47 results on '"Petro, Lucy S."'
Search Results
2. The representation of occluded image regions in area V1 of monkeys and humans
- Author
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Papale, Paolo, Wang, Feng, Morgan, A. Tyler, Chen, Xing, Gilhuis, Amparo, Petro, Lucy S., Muckli, Lars, Roelfsema, Pieter R., and Self, Matthew W.
- Published
- 2023
- Full Text
- View/download PDF
3. Experience-dependent predictions of feedforward and contextual information in mouse visual cortex
- Author
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Seignette, Koen, primary, de Kraker, Leander, additional, Papale, Paolo, additional, Petro, Lucy S, additional, Hobo, Barbara, additional, Montijn, Jorrit S, additional, Self, Matthew W, additional, Larkum, Matthew E, additional, Roelfsema, Pieter R, additional, Muckli, Lars, additional, and Levelt, Christiaan N, additional
- Published
- 2024
- Full Text
- View/download PDF
4. The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing
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Amunts, Katrin, Axer, Markus, Banerjee, Swati, Bitsch, Lise, Bjaalie, Jan G., Brauner, Philipp, Brovelli, Andrea, Calarco, Navona, Carrere, Marcel, Caspers, Svenja, Charvet, Christine J., Cichon, Sven, Cools, Roshan, Costantini, Irene, D'Angelo, Egidio Ugo, Bonis, Giulia De, Deco, Gustavo, DeFelipe, Javier, Destexhe, Alain, Dickscheid, Timo, Diesmann, Markus, Düzel, Emrah, Eickhoff, Simon B., Einevoll, Gaute, Eke, Damian, Engel, Andreas K., Evans, Alan C., Evers, Kathinka, Fedorchenko, Nataliia, Forkel, Stephanie J., Fousek, Jan, Friederici, Angela D., Friston, Karl, Furber, Stephen, Geris, Liesbet, Goebel, Rainer, Güntürkün, Onur, Hamid, Aini Ismafairus Abd, Herold, Christina, Hilgetag, Claus C., Hölter, Sabine M., Ioannidis, Yannis, Jirsa, Viktor, Kashyap, Sriranga, Kasper, Burkhard S., d’Exaerde, Alban de Kerchove, Kooijmans, Roxana, Koren, István, Kotaleski, Jeanette Hellgren, Kiar, Gregory, Klijn, Wouter, Klüver, Lars, Knoll, Alois C., Krsnik, Zeljka, Kämpfer, Julia, Larkum, Matthew E, Linne, Marja-Leena, Lippert, Thomas, Abdullah, Jafri Malin, Maio, Paola Di, Magielse, Neville, Maquet, Pierre, Mascaro, Anna Letizia Allegra, Marinazzo, Daniele, Mejias, Jorge, Meyer-Lindenberg, Andreas, Migliore, Michele, Michael, Judith, Morel, Yannick, Morin, Fabrice O., Muckli, Lars, Nagels, Guy, Oden, Lena, Palomero-Gallagher, Nicola, Panagiotaropoulos, Fanis, Paolucci, Pier Stanislao, Pennartz, Cyriel, Peeters, Liesbet M., Petkoski, Spase, Petkov, Nicolai, Petro, Lucy S., Petrovici, Mihai A., Pezzulo, Giovanni, Roelfsema, Pieter, Ris, Laurence, Ritter, Petra, Rockland, Kathleen, Rotter, Stefan, Rowald, Andreas, Ruland, Sabine, Ryvlin, Philippe, Salles, Arleen, Sanchez-Vives, Maria V., Schemmel, Johannes, Senn, Walter, Sousa, Alexandra A. de, Ströckens, Felix, Thirion, Bertrand, Uludağ, Kâmil, Vanni, Simo, Albada, Sacha Jennifer van, Vanduffel, Wim, Vezoli, Julien, Vincenz-Donnelly, Lisa, Walter, Florian, Zaborszky, Laszlo, Amunts, Katrin, Axer, Markus, Banerjee, Swati, Bitsch, Lise, Bjaalie, Jan G., Brauner, Philipp, Brovelli, Andrea, Calarco, Navona, Carrere, Marcel, Caspers, Svenja, Charvet, Christine J., Cichon, Sven, Cools, Roshan, Costantini, Irene, D'Angelo, Egidio Ugo, Bonis, Giulia De, Deco, Gustavo, DeFelipe, Javier, Destexhe, Alain, Dickscheid, Timo, Diesmann, Markus, Düzel, Emrah, Eickhoff, Simon B., Einevoll, Gaute, Eke, Damian, Engel, Andreas K., Evans, Alan C., Evers, Kathinka, Fedorchenko, Nataliia, Forkel, Stephanie J., Fousek, Jan, Friederici, Angela D., Friston, Karl, Furber, Stephen, Geris, Liesbet, Goebel, Rainer, Güntürkün, Onur, Hamid, Aini Ismafairus Abd, Herold, Christina, Hilgetag, Claus C., Hölter, Sabine M., Ioannidis, Yannis, Jirsa, Viktor, Kashyap, Sriranga, Kasper, Burkhard S., d’Exaerde, Alban de Kerchove, Kooijmans, Roxana, Koren, István, Kotaleski, Jeanette Hellgren, Kiar, Gregory, Klijn, Wouter, Klüver, Lars, Knoll, Alois C., Krsnik, Zeljka, Kämpfer, Julia, Larkum, Matthew E, Linne, Marja-Leena, Lippert, Thomas, Abdullah, Jafri Malin, Maio, Paola Di, Magielse, Neville, Maquet, Pierre, Mascaro, Anna Letizia Allegra, Marinazzo, Daniele, Mejias, Jorge, Meyer-Lindenberg, Andreas, Migliore, Michele, Michael, Judith, Morel, Yannick, Morin, Fabrice O., Muckli, Lars, Nagels, Guy, Oden, Lena, Palomero-Gallagher, Nicola, Panagiotaropoulos, Fanis, Paolucci, Pier Stanislao, Pennartz, Cyriel, Peeters, Liesbet M., Petkoski, Spase, Petkov, Nicolai, Petro, Lucy S., Petrovici, Mihai A., Pezzulo, Giovanni, Roelfsema, Pieter, Ris, Laurence, Ritter, Petra, Rockland, Kathleen, Rotter, Stefan, Rowald, Andreas, Ruland, Sabine, Ryvlin, Philippe, Salles, Arleen, Sanchez-Vives, Maria V., Schemmel, Johannes, Senn, Walter, Sousa, Alexandra A. de, Ströckens, Felix, Thirion, Bertrand, Uludağ, Kâmil, Vanni, Simo, Albada, Sacha Jennifer van, Vanduffel, Wim, Vezoli, Julien, Vincenz-Donnelly, Lisa, Walter, Florian, and Zaborszky, Laszlo
- Abstract
In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.
