A new measure of clustering and switching based on bigrams

Maria K. Wolters; Sarah E. MacPherson; Jinseon You; Rize Jin; Seung-Cheol Baek; Jong C. Park

A new measure of clustering and switching based on bigrams

Maria K. Wolters, Sarah E. MacPherson, Jinseon You, Rize Jin, Seung-Cheol Baek, Jong C. Park

Research output: Contribution to conference › Poster › peer-review

Abstract

The category fluency task (CFT) provides important information about executive abilities such as initiation, set-shifting and inhibition. CFT sequences are generated by retrieving groups of related words (“clusters“) from semantic
memory. Manual annotation schemes have been developed for inferring these clusters from transcribed CFT sequences (Troyer 2008), but these are time-consuming and require training. We propose an automatic analysis technique that is based on a simple statistical model of CFT sequences.
This model can be easily adapted to different languages and domains, given sufficient training data. CFT sequences (domain “animals“) were generated by 104 younger adults aged 18-34 years and 100 older adults aged 50-84 years
who were native speakers of UK English. The sequences were categorised both manually and using our automated method with key measures such as the number of switches significantly correlating (rho=0.4, 95% CI [0.28-0.51]). Both
methods also resulted in the significant age differences that are consistently reported in the cognitive aging literature.

Original language	English
Pages	241
Publication status	Published - 2015
Event	56th Annual Meeting of the Psychonomic Society - Chicago, United States Duration: 19 Nov 2015 → 22 Nov 2015

Conference

Conference	56th Annual Meeting of the Psychonomic Society
Country/Territory	United States
City	Chicago
Period	19/11/15 → 22/11/15

Access to Document

Wolters et al 2015 PsychonomicsFinal published version, 264 KB

Cite this

@conference{19fc024323a64cc489aab03643178a09,

title = "A new measure of clustering and switching based on bigrams",

abstract = "The category fluency task (CFT) provides important information about executive abilities such as initiation, set-shifting and inhibition. CFT sequences are generated by retrieving groups of related words (“clusters“) from semanticmemory. Manual annotation schemes have been developed for inferring these clusters from transcribed CFT sequences (Troyer 2008), but these are time-consuming and require training. We propose an automatic analysis technique that is based on a simple statistical model of CFT sequences.This model can be easily adapted to different languages and domains, given sufficient training data. CFT sequences (domain “animals“) were generated by 104 younger adults aged 18-34 years and 100 older adults aged 50-84 yearswho were native speakers of UK English. The sequences were categorised both manually and using our automated method with key measures such as the number of switches significantly correlating (rho=0.4, 95% CI [0.28-0.51]). Bothmethods also resulted in the significant age differences that are consistently reported in the cognitive aging literature. ",

author = "Wolters, {Maria K.} and MacPherson, {Sarah E.} and Jinseon You and Rize Jin and Seung-Cheol Baek and Park, {Jong C.}",

year = "2015",

language = "English",

pages = "241",

note = "56th Annual Meeting of the Psychonomic Society ; Conference date: 19-11-2015 Through 22-11-2015",

}

TY - CONF

T1 - A new measure of clustering and switching based on bigrams

AU - Wolters, Maria K.

AU - MacPherson, Sarah E.

AU - You, Jinseon

AU - Jin, Rize

AU - Baek, Seung-Cheol

AU - Park, Jong C.

PY - 2015

Y1 - 2015

N2 - The category fluency task (CFT) provides important information about executive abilities such as initiation, set-shifting and inhibition. CFT sequences are generated by retrieving groups of related words (“clusters“) from semanticmemory. Manual annotation schemes have been developed for inferring these clusters from transcribed CFT sequences (Troyer 2008), but these are time-consuming and require training. We propose an automatic analysis technique that is based on a simple statistical model of CFT sequences.This model can be easily adapted to different languages and domains, given sufficient training data. CFT sequences (domain “animals“) were generated by 104 younger adults aged 18-34 years and 100 older adults aged 50-84 yearswho were native speakers of UK English. The sequences were categorised both manually and using our automated method with key measures such as the number of switches significantly correlating (rho=0.4, 95% CI [0.28-0.51]). Bothmethods also resulted in the significant age differences that are consistently reported in the cognitive aging literature.

AB - The category fluency task (CFT) provides important information about executive abilities such as initiation, set-shifting and inhibition. CFT sequences are generated by retrieving groups of related words (“clusters“) from semanticmemory. Manual annotation schemes have been developed for inferring these clusters from transcribed CFT sequences (Troyer 2008), but these are time-consuming and require training. We propose an automatic analysis technique that is based on a simple statistical model of CFT sequences.This model can be easily adapted to different languages and domains, given sufficient training data. CFT sequences (domain “animals“) were generated by 104 younger adults aged 18-34 years and 100 older adults aged 50-84 yearswho were native speakers of UK English. The sequences were categorised both manually and using our automated method with key measures such as the number of switches significantly correlating (rho=0.4, 95% CI [0.28-0.51]). Bothmethods also resulted in the significant age differences that are consistently reported in the cognitive aging literature.

M3 - Poster

SP - 241

T2 - 56th Annual Meeting of the Psychonomic Society

Y2 - 19 November 2015 through 22 November 2015

ER -

A new measure of clustering and switching based on bigrams

Abstract

Conference

Access to Document

Fingerprint

Cite this