当前位置: X-MOL 学术Neuropsychologia › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Artificially-generated scenes demonstrate the importance of global scene properties for scene perception.
Neuropsychologia ( IF 2.0 ) Pub Date : 2020-03-13 , DOI: 10.1016/j.neuropsychologia.2020.107434
Assaf Harel 1 , Mavuso W Mzozoyana 2 , Hamada Al Zoubi 2 , Jeffrey D Nador 1 , Birken T Noesen 1 , Matthew X Lowe 3 , Jonathan S Cant 4
Affiliation  

Recent electrophysiological research highlights the significance of global scene properties (GSPs) for scene perception. However, since real-world scenes span a range of low-level stimulus properties and high-level contextual semantics, GSP effects may also reflect additional processing of such non-global factors. We examined this question by asking whether Event-Related Potentials (ERPs) to GSPs will still be observed when specific low- and high-level scene properties are absent from the scene. We presented participants with computer-based artificially-manipulated scenes varying in two GSPs (spatial expanse and naturalness) which minimized other sources of scene information (color and semantic object detail). We found that the peak amplitude of the P2 component was sensitive to the spatial expanse and naturalness of the artificially-generated scenes: P2 amplitude was higher to closed than open scenes, and in response to manmade than natural scenes. A control experiment showed that the effect of Naturalness on the P2 is not driven by local texture information, while earlier effects of naturalness, expressed as a modulation of the P1 and N1 amplitudes, are sensitive to texture information. Our results demonstrate that GSPs are processed robustly around 220 ms and that P2 can be used as an index of global scene perception.



中文翻译:

人工生成的场景证明了全局场景属性对于场景感知的重要性。

最近的电生理研究突出了全局场景属性(GSP)对于场景感知的重要性。但是,由于现实世界的场景跨越了一系列的低级刺激属性和高级上下文语义,因此GSP效果也可能反映了对此类非全局因素的额外处理。我们通过询问场景中缺少特定的低级和高级场景属性时是否仍会观察到GSP的事件相关电位(ERP)来研究此问题。我们向参与者展示了基于计算机的人工操纵场景,它们在两个GSP(空间扩展和自然度)中有所不同,从而最大程度地减少了其他场景信息源(颜色和语义对象细节)。我们发现,P2分量的峰值幅度对人工生成场景的空间扩展性和自然度敏感:P2幅度比开放场景要高到封闭场景,而对人为响应要比自然场景高。对照实验表明,自然性对P2的影响不受局部纹理信息的驱动,而较早的自然性影响(表示为P1和N1幅度的调制)对纹理信息敏感。我们的结果表明,GSP在220 ms左右得到了稳健的处理,P2可以用作全局场景感知的指标。而较早的自然效果(表示为P1和N1幅度的调制)对纹理信息敏感。我们的结果表明,GSP在220 ms左右得到了稳健的处理,P2可以用作全局场景感知的指标。而较早的自然效果(表示为P1和N1幅度的调制)对纹理信息敏感。我们的结果表明,GSP在220 ms左右得到了稳健的处理,P2可以用作全局场景感知的指标。

更新日期:2020-03-16
down
wechat
bug