Theory of Embodied Cognition

The stance of embodied cognition holds that a continuous link exists between the sensorimotor level and abstract cognitive processes. Cognition even in abstract tasks relies on the same mechanisms and shares neural resources with sensory processing and motor planning.

Our research concretizes these views with the help of Dynamic Field Theory. This theoretical framework describes cognitive processing through the evolution of activation patterns at the level of neural populations, which has proven to be the level of description that is most directly linked to behavior.

Our research integrates theoretical considerations and modeling with both neural data and results from behavioral experiments.

Higher Cognition

Understanding the neural basis of higher cognitive processes such as relational reasoning, through both theoretical models and experimental work

Movement planning

Experimental work and theoretical analysis of sequential arm movements, using the concept of uncontrolled manifold.

Perception and Memory

Dynamic Neural Field models of visual working memory, spatial transformations, change detection, and visual scene representation.

Lins, J., & Schöner, G.. (in press). Mouse Tracking Shows Attraction to Alternative Targets While Grounding Spatial Relations. In Proceedings of the 39th Annual Conference of the Cognitive Science Society (to appear). Austin, TX: Cognitive Science Society.
Knips, G., Zibner, S. K. U., Reimann, H., & Schöner, G.. (2017). A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating. Frontiers in Neurorobotics, 11(March), 9:1–14. http://doi.org/10.3389/fnbot.2017.00009
Lomp, O., Faubel, C., & Schöner, G.. (2017). A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity. Frontiers in Neurorobotics, 11(April), 23. http://doi.org/10.3389/fnbot.2017.00023
Richter, M., Lins, J., & Schöner, G.. (2017). A neural dynamic model generates descriptions of object-oriented actions. Topics in Cognitive Science, 9(1), 35–47. http://doi.org/10.1111/tops.12240
Hock, H. S., & Schöner, G.. (2016). Nonlinear dynamics in the perceptual grouping of connected surfaces. Vision Research, 126, 80–96. http://doi.org/10.1016/j.visres.2015.06.006
Lomp, O., Richter, M., Zibner, S. K. U., & Schöner, G.. (2016). Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar. Frontiers in Neurorobotics, 10(November), 14. http://doi.org/10.3389/fnbot.2016.00014
Park, E., Reimann, H., & Schöner, G.. (2016). Coordination of muscle torques stabilizes upright standing posture: an UCM analysis. Experimental Brain Research, 234(6), 1757–1767. http://doi.org/10.1007/s00221-016-4576-x
Raket, L. L., Grimme, B., Schöner, G., Igel, C., & Markussen, B. (2016). Separating Timing, Movement Conditions and Individual Differences in the Analysis of Human Movement. PLoS Computational Biology, 12(9), 1–27. http://doi.org/10.1371/journal.pcbi.1005092
Rekauzke, S., Nortmann, N., Staadt, R., Hock, H. S., Schöner, G., & Jancke, D.. (2016). Temporal asymmetry in dark-bright processing initiates propagating activity across primary visual cortex. J Neurosci, 36(6), 1902–1913.
Tekülve, J., Zibner, S. K. U., & Schöner, G.. (2016). A neural process model of learning to sequentially organize and activate pre-reaches. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2016 Joint IEEE International Conferences on.
Hansen, E., Grimme, B., Reimann, H., & Schöner, G.. (2015). Carry-over coarticulation in joint angles. Experimental Brain Research, 233(9), 2555–2569. http://doi.org/10.1007/s00221-015-4327-4
Lobato, D., Sandamirskaya, Y., Richter, M., & Schöner, G.. (2015). Parsing of action sequences: A neural dynamics approach. Paladyn, Journal of Behavioral Robotics, 6(1), 119–135. http://doi.org/10.1515/pjbr-2015-0008
Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L. (2015). Task-specific stability of abundant systems: Structure of variance and motor equivalence. Neuroscience, 310, 600–615. http://doi.org/10.1016/j.neuroscience.2015.09.071
Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L. (2015). Motor equivalence during multi-finger accurate force production. Experimental Brain Research, 233, 487–502. http://doi.org/10.1007/s00221-014-4128-1
Reimann, H., Lins, J., & Schöner, G.. (2015). The Dynamics of Neural Activation Variables. Paladyn, Journal of Behavioral Robotics, 6(1), 57–70.
Zibner, S. K. U., Tekülve, J., & Schöner, G.. (2015). The Neural Dynamics of Goal-Directed Arm Movements: A Developmental Perspective. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 154–161).
