Prediction of Attention Groups and Big Five Personality Traits from Gaze Features Collected from an Outlier Search Game.

IF 2.7 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Rachid Rhyad Saboundji, Kinga Bettina Faragó, Violetta Firyaridi
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引用次数: 0

Abstract

This study explores the intersection of personality, attention and task performance in traditional 2D and immersive virtual reality (VR) environments. A visual search task was developed that required participants to find anomalous images embedded in normal background images in 3D space. Experiments were conducted with 30 subjects who performed the task in 2D and VR environments while their eye movements were tracked. Following an exploratory correlation analysis, we applied machine learning techniques to investigate the predictive power of gaze features on human data derived from different data collection methods. Our proposed methodology consists of a pipeline of steps for extracting fixation and saccade features from raw gaze data and training machine learning models to classify the Big Five personality traits and attention-related processing speed/accuracy levels computed from the Group Bourdon test. The models achieved above-chance predictive performance in both 2D and VR settings despite visually complex 3D stimuli. We also explored further relationships between task performance, personality traits and attention characteristics.

从离群搜索游戏中收集的目光特征预测注意力群体和五大人格特质
本研究探讨了在传统 2D 和沉浸式虚拟现实(VR)环境中个性、注意力和任务表现之间的交集。研究人员开发了一项视觉搜索任务,要求受试者在三维空间中找到嵌入正常背景图像中的异常图像。30 名受试者在 2D 和 VR 环境中完成了这项任务,同时对他们的眼球运动进行了跟踪。在进行探索性相关性分析后,我们应用机器学习技术研究了凝视特征对不同数据收集方法得出的人类数据的预测能力。我们提出的方法由一系列步骤组成,包括从原始凝视数据中提取固定和囊状移动特征,以及训练机器学习模型来对五大人格特质和从小组布尔登测试中计算出的与注意力相关的处理速度/准确度水平进行分类。尽管视觉上的三维刺激非常复杂,但这些模型在 2D 和 VR 环境中都取得了超出预期的预测效果。我们还进一步探索了任务表现、人格特质和注意力特征之间的关系。
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
Journal of Imaging
Journal of Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.90
自引率
6.20%
发文量
303
审稿时长
7 weeks
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