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.

我们如何感知视觉场景很大程度上取决于注视位置的选择。对于这个选择过程，视觉注意力在两个方面发挥着关键作用。首先，图像特征吸引视觉注意力，这一事实可以通过与时间无关的注视模型很好地捕捉到。其次，扫视期间的毫秒级注意力动态驱动我们的目光从一个位置转移到下一个位置。这两个相关的注意力研究领域在理论上和实验上通常被认为是独立的。在这里，我们通过证明周期性注意力动态改善对扫描路径统计的预测来将这两个研究领域联系起来。在数学模型中，我们将循环隐性注意力与动态扫描路径生成相结合。我们的模型使用贝叶斯推理再现了扫视幅度分布、角度统计、扫视间转动角度及其对注视持续时间的影响以及个体间差异。因此，我们的结果支持了偶尔关注与注视统计的相关性。