Gaze Transition Entropy

IF 1.9 4区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

ACM Transactions on Applied Perception Pub Date : 2015-12-10 DOI:10.1145/2834121

Krzysztof Krejtz, A. Duchowski, T. Szmidt, I. Krejtz, Fernando González Perilli, A. Pires, A. Vilaró, N. Villalobos

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引用次数: 94

Abstract

This article details a two-step method of quantifying eye movement transitions between areas of interest (AOIs). First, individuals' gaze switching patterns, represented by fixated AOI sequences, are modeled as Markov chains. Second, Shannon's entropy coefficient of the fit Markov model is computed to quantify the complexity of individual switching patterns. To determine the overall distribution of attention over AOIs, the entropy coefficient of individuals' stationary distribution of fixations is calculated. The novelty of the method is that it captures the variability of individual differences in eye movement characteristics, which are then summarized statistically. The method is demonstrated on gaze data collected from two studies, during free viewing of classical art paintings. Normalized Shannon's entropy, derived from individual transition matrices, is related to participants' individual differences as well as to either their aesthetic impression or recognition of artwork. Low transition and high stationary entropies suggest greater curiosity mixed with a higher subjective aesthetic affinity toward artwork, possibly indicative of visual scanning of the artwork in a more deliberate way. Meanwhile, both high transition and stationary entropies may be indicative of recognition of familiar artwork.

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凝视转移熵

本文详细介绍了一种量化感兴趣区域(aoi)之间眼球运动转换的两步方法。首先，用马尔可夫链对个体注视转换模式进行建模，并以注视AOI序列为表征。其次，计算拟合马尔可夫模型的Shannon熵系数，量化个体切换模式的复杂性;为了确定注意力在aoi上的总体分布，计算了个体注视的平稳分布的熵系数。该方法的新颖之处在于，它捕捉到了眼动特征的个体差异的可变性，然后对其进行统计总结。该方法在两项研究中进行了验证，这些研究是在自由观看古典艺术绘画时收集的凝视数据。归一化香农熵来源于个体转移矩阵，它与参与者的个体差异以及审美印象或对艺术品的识别有关。低过渡和高平稳熵表明更大的好奇心混合着对艺术品更高的主观审美亲和力，可能表明以更深思熟虑的方式对艺术品进行视觉扫描。同时，高过渡熵和静止熵都可能表明对熟悉艺术品的识别。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Transactions on Applied Perception 工程技术-计算机：软件工程

CiteScore

3.70

自引率

0.00%

发文量

审稿时长

12 months

期刊介绍： ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields. The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.