The initial phase of auditory and visual scene analysis
The initial phase of auditory and visual scene analysis
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听觉和视觉场景分析的初始阶段
DOI：
10.1098/rstb.2011.0368
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发表时间：
2012-04-05
影响因子：
6.3
通讯作者：
Pressnitzer, Daniel
中科院分区：
文献类型：
--
作者：
Hupe, Jean-Michel;Pressnitzer, Daniel
听觉流和视觉格子已被广泛用于研究每种模式的感知组织。两种刺激都可以在分组（一个对象）和分割（两个对象）解释之间产生双稳态交替。它们还具有两个独特的特征：（i）在刺激呈现开始时，组织开始对分组解释产生系统性偏见； (ii) 该第一感知具有“惯性”；它比后续的持续时间更长。结果，形成不同物体的概率随着时间的推移而增加，这是听觉流的行为和神经生理学数据的里程碑。在这里，我们表明第一感知偏差和惯性是独立的。在格子感知中，惯性是由于透明（分裂）解释中的深度排序模糊性造成的，这使得格子感知三稳态而不是双稳态：消除深度模糊性的实验操作抑制了惯性。然而，第一感知偏差仍然存在。我们尝试通过引入流之间的电平差异来对听觉流进行类似的操作，以偏置哪个流将出现在感知前景中。在这里，惯性和第一感知偏差仍然存在。因此，我们认为感知组织开始的关键共同特征是分组偏差，这可能与从时间/空间局部计算到时间/空间全局计算的转变有关。
Auditory streaming and visual plaids have been used extensively to study perceptual organization in each modality. Both stimuli can produce bistable alternations between grouped (one object) and split (two objects) interpretations. They also share two peculiar features: (i) at the onset of stimulus presentation, organization starts with a systematic bias towards the grouped interpretation; (ii) this first percept has 'inertia'; it lasts longer than the subsequent ones. As a result, the probability of forming different objects builds up over time, a landmark of both behavioural and neurophysiological data on auditory streaming. Here we show that first percept bias and inertia are independent. In plaid perception, inertia is due to a depth ordering ambiguity in the transparent (split) interpretation that makes plaid perception tristable rather than bistable: experimental manipulations removing the depth ambiguity suppressed inertia. However, the first percept bias persisted. We attempted a similar manipulation for auditory streaming by introducing level differences between streams, to bias which stream would appear in the perceptual foreground. Here both inertia and first percept bias persisted. We thus argue that the critical common feature of the onset of perceptual organization is the grouping bias, which may be related to the transition from temporally/spatially local to temporally/spatially global computation.