M
Marieke Mur
Researcher at University of Western Ontario
Publications - 37
Citations - 5963
Marieke Mur is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Temporal cortex & Fusiform face area. The author has an hindex of 15, co-authored 37 publications receiving 4933 citations. Previous affiliations of Marieke Mur include University of Cambridge & Maastricht University.
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Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience
TL;DR: A new experimental and data-analytical framework called representational similarity analysis (RSA) is proposed, in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs.
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Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey
Nikolaus Kriegeskorte,Marieke Mur,Marieke Mur,Douglas A. Ruff,Roozbeh Kiani,Jerzy Bodurka,Hossein Esteky,Keiji Tanaka,Peter A. Bandettini +8 more
TL;DR: It is suggested that primate IT across species may host a common code, which combines a categorical and a continuous representation of objects.
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Local discriminability determines the strength of holistic processing for faces in the fusiform face area.
Valerie Goffaux,Valerie Goffaux,Valerie Goffaux,Christine Schiltz,Marieke Mur,Marieke Mur,Rainer Goebel +6 more
TL;DR: The present findings confirm the co-existence of holistic and featural representations in the FFA and establish FFA as the main contributor to the featural/holistic representational mode switches determined by local discriminability.
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Revealing representational content with pattern-information fMRI—an introductory guide
TL;DR: This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.
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Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements.
TL;DR: This work proposes a method for the inverse process: inferring the pairwise dissimilarities from multiple 2D arrangements of items, based on multiple arrangements of item subsets, designed by an adaptive algorithm that aims to provide optimal evidence for the dissimilarity estimates.