海马在新型的多步预测过程中快速整合序列表示
Hannah Tarder-Stoll1, Christopher Baldassano2, Mariam Aly3
1Department of Psychology, Glendon Campus, York University, Toronto, ON, M4N 3M6.
bioRxiv : the preprint server for biology
|September 26, 2025
概括
海马集成新的信息来更新扩展序列的记忆表征,在环境变化时实现灵活的预测. 这种学习过程塑造了适应性行为的神经活动.
科学领域:
- 神经科学是一个神经科学.
- 认知心理学 认知心理学
- 计算神经科学是一种神经科学.
背景情况:
- 海马对于形成暂时延长序列的记忆和预测未来事件至关重要.
- 有效的预测需要在环境环境发生变化时更新这些记忆.
研究的目的:
- 调查新学习如何修改扩展序列的海马体表征.
- 检查这种更新过程与灵活的未来事件预测之间的关系.
主要方法:
- 人类参与者在虚拟现实中学习了环境的序列.
- 引入了新的过渡来连接以前分开的序列.
- 功能磁共振成像 (fMRI) 用于测量预测任务期间的大脑活动.
主要成果:
- 海马集成了新连接的序列的表示,在试验中显示了活动模式的相似性.
- 这些集成的表征在学习后迅速形成,并包括来自原始序列的元素加上新的活动模式.
- 海马活动模式预测了参与者在行为上更新预测的能力.
结论:
- 海马可以动态地将新信息整合到现有的序列表示中.
- 这种整合通过在不断变化的环境中实现灵活的预测,促进了适应性行为反应.
- 结构化知识是通过持续学习和海马体表征更新而产生的.
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