自我的游戏:身份和经验作为积极的推理
1Rotman School of Management, University of Toronto, ON, Canada.
概括
积极推断模型将大脑作为一个统计引擎,最大限度地减少不确定性. 这一框架扩展到自我认识,将"自我游戏"概念化为不断更新贝叶斯式自我表现,以减少不确定性.
科学领域:
- 认知神经科学 认知神经科学
- 计算精神病学是一种计算精神病学.
- 思想的哲学 思想的哲学
背景情况:
- 积极推断模型神经系统作为预测引擎,最大限度地减少感官不确定性.
- 感知和行为是由减少不确定性的必要驱动的.
研究的目的:
- 将主动推断扩展到社会代理人的自我推断.
- 将自我认识概念化为"自我游戏" - - 贝叶斯更新过程.
- 模拟语义和情节性自我表示之间的动态相互作用.
主要方法:
- 贝叶斯推论和自我表现的更新.
- 模拟身份和经验之间的双向预测循环.
- 使用主动推理原则来解释自我相关的不确定性减少.
主要成果:
- 自我知识是从不断的贝叶斯更新自我表现中产生的.
- 存在一个双向循环,其中语义身份塑造了情节性经验,反之亦然.
- 新兴的自我表现是那些在给定证据下具有最高后置概率的自我表现.
结论:
- 自我不是固定的,而是通过积极的推理被适应性地塑造.
- "自我游戏"为理解自我提供了一个计算框架.
- 这种模型为大脑如何在自我相关的不确定性中导航提供了新的见解.
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