- Published
- 2024
5. Retinotopic biases in contextual feedback signals to V1 for object and scene processing
- Author
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Bennett, Matthew A., primary, Petro, Lucy S., additional, Abbatecola, Clement, additional, and Muckli, Lars, additional
- Published
- 2024
- Full Text
- View/download PDF
6. The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing
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Amunts, Katrin, primary, Axer, Markus, additional, Banerjee, Swati, additional, Bitsch, Lise, additional, Bjaalie, Jan G., additional, Brauner, Philipp, additional, Brovelli, Andrea, additional, Calarco, Navona, additional, Carrere, Marcel, additional, Caspers, Svenja, additional, Charvet, Christine J., additional, Cichon, Sven, additional, Cools, Roshan, additional, Costantini, Irene, additional, D'Angelo, Egidio Ugo, additional, De Bonis, Giulia, additional, Deco, Gustavo, additional, DeFelipe, Javier, additional, Destexhe, Alain, additional, Dickscheid, Timo, additional, Diesmann, Markus, additional, Düzel, Emrah, additional, Eickhoff, Simon B., additional, Einevoll, Gaute, additional, Eke, Damian, additional, Engel, Andreas K., additional, Evans, Alan C., additional, Evers, Kathinka, additional, Fedorchenko, Nataliia, additional, Forkel, Stephanie J., additional, Fousek, Jan, additional, Friederici, Angela D., additional, Friston, Karl, additional, Furber, Stephen, additional, Geris, Liesbet, additional, Goebel, Rainer, additional, Güntürkün, Onur, additional, Hamid, Aini Ismafairus Abd, additional, Herold, Christina, additional, Hilgetag, Claus C., additional, Hölter, Sabine M., additional, Ioannidis, Yannis, additional, Jirsa, Viktor, additional, Kashyap, Sriranga, additional, Kasper, Burkhard S., additional, d’Exaerde, Alban de Kerchove, additional, Kooijmans, Roxana, additional, Koren, István, additional, Kotaleski, Jeanette Hellgren, additional, Kiar, Gregory, additional, Klijn, Wouter, additional, Klüver, Lars, additional, Knoll, Alois C., additional, Krsnik, Zeljka, additional, Kämpfer, Julia, additional, Larkum, Matthew E, additional, Linne, Marja-Leena, additional, Lippert, Thomas, additional, Abdullah, Jafri Malin, additional, Maio, Paola Di, additional, Magielse, Neville, additional, Maquet, Pierre, additional, Mascaro, Anna Letizia Allegra, additional, Marinazzo, Daniele, additional, Mejias, Jorge, additional, Meyer-Lindenberg, Andreas, additional, Migliore, Michele, additional, Michael, Judith, additional, Morel, Yannick, additional, Morin, Fabrice O., additional, Muckli, Lars, additional, Nagels, Guy, additional, Oden, Lena, additional, Palomero-Gallagher, Nicola, additional, Panagiotaropoulos, Fanis, additional, Paolucci, Pier Stanislao, additional, Pennartz, Cyriel, additional, Peeters, Liesbet M., additional, Petkoski, Spase, additional, Petkov, Nicolai, additional, Petro, Lucy S., additional, Petrovici, Mihai A., additional, Pezzulo, Giovanni, additional, Roelfsema, Pieter, additional, Ris, Laurence, additional, Ritter, Petra, additional, Rockland, Kathleen, additional, Rotter, Stefan, additional, Rowald, Andreas, additional, Ruland, Sabine, additional, Ryvlin, Philippe, additional, Salles, Arleen, additional, Sanchez-Vives, Maria V., additional, Schemmel, Johannes, additional, Senn, Walter, additional, de Sousa, Alexandra A., additional, Ströckens, Felix, additional, Thirion, Bertrand, additional, Uludağ, Kâmil, additional, Vanni, Simo, additional, van Albada, Sacha Jennifer, additional, Vanduffel, Wim, additional, Vezoli, Julien, additional, Vincenz-Donnelly, Lisa, additional, Walter, Florian, additional, and Zaborszky, Laszlo, additional
- Published
- 2024
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7. Cortical feedback signals generalise across different spatial frequencies of feedforward inputs
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Revina, Yulia, Petro, Lucy S., and Muckli, Lars
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- 2018
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8. The Spatial Precision of Contextual Feedback Signals in Human V1
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Petro, Lucy S., primary, Smith, Fraser W., additional, Abbatecola, Clement, additional, and Muckli, Lars, additional
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- 2023
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9. Forecasting Faces in the Cortex: Comment on ‘High-Level Prediction Signals in a Low-Level Area of the Macaque Face-Processing Hierarchy’, by Schwiedrzik and Freiwald, Neuron (2017)
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Petro, Lucy S. and Muckli, Lars
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- 2018
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10. Diagnostic information use to understand brain mechanisms of facial expression categorization
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Petro, Lucy S.
