使用扩散模型进行象征性算术任务,以检查衰老效应
1Department of Psychology, The Ohio State University.
Journal of experimental psychology. Learning, memory, and cognition
|October 17, 2024
概括
较年长的成年人在数学的任务中增加了非决策时间. 虽然一些任务的决策证据增加了,但在记忆任务中决策速度下降,揭示了与年龄相关的认知变化.
科学领域:
- 认知心理学 认知心理学
- 衰老的神经科学 衰老的神经科学
- 量化心理学 量化心理学
背景情况:
- 象征性算术对于日常生活至关重要.
- 衰老会影响认知功能,包括数字处理.
- 扩散模型为分析决策过程提供了一个框架.
研究的目的:
- 通过基于模型的分析,研究符号数学的与年龄相关的变化.
- 检查老龄化如何影响决策的不同组成部分.
- 在任务之间识别认知过程中的一致的个体差异.
主要方法:
- 运用了三个象征性算术任务:数字区分,数字记忆和数字直线.
- 应用了标准的双选择扩散模型和空间连续扩散模型.
- 分析了三个年龄组 (年轻人,60-69岁,70-90岁) 的准确性和响应时间分布.
主要成果:
- 不决策时间 (编码/响应) 在任务中随着年龄的增长而增加.
- 边界设置 (需要证据) 在区分和记忆任务中增加,但在数列任务中没有增加.
- 老年人对于数字记忆任务的漂移率 (决策证据积累) 降低了,但对于更轻松的任务却没有降低.
结论:
- 基于模型的分析成功地解决了与年龄相关的绩效差异.
- 老龄化明显地影响认知任务的不同组成部分.
- 在不同任务的决策过程中观察到一致的个体差异,突出显示了扩散模型的实用性.
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