在处理数字和序列刺激时,将空间表示与极性编码分开,在四向分类任务中进行四向分类
Qiangqiang Wang1, Jiayi Lou1, Mengxia Li1
1School of Teacher Education, Huzhou University, China.
Acta psychologica
|April 26, 2024
概括
在数和序列处理中观察到的响应代码的空间数值关联 (SNARC) 效应是由空间表示驱动的,而不是极性编码. 这一发现支持大小刺激的空间表示理论.
科学领域:
- 认知心理学 认知心理学
- 神经科学是一个神经科学.
- 人与计算机的交互
背景情况:
- 响应代码的空间数值关联 (SNARC) 效应是数量和序列处理方面记录良好的现象,几十年来一直在研究.
- 现有的理论提出空间表示或极性编码作为SNARC效应的基本机制.
- 关于哪种理论最能解释不同类型的刺激中SNARC效应的含糊性仍然存在.
研究的目的:
- 调查SNARC在处理数值和序列刺激中的效应主要是由于空间表示或极性编码.
- 使用一系列受控实验,区分空间表示理论和极性编码理论.
- 为了澄清SNARC效应背后的认知机制.
主要方法:
- 进行了五项实验,使用四向分类任务与中央呈现的刺激进行了五项实验.
- 刺激包括阿拉伯数字,序列刺激,顺序序列和缺乏固有的空间信息的中国字符.
- 参与者使用一致或不一致的关键映射 (A,S,K,L或L,K,S,A) 响应刺激.
主要成果:
- 在处理阿拉伯数字和顺序刺激时,无论映射的复杂性如何,都始终观察到SNARC效应 (实验1-4).
- 重要的是,当刺激是中文字符时,SNARC效应是不存在的,这些字符缺乏隐含的空间信息 (实验5).
- 这些发现表明SNARC效应严重依赖空间特性.
结论:
- 在大小处理中的SNARC效应,包括数字和序列,从根本上植根于这些刺激的空间表示.
- 这些结果为空间表示理论在极性编码理论上提供了强有力的支持.
- 这项研究阐明了SNARC效应的认知基础,强调了空间认知的作用.
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