To Google or Not: Differences on How Online Searches Predict Names and Faces

Carmen Moret-Tatay, Abigail G. Wester and Daniel Gamermann
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Carmen Moret-Tatay: Escuela de Doctorado, Universidad Católica de Valencia San Vicente Mártir, San Agustín 3, Esc. A, Entresuelo 1, 46002 València, Spain
Abigail G. Wester: Department of Psychology, Neuroscience and Languages. Regis University, 3333 Regis Blvd, Denver, CO 80221, USA
Daniel Gamermann: Instituto de Física—Universidade Federal do Rio Grande do Sul (UFRGS), Av, Bento Gonçalves 9500, 15051 CEP 91501-970, Porto Alegre, Brazil

Mathematics, 2020, vol. 8, issue 11, 1-10

Abstract: 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.

Keywords: Bayesian inference; logarithmic transformation; word recognition; face recognition; word frequency effect; contextual diversity; computer calculation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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