在决策领域共享的潜在认知机制的联合建模
Niek Stevenson1, Reilly J Innes1, Russell J Boag1
1Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.
Computational brain & behavior
|March 1, 2024
概括
证据积累模型 (EAMs) 的参数在大多数决策任务中显示出跨时间点的一致性. 联合建模显示,信息处理能力在各个领域之间有联系,但响应的谨慎性和紧迫性各不相同.
科学领域:
- 认知心理学 认知心理学
- 决策科学 决策科学 决策科学
- 计算神经科学是一种神经科学.
背景情况:
- 证据积累模型 (EAM) 被广泛用于理解决策.
- 一个关键的假设是EAM参数在不同决策领域的一致性.
- 这些认知构造的稳定性随着时间的推移和跨任务需要经验验证.
研究的目的:
- 调查EAM参数在不同决策领域的一致性.
- 检查EAM参数在不同时间点的稳定性.
- 探索决策中的潜在认知构造之间的关系.
主要方法:
- 利用一种新的联合建模方法,同时分析来自多个决策领域的参数.
- 在联合模型中纳入因子分析,以确定参数之间的潜在关系.
- 考虑到测量错误和参数估计的不确定性.
主要成果:
- 在四个决策任务中,EAM参数在三个时间点中显示出一致性.
- 一个双因素的联合模型显示,信息处理能力在不同决策领域之间始终相关.
- 其他认知构造,如响应谨慎和紧迫性,显示了特定领域的可比性.
结论:
- 对于大多数决策任务,EAM参数具有时间稳定性.
- 信息处理能力似乎是影响跨领域决策的一般认知因素.
- 响应的谨慎性和紧迫性的特定领域的变化表明了取决于背景的决策策略.
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