- Subjects
150.724 ,BF Psychology ,Q Science (General) - Abstract
Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visual system via fixations reflect diagnostic information use. Inputs to the visual system dictate the information distributed to cognitive systems during the seamless and rapid categorization of expressive faces. How we perform eye-movements during this task informs how task-driven and stimulus-driven mechanisms interact to guide the extraction of information supporting recognition. We recorded eye movements of observers who categorized the six basic categories of facial expressions. We use a measure of task-relevant information (diagnosticity) to discuss oculomotor behaviour, with focus on two findings. Firstly, fixated regions reveal expression differences. Secondly, by examining fixation sequences, the intersection of fixations with diagnostic information increases in a sequence of fixations. This suggests a top-down drive to acquire task-relevant information, with different functional roles for first and final fixations. A combination of psychophysical studies of visual recognition together with the EEG (electroencephalogram) signal is used to infer the dynamics of feature extraction and use during the recognition of facial expressions in Chapter 3. The results reveal a process that integrates visual information over about 50 milliseconds prior to the face-sensitive N170 event-related potential, starting at the eye region, and proceeding gradually towards lower regions. The finding that informative features for recognition are not processed simultaneously but in an orderly progression over a short time period is instructive for understanding the processes involved in visual recognition, and in particular the integration of bottom-up and top-down processes. In Chapter 4 we use fMRI to investigate the task-dependent activation to diagnostic features in early visual areas, suggesting top-down mechanisms as V1 traditionally exhibits only simple response properties. Chapter 3 revealed that diagnostic features modulate the temporal dynamics of brain signals in higher visual areas. Within the hierarchical visual system however, it is not known if an early (V1/V2/V3) sensitivity to diagnostic information contributes to categorical facial judgements, conceivably driven by top-down signals triggered in visual processing. Using retinotopic mapping, we reveal task-dependent information extraction within the earliest cortical representation (V1) of two features known to be differentially necessary for face recognition tasks (eyes and mouth). This strategic encoding of face images is beyond typical V1 properties and suggests a top-down influence of task extending down to the earliest retinotopic stages of visual processing. The significance of these data is discussed in the context of the cortical face network and bidirectional processing in the visual system. The visual cognition of facial expression processing is concerned with the interactive processing of bottom-up sensory-driven information and top-down mechanisms to relate visual input to categorical judgements. The three experiments presented in this thesis are summarized in Chapter 5 in relation to how diagnostic features can be used to explore such processing in the human brain leading to proficient facial expression categorization.
