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

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.

[1]  Joachim Vandekerckhove,et al.  Editorial: Bayesian methods for advancing psychological science , 2018, Psychonomic bulletin & review.

[2]  Martin Magdin,et al.  Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System , 2019, Int. J. Interact. Multim. Artif. Intell..

[3]  Daniel Fitousi.  Linking the Ex-Gaussian Parameters to Cognitive Stages: Insights from the Linear Ballistic Accumulator (LBA) Model , 2020 .

[4]  Emmanuel J. Barbeau,et al.  How Fast is Famous Face Recognition? , 2012, Front. Psychology.

[5]  The mediational role of distracting stimuli in emotional word recognition , 2018, Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS.

[6]  M. Brysbaert,et al.  Assessing the Usefulness of Google Books’ Word Frequencies for Psycholinguistic Research on Word Processing , 2011, Front. Psychology.

[7]  Rebecca Treiman,et al.  The English Lexicon Project , 2007, Behavior research methods.

[8]  Anežka Kuzmičová,et al.  Just Google It: An Approach on Word Frequencies Based on Online Search Result , 2018, The Journal of general psychology.

[9]  Michael J Cortese,et al.  Visual word recognition of single-syllable words. , 2004, Journal of experimental psychology. General.

[10]  Manju Khari,et al.  Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks , 2019, Int. J. Interact. Multim. Artif. Intell..

[11]  P. Alexander,et al.  Reading on Paper and Digitally: What the Past Decades of Empirical Research Reveal , 2017 .

[12]  Inmaculada Baixauli Fortea,et al.  Challenges and insights for the visual system: Are face and word recognition two sides of the same coin? , 2020, Journal of Neurolinguistics.

[13]  Kenneth I Forster,et al.  DMDX: A Windows display program with millisecond accuracy , 2003, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[14]  Michael D. Dodd,et al.  How faces (and cars) may become special , 2019, Vision Research.

[15]  Isabel Gauthier,et al.  Gender and hometown population density interact to predict face recognition ability , 2019, Vision Research.

[16]  Kate Nation,et al.  Learning Words Via Reading: Contextual Diversity, Spacing, and Retrieval Effects in Adults , 2019, Cogn. Sci..

[17]  A. Jacobs,et al.  The word frequency effect: a review of recent developments and implications for the choice of frequency estimates in German. , 2011, Experimental psychology.

[18]  Gongfa Li,et al.  Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity , 2019, Mathematics.

[19]  S. Dehaene,et al.  The unique role of the visual word form area in reading , 2011, Trends in Cognitive Sciences.

[20]  J. Gabrieli,et al.  Early development of letter specialization in left fusiform is associated with better word reading and smaller fusiform face area. , 2018, Developmental science.

[21]  T. Q. Irigaray,et al.  Can You Identify These Celebrities? A Network Analysis on Differences between Word and Face Recognition , 2020 .

[22]  C. Moret-Tatay,et al.  Profiles on the Orientation Discrimination Processing of Human Faces , 2020, International journal of environmental research and public health.

[23]  Boris New,et al.  Worldlex: Twitter and blog word frequencies for 66 languages , 2015, Behavior Research Methods.

[24]  Martin Wegrzyn,et al.  Mapping the emotional face. How individual face parts contribute to successful emotion recognition , 2017, PloS one.

[25]  C. Oliveira,et al.  The Effect of Corrective Feedback on Performance in Basic Cognitive Tasks: An Analysis of RT Components , 2016, Psychologica Belgica.

[26]  Nuria Senent-Capuz,et al.  Gender, coping, and mental health: A Bayesian network model analysis , 2016 .

[27]  Manuel Perea,et al.  Contextual diversity facilitates learning new words in the classroom , 2017, PloS one.

[28]  Batyrkhan Sultanovich Omarov,et al.  Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training , 2018, Int. J. Interact. Multim. Artif. Intell..

[29]  C. Marra,et al.  Recognition disorders for famous faces and voices: a review of the literature and normative data of a new test battery , 2016, Neurological Sciences.

[30]  E. Navarro‐Pardo,et al.  ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density , 2017, Front. Psychol..

[31]  Ana-María Ruiz-Ruano,et al.  El componente social de la amenaza híbrida y su detección con modelos bayesianos/ The Social Component of the Hybrid Threat and its Detection with Bayesian Models , 2019 .

[32]  Marc Brysbaert,et al.  Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English , 2009, Behavior research methods.

[33]  B. Duchaine,et al.  Acquired prosopagnosia without word recognition deficits , 2015, Cognitive neuropsychology.

[34]  Guangmin Hu,et al.  A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network , 2020, Inf..

[35]  C. Kesavadas,et al.  Novel Face-Name Paired Associate Learning and Famous Face Recognition in Mild Cognitive Impairment: A Neuropsychological and Brain Volumetric Study , 2019, Dementia and Geriatric Cognitive Disorders Extra.

[36]  María M. Abad-Grau,et al.  Points of Significance: Bayesian networks , 2015, Nature Methods.

[37]  Douglas L. Allaire,et al.  MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models , 2019, AAAI.

[38]  C. Cesarano,et al.  Some New Oscillation Results for Fourth-Order Neutral Differential Equations , 2020 .

[39]  C. Moret-Tatay,et al.  Testing Motivational Theories in Music Education: The Role of Effort and Gratitude , 2019, Front. Behav. Neurosci..

[40]  Rodica Ianole-Călin,et al.  Exploring the Link between Academic Dishonesty and Economic Delinquency: A Partial Least Squares Path Modeling Approach , 2019, Mathematics.

[41]  Regina Nuzzo,et al.  Scientific method: Statistical errors , 2014, Nature.

[42]  C. Papagno,et al.  Famous face recognition and naming test: a normative study , 2002, Neurological Sciences.

[43]  Nathaniel J. Smith,et al.  The effect of word predictability on reading time is logarithmic , 2013, Cognition.

[44]  A. Caramazza,et al.  The Inversion, Part-Whole, and Composite Effects Reflect Distinct Perceptual Mechanisms With Varied Relationships to Face Recognition , 2017, Journal of experimental psychology. Human perception and performance.

[45]  Gordon D. A. Brown,et al.  Contextual Diversity, Not Word Frequency, Determines Word-Naming and Lexical Decision Times , 2006, Psychological science.