一种动态刺激分布方法,用于高效的大规模双对关系数据收集
Zhang Chuyin1,2, Lin Lipeng1,2, Naotsugu Tsuchiya1,2,3,4,5
1School of Psychological Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Melbourne, Victoria, Australia.
MethodsX
|March 11, 2026
概括
本研究介绍了一种有效的在线收集意识数据的方法. 它平衡了刺激对呈现,降低了数据收集成本,提高了意识结构表征的可靠性.
科学领域:
- 认知科学 认知科学
- 心理学 心理学 心理学
- 计算神经科学是一种神经科学.
背景情况:
- 主观比较为理解意识提供了有价值的数据.
- 传统的对对比需要N2试验,这对大规模数据收集构成了挑战.
- 在线平台有助于参与者招聘,但往往由于数据分配效率低下而受到影响.
研究的目的:
- 开发一种方法来最大限度地减少刺激对呈现频率的变异.
- 提高在线意识研究数据收集的效率和统一性.
- 为管理大型实验数据集中的对判断提供实用解决方案.
主要方法:
- 将PsychoPy与GitHub Gist集成,以记录刺激对的呈现频率.
- 开发JavaScript代码用于在线实验中的动态试验选择.
- 实施一个系统,读取,选择和更新每个参与者的刺激对频率.
主要成果:
- 拟议方法在平衡跨刺激对的试验数量方面表现出的有效性.
- 启用了频率的动态平衡,以实现更有效的数据收集.
- 通过使用 GitHub Gist.成功存储和管理演示频率.
结论:
- 这种新方法显著提高了在线研究中主观比较的数据收集效率.
- 这种方法为表征意识结构提供了一个可扩展的解决方案.
- PsychoPy和GitHub Gist的整合为未来实验心理学研究提供了一个强大的框架.
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