Gallagher Maria, Haynes Joshua D, Culling John F, Freeman Tom C A
School of Psychology, University of Kent, Canterbury, UK.
School of Psychology, Cardiff University, Cardiff, UK.
J Vis. 2025 Feb 3;25(2):8. doi: 10.1167/jov.25.2.8.
Despite good evidence for optimal audio-visual integration in stationary observers, few studies have considered the impact of self-movement on this process. When the head and/or eyes move, the integration of vision and hearing is complicated, as the sensory measurements begin in different coordinate frames. To successfully integrate these signals, they must first be transformed into the same coordinate frame. We propose that audio and visual motion cues are separately transformed using self-movement signals, before being integrated as body-centered cues to audio-visual motion. We tested this hypothesis using a psychophysical audio-visual integration task in which participants made left/right judgments of audio, visual, or audio-visual targets during self-generated yaw head rotations. Estimates of precision and bias from the audio and visual conditions were used to predict performance in the audio-visual conditions. We found that audio-visual performance was predicted well by models that suggested the transformation of cues into common coordinates but could not be explained by a model that did not rely on coordinate transformation before integration. We also found that precision specifically was better predicted by a model that accounted for shared noise arising from signals encoding head movement. Taken together, our findings suggest that motion perception in active observers is based on the integration of partially correlated body-centered signals.
尽管有充分证据表明静止观察者存在最佳视听整合，但很少有研究考虑自我运动对这一过程的影响。当头部和/或眼睛移动时，视觉和听觉的整合会变得复杂，因为感官测量始于不同的坐标系。为了成功整合这些信号，它们必须首先被转换到同一坐标系中。我们提出，视听运动线索在作为以身体为中心的视听运动线索进行整合之前，先利用自我运动信号分别进行转换。我们使用一项心理物理学视听整合任务对这一假设进行了测试，在该任务中，参与者在自主产生的偏航头部旋转过程中对听觉、视觉或视听目标进行左右判断。来自听觉和视觉条件的精度和偏差估计用于预测视听条件下的表现。我们发现，那些表明线索转换到共同坐标的模型能够很好地预测视听表现，但一个在整合前不依赖坐标转换的模型无法解释这种表现。我们还发现，一个考虑了编码头部运动信号产生的共享噪声的模型能更好地预测精度。综上所述，我们的研究结果表明，主动观察者的运动感知是基于部分相关的以身体为中心的信号的整合。
