文化进化的神经网络模型
A Neural Network model of Cultural Evolution
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中文总结 AI 辅助
研究构建基于简单神经网络的文化进化透明模型，通过线性组合隐藏‘原因’生成输入向量，智能体以非线性、赫布方式学习。借助‘Light’交流机制，群体能以棘轮状方式逐步学习更好的解决方案，有助于洞察文化进化机制。
中文摘要 AI 辅助
有人提出人类智能由一种名为文化进化的棘轮状过程支撑，个体产生的思想通过社会学习选择性传播并取代旧的、成效低的思想，有用思想得以积累。虽社会和个体学习被认为以类似人工神经网络方式通过突触强度的选择性活动依赖调整实现，但将神经网络纳入文化进化模型的尝试较少，引发了关于文化特征如何产生、转变、传播和选择的争议与不确定性。我们构建了基于简单神经网络的文化进化透明模型，表明交流的智能体群体能逐步更好地描述环境。具体通过线性组合隐藏‘原因’生成输入向量，智能体以非线性、赫布方式学习，发现‘分解’数据以揭示隐藏原因的突触权重。此前表明若智能体以‘Light’方式交流隐藏原因估计，交互群体能在多数非交互个体无法做到的条件下学习分解权重。本文表明有‘Light’时群体能以棘轮状方式逐步学习更好的解决方案。虽模型高度简化，但能洞察迄今模糊的文化进化机制，为更复杂但仍相对透明的分析提供了基础。
英文摘要
It has been proposed (Richerson and Boyd, 2008) that human intelligence is underpinned by a ratchet-like process called Cultural Evolution in which ideas, originated by individuals, can selectively spread by social learning and replace older, less fruitful ones. Useful ideas can thus accumulate beyond the lifetime of individuals. Although both social and individual learning are thought to be achieved by the selective activity-dependent adjustment of synaptic strengths in an artificial neural net-like manner, there have been relatively few attempts to incorporate neural networks into Cultural Evolution models. This has led to controversy and uncertainty about how cultural traits are created, transformed, transmitted and selected. We have constructed a transparent model of Cultural Evolution based on simple neural networks, and here we show that a population of communicating agents can learn progressively better descriptions of its environment. Specifically we generate input vectors by linearly combining hidden "causes", which agents can learn from in a nonlinear, Hebbian, manner, thus discovering synaptic weights that "unmix" the data to reveal the hidden causes. We previously showed that if agents can communicate their current estimates of hidden causes to other agents, in a selective manner we call "Light", the interacting population can learn unmixing weights under conditions where most noninteracting individuals cannot. Here we show that with "Light" a population can learn progressively better solutions, in a ratchet-like manner. Although our model is highly simplified, this simplicity allows insight into cultural evolution mechanisms that have hitherto been obscure, and provides a stepping stone to more complex, but still relatively transparent, analyses.
