人工智能可以看到你:马基雅维利主义和外向性反映在眼睛运动中
Elina Tsigeman1, Viktoria Zemliak2, Maxim Likhanov3
1Laboratory for Social & Cognitive Informatics, HSE University, Saint-Petersburg, Russia.
PloS one
|August 28, 2024
概括
机器学习算法可以从自然环境中的眼动中预测一些人格特征,但准确性取决于特征和背景. 需要进一步的研究来理解这些复杂的相互作用.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 人与计算机的交互
背景情况:
- 以前的研究将人格特征与受控环境中的视觉行为模式联系起来.
- 这项研究研究了在自然环境中使用眼睛跟踪数据的个性特征预测.
- 它比较了多个机器学习算法的预测精度.
研究的目的:
- 在一个交互式博物馆中探索青少年的个性特征和眼动模式之间的关联.
- 评估各种机器学习算法的有效性,从眼睛跟踪数据中预测人格特征.
- 为了比较自然环境中的预测准确性与基于实验室的研究.
主要方法:
- 青少年 (N=35) 填写了个性调查问卷 (五大库存,短黑暗三元组).
- 在博物馆参观期间,用头部安装的眼睛追踪器记录了眼睛的运动.
- 八个机器学习算法 (随机森林,Adaboost等) 分析眼动数据以进行特征预测.
主要成果:
- 机器学习模型以34%-48%的准确度预测了人格特征,超过了机会水平.
- 马基雅维利主义和外向显示出比良心,自恋和精神病更好的可预测性.
- 在自然环境中的预测准确度与之前的生态有效研究相比,但低于基于实验室的研究.
结论:
- 眼动特征可以预测某些个性特征,不同特征的成功程度各不相同.
- 特征的可预测性受到情境因素 (例如,新性,复杂性) 和使用的特定机器学习算法的影响.
- 对特征-情况-算法相互作用的进一步调查对于完善预测模型至关重要.
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