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6 - Dynamical Systems Approaches to Cognition

from Part II - Cognitive Modeling Paradigms

Published online by Cambridge University Press:  21 April 2023

By
Gregor Schöner
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

Dynamical systems thinking originated from the sensory-motor domain, but is hypothesized to reach all forms of cognition.Dynamic field theory (DFT) is a mathematically specific, neurally grounded formalization of dynamical systems thinking. Stable states of neural activation, realized as localized activation patterns in low-dimensional neural fields are the units of representation. Their dynamic instabilities lead to the emergence of events at discrete moments in time from continuous-time dynamics. These enable sequences of neural processing steps and flexible binding of multiple localist representations within neural dynamic architectures. Stability enables linking DFT accounts to sensory-motor systems and closed-loop behavior. Instabilities and coordinate transforms are key to reaching the flexibility and productivity of higher cognition. This chapter discusses the relationship between DFT and other approaches to cognition.

Keywords

neural dynamicssensory-motor processesselection decisionsworking memorysequence generationperceptual groundingcoordinate transforms
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 210 - 241
Publisher: Cambridge University Press
Print publication year: 2023

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References

Amari, S. (1977). Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics, 27, 77–87.Google Scholar
Andersen, R. A., Essick, G. K., & Siegel, R. M. (1985). Encoding of spatial location by posterior parietal neurons. Science, 230(4724), 456–458.CrossRefGoogle ScholarPubMed
Ashby, R. W. (1956). An Introduction to Cybernetics. London: Chapman & Hall Ltd.Google Scholar
Bastian, A., Riehle, A., Erlhagen, W., & Schöner, G. (1998). Prior information preshapes the population representation of movement direction in motor cortex. Neuroreports, 9, 315–319.Google Scholar
Bastian, A., Schöner, G., & Riehle, A. (2003). Preshaping and continuous evolution of motor cortical representations during movement preparation. European Journal of Neuroscience, 18, 2047–2058.Google Scholar
Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3), 91–99.CrossRefGoogle ScholarPubMed
Bicho, E., Louro, L., & Erlhagen, W. (2010). Integrating verbal and nonverbal communication in a dynamic neural field architecture for human-robot interaction. Frontiers in Neurorobotics, 4(5), 1–13.Google Scholar
Bicho, E., Mallet, P., & Schöner, G. (2000). Target representation on an autonomous vehicle with low-level sensors. The International Journal of Robotics Research, 19, 424–447.CrossRefGoogle Scholar
Botvinick, M. M., & Plaut, D. C. (2006). Short-term memory for serial order: a recurrent neural network model. Psychological Review, 113(2), 201–233.CrossRefGoogle Scholar
Bowers, J. S. (2017). Grandmother cells and localist representations: a review of current thinking. Language, Cognition and Neuroscience, 32(3), 257–273.CrossRefGoogle Scholar
Braitenberg, V. (1984). Vehicles: Experiments in Synthetic Psychology. Cambridge, MA: MIT Press.Google Scholar
Buonomano, D. V., & Laje, R. (2010 ). Population clocks: motor timing with neural dynamics. Trends in Cognitive Sciences, 14(12), 520–527.Google Scholar
Buss, A. T., & Spencer, J. P. (2014). The emergent executive: a dynamic field theory of the development of executive function. Monographs of the Society for Research in Child Development, 79(2), 1–103.Google Scholar
Chrysikou, E. G., Casasanto, D., & Thompson-Schill, S. L. (2017). Motor experience influences object knowledge. Journal of Experimental Psychology: General, 146(3), 395–408.CrossRefGoogle ScholarPubMed
Clearfield, M. W., Dineva, E., Smith, L. B., Diedrich, F. J., & Thelen, E. (2009). Cue salience and infant perseverative reaching: tests of the dynamic field theory. Developmental Science, 12(1), 26–40.CrossRefGoogle ScholarPubMed
