| 60 | 0 | 79 |
| 下载次数 | 被引频次 | 阅读次数 |
针对高生态效能复杂情境下眼动实验面临的方法学异质性与可重复性挑战，文章基于2014—2025年间253项跨学科实证研究的系统综述，识别报告不全与指标定义冲突等缺陷，构建了包含51项指标的层级分类体系，制定了跨学科眼动报告检查清单；同步开发了基于Python的开源计算工具集（IER-Metrics）；并引入了动态包围盒算法，以优化复杂场景下的熵值计算。实证验证显示，应用跨学科眼动报告清单使评审一致性（Cohen’s ■）由0.61提升至0.86，方法学漏报的检出率提升约200%；基准测试证实，自研工具集在最近邻指数等核心指标上与商业软件具有极高的一致性（r=0.923）。该体系为复杂情境下的眼动研究提供了标准化的评价准则与可靠的计算基础设施，对于推动眼动实验的透明化与开放科学实践具有应用价值。
Abstract：[Objective] As eye-tracking research increasingly transitions from constrained laboratory environments to complex， high-ecological-validity scenarios， such as virtual reality， autonomous driving， and clinical diagnostics， methodological heterogeneity has emerged as a primary barrier to reproducibility. Current research lacks a unified standard for documenting hardware constraints， data quality baselines， and event-parsing algorithms. This study aims to address these critical inconsistencies by establishing a standardized reporting protocol and providing a transparent computational infrastructure. This research is motivated by the widespread “black-box” nature of proprietary analysis software and the limited transparency in data quality disclosure within the interdisciplinary eye-tracking community. [Methods] The research was conducted in two distinct stages. First， a systematic review following the PRISMA 2020 guidelines was performed to quantify prevailing reporting practices. A final corpus of 253 empirical studies published between 2014 and 2025 was retrieved from major databases， including Web of Science and Scopus. Based on the identified reporting deficiencies， the Interdisciplinary Eye-Tracking Reporting Checklist (IER-Checklist) was developed， comprising 51 items organized into five dimensions: Participants and Design; Apparatus and Environment; Calibration and Validation; Processing and Event Detection; and Quality and Statistics. Concurrently， the IER-Metrics toolkit， an open-source， Python-based environment， was developed for automated metric extraction. The toolkit implements a multistage protocol， including automated handling of missing data through linear or cubic spline interpolation， physiological blink artifact removal， and oculomotor event parsing using the velocity-threshold identification (I-VT) algorithm. A key algorithmic innovation is the implementation of a dynamic bounding box for stationary gaze entropy (SGE) calculations. This approach isolates the instantaneous working visual field to prevent probability distribution bias caused by head movements in dynamic environments. To assess the framework’s effectiveness， four postdoctoral experts conducted a between-subjects validation study by evaluating 80 empirical manuscripts. Furthermore， the computational accuracy of the IER-Metrics toolkit was benchmarked against established commercial software using a dataset comprising 95 independent recording sessions and examining advanced spatiotemporal features， such as the nearest neighbor index (NNI) and SGE. [Results] The systematic review revealed that only 35% of the literature reported essential measures of spatial accuracy and data loss rates， whereas fixation thresholds ranged from 60 ms to 200 ms without explicit justification. Experimental validation showed that application of the IER-Checklist significantly improved evaluative consistency， with inter-rater agreement (Cohen’s ■) increasing from 0.61 under traditional heuristic assessment to 0.86. Moreover， evaluators using the IER-Checklist identified a significantly higher mean number of methodological omissions (25.15 per manuscript) than those in the traditional assessment group (8.18 per manuscript， P < .001). In the computational benchmarking， IER-Metrics demonstrated high concordance with commercial software for the NNI (r = 0.923， R2 = 0.853). For SGE calculations， although a correlation of r = 0.744 was observed， the self-developed toolkit demonstrated greater sensitivity. This outcome was attributed to the dynamic bounding box algorithm， which effectively handled ego-motion data that fixed-grid commercial algorithms often underestimate because of extensive zero-probability peripheral zones. Finally， the toolkit achieved a mean saccade detection accuracy exceeding 98% compared with industry gold standards. [Conclusions] This study establishes a standardized evaluation system and a reproducible computational environment for interdisciplinary eye-tracking research. By integrating the 51-item IER-Checklist with the open-source IER-Metrics toolkit， the proposed framework enables researchers to reduce selection bias and ensure algorithmic transparency. The findings underscore that standardized reporting is an operational necessity in naturalistic research settings. Ultimately， this framework supports the transition toward open science by reducing reliance on proprietary software and establishing a structured mechanism for rigorous experimental design in human–environment interaction research.
