PortalPublikationenA Two-Way Street between Attention and Learning

A Two-Way Street between Attention and Learning

Standard

A Two-Way Street between Attention and Learning. / Rusch, Tessa; Korn, Christoph W; Gläscher, Jan.

in: NEURON, Jahrgang 93, Nr. 2, 18.01.2017, S. 256-258.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › Andere (Vorworte u.ä.) › Forschung

Harvard

Rusch, T, Korn, CW & Gläscher, J 2017, 'A Two-Way Street between Attention and Learning', NEURON, Jg. 93, Nr. 2, S. 256-258. https://doi.org/10.1016/j.neuron.2017.01.005

APA

Rusch, T., Korn, C. W., & Gläscher, J. (2017). A Two-Way Street between Attention and Learning. NEURON, 93(2), 256-258. https://doi.org/10.1016/j.neuron.2017.01.005

Vancouver

Rusch T, Korn CW, Gläscher J. A Two-Way Street between Attention and Learning. NEURON. 2017 Jan 18;93(2):256-258. https://doi.org/10.1016/j.neuron.2017.01.005

Bibtex

@article{5fa997fe152b43808826b2b51d686261,
title = "A Two-Way Street between Attention and Learning",
abstract = "In an elegant model-based fMRI study, Leong et al. (2017) demonstrate how attention and learning interact to facilitate value-based decision-making. They combine computational modeling with empirical measures of attentional selection derived from eye-tracking data and multivariate pattern analyses.",
keywords = "Attention, Decision Making, Humans, Learning, Magnetic Resonance Imaging, Journal Article",
author = "Tessa Rusch and Korn, {Christoph W} and Jan Gl{\"a}scher",
note = "Editorial",
year = "2017",
month = jan,
day = "18",
doi = "10.1016/j.neuron.2017.01.005",
language = "English",
volume = "93",
pages = "256--258",
journal = "NEURON",
issn = "0896-6273",
publisher = "Cell Press",
number = "2",

}

RIS

TY - JOUR

T1 - A Two-Way Street between Attention and Learning

AU - Rusch, Tessa

AU - Korn, Christoph W

AU - Gläscher, Jan

N1 - Editorial

PY - 2017/1/18

Y1 - 2017/1/18

N2 - In an elegant model-based fMRI study, Leong et al. (2017) demonstrate how attention and learning interact to facilitate value-based decision-making. They combine computational modeling with empirical measures of attentional selection derived from eye-tracking data and multivariate pattern analyses.

AB - In an elegant model-based fMRI study, Leong et al. (2017) demonstrate how attention and learning interact to facilitate value-based decision-making. They combine computational modeling with empirical measures of attentional selection derived from eye-tracking data and multivariate pattern analyses.

KW - Attention

KW - Decision Making

KW - Humans

KW - Learning

KW - Magnetic Resonance Imaging

KW - Journal Article

U2 - 10.1016/j.neuron.2017.01.005

DO - 10.1016/j.neuron.2017.01.005

M3 - Other (editorial matter etc.)

C2 - 28103475

VL - 93

SP - 256

EP - 258

JO - NEURON

JF - NEURON

SN - 0896-6273

IS - 2

ER -