Emotion

Computational modeling

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

The neural basis of human emotions is difficult to study, because emotions are primarily subjective and nondeterministic. To find basic principles of emotions and their underlying mechanisms, neuroscientists typically study specific emotions, using specific tasks. They use a combination of animal and human preparations, yielding various types of data, from single neuron firing patterns, to activation levels of a whole brain area. The approach, while rigorous, is slow and yields an increasingly complex body of often conflicting data. An integrative approach is needed. As described in this article, computational models of emotion have emerged as a promising tool for integration. Because these models require that all assumptions be made explicit, they offer a new language in which to express and test hypotheses and to explain and predict neural mechanisms.

Original languageEnglish (US)
Title of host publicationEncyclopedia of Neuroscience
PublisherElsevier Ltd
Pages909-913
Number of pages5
ISBN (Print)9780080450469
DOIs
StatePublished - 2010

Fingerprint

Emotions
Language
Neurons
Brain

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Fellous, J-M. (2010). Emotion: Computational modeling. In Encyclopedia of Neuroscience (pp. 909-913). Elsevier Ltd. https://doi.org/10.1016/B978-008045046-9.01845-3

Emotion : Computational modeling. / Fellous, Jean-Marc.

Encyclopedia of Neuroscience. Elsevier Ltd, 2010. p. 909-913.

Research output: Chapter in Book/Report/Conference proceedingChapter

Fellous, J-M 2010, Emotion: Computational modeling. in Encyclopedia of Neuroscience. Elsevier Ltd, pp. 909-913. https://doi.org/10.1016/B978-008045046-9.01845-3
Fellous J-M. Emotion: Computational modeling. In Encyclopedia of Neuroscience. Elsevier Ltd. 2010. p. 909-913 https://doi.org/10.1016/B978-008045046-9.01845-3
Fellous, Jean-Marc. / Emotion : Computational modeling. Encyclopedia of Neuroscience. Elsevier Ltd, 2010. pp. 909-913
@inbook{d8822f7827d64b4aa620499befd845cd,
title = "Emotion: Computational modeling",
abstract = "The neural basis of human emotions is difficult to study, because emotions are primarily subjective and nondeterministic. To find basic principles of emotions and their underlying mechanisms, neuroscientists typically study specific emotions, using specific tasks. They use a combination of animal and human preparations, yielding various types of data, from single neuron firing patterns, to activation levels of a whole brain area. The approach, while rigorous, is slow and yields an increasingly complex body of often conflicting data. An integrative approach is needed. As described in this article, computational models of emotion have emerged as a promising tool for integration. Because these models require that all assumptions be made explicit, they offer a new language in which to express and test hypotheses and to explain and predict neural mechanisms.",
author = "Jean-Marc Fellous",
year = "2010",
doi = "10.1016/B978-008045046-9.01845-3",
language = "English (US)",
isbn = "9780080450469",
pages = "909--913",
booktitle = "Encyclopedia of Neuroscience",
publisher = "Elsevier Ltd",

}

TY - CHAP

T1 - Emotion

T2 - Computational modeling

AU - Fellous, Jean-Marc

PY - 2010

Y1 - 2010

N2 - The neural basis of human emotions is difficult to study, because emotions are primarily subjective and nondeterministic. To find basic principles of emotions and their underlying mechanisms, neuroscientists typically study specific emotions, using specific tasks. They use a combination of animal and human preparations, yielding various types of data, from single neuron firing patterns, to activation levels of a whole brain area. The approach, while rigorous, is slow and yields an increasingly complex body of often conflicting data. An integrative approach is needed. As described in this article, computational models of emotion have emerged as a promising tool for integration. Because these models require that all assumptions be made explicit, they offer a new language in which to express and test hypotheses and to explain and predict neural mechanisms.

AB - The neural basis of human emotions is difficult to study, because emotions are primarily subjective and nondeterministic. To find basic principles of emotions and their underlying mechanisms, neuroscientists typically study specific emotions, using specific tasks. They use a combination of animal and human preparations, yielding various types of data, from single neuron firing patterns, to activation levels of a whole brain area. The approach, while rigorous, is slow and yields an increasingly complex body of often conflicting data. An integrative approach is needed. As described in this article, computational models of emotion have emerged as a promising tool for integration. Because these models require that all assumptions be made explicit, they offer a new language in which to express and test hypotheses and to explain and predict neural mechanisms.

UR - http://www.scopus.com/inward/record.url?scp=84856607657&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856607657&partnerID=8YFLogxK

U2 - 10.1016/B978-008045046-9.01845-3

DO - 10.1016/B978-008045046-9.01845-3

M3 - Chapter

SN - 9780080450469

SP - 909

EP - 913

BT - Encyclopedia of Neuroscience

PB - Elsevier Ltd

ER -