学习的多个时间尺度,通过在感知决策过程中证据积累过程的变化来表明
Aaron Cochrane1,2,3, Chris R Sims4, Vikranth R Bejjanki5
1University of Geneva, Geneva, Switzerland. aaron_cochrane@brown.edu.
NPJ science of learning
|June 8, 2023
概括
这项研究揭示了学习如何影响决策过程. 感知灵敏度随着经验的不断提高,而行动值在会话中独立调整.
科学领域:
- 认知神经科学 认知神经科学
- 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:
- 证据积累模型对于理解决策至关重要.
- 这些模型在学习过程中的应用仍然不太被探索.
研究的目的:
- 研究学习如何改变感知决策的组成部分.
- 描述一项多天任务中性能变化的动态.
主要方法:
- 利用了一个动态随机点运动方向区分任务.
- 应用连续时间学习模型来分析漂移扩散模型参数 (漂移率,响应边界).
主要成果:
- 漂移率 (感知灵敏度) 在累积试验中呈指数级提高.
- 响应边界 (行动值) 在会话内调整,但在几天内独立调整.
结论:
- 学习涉及不同的过程:持续的感知调整和特定会话的值调整.
- 这为决策任务中的行为适应提供了细微的理解.
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