关联式学习和积极的推理
Petr Anokhin1, Artyom Sorokin2, Mikhail Burtsev3
1AIRI, Moscow, Russia 121170 anokhin@airi.net.
Neural computation
|September 23, 2024
概括
本研究结合了使用自由能源原则的关联式学习模型. 它通过尽量减少惊喜和预测错误来解释学习现象,如阻塞和覆盖.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 关联式学习将基于共发生的刺激连接在一起,这是理解行为的基础.
- 像Rescorla-Wagner这样的经典模型通过奖励预测错误来解释学习,但缺乏范围.
- 现有的模型很难涵盖学习现象的全部范围.
研究的目的:
- 将自由能量原理应用于关联式学习,将其定义为最小化不确定性.
- 将自由能源原则与Rescorla-Wagner模型联系起来,并探索学习的信息方面.
- 在主动推理框架内模拟诸如阻断,遮蔽和潜伏抑制等行为现象.
主要方法:
- 利用自由能量原理,将学习模式作为惊喜最小化.
- 调查信息方面,惊喜类型和预测错误.
- 应用主动推理框架来解释行为现象和注意力.
主要成果:
- 自由能源原则为协会式学习提供了一个统一的框架.
- 行为现象是通过整合注意力的信息和新方面来建模的.
- 展示了免费能源最小化和基于预测错误的学习之间的联系.
结论:
- 自由能源原则为协会式学习提供了一个全面的理论基础.
- 它整合了多种不同的经验模型,并解释了复杂的行为现象.
- 提供了一个理解大脑在学习中的计算过程的框架.
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