构建目标导向的认知图表
bioRxiv : the preprint server for biology
|February 27, 2026
概括
本研究引入了Sparse认知图框架,以解释奖励如何塑造学习和决策. 该模型揭示了奖励偏差如何影响内部关系结构的灵活,以目标为导向的行为.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 灵活的,以目标为导向的行为依赖于学习预测关系.
- 了解奖励如何影响学习过渡结构至关重要,但不完整.
研究的目的:
- 引入一种新的强化学习框架,即Sparse认知图 (SCG).
- 模拟奖励如何塑造学习过渡结构并影响决策.
- 为灵活的,以目标为导向的行为提供统一的机制解释.
主要方法:
- 开发了Sparse认知图 (SCG) 强化学习框架.
- 将SCG模型应用于人类行为和小鼠光遗传数据集.
- 分析了推断图形拓和动态图形演变.
主要成果:
- SCG框架成功地模拟了跨任务的学习和决策.
- 推断图形拓学的差异解释了不同的行为模式.
- 动态演变的图表重现了正规的两步任务签名.
- 在小鼠中奖励选择性增强的学习过渡之前的理想结果.
- 多巴胺刺激效应与增强的过渡学习一致.
结论:
- 该SCG提供了一个机械的帐户,奖励依赖的学习如何塑造内部的关系结构.
- 这个框架支持灵活的,以目标为导向的行为,通过偏向过渡学习.
- 该模型可以生成可测试的预测,用于图形输入和目标状态中的神经特征.
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