SingleMALD: 调查听力词汇决策中的实践影响.
Filip Nenadić1, Katarina Bujandrić2, Matthew C Kelley3
1Department of Linguistics, University of Potsdam, Haus 14 (Room II.14.313) Karl-Liebknecht-Straße 24-25, 14476, Potsdam, Germany. filip.nenadic@uni-potsdam.de.
Behavior research methods
|April 2, 2025
概括
单一MALD数据集是一项大型听觉词汇决策研究,揭示了广泛的参与者测试可以影响表现. 虽然数据仍然可用,但建议考虑参与者的经验,以便进行准确的分析.
科学领域:
- 心理语言学 心理语言学
- 认知心理学 认知心理学
- 计算语言学 计算语言学
背景情况:
- 听觉词汇决策任务对于理解口语识别至关重要.
- 现有的数据集,如大规模听力词汇决策 (MALD),提供了有价值的见解,但参与者响应的深度不同.
- 需要大规模的听觉词汇决策数据,每位参与者提供广泛的响应,以补充现有资源.
研究的目的:
- 介绍和描述SingleMALD数据集,这是一个用于听觉词汇决策研究的新型资源.
- 调查广泛的参与者测试对听觉词汇决策任务的表现的影响.
- 检查参与者经验如何影响词汇变量 (词汇频率,语音邻里密度) 与任务执行之间的关系.
主要方法:
- 开发了SingleMALD,这是一个具有完全交叉设计的大规模听觉词汇决策研究.
- 收集了40名英语母语人士的200多万次试验,在67次会议中,他们对超过26,000个单词和9000个伪词作出了回应.
- 使用参与者内部设计,每种刺激都有大量的反应,与MALD的多听者方法形成鲜明对比.
主要成果:
- 随着测试的进展,SingleMALD参与者表现出优先考虑速度而不是准确性的趋势.
- 单词频率和语音社区密度对参与者的表现的影响随着会议数量的增加而变化.
- 观察到的性能和预测器关系的变化并不是很大的,这表明数据可用性.
结论:
- 单一MALD数据集为听觉词汇决策研究提供了一个有价值的,免费可用的资源.
- 在听觉词汇决策任务中进行广泛的测试可以微妙地改变参与者的表现和词汇因素的影响.
- 使用广泛测试参与者的数据进行统计分析时,应考虑和控制参与者经验水平.
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