Institution
Trinity College, Dublin
Education•Dublin, Dublin, Ireland•
About: Trinity College, Dublin is a education organization based out in Dublin, Dublin, Ireland. It is known for research contribution in the topics: Population & Context (language use). The organization has 20576 authors who have published 48296 publications receiving 1780313 citations.
Topics: Population, Context (language use), Irish, Health care, Mental health
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors present a theory of fatigue behavior in materials which encompasses two areas of the subject (the behaviour of cracks and the behaviour of notches) and also accounts for size effects in these two types of geometrical feature.
620 citations
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TL;DR: It is shown that single-trial unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multispeaker environment and a significant correlation between the EEG-based measure of attention and performance on a high-level attention task is shown.
Abstract: How humans solve the cocktail party problem remains unknown. However, progress has been made recently thanks to the realization that cortical activity tracks the amplitude envelope of speech. This has led to the development of regression methods for studying the neurophysiology of continuous speech. One such method, known as stimulus-reconstruction, has been successfully utilized with cortical surface recordings and magnetoencephalography (MEG). However, the former is invasive and gives a relatively restricted view of processing along the auditory hierarchy, whereas the latter is expensive and rare. Thus it would be extremely useful for research in many populations if stimulus-reconstruction was effective using electroencephalography (EEG), a widely available and inexpensive technology. Here we show that single-trial (≈60 s) unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multispeaker environment. Furthermore, we show a significant correlation between our EEG-based measure of attention and performance on a high-level attention task. In addition, by attempting to decode attention at individual latencies, we identify neural processing at ∼200 ms as being critical for solving the cocktail party problem. These findings open up new avenues for studying the ongoing dynamics of cognition using EEG and for developing effective and natural brain– computer interfaces.
620 citations
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TL;DR: This work isolated a freely evolving decision variable signal in human subjects that exhibited every aspect of the dynamics observed in its single-neuron counterparts and tracked cumulative evidence even in the absence of overt action.
Abstract: This study uses EEG in humans to isolate and track an evolving, domain-general decision signal, which varies with accumulated evidence, but is independent of overt actions.
619 citations
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TL;DR: An in depth understanding of biomaterial cues to selectively polarize macrophages may prove beneficial in the design of a new generation of ‘immuno-informed’ biomaterials that can positively interact with the immune system to dictate a favorable macrophage response following implantation.
617 citations
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Ghent University1, University of California, San Diego2, Leiden University3, Dresden University of Technology4, Stanford University5, University of Maryland, College Park6, Indiana University7, University of Cambridge8, Cardiff University9, University of Western Ontario10, Monash University, Clayton campus11, University of Toronto12, University of Vermont13, University of Oregon14, University of Tasmania15, University of Oslo16, Utrecht University17, Katholieke Universiteit Leuven18, Yale University19, Vanderbilt University20, University of Amsterdam21, Anglia Ruskin University22, Indian Institute of Science23, Queen's University24, King's College London25, Michigan State University26, University of Iowa27, Trinity College, Dublin28
TL;DR: The goal is to facilitate a more accurate use of the stop-signal task and provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
Abstract: Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
617 citations
Authors
Showing all 20853 results
Name | H-index | Papers | Citations |
---|---|---|---|
Edward Giovannucci | 206 | 1671 | 179875 |
Robin M. Murray | 171 | 1539 | 116362 |
Mark E. Cooper | 158 | 1463 | 124887 |
Stephen J. O'Brien | 153 | 1062 | 93025 |
Amartya Sen | 149 | 689 | 141907 |
Kevin Murphy | 146 | 728 | 120475 |
Peter M. Visscher | 143 | 694 | 118115 |
Mihai G. Netea | 142 | 1170 | 86908 |
Kristine Yaffe | 136 | 794 | 72250 |
Cisca Wijmenga | 136 | 668 | 86572 |
David A. Jackson | 136 | 1095 | 68352 |
Patrick F. Sullivan | 133 | 594 | 92298 |
Thomas N. Williams | 132 | 1145 | 95109 |
Paul Brennan | 132 | 1221 | 72748 |
David Taylor | 131 | 2469 | 93220 |