[1] Just M A， Carpenter P A. A theory of reading: From eye fixations to comprehension[J]. Psychological Review， 1980， 87(4): 329–354.
[2] Hu X Y， Lodewijks G. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue[J]. Journal of Safety Research， 2020， 72: 173–187.
[3] Chen K， Wan T， Matsuda N， et al. PEA-PODs: Perceptual evaluation of algorithms for power optimization in XR displays[J]. ACM Transactions on Graphics， 2024， 43(4): 67.
[4] Chen M， Burke R R， Hui S K， et al. Understanding shoppers’ attention to price information at the point of consideration using in-store ambulatory eye-tracking[J]. Journal of Retailing， 2024， 100(3): 439–455.
[5] Holmqvist K， Nyström M， Andersson R， et al. Eye tracking: A comprehensive guide to methods and measures[M]. Oxford: Oxford University Press， 2011.
[6] Wong A Y， Moss J. Metacomprehension and regressions during reading and rereading[J]. Metacognition and Learning， 2022， 17(1): 53–71.
[7] Špakov O， Istance H， Hyrskykari A， et al. Improving the performance of eye trackers with limited spatial accuracy and low sampling rates for reading analysis by heuristic fixation- to-word mapping[J]. Behavior Research Methods， 2019， 51(6): 2661–2687.
[8] Kanovský M， Halamová J， Strnádelová B， et al. Pupil size variation in primary facial expressions-testing potential biomarker of self-criticism[J]. Artificial Intelligence Review， 2022， 55(3): 2001–2022.
[9] Carter B T， Luke S G. Best practices in eye tracking research[J]. International Journal of Psychophysiology， 2020， 155: 49–62.
[10] Page M J， McKenzie J E， Bossuyt P M， et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews[J]. International Journal of Surgery， 2021， 88: 105906.
[11] 刘永贵， 申灵灵， 张刚要， 等. 复杂场景下数字化教学视频拍摄与评价虚拟仿真实验设计[J]. 实验技术与管理， 2019， 36(10): 115–119.
[12] Berčík J， Paluchová J， Neomániová K. Neurogastronomy as a tool for evaluating emotions and visual preferences of selected food served in different ways[J]. Foods， 2021， 10(2): 354.
[13] Bochynska A， Vulchanova M， Vulchanov V， et al. Spatial language difficulties reflect the structure of intact spatial representation: Evidence from high-functioning autism[J]. Cognitive Psychology， 2020， 116: 101249.
[14] Giannakos M N， Sharma K， Pappas I O， et al. Multimodal data as a means to understand the learning experience[J]. International Journal of Information Management， 2019， 48: 108–119.
[15] Capponi M， Gervasi R， Mastrogiacomo L， et al. Assembly complexity and physiological response in human-robot collaboration: Insights from a preliminary experimental analysis[J]. Robotics and Computer-Integrated Manufacturing， 2024， 89: 102789.
[16] Liu L H， Qu H Y， Ma Y M， et al. Restorative benefits of urban green space: Physiological， psychological restoration and eye movement analysis[J]. Journal of Environmental Management， 2022， 301: 113930.
[17] Gruden C， Ištoka Otković I， Šraml M. Pedestrian safety at roundabouts: Their crossing and glance behavior in the interaction with vehicular traffic[J]. Accident Analysis & Prevention， 2021， 159: 106290.
[18] Economides J R， Dilbeck M D， Gentry T N， et al. Ambulatory monitoring with eye tracking glasses to assess the severity of intermittent exotropia[J]. American Journal of Ophthalmology， 2023， 250: 120–129.
