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相关概念视频

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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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...
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Language01:16

Language

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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...
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Components of Language01:24

Components of Language

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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.
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Language Development01:22

Language Development

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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...
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Language and Cognition01:27

Language and Cognition

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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.
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Relative Risk01:12

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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相关实验视频

Updated: Feb 4, 2026

A Method to Study Adaptation to Left-Right Reversed Audition
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一个大语言模型工作流程可审计的大脑风险分层和预居留奖学金:技术报告.

Amir Akhavan1, Swapan Nath2

  • 1Medicine, Anne Burnett Marion School of Medicine at Texas Christian University, Fort Worth, USA.

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概括

本研究介绍了在医学教育中使用大型语言模型 (LLM) 来教授人工智能 (AI) 识字的指导框架. 它展示了LLM如何支持严格的学术工作,并为学员培养可审计的人工智能使用技能.

关键词:
人工智能的人工智能是人工智能.脑部,脑部.证据综合 证据综合大型语言模型.医学教育 医学教育导师制 导师制 导师制 导师制快速的工程迅速的工程风险分层的风险分层.本科医学教育 本科医学教育

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相关实验视频

Last Updated: Feb 4, 2026

A Method to Study Adaptation to Left-Right Reversed Audition
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6.9K
Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting
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科学领域:

  • 医学教育 医学教育
  • 人工智能在医学中的应用
  • 学术工作流程学术工作流程

背景情况:

  • 医学实习生需要透明的方法来将大型语言模型 (LLM) 整合到学术工作中.
  • 现有的人工智能 (AI) 识字课程缺乏用于LLM的可审计框架.
  • 发展结构化的人工智能使用技能对于预入住资准备至关重要.

研究的目的:

  • 描述一个指导教育框架,教AI素养和结构化的LLM使用医疗学员.
  • 将临床病例报告转化为可重复的,LLM支持的风险分层模型.
  • 促进严谨,验证和透明的人工智能参与学术活动.

主要方法:

  • 使用结构化临床变量重建了一例未被识别的大脑.
  • 在标准化提示分类账中记录了LLM交互,记录了理由和决策.
  • 根据文献支持的指标,开发了一个临时的神经病变恶化脑得分 (NDBAS v0.1).

主要成果:

  • 与手工方法相比,LLM辅助合成加速了证据审查.
  • 该框架产生了三个课程文物:一个案例附录,提示账本和可变字典.
  • 该指数案例获得了5的NDBAS v0.1评分,表明风险中等.

结论:

  • 这种结构化,审计准备的LLM工作流程增强了医疗学员的AI素养和学术成果.
  • "提示账本加上询问-验证-修改"模式有助于透明的证据综合和概念模型构建.
  • 在适当的治理下,指导的LLM工作流可以为学习者在临床培训中负责任的AI参与做好准备.