Zibner, S. K. U., Tekülve, J., & Schöner, G.. (2015). The Sequential Organization of Movement is Critical to the Development of Reaching: A Neural Dynamics Account. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 39–46).
Lomp, O., Terzić, K., Faubel, C., du Buf, J. M. H., & Schöner, G.. (2014). Instance-based Object Recognition with Simultaneous Pose Estimation Using Keypoint Maps and Neural Dynamics. In S. Wermter, Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P. D., , S. M., et al. (Eds.), ICANN 2014 (Vol. 8681, pp. 451–458). Hamburg.
Hernandes, A. C., Guerrero, H. B., Becker, M., Jokeit, J. S., & Schöner, G.. (2014). A comparison between reactive potential fields and Attractor Dynamics. 2014 IEEE 5th Colombian Workshop on Circuits and Systems, CWCAS 2014 - Conference Proceedings, (3), 0–3. http://doi.org/10.1109/CWCAS.2014.6994609
Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.. (2014). A neural dynamics architecture for grasping that integrates perception and movement generation and enables on-line updating. In International Conference on Intelligent Robots and Systems (IROS) (pp. 646–653).
Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.. (2014). Reaching and grasping novel objects: Using neural dynamics to integrate and organize scene and object perception with movement generation. In International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB) (pp. 416–423).
Lins, J., & Schöner, G.. (2014). Neural Fields. In S. Coombes, beim Graben, P., Potthast, R., & , J. W. (Eds.) (pp. 319–339). Springer Berlin Heidelberg.
Luciw, M., Kazerounian, S., Sandamirskaya, Y., Schöner, G., & Schmidhuber, J. (2014). Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics. In Simulation of Adaptive Behavior, SAB.
Maruyama, S., Dineva, E., Spencer, J. P., & Schöner, G.. (2014). Change occurs when body meets environment: A review of the embodied nature of development. Japanese Psychological Research, 56, 385–401. http://doi.org/10.1111/jpr.12065
Norman, J., Hock, H., & Schoner, G.. (2014). Contrasting accounts of direction and shape perception in short-range motion: Counterchange compared with motion energy detection. Attention, perception & psychophysics, 76, 1350–70. http://doi.org/10.3758/s13414-014-0650-2
Oubbati, F., Richter, M., & Schöner, G.. (2014). A neural dynamics to organize timed movement : Demonstration in a robot ball bouncing task. In 4th International Conference on Development and Learning and on Epigenetic Robotics (pp. 291–298). Palazzo Ducale, Genoa, Italy.
Richter, M., Lins, J., Schneegans, S., Sandamirskaya, Y., & Schöner, G.. (2014). Autonomous Neural Dynamics to Test Hypotheses in a Model of Spatial Language. In P. Bello, Guarini, M., McShane, M., & Scassellati, B. (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 2847–2852). Austin, TX: Cognitive Science Society.
Richter, M., Lins, J., Schneegans, S., & Schöner, G.. (2014). A neural dynamic architecture resolves phrases about spatial relations in visual scenes. In 24th International Conference on Artificial Neural Networks (ICANN) (pp. 201–208). Heidelberg, Germany: Springer.
Schneegans, S., Spencer, J. P., Schöner, G., Hwang, S., & Hollingworth, A. (2014). Dynamic interactions between visual working memory and saccade target selection. Journal of vision, 14(11), 9.
Scholz, J. P., & Schöner, G.. (2014). Use of the Uncontrolled Manifold (UCM) Approach to Understand MotorVariability, Motor Equivalence, and Self-motion. In M. F. Levin (Ed.), Progress in Motor Control (Vol. 826, p. Chapter 7). Springer International Publishing. http://doi.org/10.1007/978-3-319-47313-0
Schöner, G. (2014). Embodied Cognition, Neural Field Models of. In Encyclopedia of Computational Neuroscience (pp. 1084–1092). Springer Berlin Heidelberg.
Schöner, G. (2014). Dynamical Systems Thinking: From Metaphor to Neural Theory. In P. C. M. Molenaar, Lerner, R. M., & Newell, K. M. (Eds.), Handbook of Developmental Systems Theory and Methodology (pp. 188–219). New York, New York, USA: Guilford Publications.
Schöner, G., & Nowak, E. (2014). Coordination Dynamics. In D. Jaeger & Jung, R. (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–3). New York, NY: Springer New York. http://doi.org/10.1007/978-1-4614-7320-6_63-1
Kazerounian, S., Luciw, M., Richter, M., & Sandamirskaya, Y.. (2013). Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics. In International Joint Conference on Neural Networks (IJCNN).
Lomp, O., Zibner, S. K. U., Richter, M., Ranó, I., & Schöner, G.. (2013). A software framework for cognition, embodiment, dynamics, and autonomy in robotics: cedar. In Artificial Neural Networks and Machine Learning–ICANN 2013 (pp. 475–482). Springer.
