To Google or not : differences on how online searches predict names and faces
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/217282 |
Resumo: | Word and face recognition are processes of interest for a large number of fields, including both clinical psychology and computer calculations. The research examined here aims to evaluate the role of an online frequency’s ability to predict both face and word recognition by examining the stability of these processes in a given amount of time. The study will further examine the differences between traditional theories and current contextual frequency approaches. Reaction times were recorded through both a logarithmic transformation and through a Bayesian approach. The Bayes factor notation was employed as an additional test to support the evidence provided by the data. Although differences between face and name recognition were found, the results suggest that latencies for both face and name recognition are stable for a period of six months and online news frequencies better predict reaction time for both classical frequentist analyses. These findings support the use of the contextual diversity approach. |
id |
UFRGS-2_578df4f773fc5cd45f261502b6cb6a7c |
---|---|
oai_identifier_str |
oai:www.lume.ufrgs.br:10183/217282 |
network_acronym_str |
UFRGS-2 |
network_name_str |
Repositório Institucional da UFRGS |
repository_id_str |
|
spelling |
Moret-Tatay, CarmenWester, Abigail G.Gamermann, Daniel2021-01-14T04:10:22Z20202227-7390http://hdl.handle.net/10183/217282001119643Word and face recognition are processes of interest for a large number of fields, including both clinical psychology and computer calculations. The research examined here aims to evaluate the role of an online frequency’s ability to predict both face and word recognition by examining the stability of these processes in a given amount of time. The study will further examine the differences between traditional theories and current contextual frequency approaches. Reaction times were recorded through both a logarithmic transformation and through a Bayesian approach. The Bayes factor notation was employed as an additional test to support the evidence provided by the data. Although differences between face and name recognition were found, the results suggest that latencies for both face and name recognition are stable for a period of six months and online news frequencies better predict reaction time for both classical frequentist analyses. These findings support the use of the contextual diversity approach.application/pdfengMathematics. Basel. Vol. 8, no. 11 (Nov. 2020), 10 p.Inferência bayesianaTransformacoes logaritmicasReconhecimento de palavrasCálculo computacionalBayesian inferenceLogarithmic transformationWord recognitionFace recognitionWord frequency effectContextual diversityComputer calculationTo Google or not : differences on how online searches predict names and facesEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001119643.pdf.txt001119643.pdf.txtExtracted Texttext/plain38335http://www.lume.ufrgs.br/bitstream/10183/217282/2/001119643.pdf.txt1e0680d3061e06533024311577e61cd0MD52ORIGINAL001119643.pdfTexto completo (inglês)application/pdf1835970http://www.lume.ufrgs.br/bitstream/10183/217282/1/001119643.pdf2d2408b25458ab45f398190295ed1a6aMD5110183/2172822023-07-15 03:26:58.038054oai:www.lume.ufrgs.br:10183/217282Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-07-15T06:26:58Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
To Google or not : differences on how online searches predict names and faces |
title |
To Google or not : differences on how online searches predict names and faces |
spellingShingle |
To Google or not : differences on how online searches predict names and faces Moret-Tatay, Carmen Inferência bayesiana Transformacoes logaritmicas Reconhecimento de palavras Cálculo computacional Bayesian inference Logarithmic transformation Word recognition Face recognition Word frequency effect Contextual diversity Computer calculation |
title_short |
To Google or not : differences on how online searches predict names and faces |
title_full |
To Google or not : differences on how online searches predict names and faces |
title_fullStr |
To Google or not : differences on how online searches predict names and faces |
title_full_unstemmed |
To Google or not : differences on how online searches predict names and faces |
title_sort |
To Google or not : differences on how online searches predict names and faces |
author |
Moret-Tatay, Carmen |
author_facet |
Moret-Tatay, Carmen Wester, Abigail G. Gamermann, Daniel |
author_role |
author |
author2 |
Wester, Abigail G. Gamermann, Daniel |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Moret-Tatay, Carmen Wester, Abigail G. Gamermann, Daniel |
dc.subject.por.fl_str_mv |
Inferência bayesiana Transformacoes logaritmicas Reconhecimento de palavras Cálculo computacional |
topic |
Inferência bayesiana Transformacoes logaritmicas Reconhecimento de palavras Cálculo computacional Bayesian inference Logarithmic transformation Word recognition Face recognition Word frequency effect Contextual diversity Computer calculation |
dc.subject.eng.fl_str_mv |
Bayesian inference Logarithmic transformation Word recognition Face recognition Word frequency effect Contextual diversity Computer calculation |
description |
Word and face recognition are processes of interest for a large number of fields, including both clinical psychology and computer calculations. The research examined here aims to evaluate the role of an online frequency’s ability to predict both face and word recognition by examining the stability of these processes in a given amount of time. The study will further examine the differences between traditional theories and current contextual frequency approaches. Reaction times were recorded through both a logarithmic transformation and through a Bayesian approach. The Bayes factor notation was employed as an additional test to support the evidence provided by the data. Although differences between face and name recognition were found, the results suggest that latencies for both face and name recognition are stable for a period of six months and online news frequencies better predict reaction time for both classical frequentist analyses. These findings support the use of the contextual diversity approach. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020 |
dc.date.accessioned.fl_str_mv |
2021-01-14T04:10:22Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/217282 |
dc.identifier.issn.pt_BR.fl_str_mv |
2227-7390 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001119643 |
identifier_str_mv |
2227-7390 001119643 |
url |
http://hdl.handle.net/10183/217282 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Mathematics. Basel. Vol. 8, no. 11 (Nov. 2020), 10 p. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
instname_str |
Universidade Federal do Rio Grande do Sul (UFRGS) |
instacron_str |
UFRGS |
institution |
UFRGS |
reponame_str |
Repositório Institucional da UFRGS |
collection |
Repositório Institucional da UFRGS |
bitstream.url.fl_str_mv |
http://www.lume.ufrgs.br/bitstream/10183/217282/2/001119643.pdf.txt http://www.lume.ufrgs.br/bitstream/10183/217282/1/001119643.pdf |
bitstream.checksum.fl_str_mv |
1e0680d3061e06533024311577e61cd0 2d2408b25458ab45f398190295ed1a6a |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS) |
repository.mail.fl_str_mv |
|
_version_ |
1801225007118942208 |