Higher Cognition Theory of Embodied Cognition

Publications

    2022

  • A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
    Sabinasz, D., & Schöner, G.
    Topics in Cognitive Science
  • Bridging DFT and DNNs: A neural dynamic process model of scene representation, guided visual search and scene grammar in natural scenes
    Grieben, R., & Schöner, G.
    In J. Culbertson, Perfors, A., Rabagliati, H., & Ramenzoni, V. (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society
  • A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
    Sabinasz, D., & Schöner, G.
    In J. Culbertson, Perfors, A., Rabagliati, H., & Ramenzoni, V. (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society
  • 2021

  • A neural dynamic process model of combined bottom-up and top-down guidance in triple conjunction visual search
    Grieben, R., & Schöner, G.
    In T. Fitch, Lamm, C., Leder, H., & Teßmar-Raible, K. (Eds.), Proceedings of the 43rd Annual Conference of the Cognitive Science Society
  • 2020

  • Scene memory and spatial inhibition in visual search: A neural dynamic process model and new experimental evidence
    Grieben, R., Tekülve, J., Zibner, S. K. U., Lins, J., Schneegans, S., & Schöner, G.
    Attention, Perception, & Psychophysics
  • 2019

  • Computer mouse tracking reveals motor signatures in a cognitive task of spatial language grounding
    Lins, J., & Schöner, G.
    Attention, Perception, & Psychophysics
  • The Dynamics of Neural Populations Capture the Laws of the Mind
    Schöner, G.
    Topics in Cognitive Science, 1–15
  • 2018

  • Sequences of discrete attentional shifts emerge from a neural dynamic architecture for conjunctive visual search that operates in continuous time
    Grieben, R., Tekülve, J., Zibner, S. K. U., Schneegans, S., & Schöner, G.
    In T. T. Rogers, Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society
  • A Neural Dynamic Architecture That Autonomously Builds Mental Models
    Kounatidou, P., Richter, M., & Schöner, G.
    In T. T. Rogers, Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 643–648)

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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