Luciw, M., Kazerounian, S., Lakhmann, K., Richter, M., & Sandamirskaya, Y.. (2013). Learning the Perceptual Conditions of Satisfaction of Elementary Behaviors. In Robotics: Science and Systems (RSS), Workshop "Active Learning in Robotics: Exploration, Curiosity, and Interaction".
Sandamirskaya, Y., Zibner, S. K. U., Schneegans, S., & Schöner, G.. (2013). Using Dynamic Field Theory to Extend the Embodiment Stance toward Higher Cognition. New Ideas in Psychology, 31(3), 322–339.
Grimme, B., Lipinski, J., & Schöner, G.. (2012). Naturalistic arm movements during obstacle avoidance in 3D and the identification of movement primitives. Experimental brain research, 222(3), 185–200. http://doi.org/10.1007/s00221-012-3205-6
Duran, B., Sandamirskaya, Y., & Schöner, G.. (2012). A Dynamic Field Architecture for the Generation of Hierarchically Organized Sequences. In A. E. P. Villa, Duch, W., Érdi, P., Masulli, F., & Palm, G. (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2012 (Vol. 7552, pp. 25–32). Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-642-33269-2_4
Klaes, C., Schneegans, S., Schöner, G., & Gail, A. (2012). Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making. PLoS computational biology, 8(11), e1002774.
Lins, J., Schneegans, S., Spencer, J., & Schöner, G.. (2012). The Function and Fallibility of Visual Feature Integration: A Dynamic Neural Field Model of Illusory Conjunctions. Frontiers in Computational Neuroscience, (128). http://doi.org/10.3389/conf.fncom.2012.55.00128
Lipinski, J., Schneegans, S., Sandamirskaya, Y., Spencer, J. P., & Schöner, G.. (2012). A Neuro-Behavioral Model of Flexible Spatial Language Behaviors. Journal of Experimental Psychology: Learning, Memory and Cognition., 38(6), 1490–1511.
Park, E., Schöner, G., & Scholz, J. P. (2012). Functional synergies underlying control of upright posture during changes in head orientation. PLoS ONE, 7(8), 1–12. http://doi.org/10.1371/journal.pone.0041583
Richter, M., Sandamirskaya, Y., & Schöner, G.. (2012). A robotic architecture for action selection and behavioral organization inspired by human cognition. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS.
Schneegans, S., & Schöner, G.. (2012). A neural mechanism for coordinate transformation predicts pre-saccadic remapping. Biological cybernetics, 106(2), 89–109.
Scholz, J. P., Park, E., Jeka, J. J., Schöner, G., & Kiemel, T. (2012). How visual information links to multijoint coordination during quiet standing. Experimental Brain Research, 222, 229–239. http://doi.org/10.1007/s00221-012-3210-9
van Hengel, U., Sandamirskaya, Y., Schneegans, S., & Schöner, G.. (2012). A neural-dynamic architecture for flexible spatial language: intrinsic frames, the term “between”, and autonomy. In 21st IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man) 2012 (pp. 150–157).
Hock, H. S., Schöner, G., Brownlow, S., & Taler, D. (2011). The temporal dynamics of global-to-local feedback in the formation of hierarchical motion patterns: psychophysics and computational simulations. Attention, perception & psychophysics, 73(4), 1171–94. http://doi.org/10.3758/s13414-011-0105-y
Grimme, B., Fuchs, S., Perrier, P., & Schöner, G.. (2011). Limb versus speech motor control: a conceptual review. Motor control, 15(1), 5–33. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21339512
Reimann, H., Iossifidis, I., & Schöner, G.. (2011). Autonomous movement generation for manipulators with multiple simultaneous constraints using the attractor dynamics approach. In 2011 IEEE International Conference on Robotics and Automation, ICRA2011.
Sandamirskaya, Y., Richter, M., & Schöner, G.. (2011). A neural-dynamic architecture for behavioral organization of an embodied agent. In IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011) (pp. 1–7).
Scholz, J. P., Dwight-Higgin, T., Lynch, J. E., Tseng, Y. -W., Martin, V., & Schöner, G.. (2011). Motor equivalence and self-motion induced by different movement speeds. Experimental Brain Research, 209(3), 319–332. http://doi.org/10.1007/s00221-011-2541-2
Zibner, S. K. U., Faubel, C., Iossifidis, I., & Schöner, G.. (2011). Dynamic Neural Fields as Building Blocks of a Cortex-Inspired Architecture for Robotic Scene Representation. IEEE Transactions on Autonomous Mental Development, 3(1), 74–91.
Zibner, S. K. U., Faubel, C., & Schöner, G.. (2011). Making a robotic scene representation accessible to feature and label queries. In Proceedings of the First Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011).
