Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes

自闭症 对话 心理学 判决 认知心理学 计算机科学 自闭症谱系障碍 脆性X综合征 人工智能 发展心理学 自然语言处理 沟通 精神科
作者
Joseph C. Y. Lau,Emily B. Landau,Qingcheng Zeng,Ronghui Zhang,Stephanie Crawford,Rob Voigt,Molly Losh
出处
期刊:Autism [SAGE Publishing]
标识
DOI:10.1177/13623613241304488
摘要

Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor intensive, and not readily applied in ample sample sizes. This proof-of-concept methodological study employed an artificial intelligence pre-trained language model, Bidirectional Encoder Representations from Transformers, as a tool to address such challenges. We applied Bidirectional Encoder Representations from Transformers to computationally index pragmatic-related variability in autism and in genetically related phenotypes displaying pragmatic differences, namely, in parents of autistic individuals, fragile X syndrome, and FMR1 premutation. Findings suggest that without model fine-tuning, Bidirectional Encoder Representations from Transformers’s Next Sentence Prediction module was able to derive estimates that differentiate autistic from non-autistic groups. Moreover, such computational estimates correlated with manually coded characterization of pragmatic abilities that contribute to conversational coherence, not only in autism but also in the other genetically related phenotypes. This study represents a step forward in evaluating the efficacy of artificial intelligence language models for capturing clinically important pragmatic differences and variability related to autism, showcasing the potential of artificial intelligence to provide automatized, efficient, and objective tools for pragmatic characterization to help advance the field. Lay abstract Autism is clinically defined by challenges with social language, including difficulties offering on-topic language in a conversation. Similar differences are also seen in genetically related conditions such as fragile X syndrome (FXS), and even among those carrying autism-related genes who do not have clinical diagnoses (e.g., the first-degree relatives of autistic individuals and carriers of the FMR1 premutation), which suggests there are genetic influences on social language related to the genes involved in autism. Characterization of social language is therefore important for informing potential intervention strategies and understanding the causes of communication challenges in autism. However, current tools for characterizing social language in both clinical and research settings are very time and labor intensive. In this study, we test an automized computational method that may address this problem. We used a type of artificial intelligence known as pre-trained language model to measure aspects of social language in autistic individuals and their parents, non-autistic comparison groups, and individuals with FXS and the FMR1 premutation. Findings suggest that these artificial intelligence approaches were able to identify differences in social language in autism, and to provide insight into the individuals’ ability to keep a conversation on-topic. These findings also were associated with broader measures of participants’ social communication ability. This study is one of the first to use artificial intelligence models to capture important differences in social language in autism and genetically related groups, demonstrating how artificial intelligence might be used to provide automatized, efficient, and objective tools for language characterization.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
matthew完成签到,获得积分10
1秒前
1秒前
1秒前
沙亮完成签到 ,获得积分10
2秒前
Hello应助吵吵robot采纳,获得10
2秒前
weige发布了新的文献求助10
2秒前
共享精神应助城南徐师傅采纳,获得10
3秒前
proton发布了新的文献求助10
3秒前
CipherSage应助自信小笼包采纳,获得10
4秒前
大模型应助matthew采纳,获得10
5秒前
眉间一把刀完成签到,获得积分10
5秒前
5秒前
maomao发布了新的文献求助10
6秒前
6秒前
科研通AI2S应助naturehome采纳,获得10
8秒前
爆米花应助好宝宝采纳,获得10
9秒前
科研通AI2S应助Victoria采纳,获得10
9秒前
盛弟发布了新的文献求助10
10秒前
Myles发布了新的文献求助10
10秒前
研友_ZGAWYL完成签到,获得积分10
10秒前
11秒前
yookia应助烂漫的冰蓝采纳,获得10
13秒前
现代的东蒽完成签到,获得积分10
13秒前
13秒前
13秒前
上官若男应助幼汁汁鬼鬼采纳,获得10
13秒前
阿秋秋秋完成签到 ,获得积分10
14秒前
在水一方应助粥粥爱糊糊采纳,获得10
14秒前
16秒前
喻紫寒发布了新的文献求助10
16秒前
杰小瑞发布了新的文献求助30
16秒前
Gengar发布了新的文献求助10
16秒前
17秒前
张晶晶发布了新的文献求助10
18秒前
19秒前
19秒前
星辰大海应助飞星流采纳,获得10
19秒前
香蕉觅云应助诺之采纳,获得10
19秒前
万能图书馆应助frl采纳,获得10
21秒前
22秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988732
求助须知:如何正确求助?哪些是违规求助? 3531027
关于积分的说明 11252281
捐赠科研通 3269732
什么是DOI,文献DOI怎么找? 1804764
邀请新用户注册赠送积分活动 881869
科研通“疑难数据库(出版商)”最低求助积分说明 809021