Abstract Integrating information across our various sensory modalities results in striking behavioral benefits. This integration depends on a variety of factors, among which are the effectiveness of the stimuli and the relative timing between them. Both of these factors physically vary as a function of the distance between stimuli and the observer: intensity decreases as a function of distance for both auditory and visual stimuli, while the relative timing of energy arriving at the sensory organs differs due to differing transmission speeds. As a result, the depth at which multisensory stimuli are presented is likely to be an important factor in the gain that is derived from integrating them. Here, we use a computational approach – the Time-Window-of Integration (TWIN) framework – to examine differences in simultaneity judgments and reaction times to audiovisual stimuli presented at two depths. Using the TWIN model, we tested whether the observed behavior could be explained solely on the basis of differences in peripheral processing times, on the basis of changes in the temporal binding window (TBW), or by a combination of both factors. The results indicated that a model allowing for different TBWs for near and far space best accounts for the observed data in the majority of participants. However, the best overall model (regardless of the number of parameters) was a model containing both distance-dependent peripheral processing times and TBWs. Interestingly, TBWs were found not to expand from near to far space, but rather to get smaller. Taken together, the results indicate that distance is an additional factor in multisensory integration, above its impact on relative timing and intensity.

中文翻译：

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视听深度整合：使用 TWIN 模型模拟距离和刺激效果的影响

摘要在我们的各种感官方式中整合信息会带来显着的行为益处。这种整合取决于多种因素，其中包括刺激的有效性和它们之间的相对时间。这两个因素在物理上都随着刺激和观察者之间的距离而变化：强度随着听觉和视觉刺激的距离而降低，而能量到达感觉器官的相对时间因传输速度不同而不同。因此，呈现多感官刺激的深度很可能是对它们进行整合所获得的增益的重要因素。这里，我们使用计算方法——时间窗口积分 (TWIN) 框架——来检查同时性判断和对两个深度呈现的视听刺激的反应时间的差异。使用 TWIN 模型，我们测试了观察到的行为是否可以仅根据外围处理时间的差异、时间绑定窗口 (TBW) 的变化或这两种因素的组合来解释。结果表明，允许近距和远距空间不同 TBW 的模型最能解释大多数参与者的观测数据。然而，最好的整体模型（无论参数数量如何）是包含与距离相关的外围处理时间和 TBW 的模型。有趣的是，发现 TBW 不会从近空间扩展到远空间，而是变小。