كيف تتعلم الأفكار من بعضها البعض سريع

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2607.24886

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.