Compte, A., Brunel, N., Goldman-Rakic, P. S., & Wang, X.-J. (2000). Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cerebral Cortex, 10, 910–923.Google Scholar
Coombes, S., beim Graben, P., Potthast, R., & Wright, J. (Eds.). (2014). Neural Fields: Theory and Applications. New York, NY: Springer Verlag.CrossRefGoogle Scholar
Desimone, R. (1998). Visual attention mediated by biased competition in extrastriate visual cortex. Philosophical Transactions of the Royal Society B: Biological Sciences, 353(1373), 1245–1255.Google Scholar
Dineva, E., & Schöner, G. (2018). How infants’ reaches reveal principles of sensorimotor decision making. Connection Science, 30(1), 53–80.CrossRefGoogle Scholar
Douglas, R. J., Martin, K. A. C., & Whitteridge, D. (1989). Microcircuit for neocortex. Neural Computation, 1, 480–488.CrossRefGoogle Scholar
Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience Supplement, 3, 1184–1191.Google Scholar
Eliasmith, C. (2005). A unified approach to building and controlling spiking attractor networks. Neural Computation, 17, 1276–1314.Google Scholar
Eliasmith, C., Stewart, T. C., Choo, X., et al. (2012). A large-scale model of the functioning brain. Science, 338(6111), 1202–1205.CrossRefGoogle ScholarPubMed
Eliasmith, C., & Trujillo, O. (2014). The use and abuse of large-scale brain models. Current Opinion in Neurobiology, 25, 1–6.Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179–211.Google Scholar
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.CrossRefGoogle ScholarPubMed
Erlhagen, W., & Schöner, G. (2002). Dynamic field theory of movement preparation. Psychological Review, 109(3), 545–572.Google Scholar
Ermentrout, B. (1998). Neural networks as spatio-temporal pattern-forming systems. Reports on Progress in Physics, 61, 353–430.Google Scholar
Fauconnier, G., & Turner, M. (2002). The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. New York, NY: Basic Books.Google Scholar
Fuster, J. M. (1995). Memory in the Cerebral Cortex: An Empirical Approach to Neural Networks in the Human and Nonhuman Primate. Cambridge, MA: MIT Press.Google Scholar
Gardenfors, P. (2000). Conceptual Spaces: The Geometry of Thought. Boston, MA: MIT Press.Google Scholar
Gayler, R. (2003). Vector symbolic architectures answer Jackendoff’s challenges for cognitive neuroscience. In Slezak, P. (Ed.), ICCS/ASCS International Conference on Cognitive Science (pp. 133–138). Sydney, Australia: University of New South Wales.Google Scholar
Georgopoulos, A. P., Taira, M., & Lukashin, A. (1993). Cognitive neurophysiology of the motor cortex. Science, 260(5104), 47–52.CrossRefGoogle ScholarPubMed
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge: Cambridge University Press.Google Scholar
Gibson, J. J. (1966). The Senses Considered as Perceptual Systems. Boston, MA: Houghton Mifflin Co.Google Scholar
Grabska-Barwińska, A., Distler, C., Hoffmann, K. P., & Jancke, D. (2009). Contrast independence of cardinal preference: stable oblique effect in orientation maps of ferret visual cortex. European Journal of Neuroscience, 29(6), 1258–1270.Google Scholar
Grieben, R., Tekülve, J., Zibner, S. K. U., Lins, J., Schneegans, S., & Schöner, G. (2020). Scene memory and spatial inhibition in visual search. Attention, Perception, and Psychophysics, 82, 775–798.Google Scholar
Grossberg, S. (1970). Some networks that can learn, remember, and reproduce any number of complicated space-time patterns, II. Studies in Applied Mathematics, XLIX,(2), 135–166.Google Scholar
Grossberg, S. (2021). Conscious Mind, Resonant Brain: How Each Brain Makes a Mind. Oxford: Oxford University Press.Google Scholar
Henson, R. N. A., & Burgess, N. (1997). Representations of serial order. In Bullinaria, J. A., Glasspool, D. W., & Houghton, G. (Eds.), Connectionist Representations (pp. 283–300). New York, NY: Springer Verlag.Google Scholar
Hopfield, J. J., & Tank, D. W. (1986). Computing with neural circuits: a model. Science, 233, 625–633.Google Scholar
