Suppr超能文献

何时才足够?确定场景观看研究参与者样本量的实证指南。

When is enough enough? Empirical guidelines to determine participant sample size for scene viewing studies.

作者信息

Hoogerbrugge Alex J, Hooge Ignace T C, Hessels Roy S, Strauch Christoph

机构信息

Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands.

出版信息

Behav Res Methods. 2025 Jul 28;57(9):241. doi: 10.3758/s13428-025-02754-8.

Abstract

Eye tracking is widely used to study where spatial attention is allocated across stimuli. However, determining a sufficient and efficient number of participants for such studies remains a challenge. While clear guidelines have been established for many classical statistical tests, no straightforward participant sample size guidelines exist for the comparison of gaze distribution maps and area-of-interest analyses - two of the most prominent analyses in scene viewing studies. Just how many participants should be included for reliable and reproducible gaze estimations? We here utilized gaze data to a single static image, viewed by 1248 individuals (dataset 1), and gaze data to 200+ images, viewed by 84 participants each (dataset 2). Researchers can assess which of these datasets and analysis types most resemble their setup and determine their sample size accordingly. Although we cannot provide a one-size-fits-all sample size recommendation, we show progressively diminishing returns for a range of sample sizes and for two typical study types. For example, when using Normalized Saliency Score as a metric of distribution map similarity, a 5% relative increase requires increases in sample size from 13 20 34 participants (based on dataset 1) or from 10 16 32 participants (based on dataset 2). Alternatively, when analyzing the number of visits to certain areas of interest, a 25% decrease in outcome variance requires increases in sample size from 13 24 44. We provide easy-to-use guidelines and reference tables to determine scene viewing participant sample size for academics and industry professionals alike.

摘要

眼动追踪被广泛用于研究空间注意力在不同刺激上的分配情况。然而,为这类研究确定足够且有效的参与者数量仍然是一项挑战。虽然已经为许多经典统计检验建立了明确的指导方针,但对于注视分布图比较和感兴趣区域分析(场景观看研究中最突出的两种分析方法),却没有直接的参与者样本量指导方针。究竟应该纳入多少参与者才能获得可靠且可重复的注视估计呢?我们在此利用了由1248人观看一张静态图像的注视数据(数据集1),以及由84名参与者每人观看200多张图像的注视数据(数据集2)。研究人员可以评估这些数据集中的哪一个以及哪种分析类型与他们的设置最为相似,并据此确定样本量。尽管我们无法提供适用于所有情况的样本量建议,但我们展示了一系列样本量以及两种典型研究类型的收益递减情况。例如,当使用归一化显著性分数作为分布图相似性的度量标准时,相对增加5%需要将样本量从13名、20名、34名参与者(基于数据集1)或从10名、16名、32名参与者(基于数据集2)增加。或者,当分析对某些感兴趣区域的访问次数时,结果方差减少25%需要将样本量从13名、24名、44名增加。我们提供了易于使用的指导方针和参考表,以便为学术界和行业专业人士确定场景观看参与者的样本量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b435/12304073/a0c957d20ae7/13428_2025_2754_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验