國立高雄大學圖資館 |

登入

回首頁

Cognition, Modality, and Language in Healthy Young Adults.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Cognition, Modality, and Language in Healthy Young Adults.
作者:	Finley, Ann Marie.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2023
面頁冊數:	117 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
附註:	Advisor: Bedore, Lisa.
Contained By:	Dissertations Abstracts International85-07B.
標題:	Language.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30813294
ISBN:	9798381380460

Cognition, Modality, and Language in Healthy Young Adults.
Finley, Ann Marie.

Cognition, Modality, and Language in Healthy Young Adults. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 117 p.

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

Thesis (Ph.D.)--Temple University, 2023.

This item must not be sold to any third party vendors.

Measures drawn from language samples (e.g., discourse measures) are used in clinical and research settings as a functional measure of language and cognitive abilities. In narrative elicitation tasks, discourse measures reliably vary by the type of prompt used to collect a language sample. Additionally, language features tend to very along with communicative context, topic, and modality (e.g., oral vs. written). However, until recent years, technology had not advanced sufficiently to support large-scale study of spoken language data. In this project, we used natural language processing and machine learning methods to examine the intersection of discourse measures, language modality, and cognition (i.e., working memory) in healthy young adults. In Experiment 1, we used a computational approach to examine discourse measures in spoken and written English. We achieved >90% accuracy in binary classification (e.g., spoken/written). In Experiment 2, we took a behavioral approach, studying working memory and narrative discourse measures in a cohort of healthy young adults. We predicted that working memory would predict informativity in participants’ narrative language samples. We found mixed results for our two measures of informativity (e.g., the Measure of Textual Lexical Diversity and Shannon entropy). We attributed the observed differences in these two measures to the fact that, while both serve to measure new or unique information, MTLD indexes additional linguistic information (e.g., semantic, lexical). In contrast, Shannon entropy is based on word co-occurrence statistics. We interpret our overall results as support for the potential utility of machine learning in language research and potential for future research and clinical implementations.

ISBN: 9798381380460Subjects--Topical Terms:

180769
Language.
Subjects--Index Terms:

Discourse

Cognition, Modality, and Language in Healthy Young Adults.
LDR:02967nmm a2200397 4500 001 655879
005 20240414211956.5
006 m o d
007 cr#unu||||||||
008 240620s2023 ||||||||||||||||| ||eng d
020 $a 9798381380460
035 $a (MiAaPQ)AAI30813294
035 $a AAI30813294
040 $a MiAaPQ $c MiAaPQ
100 1 $a Finley, Ann Marie. $0 (orcid)0000-0003-3368-3912 $3 967072
245 1 0 $a Cognition, Modality, and Language in Healthy Young Adults.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 117 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
500 $a Advisor: Bedore, Lisa.
502 $a Thesis (Ph.D.)--Temple University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Measures drawn from language samples (e.g., discourse measures) are used in clinical and research settings as a functional measure of language and cognitive abilities. In narrative elicitation tasks, discourse measures reliably vary by the type of prompt used to collect a language sample. Additionally, language features tend to very along with communicative context, topic, and modality (e.g., oral vs. written). However, until recent years, technology had not advanced sufficiently to support large-scale study of spoken language data. In this project, we used natural language processing and machine learning methods to examine the intersection of discourse measures, language modality, and cognition (i.e., working memory) in healthy young adults. In Experiment 1, we used a computational approach to examine discourse measures in spoken and written English. We achieved >90% accuracy in binary classification (e.g., spoken/written). In Experiment 2, we took a behavioral approach, studying working memory and narrative discourse measures in a cohort of healthy young adults. We predicted that working memory would predict informativity in participants’ narrative language samples. We found mixed results for our two measures of informativity (e.g., the Measure of Textual Lexical Diversity and Shannon entropy). We attributed the observed differences in these two measures to the fact that, while both serve to measure new or unique information, MTLD indexes additional linguistic information (e.g., semantic, lexical). In contrast, Shannon entropy is based on word co-occurrence statistics. We interpret our overall results as support for the potential utility of machine learning in language research and potential for future research and clinical implementations.
590 $a School code: 0225.
650 4 $a Language. $3 180769
650 4 $a Cognitive psychology. $3 179548
650 4 $a Computer science. $3 199325
653 $a Discourse
653 $a Machine learning
653 $a Working memory
653 $a Spoken language
653 $a Clinical implementations
690 $a 0679
690 $a 0633
690 $a 0984
710 2 $a Temple University. $b Communication Sciences. $3 967073
773 0 $t Dissertations Abstracts International $g 85-07B.
790 $a 0225
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30813294