Jancke, D., Erlhagen, W., Dinse, H. R., et al. (1999). Parametric population representation of retinal location: neuronal interaction dynamics in cat primary visual cortex. Journal of Neuroscience, 19, 9016–9028.Google Scholar
Johnson, J., Spencer, J., Luck, S., & Schöner, G. (2009). A dynamic neural field model of visual working memory and change detection. Psychological Science, 20(5) 568–577.Google Scholar
Johnson, J. S., Simmering, V. R., & Buss, A. T. (2014). Beyond slots and resources: grounding cognitive concepts in neural dynamics. Attention, Perception, and Psychophysics, 76(6), 1630–1654.Google Scholar
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.Google Scholar
Knierim, J. J., & Zhang, K. (2012). Attractor dynamics of spatially correlated neural activity in the limbic system. Annual Review of Neuroscience, 35(1), 267–285.Google Scholar
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(9), 1–14.CrossRefGoogle ScholarPubMed
Kounatidou, P., Richter, M., & Schöner, G. (2018). A neural dynamic architecture that autonomously builds mental models. In Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 1–6).Google Scholar
Kreiser, R., Aathmani, D., Quio, N., Indiveri, G., & Sandamirskaya, Y. (2018). Organizing sequential memory in a neuromorphic device using dynamic neural fields. Frontiers in Neuroscience, 12(717), 1–17.Google Scholar
Lakoff, G. J., & Johnson, M. (1999). Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought. New York, NY: Basic Books.Google Scholar
Latash, M. L. (2008). Synergy. New York, NY: Oxford University Press.CrossRefGoogle Scholar
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.Google Scholar
Marino, R. A., Trappenberg, T. P., Dorris, M., & Munoz, D. P. (2012). Spatial interactions in the superior colliculus predict saccade behavior in a neural field model. Journal of Cognitive Neuroscience, 24(2), 315–336.CrossRefGoogle Scholar
Markounikau, V., Igel, C., Grinvald, A., & Jancke, D. (2010). A dynamic neural field model of mesoscopic cortical activity captured with voltage-sensitive dye imaging. PLoS Computational Biology, 6(9), e1000919.CrossRefGoogle ScholarPubMed
Milde, M. B., Blum, H., Dietmüller, A., et al. (2017). Obstacle avoidance and target acquisition for robot navigation using a mixed signal analog/digital neuromorphic processing system. Frontiers in Neurorobotics, 11(28), 1–17.Google Scholar
Moran, D. W., & Schwartz, A. B. (1999). Motor cortical representation of speed and direction during reaching movement. Journal of Neurophysiology, 82, 2676–2692.Google Scholar
Oksendal, B. (2013). Stochastic Differential Equations: An Introduction with Applications (6th ed.). Berlin and Heidelberg: Springer.Google Scholar
O’Reilly, R. C. (2006). Biologically based computational models of high-level cognition. Science, 314, 91–94.Google Scholar
Perko, L. (2001). Differential Equations and Dynamical Systems (3rd ed.). Berlin: Springer Verlag.CrossRefGoogle Scholar
Perone, S., & Spencer, J. P. (2013). Autonomy in action: linking the act of looking to memory formation in infancy via dynamic neural fields. Cognitive Science, 37(1), 1–60.CrossRefGoogle ScholarPubMed
Perone, S., & Spencer, J. P. (2014). The co-development of looking dynamics and discrimination performance. Developmental Psychology, 50(3), 837–852.Google Scholar
Port, R., & van Gelder, R. (Eds.). (1995). Mind as Motion: Explorations in the Dynamics of Cognition. Cambridge, MA: MIT Press.Google Scholar
Pouget, A., & Snyder, L. H. (2000). Computational approaches to sensorimotor transformations. Nature Neuroscience Supplement, 3, 1192–1198.Google Scholar
Ramsey, W. M. (2007). Representation Reconsidered. Cambridge: Cambridge University Press.Google Scholar
Richter, M., Lins, J., & Schöner, G. (2017). A neural dynamic model generates descriptions of object-oriented actions. Topics in Cognitive Science, 9, 35–47.Google Scholar
Richter, M., Lins, J., & Schöner, G. (2021). A neural dynamic model for the perceptual grounding of spatial and movement relations. Cognitive Science, 45, e13405.Google Scholar
