神经科学与心理学2024nature.com编辑精选
自然语言指令诱导神经元网络中的泛化
Natural language instructions induce generalization in networks of neurons
专业 · 约 60 分钟读完前置知识：神经网络基础、认知神经科学、组合泛化
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这篇 Nature Neuroscience 论文研究自然语言指令如何使神经网络（包括生物神经网络）产生更灵活的泛化能力。作者提出语言信息作为一种“脚手架”，支持组合式指令下的条件推理，并给出若干可实验验证的预测，说明语言信息需如何被表征才能促进人脑的灵活通用认知。
HN 关注点：语言与神经网络泛化能力的关系，以及该研究对理解人类认知和 LLM 作为大脑模型的启示；讨论焦点之一是论文“必须（must）”这类强断言是否站得住脚。
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这几条评论里：支持 1 质疑 1 补充 2
质疑质疑论文用简化神经元模型却做出“必须”式的强因果断言，与脉冲时序、树突计算等新证据不符
论文称“语言信息必须以某种方式被表征才能促进灵活通用认知”。但近年研究表明，即使果蝇对运动刺激的偏好方向也依赖脉冲时序，小鼠恐惧条件化涉及树突区室化，人类单个神经元甚至能做 XOR。如果把“必须”改成“可以用……建模”我会少些异议。人工神经元模型显然是过度简化的，为什么“必须”这种强结论能成立？
nyrikki查看英文原文
>Tasks that are instructed using conditional clauses also require a simple form of deductive reasoning (if p then q else s) > Our models ofer several experimentally testable predictions outlining how linguistic information must be represented to facilitate flexible and general cognition in the human brain. Aren't those claims falsified by more recent studies that show that even in flys, preferred direction to a moving stimulus uses the timing of spikes. And that fear conditioning in even mice uses Dendritic Compartmentalization? Or that humans can even do xor with a single neuron. If "must be represented" was "may be modeled by" I would have less of an issue and obviously spikey artificial NNs have had problems with riddled basins and make autograd problematic in general. So ANNs need to be binary and it is best to model biological neurons as such for practical models... but can someone please clarify why 'must' can apply when using what we know now is an oversimplified artificial neuron models? Here are a couple of recent papers but I think dendritic compartmentalization and spike timing sensitivity has been established for over a decade. https://pubmed.ncbi.nlm.nih.gov/35701166/ https://www.sciencedirect.com/science/article/pii/S0092867418311061
补充主张像空气动力学反哺鸟类学那样，把 LLM 作为人类思维的模型反哺神经科学研究
鸟类启发了飞机，后来空气动力学又反哺鸟类学。我一直期待把 LLM 当作人类思维的模型来评估。复杂度和规模一直制约着神经科学，从生物底层构建高级大脑模型遥遥无期。一台会像人一样写作的机器已经造出来了，其设计虽在很多地方很不生物化，但为什么不看看这个“兔子洞”有多深？语言是思维核心的直觉早于 LLM，但与 LLM 的表现一致。
worstspotgain查看英文原文
Birds inspired planes. Later, aerodynamics fed back into ornithology. I've been waiting for LLMs to be evaluated as a model of human thought. Complexity and scale have held neuroscience back. It's nowhere close to building a high-level brain model up from biological primitives. Like ornithology, it could use some feedback. All arguments about AGI aside, a machine was built that writes like a human. Its design is very un-biological in places, so it's tempting to dismiss it. Why not see how deep the rabbit hole goes? Like all conjectures it just might surprise us. For one, the intuition that language is central to thought predates LLMs, but it's certainly consistent with it.
支持认同论文观点：语言充当了组合式认知任务的潜在空间/脚手架
仿佛语言本身就是这些心理物理任务的潜在空间，尤其是组合式指令。论文把它描述为脚手架也很贴切。
kevindamm查看英文原文
It's as if language is itself the latent space for these psychophysical tasks, especially compositional instruction. Their description of it as a scaffolding also seems apt.
补充认为生物智能与未来的人工智能本质等价，不应有优劣之分
显然，生物的智能存在与未来工厂里造出的智能存在本质上是同一枚硬币的两面，只是物理构成不同。今天的人类是由细胞驱动的生物 AI，未来的智能体则运行在晶体管上。当两者展现出相当的智能、情感和学习经历时，界限会越来越模糊。届时凭什么认为碳基的智能体天然优于硅基的？
retskrad查看英文原文
It's clear that both biological sentient beings and sentient being made in factories in the future will essentially be two sides of the same coin, differing only in their physical composition. Humans today operate as biological AI, powered by cells, and the sentient beings instead operate on transistors. As we progress toward a future where both sentient beings exhibit comparable intelligence, emotions, and learned experiences, the distinction between the two becomes increasingly blurred. It wouldn't be crazy to think that we'll program a person made out of transistors to go through the same life as a biological human. In such a scenario, why should we consider the sentient being made of cells inherently superior to its transistor-based counterpart?
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