Psychology Department, Northeastern University, Boston, MA, United States of America.
PLoS One. 2022 Nov 28;17(11):e0277691. doi: 10.1371/journal.pone.0277691. eCollection 2022.
Growing evidence links eye movements and cognitive functioning, however there is debate concerning what image content is fixated in natural scenes. Competing approaches have argued that low-level/feedforward and high-level/feedback factors contribute to gaze-guidance. We used one low-level model (Graph Based Visual Salience, GBVS) and a novel language-based high-level model (Global Vectors for Word Representation, GloVe) to predict gaze locations in a natural image search task, and we examined how fixated locations during this task vary under increasing levels of cognitive load. Participants (N = 30) freely viewed a series of 100 natural scenes for 10 seconds each. Between scenes, subjects identified a target object from the scene a specified number of trials (N) back among three distracter objects of the same type but from alternate scenes. The N-back was adaptive: N-back increased following two correct trials and decreased following one incorrect trial. Receiver operating characteristic (ROC) analysis of gaze locations showed that as cognitive load increased, there was a significant increase in prediction power for GBVS, but not for GloVe. Similarly, there was no significant difference in the area under the ROC between the minimum and maximum N-back achieved across subjects for GloVe (t(29) = -1.062, p = 0.297), while there was a cohesive upwards trend for GBVS (t(29) = -1.975, p = .058), although not significant. A permutation analysis showed that gaze locations were correlated with GBVS indicating that salient features were more likely to be fixated. However, gaze locations were anti-correlated with GloVe, indicating that objects with low semantic consistency with the scene were more likely to be fixated. These results suggest that fixations are drawn towards salient low-level image features and this bias increases with cognitive load. Additionally, there is a bias towards fixating improbable objects that does not vary under increasing levels of cognitive load.
越来越多的证据表明眼球运动与认知功能有关，但对于在自然场景中注视什么图像内容存在争议。有竞争的方法认为，低水平/前馈和高水平/反馈因素有助于注视引导。我们使用了一种低水平模型（基于图的视觉显著性，GBVS）和一种新的基于语言的高水平模型（词向量全局表示，GloVe）来预测自然图像搜索任务中的注视位置，并研究了在认知负荷增加的情况下注视位置如何变化。参与者（N=30）自由观看了一系列 100 个自然场景，每个场景观看 10 秒。在场景之间，被试从场景中识别出与三个干扰物同类型但来自不同场景的目标物体，指定的试次（N）为前 N 次。N 回是自适应的：当两次正确试验后，N 回增加，一次错误试验后，N 回减少。注视位置的接收者操作特征（ROC）分析表明，随着认知负荷的增加，GBVS 的预测能力显著提高，但 GloVe 则不然。同样，对于 GloVe，在被试之间达到的最小和最大 N 回之间，ROC 下的面积没有显著差异（t(29)=-1.062，p=0.297），而对于 GBVS，则有一个向上的一致趋势（t(29)=-1.975，p=0.058），尽管不显著。置换分析表明，注视位置与 GBVS 相关，表明显著特征更有可能被注视。然而，注视位置与 GloVe 呈负相关，表明与场景语义一致性低的物体更有可能被注视。这些结果表明，注视被吸引到显著的低水平图像特征，并且这种偏差随着认知负荷的增加而增加。此外，存在一种注视不太可能的物体的偏差，而这种偏差不会随着认知负荷的增加而变化。
