嵌入式神经网络中的社会偶然性依赖于共同构建的动态机制
Gabriel J Severino1, Sasha L Winkler1,2, Randall D Beer1,3,4
1Cognitive Science Program, Indiana University Bloomington , Bloomington, IN, USA.
概括
生物通过神经机制来检测社会偶然性,将时间线索与条件稳定性相结合. 这允许在社会互动中进行适应性反应,这对于跨物种的学习和行为至关重要.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
- 行为科学是一种行为科学.
背景情况:
- 社会偶然性,即相互影响的个体之间的可靠反应能力,是适应性行为的基础.
- 了解社交偶然性的神经机制是解读社交互动的关键.
研究的目的:
- 调查社会突发事件检测和应对背后的一般原则和神经机制.
- 在最小的社交互动任务中分析体现的循环神经网络.
主要方法:
- 在感知交叉任务中训练和分析体现的循环神经网络 (RNN) 群体.
- 采用强度测试来隔离有效的电路和动态系统分析来识别机制.
- 分析行为策略和反参数关系.
主要成果:
- 根据国家历史和合作伙伴行为,确定了代理人采用的四种不同的行为策略.
- 证明社会偶然性依赖于反参数之间的规模关系,而不是单一的特征.
- 发现了一个共享的神经机制,涉及将偶然线索与条件稳定性合起来.
结论:
- 社会突发性的一个核心机制是将感官输入中的特定时间模式 (突发性线索) 与只有在适当条件下 (条件稳定性) 才能确保稳定的系统结构联系起来.
- 这种机制使生物能够有效地检测和响应他人的行为,促进社会学习和互动.
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