的注视来自动生成的(Foulsham & Cohn,2020)。我们发现,这些注视面板比全景面板诱发的 N300 更小,表明物体识别的成本降低,但它们也诱发了稍大的 N400 响应,表明在内容受限的情况下访问语义记忆的成本更高。在注视面板在序列中的所有位置都保持不变的序列中,面板也诱发了更大的后 P600,这意味着在整个序列中需要更多的更新或修正过程。总之,这些发现表明,将视觉场景限制在其关键部分会触发与信息密度相关的各种过程,还会触发与将其集成到序列上下文中相关的各种过程。

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