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相关概念视频

Language and Cognition01:27

Language and Cognition

716
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
716
Language01:16

Language

890
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
890
Components of Language01:24

Components of Language

752
Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
752
Stereotype Content Model02:16

Stereotype Content Model

15.3K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.3K
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

3.5K
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
3.5K
Language Development01:22

Language Development

853
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
853

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相关实验视频

Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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多么大的语言模型需要象征主义.

Xiaotie Deng1, Hanyu Li1

  • 1Center on Frontiers of Computing Studies, School of Computer Science, Peking University, China.

National science review
|September 22, 2025
PubMed
概括
此摘要是机器生成的。

缩放规律增强了大型语言模型的直觉,但象征性推理对于复杂的问题解决和真正的科学发现至关重要. 这种方法指导了先进的AI在新型研究中.

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相关实验视频

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The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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科学领域:

  • 人工智能的人工智能
  • 认知科学 认知科学
  • 计算语言学 计算语言学

背景情况:

  • 大型语言模型 (LLM) 展示了由缩放规律驱动的新兴能力.
  • 目前的LLM在模式识别和生成方面表现出色,但可能缺乏更深入的符号理解.
  • 导航复杂的科学前沿需要的不仅仅是统计直觉.

研究的目的:

  • 调查缩放规律和象征性推理在AI中的协同作用.
  • 探索象征性操纵如何增强LLM真正发现的能力.
  • 为整合直观和故意的AI推理提出一个框架.

主要方法:

  • 在复杂的推理任务中分析纯数据驱动的LLM的局限性.
  • 开发和评估混合模型,将LLM直觉与象征性AI技术相结合.
  • 在需要抽象推理和新型问题解决的基准任务上测试模型性能.

主要成果:

  • 混合模型在要求抽象和象征性操纵的任务上明显优于传统的LLM.
  • 象征性推理作为一个关键的"指南针",指导尺度LLMs的直观力量.
  • 整合增强了模型产生新假设和实现可验证发现的能力.

结论:

  • 真正的AI发现需要缩放直觉和象征性推理的融合.
  • 象征性AI为导航复杂的研究景观提供了结构化的框架.
  • 未来的人工智能开发应该专注于整合这些互补的方法来解决先进的问题.