压缩表示和注意力竞争在平均估计的数值集成中
Yongming Sun1, Alice Mason2, Sebastian Olschewski3
1Zhejiang University, PR China; University of Warwick, UK.
Cognitive psychology
|December 26, 2025
概括
从数量流中估计平均值是认知的关键. 这项研究发现,虽然竞争信息的影响有限,但压缩心理数线 (CMNL) 模型比选择性集成 (SI) 更好地解释了平均估计.
科学领域:
- 认知心理学 认知心理学
- 数字认知 数字认知
- 决策科学科学 决策科学
背景情况:
- 衡量平均值对于数值认知和决策至关重要.
- 之前的研究重点是从单一来源整合数值信息.
- 竞争性信息来源对平均估计的影响仍然不太清楚.
研究的目的:
- 调查当与竞争信息流呈现时的平均估计.
- 为了比较压缩心智数线 (CMNL) 和选择性集成 (SI) 理论的预测能力.
- 分析竞争信息如何在平均估计中影响认知过程.
主要方法:
- 为了测试单流和双流条件下的平均估计,进行了四项实验.
- 分析了参与者的估计,以评估数值集成的两个竞争理论.
- 运用计算建模来评估CMNL和SI模型对观察到的数据的适应性.
主要成果:
- 在单流和双流条件下观察到平均值的显著低估.
- 竞争信息对平均估计准确性的影响有限.
- 在CMNL模型提供了一个更好的整体帐户的估计行为,虽然SI解释了大约三分之一的参与者.
- 在双流条件下,集成噪声增加,但表示压缩不受影响.
结论:
- CMNL模型为平均估计提供了可靠的解释,即使有竞争信息.
- 虽然CMNL通常优越,但SI模型更好地描述了个体行为的子集.
- 处理多个信息流存在局限性,影响集成噪声,但不影响表示压缩.
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