- Published
- 2010
11. The laminar integration of sensory inputs with feedback signals in human cortex
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Petro, Lucy S. and Muckli, Lars
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- 2017
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12. Multivoxel Pattern of Blood Oxygen Level Dependent Activity can be sensitive to stimulus specific fine scale responses
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Vizioli, Luca, De Martino, Federico, Petro, Lucy S., Kersten, Daniel, Ugurbil, Kamil, Yacoub, Essa, and Muckli, Lars
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- 2020
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13. The brain’s predictive prowess revealed in primary visual cortex
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Petro, Lucy S. and Muckli, Lars
- Published
- 2016
14. The coming decade of digital brain research - A vision for neuroscience at the intersection of technology and computing
- Author
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Amunts, Katrin, Axer, Markus, Bitsch, Lise, Bjaalie, Jan G., Brauner Philipp, Brovelli, Andrea, Calarco Navona, Caspers, Svenja, Charvet Christine, Cichon, Sven, Cools, Roshan, Changeux, Jean-Pierre, Costantini, Irene, D'Angelo, Egidio, De Bonis, Giulia, Deco, Gustavo, DeFelipe, Javier, Destexhe, Alain, Dickscheid, Timo, Diesmann, Markus, Duqué, Julie, Düzel, Emrah, Eickhoff, Simon B., Gaute Einevoll, Eke Damian, Engel, Andreas K., Evans Alan Charles, Evers, Kathinka, Fousek, Jan, Friederici, Angela D., Friston, Karl, Furber, Stephen, Geris Liesbet, Goebel, Rainer, Güntürkün, Onur, Herold Christina, De Kerchove d'Exaerde, Alban, Hellgren Kotaleski, Jeanette, Kiar Gregory, Krsnik, Zeljka, Hilgetag, Claus C., Hölter, Sabine M., Ioannidis, Yannis, Jirsa, Viktor, Kashyap Sriranga, Koren István, Klijn, Wouter, Kämpfer, Julia, Klüver, Lars, Knoll, Alois C., Larkum, Matthew E, Linne, Marja-Leena, Lippert, Thomas, Magielse Neville, Maquet, Pierre, Mascaro, Anna Letizia Allegra, Martinez, Sara Christina, Marinazzo, Daniele, Jorge Mejias, Meyer-Lindenberg, Andreas, Migliore, Michele, Michael Judith, Morel, Yannick, Morin, Fabrice, Muckli, Lars, Nagels, Guy, Oden, Lena, Panagiotaropoulos, Fanis, Paolucci, Pier Stanislao, Pennartz, Cyriel, Peeters, Liesbet M., Petkoski, Spase, Petkov, Nicolai, Petro , Lucy S., Petrovici, Mihai A., Pezzulo Giovanni, Roelfsema, Pieter, Ris, Laurence, Ritter, Petra, Rockland, Kathleen, Rotter, Stefan, Rowald, Andreas, Ruland, Sabine, Ryvlin, Philippe, Salles, Arleen, Sanchez-Vives, Maria V., Schemmel, Johannes, Sousa Alexandra A. de, Ströckens Felix, Thirion, Betrand, Uludağ Kâmil, Vanni, Simo, Van Albada, Sacha Jennifer, Vanduffel, Wim, De Vos, Winnok, Vezoli, Julien, Vincenz-Donnelly, Lisa, Walter, Florian, and Zaborszky, Laszlo
- Subjects
Digital brain research ,Brain-inspired technologies ,Brain research ,570 Life sciences ,biology ,610 Medicine & health ,Neuroscience - Abstract
Brain research has in recent years indisputably entered a new epoch, driven bysubstantialmethodologicaladvances and digitallyenabled data integration and modeling at multiple scales –from molecules to the whole system. Major advances are emerging at theintersection of neurosciencewith technology and computing. This new science of the brain integrates high-quality basic research,systematic data integration across multiple scales, a new culture of large-scale collaboration andtranslation into applications. A systematic approach, as pioneered in Europe’s Human Brain Project(HBP), will be essential in meeting the pressing medical andtechnological challenges of the comingdecade. The aims of this paper are To develop a concept for the coming decade of digital brain research To discuss it with the research community at large, with the aim of identifying points of convergence and common goals To provide a scientific framework for current and future development of EBRAINS To inform and engage stakeholders, funding organizations and research institutions regarding future digital brain research To identify and address key ethical and societal issues While we do not claim that there is a ‘one size fits all’ approach to addressing these aspects, we areconvinced that discussions around the theme of digital brain research will help drive progress in thebroader field of neuroscience. You can submit your comments here and be considered for co-authorship Or become a supporter by clicking here More information below Comments on this manuscript are welcome This manuscriptis a living document that is being further developed in a participatory process. The work has been initiated by the Science and Infrastructure Board of the Human Brain Project (HBP). Now, the entire research community is invited to contribute to shaping the vision by submitting comments.Comments can be submitted via an online commentary form here. All submitted comments will be considered and discussed. The final decision on whether edits or additions will be made to the next version of the manuscript based on an individual comment will be made by the Science and Infrastructure Board (SIB) of the Human Brain Project (HBP) at regular intervals. The final version of the manuscript will be prepared and published in the next months. Comments may be submitted until the end of May, 2023. Every researcher is welcome to provide comment or indicate support for the contents of this paper and jointly shape the future of brain research. Supporters of the paper: Pietro Avanzini, Marc Beyer, Maria Del Vecchio, Jitka Annen, Maurizio Mattia, Steven Laureys, Rosanne Edelenbosch, Rafael Yuste, Jean-Pierre Changeux, Linda Richards, Hye Weon Jessica Kim, Chrysoula Samara, Luis Miguel González de la Garza, Nikoleta Petalidou, Vasudha Kulkarni, Cesar David Rincon, Isabella O'Shea, Munira Tamim Electricwala, Nicola Palomero-Gallagher, Bernd Carsten Stahl, Bahar Hazal Yalcinkaya, Meysam Hashemi, Carola Sales Carbonell, Marcel Carrère, Anthony Randal McIntosh, Hiba Sheheitli, Abolfazl Ziaeemehr, Martin Breyton, Giovanna Ramos Queda, Anirudh NIhalani Vattikonda, Gyorgy Buzsaki George Ogoh, William Knight Torbjørn V Ness, MIchiel van der Vlag, Mu-ming Poo.