Rolls, E. T., Stringer, S. M., & Trappenberg, T. P. (2002). A unified model of spatial and episodic memory. Proceedings of the Royal Society B: Biological Sciences, 269(1496), 1087–1093. https://doi.org/10.1098/rspb.2002.2009Google Scholar
Sabinasz, D., Richter, M., Lins, J., Richter, M., & Schöner, G. (2020). Grounding spatial language in perception by combining concepts in a neural dynamic architecture. In Proceedings of the 42nd Annual Conference of the Cognitive Science Society.Google Scholar
Samuelson, L. K., Smith, L. B., Perry, L. K., & Spencer, J. P. (2011 ). Grounding word learning in space. PloS One, 6(12), e28095.Google Scholar
Sandamirskaya, Y. (2014). Dynamic neural fields as a step toward cognitive neuromorphic architectures. Frontiers in Neuroscience, 7(276), 1–13.Google Scholar
Sandamirskaya, Y. (2016). Autonomous sequence generation in dynamic field theory. In Schöner, G., Spencer, J. P., & Research Group, T. DFT (Eds.), Dynamic Thinking: A Primer on Dynamic Field Theory (pp. 353–368). New York, NY: Oxford University Press.Google Scholar
Sandamirskaya, Y., & Schöner, G. (2010). An embodied account of serial order: how instabilities drive sequence generation. Neural Networks, 10, 1164–1179.Google Scholar
Sandamirskaya, Y., & Storck, T. (2015). Learning to look and looking to remember: a neural-dynamic embodied model for generation of saccadic gaze shifts and memory formation. In Koprinkova-Hristova, P., Mladenov, V., & Kasabov, N. K. (Eds.), Artificial Neural Networks, vol. 4 (pp. 175–200). New York, NY: Springer International Publishing.Google Scholar
Schneegans, S. (2016). Sensori-motor and cognitive transformation. In Schöner, G., Spencer, J. P., & Research Group, T. DFT (Eds.), Dynamic Thinking: A Primer on Dynamic Field Theory (pp. 169–196). New York, NY: Oxford University Press.Google Scholar
Schneegans, S., & Bays, P. M. (2016). No fixed item limit in visuospatial working memory. Cortex, 83, 181–193.Google Scholar
Schneegans, S., & Schöner, G. (2012). A neural mechanism for coordinate transformation predicts pre-saccadic remapping. Biological Cybernetics, 106(2), 89–109.Google Scholar
Schneegans, S., Spencer, J. P., & Schöner, G. (2016). Integrating ‘what’ and ‘where’: visual working memory for objects in a scene. In Schöner, G., Spencer, J. P., & Research Group, T. DFT (Eds.), Dynamic Thinking: A Primer on Dynamic Field Theory (chap. 8). New York, NY: Oxford University Press.Google Scholar
Schöner, G. (2014). Dynamical systems thinking: from metaphor to neural theory. In Molenaar, P. C. M., Lerner, R. M., & Newell, K. M. (Eds.), Handbook of Developmental Systems Theory and Methodology (pp. 188–219). New York, NY: Guilford Publications.Google Scholar
Schöner, G., Faubel, C., Dineva, E., & Bicho, E. (2016). Embodied neural dynamics. In Schöner, G., Spencer, J., & Research Group, T. DFT (Eds.), Dynamic Thinking: A Primer on Dynamic Field Theory (pp. 95–118). New York, NY: Oxford University Press.Google Scholar
Schöner, G., & Kelso, J. A. (1988). Dynamic pattern generation in behavioral and neural systems. Science, 239(4847), 1513–1520.Google Scholar
Schöner, G., Spencer, J. P., & DFT Research Group, T. (2016). Dynamic Thinking: A Primer on Dynamic Field Theory. New York, NY: Oxford University Press.Google Scholar
Schöner, G., Tekülve, J., & Zibner, S. (2019). Reaching for objects : a neural process account in a developmental perspective. In Corbetta, D. & Santello, M. (Eds.), Reach-to-Grasp Behavior: Brain, Behavior and Modelling Across the Life Span (pp. 281–318). Abingdon: Taylor & Francis.Google Scholar
Schöner, G., & Thelen, E. (2006). Using dynamic field theory to rethink infant habituation. Psychological Review, 113(2), 273–299.Google Scholar
Schutte, A. R., & Spencer, J. P. (2009). Tests of the dynamic field theory and the spatial precision hypothesis: capturing a qualitative developmental transition in spatial working memory. Journal of Experimental Psychology. Human Perception and Performance, 35(6), 1698–1725.Google Scholar
