面孔加工中眼动数据分析方法的新进展  

Advances of Eye Movement Data Analysis in Face Processing

作　　者：王立卉 刘梦 王珍妮 Wang Lihui;Liu Meng;Wang Zhenni(School of Psychology,Shanghai Jiao Tong University,Shanghai,200030;Shanghai Mental Health Center,Shanghai Jiao Tong University School of Medicine,Shanghai,200030)

机构地区：[1]上海交通大学心理学院,上海200030 [2]上海交通大学医学院附属精神卫生中心,上海200030

出　　处：《心理科学》2025年第2期268-279,共12页Journal of Psychological Science

基　　金：国家自然科学基金面上项目(32271086)的资助。

摘　　要：眼动追踪是面孔加工的经典和热门研究方法。传统的数据分析方法多聚焦于注视点在面孔的空间分布和持续时间,近年来多个研究结合机器学习和计算建模开发了一系列新的数据分析方法:采用机器学习对注视点聚类和精准界定感兴趣区,提高了空间解析度和统计推论的明确性;基于多变量模式分析和表征相似性分析量化眼动模式的空间结构性;采用隐马尔科夫模型和结合新兴人工智能模型架构,建立眼动数据的时空序列量化信息采样与整合等。这些方法在空间和时间两个维度建立高度量化的指标,推动面孔认知机制的实证研究和理论进步。论文介绍眼动数据分析新方法所回答的科学问题、基本原理以及所涉及的统计推断知识,为眼动研究提供新视角和方法论依据。Eye-tracking has long been a classic and popular research method in psychological studies.Traditional analysis of eye-movement data mainly focuses on the spatial distribution and the duration of the eye fixations.In the current review,we use eye movement in face processing as an example to introduce the new methods of data analysis that have been developed in recent years.In the first part of the review,we briefly introduce the traditional methods of data analysis and discuss their limitations.The main traditional approach is to gather the fixations during face processing and to plot the fixation distribution in the form of a heatmap to show the critical facial regions for information processing.However,in the spatial dimension,the boundaries of the regions of interest(ROI)are often poorly defined,limiting the power to obtain highly quantitative results and to make conclusive statistical inferences;in the temporal domain,the dependencies between the sequential fixations are often not quantified.In the second and major part of the review,we discuss how the application of machine learning and computational modeling to the analysis of eye movement data can advance the understanding of the cognitive mechanism of visual processing.Based on recently published work,we introduce three new methods for eye movement data analysis in face processing.We elucidate the technical implementation and the open-source toolkits supporting these methods.We also cover the scientific questions and the statistical inferences related to these methods.The first method concerns how to combine machine-learning approaches to quantify the clustering of fixations and to define accurate boundaries of the face ROIs.In contrast to the intuitive fixation densities shown by the traditional heatmaps,the machine-learning approaches render quantitatively separated fixation clusters and the specific landmarks of the face ROIs.The second method is to take into account the multidimensional features of the eye movement data to reveal structural patterns of

关 键 词：眼动追踪 机器学习 时空特性 面孔加工 

分 类 号：B842[哲学宗教—基础心理学]