- Published
- 2023
15. Feedback brings scene information to the representation of occluded image regions in area V1 of monkeys and humans
- Author
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Papale, Paolo, primary, Wang, Feng, additional, Morgan, A. Tyler, additional, Chen, Xing, additional, Gilhuis, Amparo, additional, Petro, Lucy S., additional, Muckli, Lars, additional, Roelfsema, Pieter R., additional, and Self, Matthew W., additional
- Published
- 2022
- Full Text
- View/download PDF
16. The coming decade of digital brain research - A vision for neuroscience at the intersection of technology and computing
- Author
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Amunts, Katrin, Axer, Markus, Bitsch, Lise, Bjaalie, Jan G., Brovelli, Andrea, Caspers, Svenja, Cichon, Sven, Cools, Roshan, Changeux, Jean-Pierre, Costantini, Irene, D'Angelo, Egidio, De Bonis, Giulia, Deco, Gustavo, DeFelipe, Javier, Destexhe, Alain, Dickscheid, Timo, Diesmann, Markus, Duqué, Julie, Düzel, Emrah, Eickhoff, Simon B., Engel, Andreas K., Evers, Kathinka, Fousek, Jan, Friederici, Angela D., Friston, Karl, Furber, Stephen, Goebel, Rainer, Güntürkün, Onur, De Kerchove d'Exaerde, Alban, Hellgren Kotaleski, Jeanette, Krsnik, Zeljka, Hilgetag, Claus C., Hölter, Sabine M., Ioannidis, Yannis, Jirsa, Viktor, Klijn, Wouter, Kämpfer, Julia, Klüver, Lars, Knoll, Alois C., Larkum, Matthew E, Linne, Marja-Leena, Lippert, Thomas, Maquet, Pierre, Mascaro, Anna Letizia Allegra, Martinez, Sara Christina, Marinazzo, Daniele, Meyer-Lindenberg, Andreas, Migliore, Michele, Morel, Yannick, Morin, Fabrice, Muckli, Lars, Nagels, Guy, Oden, Lena, Panagiotaropoulos, Fanis, Paolucci, Pier Stanislao, Pennartz, Cyriel, Peeters, Liesbet M., Petkoski, Spase, Petkov, Nicolai, Petro , Lucy S., Petrovici, Mihai A., Roelfsema, Pieter, Ris, Laurence, Ritter, Petra, Rockland, Kathleen, Rotter, Stefan, Rowald, Andreas, Ruland, Sabine, Ryvlin, Philippe, Salles, Arleen, Sanchez-Vives, Maria V., Schemmel, Johannes, Thirion, Betrand, Vanni, Simo, Van Albada, Sacha Jennifer, Vanduffel, Wim, De Vos, Winnok, Vezoli, Julien, Vincenz-Donnelly, Lisa, Walter, Florian, and Zaborszky, Laszlo
- Subjects
Digital brain research ,Brain-inspired technologies ,Brain research ,Neuroscience - Abstract
Brain research has in recent years indisputably entered a new epoch, driven bysubstantialmethodologicaladvances and digitallyenabled data integration and modeling at multiple scales –from molecules to the whole system. Major advances are emerging at theintersection of neurosciencewith technology and computing. This new science of the brain integrates high-quality basic research,systematic data integration across multiple scales, a new culture of large-scale collaboration andtranslation into applications. A systematic approach, as pioneered in Europe’s Human Brain Project(HBP), will be essential in meeting the pressing medical andtechnological challenges of the comingdecade. The aims of this paper are To develop a concept for the coming decade of digital brain research To discuss it with the research community at large, with the aim of identifying points of convergence and common goals To provide a scientific framework for current and future development of EBRAINS To inform and engage stakeholders, funding organizations and research institutions regarding future digital brain research To identify and address key ethical and societal issues While we do not claim that there is a ‘one size fits all’ approach to addressing these aspects, we areconvinced that discussions around the theme of digital brain research will help drive progress in thebroader field of neuroscience. You can submit your comments here and be considered for co-authorship Or become a supporter by clicking here More information below Comments on this manuscript are welcome This manuscriptis a living document that is being further developed in a participatory process. The work has been initiated by the Science and Infrastructure Board of the Human Brain Project (HBP). Now, the entire research community is invited to contribute to shaping the vision by submitting comments.Comments can be submitted via an online commentary form here. All submitted comments will be considered and discussed. The final decision on whether edits or additions will be made to the next version of the manuscript based on an individual comment will be made by the Science and Infrastructure Board (SIB) of the Human Brain Project (HBP) at regular intervals. New versions of the manuscript will be published every few months on Zenodo. Comments may be submitted until the beginning of 2023. During the Human Brain Project Summit 2023, the manuscript will be adopted by HBP and non-HBP participants, and a final version will be published shortly after. Supporters of the paper: Pietro Avanzini, Marc Beyer, Maria Del Vecchio, Jitka Annen, Maurizio Mattia, Steven Laureys, Rosanne Edelenbosch, Rafael Yuste, Jean-Pierre Changeux, Linda Richards.