[19] Zhou X M， Siddiqui H， Rutherford M D. Face perception and social cognitive development in early autism: A prospective longitudinal study from 3 months to 7 years of age[J]. Child Development， 2025， 96(1): 104–121.
[20] 闫国利， 白学军. 眼动研究心理学导论: 揭开心灵之窗奥秘的神奇科学[M]. 北京: 科学出版社， 2012: 33–93.
[21] Hooge I T C， Holleman G A， Haukes N C， et al. Gaze tracking accuracy in humans: One eye is sometimes better than two[J]. Behavior Research Methods， 2019， 51(6): 2712–2721.
[22] Jiang K， Ling F Y， Feng Z X， et al. Effects of mobile phone distraction on pedestrians’ crossing behavior and visual attention allocation at a signalized intersection: An outdoor experimental study[J]. Accident Analysis & Prevention， 2018， 115: 170–177.
[23] Ladeira W J， Dalmoro M， de Oliveira Santini F， et al. More bodily motor action， less visual attention: How supermarket stimuli and consumer-related factors influence gaze behavior[J]. Journal of Retailing and Consumer Services， 2021， 59: 102403.
[24] Lee M， Jung J. Effects of textual enhancement and task manipulation on L2 learners’ attentional processes and grammatical knowledge development: A mixed methods study[J]. Language Teaching Research， 2024， 28(4): 1552–1571.
[25] Johannessen E， Szulewski A， Radulovic N， et al. Psychophysiologic measures of cognitive load in physician team leaders during trauma resuscitation[J]. Computers in Human Behavior， 2020， 111: 106393.
[26] Lei Q H， Yuan C， Lau S S Y. A quantitative study for indoor workplace biophilic design to improve health and productivity performance[J]. Journal of Cleaner Production， 2021， 324: 129168.
[27] Shi Y M， Du J， Zhu Q. The impact of engineering information format on task performance: Gaze scanning pattern analysis[J]. Advanced Engineering Informatics， 2020， 46: 101167.
[28] Kleberg J L， Frick M A， Brocki K C. Can auditory warning signals normalize eye movements in children with ADHD?[J]. European Child & Adolescent Psychiatry， 2020， 29(12): 1635– 1644.
[29] Zhou X Y， Fukuyama H， Okita Y， et al. Pupillary responses reveal autonomic regulation impairments in patients with central serous chorioretinopathy[J]. Investigative Ophthalmology & Visual Science， 2022， 63(10): 2.
[30] Mangaroska K， Sharma K， Gasevic D， et al. Exploring students’ cognitive and affective states during problem solving through multimodal data: Lessons learned from a programming activity [J]. Journal of Computer Assisted Learning， 2022， 38(1): 40–59.
[31] Bigne E， Chatzipanagiotou K， Ruiz C. Pictorial content， sequence of conflicting online reviews and consumer decision- making: The stimulus-organism-response model revisited[J]. Journal of Business Research， 2020， 115: 403–416.
[32] Du Z， Li M X， Wang K L. “The more options， the better?” Investigating the impact of the number of options on backers’ decisions in reward-based crowdfunding projects[J]. Information & Management， 2019， 56(3): 429–444.
[33] Qin X C， Fang M J， Yang D X， et al. Quantitative evaluation of attraction intensity of highway landscape visual elements based on dynamic perception[J]. Environmental Impact Assessment Review， 2023， 100: 107081.
[34] Manyakov N V， Bangerter A， Chatterjee M， et al. Visual exploration in autism spectrum disorder: Exploring age differences and dynamic features using recurrence quantification analysis [J]. Autism Research， 2018， 11(11): 1554–1566.
[35] González-Díaz I， Molina-Moreno M， Benois-Pineau J， et al. Asymmetric multi-task learning for interpretable gaze-driven grasping action forecasting[J]. IEEE Journal of Biomedical and Health Informatics， 2024， 28(12): 7517–7530.
基本信息:
中图分类号:TP311.52
引用信息:
[1]刘忠义,刘涟涟,蔡军,等.跨学科眼动实验标准化评价体系构建与工具开发[J].实验技术与管理().
基金信息:
国家自然科学基金（52278048）
2026-09-18
2026-09-18
2026-09-18