在"人工代数"中连续大小的感知加法
Nicola J Morton1, Cameron Hooson-Smith1, Kate Stuart1
1University of Canterbury, Christchurch, New Zealand.
Cognition
|December 30, 2023
概括
人类可以默认地学习添加连续的数量,如线长和亮度,反映象征性的数学. 这项研究揭示了一个令人惊叹的结论.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 数学教育教育 数学教育
背景情况:
- 天生的"数感"表示的性质 (离散与连续) 被争论.
- 这些表示的计算能力,特别是连续大小的计算能力,尚未得到充分探索.
研究的目的:
- 调查人类是否可以学习在非象征性的连续大小上进行加法.
- 为了确定这个隐式加法是否符合代数组属性而没有明确的指令.
主要方法:
- 一个新的实验任务被设计来测试连续大小 (线长,亮度) 的感知加法.
- 实验评估了群体属性:交换性,同一性,反向性和关联性.
- 采用转移设计,在基于反的培训后,测试参与者在新的配置上.
主要成果:
- 在反试验中,观察到平均反应和大小总和之间的高度相关性.
- 在转移试验上的准确性能支持在线长的组公理下隐式加法.
- 结果显示支持所有公理,除了与亮度的关联性.
结论:
- 成年人可以隐式添加连续数量,与象征性的加法原则保持一致.
- "人工代数"任务是研究隐式计算的可行方法.
- 这些发现有助于理解数学认知的基础.
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