- Published
- 2022
- Full Text
- View/download PDF
17. Numerosity Perception in Peripheral Vision
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Li, Min Susan, primary, Abbatecola, Clement, additional, Petro, Lucy S., additional, and Muckli, Lars, additional
- Published
- 2021
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18. A self-supervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes
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Svanera, Michele, primary, Morgan, Andrew T., additional, Petro, Lucy S., additional, and Muckli, Lars, additional
- Published
- 2021
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- View/download PDF
19. Increased region of surround stimulation enhances contextual feedback and feedforward processing in human V1
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Revina, Yulia, primary, Petro, Lucy S., additional, Denk-Florea, Cristina B., additional, Rao, Isa S., additional, and Muckli, Lars, additional
- Published
- 2021
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20. Neuronal codes for predictive processing in cortical layers
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Petro, Lucy S. and Muckli, Lars
- Subjects
Cognitive science ,0303 health sciences ,03 medical and health sciences ,Behavioral Neuroscience ,0302 clinical medicine ,Neuropsychology and Physiological Psychology ,Physiology ,Computer science ,Cognition ,Motif (music) ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Predictive processing as a computational motif of the neocortex needs to be elaborated into theories of higher cognitive functions that include simulating future behavioural outcomes. We contribute to the neuroscientific perspective of predictive processing as a foundation for the proposed representational architectures of the mind.
- Published
- 2020
21. Decoding face categories in diagnostic subregions of primary visual cortex
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Petro, Lucy S., Smith, Fraser W., Schyns, Philippe G., and Muckli, Lars
- Published
- 2013
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22. High-resolution line-scanning reveals distinct visual response properties across human cortical layers
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Morgan, Andrew T., primary, Nothnagel, Nils, additional, Petro, Lucy. S., additional, Goense, Jozien, additional, and Muckli, Lars, additional
- Published
- 2020
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23. A Self-Supervised Deep Neural Network for Image Completion Resembles Early Visual Cortex fMRI Activity Patterns for Occluded Scenes
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Svanera, Michele, primary, Morgan, Andrew T., additional, Petro, Lucy S., additional, and Muckli, Lars, additional
- Published
- 2020
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24. Scene Representations Conveyed by Cortical Feedback to Early Visual Cortex Can Be Described by Line Drawings
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Morgan, Andrew T., primary, Petro, Lucy S., additional, and Muckli, Lars, additional
- Published
- 2019
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25. Multivoxel Pattern of Blood Oxygen Level Dependent Activity can be sensitive to stimulus specific fine scale responses
- Author
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Vizioli, Luca, primary, De Martino, Federico, additional, Petro, Lucy S, additional, Kersten, Daniel, additional, Ugurbil, Kamil, additional, Yacoub, Essa, additional, and Muckli, Lars, additional
- Published
- 2019
- Full Text
- View/download PDF
26. The laminar integration of sensory inputs with feedback signals in human cortex
- Author
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Petro, Lucy S. and Muckli, Lars
- Subjects
0301 basic medicine ,Sensory Receptor Cells ,Process (engineering) ,Cognitive Neuroscience ,Sensory system ,Rodentia ,Experimental and Cognitive Psychology ,Article ,03 medical and health sciences ,0302 clinical medicine ,Cognition ,Arts and Humanities (miscellaneous) ,Cortex (anatomy) ,Developmental and Educational Psychology ,medicine ,Animals ,Humans ,Cortical feedback ,Explanatory gap ,Human functional brain imaging ,Cerebral Cortex ,Feedback, Physiological ,Brain Mapping ,Mechanism (biology) ,Feed forward ,Human brain ,030104 developmental biology ,medicine.anatomical_structure ,Neuropsychology and Physiological Psychology ,Psychology ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Highlights • Understanding how the cortex integrates feedback and feedforward signals is central to understanding brain function. • The data-driven framework of apical amplification which is hypothesized to have a central role in cognition is highlighted. • Human neuroimaging data provides evidence for layer-specific cortical feedback relevant for theories of predictive feedback., The cortex constitutes the largest area of the human brain. Yet we have only a basic understanding of how the cortex performs one vital function: the integration of sensory signals (carried by feedforward pathways) with internal representations (carried by feedback pathways). A multi-scale, multi-species approach is essential for understanding the site of integration, computational mechanism and functional role of this processing. To improve our knowledge we must rely on brain imaging with improved spatial and temporal resolution and paradigms which can measure internal processes in the human brain, and on the bridging of disciplines in order to characterize this processing at cellular and circuit levels. We highlight apical amplification as one potential mechanism for integrating feedforward and feedback inputs within pyramidal neurons in the rodent brain. We reflect on the challenges and progress in applying this model neuronal process to the study of human cognition. We conclude that cortical-layer specific measures in humans will be an essential contribution for better understanding the landscape of information in cortical feedback, helping to bridge the explanatory gap.
