两种学习方式的核心差异就是，有监督学习依赖外部的明确指引，而无监督学习则源于系统内部的自主 探索与规律发现。传统观点认为，学习需要依赖奖励信号（如食物、表扬）或明确反馈（如错误纠 正），但婴儿大脑展现出的惊人能力，正促使神经科学界重新审视这一认知。那么，生物大脑的无监督 学习能力到底从何而来？ 一、无监督学习是大脑预习课 无监督学习的能力并非人类独有。例如，小鼠在探索新环境时，无需奖励就能自主形成空间记忆。为了 精准地观察这一过程，美国霍华德·休斯医学研究所（HHMI）的科学家Marius Pachitariu和Carsen Stringer领导的团队，设计了一项精巧的实验[1]。 当婴儿凝视旋转的风铃时，他或她的大脑便在悄然破解光影变化的规律。无需奖励或惩罚，这种对世界 的理解就已在神经回路中生根发芽——这正是"无监督学习"的生动体现。 反观当下最先进的人工智能，要区分"猫"和"狗"的图片，也需要在大量标注数据的"喂养"下才能实现。 这种需要引导的学习方式，则被称为"有监督学习"。 在神经科学里，有监督学习表现为外部奖惩引导的神经连接强化（如条件反射），而无监督学习是大脑 自主提取环境特征（如自发形成对线条 ...

Core Argument - The article explores the relationship between large language models (LLMs) like ChatGPT and the brain's language processing mechanisms, questioning whether LLMs capture deep cognitive processes or if their predictive capabilities are merely coincidental [1][12]. Group 1: Predictive Coding Theory - Predictive coding theory, proposed by Karl Friston in the 1990s, suggests that the brain actively predicts future events and adjusts its predictions based on sensory input to minimize prediction errors [1][2]. - This theory has gained traction as it provides a coherent framework for understanding various cognitive functions, including language processing, where the brain anticipates upcoming words and sentences [3][4]. Group 2: Neural Network Language Models (NNLM) - NNLMs are artificial neural networks designed for word prediction tasks, leveraging vast amounts of natural language text for training, which allows them to learn statistical patterns across different text types [6][9]. - Recent advancements in NNLMs have led to the development of fine-tuning techniques, enabling models to adapt learned representations for various language tasks, improving performance compared to models trained from scratch [6][10]. Group 3: Neuroscience Research Using NNLM - NNLMs have been employed in neuroscience to predict brain responses to natural language, with studies showing that models based on language representations outperform those using non-contextual embeddings [10][11]. - Research indicates a strong correlation between a model's word prediction accuracy and its ability to explain brain activity, suggesting that word prediction may be fundamental to language processing [10][11]. Group 4: Alternative Explanations - Antonello and Huth challenge the predictive coding theory, proposing that the success of language models may stem from their ability to capture universal information rather than predictive capabilities [12][17]. - Their research indicates that the correlation between model performance and brain response may be due to the generalizability of the representations used, rather than evidence of predictive coding in the brain [12][14]. Group 5: Future Research Directions - Future studies should aim to identify measurable phenomena that can distinctly demonstrate whether the brain employs predictive coding during language processing, potentially providing stronger evidence for or against the theory [18].

Core Argument - The article discusses the relationship between large language models (LLMs) like ChatGPT and the brain's language processing mechanisms, questioning whether the correlation between LLM predictions and brain responses indicates a deeper cognitive similarity or is merely a statistical coincidence [2][5][15]. Group 1: Predictive Coding Theory - Predictive coding theory posits that the brain actively predicts future events and adjusts its predictions based on sensory input, aiming to minimize prediction errors [2][4]. - This theory has gained traction as it provides a unified framework for understanding various cognitive functions, including language processing [4][10]. - Evidence supporting predictive coding includes findings from neural network language models (NNLMs) that effectively explain brain activity triggered by natural language [4][11]. Group 2: Neural Network Language Models (NNLMs) - NNLMs are designed for word prediction tasks, generating probability distributions for the next word based on preceding context, and can be trained on vast amounts of natural language text [6][10]. - Recent studies indicate that NNLMs can predict brain responses to language stimuli more effectively than traditional models, suggesting a potential alignment in objectives between NNLMs and the brain [10][11]. Group 3: Alternative Explanations - Antonello and Huth challenge the notion that superior predictive capabilities of models imply that the brain also engages in predictive coding, suggesting instead that the observed correlations may stem from representational generality [15][16]. - Their research indicates that the ability of NNLMs to predict brain responses may be due to capturing universal information applicable across various language tasks, rather than a direct reflection of brain processing mechanisms [16][20]. Group 4: Research Findings - The study found a high correlation (r=0.847) between model performance in predicting the next word and its encoding performance in brain response prediction [18]. - Further analysis revealed that the generality of representations in models correlates with their encoding performance, suggesting that models with better predictive capabilities also possess broader applicability [19][20]. - The research also indicated that the encoding performance of language models peaks at certain depths, contradicting the predictions of predictive coding theory [22]. Group 5: Conclusion - The findings imply that while LLMs exhibit impressive capabilities in language tasks, attributing these abilities directly to brain-like processing may be overly simplistic [25][26]. - Future research should focus on identifying measurable phenomena that can distinctly validate or refute the predictive coding theory in the context of language processing [27].