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Titre : Competition and Control during Working Memory Type de document : texte imprimé Auteurs : Anastasia Kiyonaga, Auteur ; mark désposito, Auteur Editeur : Cambridge University Press Année de publication : 2020 Collection : Elements in Perception Importance : 56 p Format : 22 cm ISBN/ISSN/EAN : 978-1-108-70644-5 Langues : Anglais (eng) Mots-clés : علــم النفــس Résumé : Working memory and perceptual attention are related functions, engaging many similar mechanisms and brain regions. As a consequence, behavioral and neural measures often reveal competition between working memory and attention demands. Yet there remains widespread debate about how working memory operates, and whether it truly shares processes and representations with attention. This Element will examine local-level representational properties to illuminate the storage format of working memory content, as well as systems-level and brain network communication properties to illuminate the attentional processes that control working memory. The Element will integrate both cognitive and neuroscientific accounts, describing shared substrates for working memory and perceptual attention, in a multi-level network architecture that provides robustness to disruptions and allows flexible attentional control in line with goals. Competition and Control during Working Memory [texte imprimé] / Anastasia Kiyonaga, Auteur ; mark désposito, Auteur . - Cambridge University Press, 2020 . - 56 p ; 22 cm. - (Elements in Perception) .
ISBN : 978-1-108-70644-5
Langues : Anglais (eng)
Mots-clés : علــم النفــس Résumé : Working memory and perceptual attention are related functions, engaging many similar mechanisms and brain regions. As a consequence, behavioral and neural measures often reveal competition between working memory and attention demands. Yet there remains widespread debate about how working memory operates, and whether it truly shares processes and representations with attention. This Element will examine local-level representational properties to illuminate the storage format of working memory content, as well as systems-level and brain network communication properties to illuminate the attentional processes that control working memory. The Element will integrate both cognitive and neuroscientific accounts, describing shared substrates for working memory and perceptual attention, in a multi-level network architecture that provides robustness to disruptions and allows flexible attentional control in line with goals. Exemplaires(0)
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Titre : Conducting Sentiment Analysis Type de document : texte imprimé Auteurs : Lei Lei, Auteur ; dilin liu, Auteur Editeur : Cambridge University Press Année de publication : 2021 Collection : Elements in Perception Importance : 104 p Format : 23 cm ISBN/ISSN/EAN : 978-1-108-82921-2 Langues : Anglais (eng) Mots-clés : علــم النفــس Résumé : This Element provides a basic introduction to sentiment analysis, aimed at helping students and professionals in corpus linguistics to understand what sentiment analysis is, how it is conducted, and where it can be applied. It begins with a definition of sentiment analysis and a discussion of the domains where sentiment analysis is conducted and used the most. Then, it introduces two main methods that are commonly used in sentiment analysis known as supervised machine-learning and unsupervised learning (or lexicon-based) methods, followed by a step-by-step explanation of how to perform sentiment analysis with R. The Element then provides two detailed examples or cases of sentiment and emotion analysis, with one using an unsupervised method and the other using a supervised learning method. Conducting Sentiment Analysis [texte imprimé] / Lei Lei, Auteur ; dilin liu, Auteur . - Cambridge University Press, 2021 . - 104 p ; 23 cm. - (Elements in Perception) .
ISBN : 978-1-108-82921-2
Langues : Anglais (eng)
Mots-clés : علــم النفــس Résumé : This Element provides a basic introduction to sentiment analysis, aimed at helping students and professionals in corpus linguistics to understand what sentiment analysis is, how it is conducted, and where it can be applied. It begins with a definition of sentiment analysis and a discussion of the domains where sentiment analysis is conducted and used the most. Then, it introduces two main methods that are commonly used in sentiment analysis known as supervised machine-learning and unsupervised learning (or lexicon-based) methods, followed by a step-by-step explanation of how to perform sentiment analysis with R. The Element then provides two detailed examples or cases of sentiment and emotion analysis, with one using an unsupervised method and the other using a supervised learning method. Exemplaires(0)
Disponibilité aucun exemplaire
