对漂移扩散观察者在波动刺激任务中的贝叶斯信心的表达式
Joshua Calder-Travis1, Rafal Bogacz2, Nick Yeung1
1Department of Experimental Psychology, University of Oxford, UK.
Journal of mathematical psychology
|January 8, 2024
概括
本研究提出了一种计算效率高的方法来建模决策信心,并考虑刺激的变化. 这种新方法使得对复杂决策任务的信心进行更可行的试验分析成为可能.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 决策科学 决策科学 决策科学
背景情况:
- 决策信心建模对于理解认知过程至关重要.
- 现有的模型面临的挑战是,试验对试验的波动刺激的变化.
- 计算效率是复杂任务实际应用的关键.
研究的目的:
- 开发一种计算便宜的方法来建模决策信心.
- 为了将试验对试验的变化纳入随机波动的刺激中.
- 为了获得信心分布的实用表达式.
主要方法:
- 使用了带有时间依赖值的漂移扩散模型框架.
- 结合了贝叶斯的信心读取和管道证据积累.
- 以正常分布的刺激波动为信心分布的衍生表达式.
主要成果:
- 开发了可信度概率分布的新表达式.
- 考虑到漂移速率的变化和元认知噪声.
- 通过模拟验证了近似值,显示了逐试验分析的可行性.
结论:
- 衍生的表达式为建模决策信心提供了一个计算上可行的方法.
- 这种方法提高了对具有波动刺激的任务的信心的理解.
- 提供了对最佳观察者信心和经验模式的见解.
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