Honda Research Institute Europe, Offenbach/Main, Germany.
PLoS One. 2021 Mar 18;16(3):e0248166. doi: 10.1371/journal.pone.0248166. eCollection 2021.
Scan pattern analysis has been discussed as a promising tool in the context of real-time gaze-based applications. In particular, information-theoretic measures of scan path predictability, such as the gaze transition entropy (GTE), have been proposed for detecting relevant changes in user state or task demand. These measures model scan patterns as first-order Markov chains, assuming that only the location of the previous fixation is predictive of the next fixation in time. However, this assumption may not be sufficient in general, as recent research has shown that scan patterns may also exhibit more long-range temporal correlations. Thus, we here evaluate the active information storage (AIS) as a novel information-theoretic approach to quantifying scan path predictability in a dynamic task. In contrast to the GTE, the AIS provides means to statistically test and account for temporal correlations in scan path data beyond the previous last fixation. We compare AIS to GTE in a driving simulator experiment, in which participants drove in a highway scenario, where trials were defined based on an experimental manipulation that encouraged the driver to start an overtaking maneuver. Two levels of difficulty were realized by varying the time left to complete the task. We found that individual observers indeed showed temporal correlations beyond a single past fixation and that the length of the correlation varied between observers. No effect of task difficulty was observed on scan path predictability for either AIS or GTE, but we found a significant increase in predictability during overtaking. Importantly, for participants for which the first-order Markov chain assumption did not hold, this was only shown using AIS but not GTE. We conclude that accounting for longer time horizons in scan paths in a personalized fashion is beneficial for interpreting gaze pattern in dynamic tasks.
扫描模式分析已被讨论为实时基于注视的应用程序的一种有前途的工具。特别是，已经提出了基于信息论的扫描路径可预测性度量，例如注视转移熵（GTE），用于检测用户状态或任务需求的相关变化。这些度量将扫描模式建模为一阶马尔可夫链，假设仅前一个注视点的位置可以预测时间上的下一个注视点。然而，一般来说，这种假设可能并不足够，因为最近的研究表明，扫描模式也可能表现出更长的时间相关性。因此，我们在这里评估主动信息存储（AIS）作为一种新的信息论方法来量化动态任务中的扫描路径可预测性。与 GTE 不同，AIS 提供了一种统计测试和解释扫描路径数据中时间相关性的方法，这些相关性超出了前一个最后注视点。我们在驾驶模拟器实验中将 AIS 与 GTE 进行了比较，在该实验中，参与者在高速公路场景中驾驶，其中试验是根据鼓励驾驶员进行超车操作的实验操作来定义的。通过改变完成任务的剩余时间来实现两个难度级别。我们发现，个体观察者确实表现出超过单个过去注视点的时间相关性，并且观察者之间的相关性长度也有所不同。对于 AIS 或 GTE，都没有观察到任务难度对扫描路径可预测性的影响，但我们发现超车过程中可预测性显著提高。重要的是，对于那些不满足一阶马尔可夫链假设的参与者，只有使用 AIS 才能显示这一点，而 GTE 则不能。我们得出的结论是，以个性化的方式考虑扫描路径中的更长时间范围对于解释动态任务中的注视模式是有益的。
