再现现实:自然场景辨别中的多模式贡献

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING ACM Transactions on Applied Perception Pub Date : 2016-08-25 DOI:10.1145/2915917
Olli S. Rummukainen, Catarina Mendonça
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引用次数: 9

摘要

大多数关于多感觉加工的研究都集中在贫困刺激和简单任务上。因此,我们对感知真实环境的感官贡献知之甚少。在这里,我们向23名参与者提供配对比较任务,其中自然场景在三个感知上有意义的属性:运动,开放和噪音。目的是评估短时间(≤500ms)自然场景暴露时听觉和视觉模式对场景识别的贡献。这些场景在具有3D声音和周围视觉效果的沉浸式视听环境中重现。运动和开放主要是视觉属性,还有一些听觉信息的输入。在某些场景中,听觉系统能够在500ms刺激后获得与视听条件相当的运动和开放信息。噪音主要是听觉上的,但在一些场景中,视觉信息也起到了促进作用。感官权重高度不平衡,倾向于较强的模态,但在某些场景中,较弱的模态能够影响双峰估计。
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Reproducing Reality: Multimodal Contributions in Natural Scene Discrimination
Most research on multisensory processing focuses on impoverished stimuli and simple tasks. In consequence, very little is known about the sensory contributions in the perception of real environments. Here, we presented 23 participants with paired comparison tasks, where natural scenes were discriminated in three perceptually meaningful attributes: movement, openness, and noisiness. The goal was to assess the auditory and visual modality contributions in scene discrimination with short (≤500ms) natural scene exposures. The scenes were reproduced in an immersive audiovisual environment with 3D sound and surrounding visuals. Movement and openness were found to be mainly visual attributes with some input from auditory information. In some scenes, the auditory system was able to derive information about movement and openness that was comparable with audiovisual condition already after 500ms stimulation. Noisiness was mainly auditory, but visual information was found to have a facilitatory role in a few scenes. The sensory weights were highly imbalanced in favor of the stronger modality, but the weaker modality was able to affect the bimodal estimate in some scenes.
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来源期刊
ACM Transactions on Applied Perception
ACM Transactions on Applied Perception 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
22
审稿时长
12 months
期刊介绍: ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields. The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.
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