Sensitivity of the Spatial Distribution of Fixations to Variations in the Type of Task Demand and Its Relation to Visual Entropy.
Sensitivity of the Spatial Distribution of Fixations to Variations in the Type of Task Demand and Its Relation to Visual Entropy.
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DOI：
10.3389/fnhum.2021.642535
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发表时间：
2021
影响因子：
2.9
通讯作者：
Di Nocera F
中科院分区：
文献类型：
--
作者：
Maggi P;Di Nocera F
众所周知，眼部活动对精神负荷的变化很敏感，最近的研究成功地将眼睛注视的分布与精神负荷联系起来。本研究旨在验证注视点空间分布作为心理负荷量度的有效性及其对任务所施加的不同类型需求（心理、时间和身体）的敏感性。为了检验研究假设，进行了两项实验研究：实验 1 评估了空间分布指数（最近邻指数；NNI）对工作负载变化的敏感性。 30 名参与者参与了一项受试者内设计，其中不同类型的任务需求（心理、时间、身体）应用于俄罗斯方块游戏；实验 2 通过分析执行视觉空间任务（“找不同”益智游戏）期间的 1 分钟周期来研究该指数的准确性。此外，还将 NNI 与更为人所知的眼部心理负荷指数（熵率）进行了比较。数据分析显示了 NNI 与任务所施加的不同工作负载水平之间的关系。特别是：实验 1 表明，由于较高的时间需求，难度增加导致相对于基线更加分散的模式，而精神需求则导致相对于基线更加分组的注视模式；实验 2 表明，随着时间的推移，熵率和 NNI 显示出相似的模式，表明活动第一分钟后的脑力负荷很高。这表明 NNI 强调了注视组的存在，因此，熵表明扫描路径更加规则和有序。这两个指数都对工作负载的变化敏感，并且它们似乎预测了性能的下降。然而，熵率受到感兴趣区域的使用的限制，使得它不可能在动态环境中应用。相反，NNI 适用于整个扫描路径，并且它显示出对不同类型任务需求的敏感性。这些结果证实了 NNI 是一种适用于不同环境的措施，并且可以作为在高风险环境（例如控制室和运输系统）中实施的自适应系统的触发器。
Ocular activity is known to be sensitive to variations in mental workload, and recent studies have successfully related the distribution of eye fixations to the mental load. This study aimed to verify the effectiveness of the spatial distribution of fixations as a measure of mental workload and its sensitivity to different types of demands imposed by the task: mental, temporal, and physical. To test the research hypothesis, two experimental studies were run: Experiment 1 evaluated the sensitivity of an index of spatial distribution (Nearest Neighbor Index; NNI) to changes in workload. A sample of 30 participants participated in a within-subject design with different types of task demands (mental, temporal, physical) applied to Tetris game; Experiment 2 investigated the accuracy of the index through the analysis of 1-min epochs during the execution of a visual-spatial task (the “spot the differences” puzzle game). Additionally, NNI was compared to a better-known ocular mental workload index, the entropy rate. The data analysis showed a relation between the NNI and the different workload levels imposed by the tasks. In particular: Experiment 1 demonstrated that increased difficulty, due to higher temporal demand, led to a more dispersed pattern with respect to the baseline, whereas the mental demand led to a more grouped pattern of fixations with respect to the baseline; Experiment 2 indicated that the entropy rate and the NNI show a similar pattern over time, indicating high mental workload after the first minute of activity. That suggests that NNI highlights the greater presence of fixation groups and, accordingly, the entropy indicates a more regular and orderly scanpath. Both indices are sensitive to changes in workload and they seem to anticipate the drop in performance. However, the entropy rate is limited by the use of the areas of interest, making it impossible to apply it in dynamic contexts. Conversely, NNI works with the entire scanpath and it shows sensitivity to different types of task demands. These results confirm the NNI as a measure applicable to different contexts and its potential use as a trigger in adaptive systems implemented in high-risk settings, such as control rooms and transportation systems.
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DOI：
10.1518/155534307x255627
发表时间：
2007-09-01
影响因子：
2
作者：
Di Nocera, Francesco;Camilli, Marco;Terenzi, Michela
通讯作者：
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DOI：
10.1016/s0735-1097(00)00595-7
发表时间：
2000-05-01
影响因子：
24
作者：
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影响因子：
2.6
作者：
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影响因子：
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通讯作者：
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