Schutte, A. R., Spencer, J. P., & Schöner, G. (2003). Testing the dynamic field theory : working memory for locations becomes more spatially precise over development. Child Development, 74(5), 1393–1417.Google Scholar
Schwartz, A. B., Kettner, R. E., & Georgopoulos, A. P. (1988). Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement. Journal of Neuroscience, 8(8), 2913–2927.Google Scholar
Searle, J. R. (1983). Intentionality: An Essay in the Philosophy of Mind. Cambridge: Cambridge University Press.Google Scholar
Shapiro, L. (Ed.). (2019). Embodied Cognition (2nd ed.). London: Routledge.Google Scholar
Simmering, V. (2016). Working memory capacity in context: modeling dynamic processes of behavior, memory and development. Monographs of the Society for Research in Child Development, 81(3), 1–158.Google Scholar
Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46(1–2), 159–216.Google Scholar
Spencer, J. P., & Schöner, G. (2003). Bridging the representational gap in the dynamic systems approach to development. Developmental Science, 6, 392–412.Google Scholar
Spencer, J. P., Simmering, V. R., & Schutte, A. R. (2006). Toward a formal theory of flexible spatial behavior: geometric category biases generalize across pointing and verbal response types. Journal of Experimental Psychology: Human Perception and Performance, 32(2), 473–490.Google Scholar
Stewart, T. C., Tang, Y., & Eliasmith, C. (2011). A biologically realistic cleanup memory: autoassociation in spiking neurons. Cognitive Systems Research, 12(2), 84–92.Google Scholar
Strauss, S., Woodgate, P. J., Sami, S. A., & Heinke, D. (2015). Choice reaching with a LEGO arm robot (CoRLEGO): the motor system guides visual attention to movement-relevant information. Neural Networks, 72, 3–12.CrossRefGoogle ScholarPubMed
Sussillo, D., Churchland, M. M., Kaufman, M. T., & Shenoy, K. V. (2015). A neural network that finds a naturalistic solution for the production of muscle activity. Nature Neuroscience, 18(7), 1025–1033.Google Scholar
Tekülve, J., Fois, A., Sandamirskaya, Y., & Schöner, G. (2019). Autonomous sequence generation for a neural dynamic robot: scene perception, serial order, and object-oriented movement. Frontiers in Neurorobotics, 13, 208014669.Google Scholar
Tekülve, J., & Schöner, G. (2020). A neural dynamic network drives an intentional agent that autonomously learns beliefs in continuous time. IEEE Transactions on Cognitive and Developmental Systems, 99, 1–12.Google Scholar
Thelen, E., Schöner, G., Scheier, C., & Smith, L. (2001). The dynamics of embodiment: a field theory of infant perseverative reaching. Brain and Behavioral Sciences, 24, 1–33.Google Scholar
Thelen, E., & Smith, L. B. (1994). A Dynamic Systems Approach to the Development of Cognition and Action. Cambridge, MA: MIT Press.Google Scholar
Thompson, R. F., & Spencer, W. A. (1966). Habituation: a model phenomenon for the study of neuronal substrates of behavior. Psychological Review, 73(1), 16–43.CrossRefGoogle Scholar
Trappenberg, T. P. (2010). Fundamentals of Computational Neuroscience (2nd ed.). Oxford: Oxford University Press.Google Scholar
Trappenberg, T. P., Dorris, M. C., Munoz, D. P., & Klein, R. M. (2001). A model of saccade initiation based on the competitive integration of exogenous and endogenous signals in the superior colliculus. Journal of Cognitive Neuroscience, 13(2), 256–271.Google Scholar
Treisman, A. M. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97–136.Google Scholar
Tripp, B., & Eliasmith, C. (2016). Function approximation in inhibitory networks. Neural Networks, 77, 95–106.Google Scholar
Van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Brain and Behavioral Sciences, 21, 615–665.Google Scholar
Wilimzig, C., Schneider, S., & Schöner, G. (2006). The time course of saccadic decision making: dynamic field theory. Neural Networks, 19(8), 1059–1074.Google Scholar
Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12, 1–24.Google Scholar
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636.Google Scholar

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