什么可以预测生产力?理论与个体相遇

IF 1.8 1区 文学 0 LANGUAGE & LINGUISTICS Cognitive Linguistics Pub Date : 2020-02-26 DOI:10.1515/cog-2019-0026
Hendrik De Smet
{"title":"什么可以预测生产力?理论与个体相遇","authors":"Hendrik De Smet","doi":"10.1515/cog-2019-0026","DOIUrl":null,"url":null,"abstract":"Abstract Because they involve individual-level cognitive processes, psychological explanations of linguistic phenomena are in principle testable against individual behaviour. The present study draws on patterns of individual variation in corpus data to test explanations of productivity. Linguistic patterns are predicted to become more productive with higher type frequencies and lower token frequencies. This is because the formation of abstract mental representations is encouraged by varied types but counteracted by automation of high-frequency types. The predictions are tested for English -ly and -ness-derivation, as used by 698 individual journalists in the New York Times Annotated Corpus and 171 members of Parliament in the Hansard Corpus. Linear regression is used to model individual variation in productivity, in relation to type and token frequency, as well as several other predictor variables. While the expected effects are observed, there is also robust evidence of an interaction effect between type and token frequency, indicating that productivity is highest for patterns with many types and not-too-infrequent tokens. This fits best with a view of entrenchment as both a conservative and creative force in language. Further, some variation remains irreducibly individual and is not explained by currently known predictors of productivity.","PeriodicalId":51530,"journal":{"name":"Cognitive Linguistics","volume":"31 1","pages":"251 - 278"},"PeriodicalIF":1.8000,"publicationDate":"2020-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/cog-2019-0026","citationCount":"18","resultStr":"{\"title\":\"What predicts productivity? Theory meets individuals\",\"authors\":\"Hendrik De Smet\",\"doi\":\"10.1515/cog-2019-0026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Because they involve individual-level cognitive processes, psychological explanations of linguistic phenomena are in principle testable against individual behaviour. The present study draws on patterns of individual variation in corpus data to test explanations of productivity. Linguistic patterns are predicted to become more productive with higher type frequencies and lower token frequencies. This is because the formation of abstract mental representations is encouraged by varied types but counteracted by automation of high-frequency types. The predictions are tested for English -ly and -ness-derivation, as used by 698 individual journalists in the New York Times Annotated Corpus and 171 members of Parliament in the Hansard Corpus. Linear regression is used to model individual variation in productivity, in relation to type and token frequency, as well as several other predictor variables. While the expected effects are observed, there is also robust evidence of an interaction effect between type and token frequency, indicating that productivity is highest for patterns with many types and not-too-infrequent tokens. This fits best with a view of entrenchment as both a conservative and creative force in language. Further, some variation remains irreducibly individual and is not explained by currently known predictors of productivity.\",\"PeriodicalId\":51530,\"journal\":{\"name\":\"Cognitive Linguistics\",\"volume\":\"31 1\",\"pages\":\"251 - 278\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2020-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1515/cog-2019-0026\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Linguistics\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1515/cog-2019-0026\",\"RegionNum\":1,\"RegionCategory\":\"文学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"LANGUAGE & LINGUISTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Linguistics","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1515/cog-2019-0026","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
引用次数: 18

摘要

由于涉及到个体层面的认知过程,语言现象的心理学解释原则上可以通过个体行为进行检验。本研究利用语料库数据中的个体变异模式来检验生产率的解释。预计语言模式在类型频率较高和标记频率较低的情况下会变得更有效率。这是因为抽象心理表征的形成受到各种类型的鼓励,但被高频类型的自动化所抵消。对这些预测进行了英语-ly和-ness衍生的测试,《纽约时报》注释语料库中的698名记者和《英国议事录》语料库中的171名国会议员使用了这些预测。线性回归用于模拟生产率的个体变化,与类型和标记频率有关,以及其他几个预测变量。虽然可以观察到预期的效果,但也有强有力的证据表明类型和令牌频率之间存在交互作用,表明具有许多类型和不太频繁的令牌的模式的生产率最高。这最符合壕沟作为语言的保守力量和创造性力量的观点。此外,一些变异仍然是不可简化的个体,不能用目前已知的生产力预测因素来解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
What predicts productivity? Theory meets individuals
Abstract Because they involve individual-level cognitive processes, psychological explanations of linguistic phenomena are in principle testable against individual behaviour. The present study draws on patterns of individual variation in corpus data to test explanations of productivity. Linguistic patterns are predicted to become more productive with higher type frequencies and lower token frequencies. This is because the formation of abstract mental representations is encouraged by varied types but counteracted by automation of high-frequency types. The predictions are tested for English -ly and -ness-derivation, as used by 698 individual journalists in the New York Times Annotated Corpus and 171 members of Parliament in the Hansard Corpus. Linear regression is used to model individual variation in productivity, in relation to type and token frequency, as well as several other predictor variables. While the expected effects are observed, there is also robust evidence of an interaction effect between type and token frequency, indicating that productivity is highest for patterns with many types and not-too-infrequent tokens. This fits best with a view of entrenchment as both a conservative and creative force in language. Further, some variation remains irreducibly individual and is not explained by currently known predictors of productivity.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.30
自引率
17.60%
发文量
21
期刊介绍: Cognitive Linguistics presents a forum for linguistic research of all kinds on the interaction between language and cognition. The journal focuses on language as an instrument for organizing, processing and conveying information. Cognitive Linguistics is a peer-reviewed journal of international scope and seeks to publish only works that represent a significant advancement to the theory or methods of cognitive linguistics, or that present an unknown or understudied phenomenon. Topics the structural characteristics of natural language categorization (such as prototypicality, cognitive models, metaphor, and imagery); the functional principles of linguistic organization, as illustrated by iconicity; the conceptual interface between syntax and semantics; the experiential background of language-in-use, including the cultural background; the relationship between language and thought, including matters of universality and language specificity.
期刊最新文献
Using constructions to measure developmental language complexity The role of constructions in understanding predictability measures and their correspondence to word duration A related-event approach to event integration in Japanese complex predicates: iconicity, frequency, or efficiency? Multimodal constructions revisited. Testing the strength of association between spoken and non-spoken features of Tell me about it The role of entrenchment and schematisation in the acquisition of rich verbal morphology
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1