一个神经网络模型用于重建性社会学习的演变
Jacob Chisausky1,2, Inès Marguerite Daras1, Franz J Weissing3
1Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, 9747AG, The Netherlands.
Scientific reports
|April 29, 2025
概括
社会学习,即从他人那里获取信息的过程,根据环境的稳定性,以不同的方式发展. 这项研究模拟神经网络,以显示社会学习策略如何适应,影响行为和文化进化.
科学领域:
- 进化生物学是进化的生物学.
- 认知科学是一种认知科学.
- 计算神经科学是一种计算神经科学.
背景情况:
- 社会学习是一个关键的适应,但它的进化动态和信息传输中的作用不明.
- 现有的模型往往忽略了社会学习涉及基于个人认知的信息重建.
- 了解个人和社会学习之间的相互作用对于解释行为进化至关重要.
研究的目的:
- 为社会学习的演变开发一个新的建模框架,其中包括信息重建.
- 研究个人学习与各种形式的社会学习之间的相互作用.
- 探索环境因素如何影响学习策略的演变.
主要方法:
- 开发了一个计算模型,将神经网络进化与生物现实的学习机制相结合.
- 在不同稳定性的环境中进行基于个体的模拟.
- 分析了不同社会学习类型 (指导与指导) 和学习顺序对进化结果的影响.
主要成果:
- 一个有效的神经网络结构迅速演变,促进适应性行为.
- 最佳的学习策略因环境稳定性而有所不同:用于静态环境的先天性行为,用于变量环境的个人学习,以及用于中间稳定性的组合方法.
- 进化结果对社会学习的类型和序列敏感,有时会导致其他进化途径.
结论:
- 开发的建模框架提供了对社会学习的重建性质及其进化影响的见解.
- 环境稳定在很大程度上影响了学习策略的演变,有利于个人和社会学习的不同组合.
- 社会学习进化的复杂性需要进一步研究,特别是关于它在文化进化中的作用.
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