| Neural architecture for feature binding in visual working memory S Schneegans, PM Bays Journal of Neuroscience 37 (14), 3913-3925, 2017 | 227 | 2017 |
| Using RFID Snapshots for Mobile Robot Self-Localization. S Schneegans, P Vorst, A Zell EMCR, 2007 | 101 | 2007 |
| No fixed item limit in visuospatial working memory S Schneegans, PM Bays cortex 83, 181-193, 2016 | 95 | 2016 |
| Using dynamic field theory to extend the embodiment stance toward higher cognition Y Sandamirskaya, SKU Zibner, S Schneegans, G Schöner New Ideas in Psychology 31 (3), 322-339, 2013 | 89 | 2013 |
| New perspectives on binding in visual working memory S Schneegans, PM Bays British Journal of Psychology 110 (2), 207-244, 2019 | 81 | 2019 |
| Self-localization with RFID snapshots in densely tagged environments P Vorst, S Schneegans, B Yang, A Zell 2008 IEEE/RSJ international conference on intelligent robots and systems …, 2008 | 71 | 2008 |
| A neurobehavioral model of flexible spatial language behaviors. J Lipinski, S Schneegans, Y Sandamirskaya, JP Spencer, G Schöner Journal of Experimental Psychology: Learning, Memory, and Cognition 38 (6), 1490, 2012 | 67 | 2012 |
| The infant orienting with attention task: Assessing the neural basis of spatial attention in infancy S Ross‐Sheehy, S Schneegans, JP Spencer Infancy 20 (5), 467-506, 2015 | 63 | 2015 |
| Drift in neural population activity causes working memory to deteriorate over time S Schneegans, PM Bays Journal of Neuroscience 38 (21), 4859-4869, 2018 | 62 | 2018 |
| Stochastic sampling provides a unifying account of visual working memory limits S Schneegans, R Taylor, PM Bays Proceedings of the National Academy of Sciences 117 (34), 20959-20968, 2020 | 59 | 2020 |
| Dynamic field theory as a framework for understanding embodied cognition S Schneegans, G Schöner Handbook of Cognitive Science, 241-271, 2008 | 57 | 2008 |
| A neural mechanism for coordinate transformation predicts pre-saccadic remapping S Schneegans, G Schöner Biological cybernetics 106, 89-109, 2012 | 51 | 2012 |
| Dynamic interactions between visual working memory and saccade target selection S Schneegans, JP Spencer, G Schöner, S Hwang, A Hollingworth Journal of vision 14 (11), 9-9, 2014 | 49 | 2014 |
| Restoration of fMRI decodability does not imply latent working memory states S Schneegans, PM Bays Journal of Cognitive Neuroscience 29 (12), 1977-1994, 2017 | 47 | 2017 |
| Integrating "what" and "where": Visual working memory for objects in a scene S Schneegans, JP Spencer, G Schöner Dynamic Thinking: A Primer on Dynamic Field Theory, 197-226, 2015 | 46* | 2015 |
| Sensorimotor learning biases choice behavior: a learning neural field model for decision making C Klaes, S Schneegans, G Schöner, A Gail PLoS computational biology 8 (11), e1002774, 2012 | 32 | 2012 |
| Autonomous neural dynamics to test hypotheses in a model of spatial language M Richter, J Lins, S Schneegans, Y Sandamirskaya, G Schoner Proceedings of the Annual Meeting of the Cognitive Science Society 36 (36), 2014 | 27 | 2014 |
| Scene memory and spatial inhibition in visual search: A neural dynamic process model and new experimental evidence R Grieben, J Tekülve, SKU Zibner, J Lins, S Schneegans, G Schöner Attention, Perception, & Psychophysics 82, 775-798, 2020 | 21 | 2020 |
| Representation and computation in working memory P Bays, S Schneegans, WJ Ma, T Brady PsyArXiv, 2022 | 20 | 2022 |
| Location-independent feature binding in visual working memory for sequentially presented objects S Schneegans, WJ Harrison, PM Bays Attention, Perception, & Psychophysics 83 (6), 2377-2393, 2021 | 18 | 2021 |
Gregor SchonerInstitut für Neuroinformatik, Ruhr-Universität Bochum, Germanyכתובת אימייל מאומתת בדומיין rub.de