- Published
- 2016
- Full Text
- View/download PDF
27. A Perspective on Cortical Layering and Layer-Spanning Neuronal Elements
- Author
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Larkum, Matthew E., primary, Petro, Lucy S., additional, Sachdev, Robert N. S., additional, and Muckli, Lars, additional
- Published
- 2018
- Full Text
- View/download PDF
28. Unsupervised deep neural network for fMRI feedback modelling
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Svanera, Michele, primary, Morgan, Andrew T., additional, Petro, Lucy S., additional, and Muckli, Lars, additional
- Published
- 2018
- Full Text
- View/download PDF
29. Predictive feedback to V1 dynamically updates with sensory input
- Author
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Edwards, Grace, primary, Vetter, Petra, additional, McGruer, Fiona, additional, Petro, Lucy S., additional, and Muckli, Lars, additional
- Published
- 2017
- Full Text
- View/download PDF
30. Line drawings reveal the structure of internal visual models conveyed by cortical feedback
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Morgan, Andrew T, Petro, Lucy S, and Muckli, Lars
- Subjects
Structure (mathematical logic) ,0303 health sciences ,genetic structures ,Computer science ,health care facilities, manpower, and services ,education ,Line drawings ,Sensory system ,Cortical neurons ,03 medical and health sciences ,0302 clinical medicine ,Visual cortex ,medicine.anatomical_structure ,medicine ,Neuroscience ,health care economics and organizations ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Human behaviour is dependent on the ability of neuronal circuits to predict the outside world. Neuronal circuits make these predictions based on internal models. Despite our extensive knowledge of the sensory features that drive cortical neurons, we have a limited grasp on the structure of the brain's internal models. Substantial progress in neuroscience therefore depends on our ability to replicate the models that the brain creates internally. Here we record human fMRI data while presenting partially occluded visual scenes. Visual occlusion controls sensory input to subregions of visual cortex while internal models continue to influence activity in these regions. Since the observed activity is dependent on internal models, but not on sensory input, we have the opportunity to map the features of the brain's internal models. Our results show that internal models in early visual cortex are both categorical and scene-specific. We further demonstrate that behavioural line drawings provide a good description of internal model structure. These findings extend our understanding of internal models by showing that line drawings, which have been effectively used by humans to convey information about the world for thousands of years, provide a window into our brains' internal models of vision.
- Published
- 2016
31. Cortical feedback to V1 and V2 contains unique information about high-level scene structure
- Author
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Morgan, Andrew T., Petro, Lucy S., and Muckli, Lars
- Subjects
genetic structures ,nervous system ,psychological phenomena and processes - Abstract
Early visual cortical neurons receive highly selective feedforward input, which is amplified or disamplified by contextual feedback and lateral connections. A significant challenge for systems neuroscience is to measure the feature space that drives these feedback channels. We occluded visual scenes and measured non-feedforward stimulated subregions of V1 and V2 using fMRI and multi-voxel pattern analyses. We found that response patterns in these subregions contain two high-level scene features, category and depth information. Responses in non-feedforward stimulated V1 and V2 differed from each other, suggesting that feedback to these two areas has unique information content. Further, we reveal that computational models of visual processing inadequately describe early visual cortex because they do not account for the brain's internal modelling of the world.
- Published
- 2016
32. The Significance of Memory in Sensory Cortex
- Author
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Muckli, Lars and Petro, Lucy S.
- Subjects
0301 basic medicine ,genetic structures ,education ,Sensory system ,Article ,Sensory neuroscience ,03 medical and health sciences ,0302 clinical medicine ,sensory cortex ,Memory ,medicine ,Humans ,Attention ,Sensory cortex ,Spotlight ,Visual Cortex ,General Neuroscience ,Sensory memory ,Sensory maps and brain development ,prediction ,Adequate stimulus ,Cross modal plasticity ,030104 developmental biology ,medicine.anatomical_structure ,Sensory substitution ,Psychology ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Neuronal activity in early visual cortex depends on attention shifts but the contribution to working memory has remained unclear. Here, we examine neuronal activity in the different layers of the primary visual cortex (V1) in an attention-demanding and a working memory task. A current-source density analysis reveales top-down inputs in the superficial layers and layer 5, and an increase in neuronal firing rates most pronounced in the superficial and deep layers and weaker in input layer 4. This increased activity is strongest in the attention task but it is also highly reliable during working memory delays. A visual mask erases the V1 memory activity, but it reappeares at a later point in time. These results provide new insights in the laminar circuits involved in the top-down modulation of activity in early visual cortex in the presence and absence of visual stimuli., The effect of working memory on activity in primary visual cortex (V1) is not well understood. Here the authors report a clear influence of both working memory and attention on spiking activity in the superficial and deep layers of V1 with a weaker influence on input layer 4.
- Published
- 2017
33. Line drawings reveal the structure of internal visual models conveyed by cortical feedback.
- Author
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Morgan, Andrew T, primary, Petro, Lucy S, additional, and Muckli, Lars, additional
- Published
- 2016
- Full Text
- View/download PDF
34. Contextual Feedback to Superficial Layers of V1
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Muckli, Lars, De Martino, Federico, Vizioli, Luca, Petro, Lucy S, Smith, Fraser W, Ugurbil, Kamil, Goebel, R., Yacoub, Essa, Muckli, Lars, De Martino, Federico, Vizioli, Luca, Petro, Lucy S, Smith, Fraser W, Ugurbil, Kamil, Goebel, R., and Yacoub, Essa
- Abstract
Neuronal cortical circuitry comprises feedforward, lateral, and feedback projections, each of which terminates in distinct cortical layers [1-3]. In sensory systems, feedforward processing transmits signals from the external world into the cortex, whereas feedback pathways signal the brain's inference of the world [4-11]. However, the integration of feedforward, lateral, and feedback inputs within each cortical area impedes the investigation of feedback, and to date, no technique has isolated the feedback of visual scene information in distinct layers of healthy human cortex. We masked feedforward input to a region of V1 cortex and studied the remaining internal processing. Using high-resolution functional brain imaging (0.8 mm(3)) and multivoxel pattern information techniques, we demonstrate that during normal visual stimulation scene information peaks in mid-layers. Conversely, we found that contextual feedback information peaks in outer, superficial layers. Further, we found that shifting the position of the visual scene surrounding the mask parametrically modulates feedback in superficial layers of V1. Our results reveal the layered cortical organization of external versus internal visual processing streams during perception in healthy human subjects. We provide empirical support for theoretical feedback models such as predictive coding [10, 12] and coherent infomax [13] and reveal the potential of high-resolution fMRI to access internal processing in sub-millimeter human cortex.