Gera, G., Freitas, S., Latash, M., Monahan, K., Schöner, G., & Scholz, J. (2010). Motor Abundance Contributes to Resolving Multiple Kinematic Task Constraints. Motor Control, 14, 83–115.
Hock, H. S., & Schöner, G.. (2010). Measuring Perceptual Hysteresis with the Modified Method of Limits: Dynamics at the Threshold. Seeing and Perceiving, 23, 173–195.
Latash, M., Levin, M. F., Scholz, J. P., & Schöner, G.. (2010). Motor control theories and their applications. Medicina (Kaunas), 29(6), 997–1003. http://doi.org/10.1016/j.biotechadv.2011.08.021.Secreted
Sandamirskaya, Y., Lipinski, J., Iossifidis, I., & Schöner, G.. (2010). Natural human-robot interaction through spatial language: a dynamic neural fields approach. In 19th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN (pp. 600–607). Viareggio, Italy. http://doi.org/10.1109/ROMAN.2010.5598671
Sandamirskaya, Y., & Schöner, G.. (2010). An embodied account of serial order: how instabilities drive sequence generation. Neural Networks, 23(10), 1164–1179. http://doi.org/DOI: 10.1016/j.neunet.2010.07.012
Sandamirskaya, Y., & Schöner, G.. (2010). Serial order in an acting system: a multidimensional dynamic neural fields implementation. In Development and Learning, 2010. ICDL 2010. 9th IEEE International Conference on.
Zibner, S. K. U., Faubel, C., Iossifidis, I., & Schöner, G.. (2010). Scene Representation for Anthropomorphic Robots: A Dynamic Neural Field Approach. In ISR / ROBOTIK 2010. Munich, Germany.
Zibner, S. K. U., Faubel, C., Iossifidis, I., Schöner, G., & Spencer, J. P. (2010). Scenes and tracking with dynamic neural fields: How to update a robotic scene representation. In Development and Learning (ICDL), 2010 IEEE 9th International Conference on (pp. 244–250). IEEE.
Johnson, J. S., Spencer, J. P., & Schöner, G.. (2009). A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory. Brain research, 1299, 17–32. http://doi.org/10.1016/j.brainres.2009.07.008
Johnson, J. S., Spencer, J. P., & Schöner, G.. (2009). A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory. Brain research, 1299, 17–32. http://doi.org/10.1016/j.brainres.2009.07.008
Hock, H. S., Schöner, G., & Gilroy, L. (2009). A counterchange mechanism for the perception of motion. Acta psychologica, 132(1), 1–21. http://doi.org/10.1016/j.actpsy.2009.06.006
Lipinski, J., Sandamirskaya, Y., & Schöner, G.. (2009). Swing it to the Left, Swing it to the Right: Enacting Flexible Spatial Language Using a Neurodynamic Framework. Cognitive Neurodynamics, 3(4).
Lipinski, J., Sandamirskaya, Y., & Schöner, G.. (2009). An Integrative Framework for Spatial Language and Color: Robotic Demonstrations Using the Dynamic Field Theory. In 31th Annual Meeting of the Cognitive Science Society, CogSci 2009. Amstredam, NL.
Lipinski, J., Sandamirskaya, Y., & Schöner, G.. (2009). Behaviorally Flexible Spatial Communication: Robotic Demonstrations of a Neurodynamic Framework. In B. Mertsching, Hund, M., & Z., A. (Eds.), KI 2009, Lecture Notes in Artificial Intelligence (Vol. 5803, pp. 257–264). Berlin: Springer-Verlag.
Martin, V., Scholz, J. P., & Schöner, G.. (2009). Redundancy, self-motion and motor control. Neural Computation, 21(5), 1371–1414.
Tuma, M., Iossifidis, I., & Schöner, G.. (2009). Temporal stabilization of discrete movement in variable environments: an attractor dynamics approach. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 863–868).
Schneegans, S., & Schöner, G.. (2008). Dynamic Field Theory as a framework for understanding embodied cognition. In P. Calvo & Gomila, T. (Eds.), Handbook of cognitive science: An embodied approach (pp. 241–271). Amsterdam, Netherlands: Elsevier.
Jancke, D., Erlhagen, W., Schöner, G., & Dinse, H. R.. (2004). Shorter latencies for motion trajectories than for flashes in population responses of cat primary visual cortex. The Journal of Physiology, 556(3), 971–982.
Erlhagen, W., Bastian, A., Jancke, D., Riehle, A., & Schöner, G.. (1999). The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations. Journal of Neuroscience Methods, 94(1), 53–66.
Jancke, D., Erlhagen, W., Dinse, H. R., Akhavan, A. C., Giese, M., Steinhage, A., & Schöner, G.. (1999). Parametric population representation of retinal location: Neuronal interaction dynamics in cat primary visual cortex. J Neurosci, 19(20), 9016–9028.
Schöner, G., Dose, M., & Engels, C. (1995). Dynamics of behavior: Theory and applications for autonomous robot architectures. Robotics and Autonomous Systems, 16, 213–245.
Schöner, G., Haken, H., & Kelso, J. A. S. (1986). A stochastic theory of phase transitions in human hand movement. Biological Cybernetics, 53, 247–257.