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Language and Cognition

Language and Cognition

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.

Human Genetics

Human Genetics

Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...

Genetic Lingo

Genetic Lingo

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Updated: Sep 18, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

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Published on: June 25, 2019

在遗传条件下评估与衰老相关的大型语言模型性能.

Amna A Othman¹, Kendall A Flaharty¹, Suzanna E Ledgister Hanchard¹

¹Medical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892, USA.

medRxiv : the preprint server for health sciences

|June 26, 2025

概括

此摘要是机器生成的。

大型语言模型 (LLM) 在描述跨年龄的遗传条件方面表现有前途,尽管需要进一步研究临床应用. 这些人工智能工具可以生成准确的医疗信息,有助于理解与年龄相关的疾病表现和管理.

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An Experimental Paradigm for Measuring the Effects of Ageing on Sentence Processing

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Last Updated: Sep 18, 2025

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科学领域:

生物医学信息学生物医学信息学
遗传学遗传学是一个
人工智能的人工智能

背景情况:

在儿童群体中,遗传疾病经常被描述,对成人表现和管理的理解有限.
关于遗传性疾病患者的临床进展和终身护理的知识差距存在.
生成型人工智能,特别是大型语言模型 (LLM),为复杂的生物医学数据分析提供了潜在的解决方案.

研究的目的:

评估LLM在处理282种遗传疾病的与年龄有关的方面的能力.
评估LLM在生成准确的医学简报和患者-遗传学家对话方面的表现.
识别潜在的基于年龄的偏见或限制在遗传疾病信息的LLM输出中.

主要方法:

根据表现年龄和管理变化对282种遗传疾病进行了分类.
评估了Llama-2-70b-chat (70b) 和GPT-3.5 (GPT) 用于生成医疗图片,评估正确性,完整性和简洁性.
利用生成的图片作为提示,创建和评估患者遗传学家对基于年龄的管理对话.

主要成果:

70b和GPT都在生成准确的医疗图片方面表现出令人印象深刻的表现.
在LLM输出中没有观察到任何重要的总体基于年龄的偏差.
在特定领域发现了统计学上显著的差异,表明了细微的LLM绩效差异.

结论:

在处理和生成有关年龄相关遗传条件特征的信息方面,LLM表现出强大的能力.
虽然有希望,LLMs目前在遗传医学中直接临床应用的局限性.
需要进一步的开发和验证,以充分利用LLMs,在整个生命周期内全面管理遗传疾病.