- Published
- 2015
35. Contextual Feedback to Superficial Layers of V1
- Author
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Muckli, Lars, primary, De Martino, Federico, additional, Vizioli, Luca, additional, Petro, Lucy S., additional, Smith, Fraser W., additional, Ugurbil, Kamil, additional, Goebel, Rainer, additional, and Yacoub, Essa, additional
- Published
- 2015
- Full Text
- View/download PDF
36. Contributions of cortical feedback to sensory processing in primary visual cortex
- Author
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Petro, Lucy S., primary, Vizioli, Luca, additional, and Muckli, Lars, additional
- Published
- 2014
- Full Text
- View/download PDF
37. Backwards is the way forward: Feedback in the cortical hierarchy predicts the expected future
- Author
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Muckli, Lars, primary, Petro, Lucy S., additional, and Smith, Fraser W., additional
- Published
- 2013
- Full Text
- View/download PDF
38. Network interactions: non-geniculate input to V1
- Author
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Muckli, Lars, primary and Petro, Lucy S, additional
- Published
- 2013
- Full Text
- View/download PDF
39. Transmission of Facial Expressions of Emotion Co-Evolved with Their Efficient Decoding in the Brain: Behavioral and Brain Evidence
- Author
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Schyns, Philippe G., primary, Petro, Lucy S., additional, and Smith, Marie L., additional
- Published
- 2009
- Full Text
- View/download PDF
40. Inverse mapping the neuronal correlates of facial expression processing
- Author
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Schyns, Philippe G., primary, Petro, Lucy S., additional, and Smith, Marie L., additional
- Published
- 2008
- Full Text
- View/download PDF
41. Dynamics of Visual Information Integration in the Brain for Categorizing Facial Expressions
- Author
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Schyns, Philippe G., primary, Petro, Lucy S., additional, and Smith, Marie L., additional
- Published
- 2007
- Full Text
- View/download PDF
42. Diagnostic information use to understand brain mechanisms of facial expression categorization
- Author
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Petro, Lucy S. and Petro, Lucy S.
- Abstract
Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua
43. Diagnostic information use to understand brain mechanisms of facial expression categorization
- Author
-
Petro, Lucy S. and Petro, Lucy S.
- Abstract
Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua
44. Diagnostic information use to understand brain mechanisms of facial expression categorization
- Author
-
Petro, Lucy S. and Petro, Lucy S.
- Abstract
Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua
45. Diagnostic information use to understand brain mechanisms of facial expression categorization
- Author
-
Petro, Lucy S. and Petro, Lucy S.
- Abstract
Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua
46. Diagnostic information use to understand brain mechanisms of facial expression categorization
- Author
-
Petro, Lucy S. and Petro, Lucy S.
- Abstract
Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua
47. Diagnostic information use to understand brain mechanisms of facial expression categorization
- Author
-
Petro, Lucy S. and Petro, Lucy S.
- Abstract
Proficient categorization of facial expressions is crucial for normal social interaction. Neurophysiological, behavioural, event-related potential, lesion and functional neuroimaging techniques can be used to investigate the underlying brain mechanisms supporting this seemingly effortless process, and the associated arrangement of bilateral networks. These brain areas exhibit consistent and replicable activation patterns, and can be broadly defined to include visual (occipital and temporal), limbic (amygdala) and prefrontal (orbitofrontal) regions. Together, these areas support early perceptual processing, the formation of detailed representations and subsequent recognition of expressive faces. Despite the critical role of facial expressions in social communication and extensive work in this area, it is still not known how the brain decodes nonverbal signals in terms of expression-specific features. For these reasons, this thesis investigates the role of these so-called diagnostic facial features at three significant stages in expression recognition; the spatiotemporal inputs to the visual system, the dynamic integration of features in higher visual (occipitotemporal) areas, and early sensitivity to features in V1. In Chapter 1, the basic emotion categories are presented, along with the brain regions that are activated by these expressions. In line with this, the current cognitive theory of face processing reviews functional and anatomical dissociations within the distributed neural “face network”. Chapter 1 also introduces the way in which we measure and use diagnostic information to derive brain sensitivity to specific facial features, and how this is a useful tool by which to understand spatial and temporal organisation of expression recognition in the brain. In relation to this, hierarchical, bottom-up neural processing is discussed along with high-level, top-down facilitatory mechanisms. Chapter 2 describes an eye-movement study that reveals inputs to the visua
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