How we perceive a visual scene depends critically on the selection of gaze positions. For this selection process, visual attention is known to play a key role in two ways. First, image-features attract visual attention, a fact that is captured well by time-independent fixation models. Second, millisecond-level attentional dynamics around the time of saccade drives our gaze from one position to the next. These two related research areas on attention are typically perceived as separate, both theoretically and experimentally. Here we link the two research areas by demonstrating that perisaccadic attentional dynamics improve predictions on scan path statistics. In a mathematical model, we integrated perisaccadic covert attention with dynamic scan path generation. Our model reproduces saccade amplitude distributions, angular statistics, intersaccadic turning angles, and their impact on fixation durations as well as inter-individual differences using Bayesian inference. Therefore, our result lend support to the relevance of perisaccadic attention to gaze statistics.

我们如何看待视觉场景在很大程度上取决于注视位置的选择。对于此选择过程，众所周知，视觉注意力在两种方面起着关键作用。首先，图像特征吸引了视觉注意力，这一事实可以通过与时间无关的注视模型很好地捕获。其次，围绕扫视时间的毫秒级注意力动态将我们的目光从一个位置转移到另一个位置。在理论上和实验上，这两个相关的研究领域通常被认为是分开的。在这里，我们通过证明周周注意力动力学改善了对扫描路径统计的预测，将两个研究领域联系起来。在数学模型中，我们将眼周隐秘注意力与动态扫描路径生成集成在一起。我们的模型复制了扫视振幅分布，角度统计数据，使用贝叶斯推断，相声间转角及其对注视持续时间的影响以及个体间差异。因此，我们的研究结果支持了周六注意与注视统计的相关性。