Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Embodied Cognition, Dynamic Field Theory of

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_55

Synonyms

Definition

The insight that cognition is grounded in sensorimotor processing and shares many properties with motor control, captured by the notion of “embodied cognition,” has been a starting point for neural process models of cognition. Neural Field models represent spaces relevant to cognition, including physical space, perceptual feature spaces, or movement parameters in activation fields that may receive input from the sensory surfaces and may project onto motor systems. Peaks of activation are units of representation. Their positive levels of activation indicate the instantiation of a representation, while their location specifies metric values along the feature dimensions. By ensuring that peaks are stable states (attractors) of a neural activation dynamics, cognitive processes are endowed with the stability properties required when cognition is linked to sensory and motor processes. Instantiations of cognitive...

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References

  1. Amari S (1977) Dynamics of pattern formation in lateral-inhibition type neural fields. Biol Cybern 27:77–87PubMedGoogle Scholar
  2. Bastian A, Riehle A, Erlhagen W, Schöner G (1998) Prior information preshapes the population representation of movement direction in motor cortex. Neuroreports 9:315–319Google Scholar
  3. Bicho E, Mallet P, Schöner G (2000) Target representation on an autonomous vehicle with low-level sensors. Int J Robotics Res 19:424–447Google Scholar
  4. Cisek P (2006) Integrated neural processes for defining potential actions and deciding between them: a computational model. J Neurosci 26(38):9761–9770PubMedGoogle Scholar
  5. Cohen MR, Newsome WT (2009) Estimates of the contribution of single neurons to perception depend on timescale and noise correlation. J Neurosci 29(20):6635–6648PubMedCentralPubMedGoogle Scholar
  6. Erlhagen W, Schöner G (2002) Dynamic field theory of movement preparation. Psychol Rev 109:545–572PubMedGoogle Scholar
  7. 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. J Neurosci Methods 94:53–66PubMedGoogle Scholar
  8. Erlhagen W, Mukovskiy A, Bicho E (2006) A dynamic model for action understanding and goal-directed imitation. Brain Res 1083(1):174–188PubMedGoogle Scholar
  9. Fix J, Rougier N, Alexandre F (2011) A dynamic neural field approach to the covert and overt deployment of spatial attention. Cognit Comput 3(1):279–293Google Scholar
  10. Fuster JM (2005) Cortex and mind – unifying cognition. Oxford University Press, OxfordGoogle Scholar
  11. Hock HS, Schöner G, Giese MA (2003) The dynamical foundations of motion pattern formation: stability, selective adaptation, and perceptual continuity. Percept Psychophys 65:429–457PubMedGoogle Scholar
  12. Jancke D, Erlhagen W, Dinse HR, Akhavan AC, Giese M, Steinhage A et al (1999) Parametric population representation of retinal location: {N}euronal interaction dynamics in cat primary visual cortex. J Neurosci 19:9016–9028PubMedGoogle Scholar
  13. Johnson JS, Spencer JP, Schöner G (2008) Moving to higher ground: the dynamic field theory and the dynamics of visual cognition. New Ideas Psychol 26:227–251PubMedCentralPubMedGoogle Scholar
  14. Johnson JS, Spencer JP, Luck SJ, Schöner G (2009) A dynamic neural field model of visual working memory and change detection. Psychol Sci 20:568–577PubMedCentralPubMedGoogle Scholar
  15. Kopecz K, Schöner G (1995) Saccadic motor planning by integrating visual information and pre-information on neural, dynamic fields. Biol Cybern 73:49–60PubMedGoogle Scholar
  16. Lipinski J, Schneegans S, Sandamirskaya Y, Spencer JP, Schöner G (2012) A neuro-behavioral model of flexible spatial language behaviors. J Exp Psychol Learn Mem Cogn 38(6):1490–1511PubMedCentralPubMedGoogle Scholar
  17. 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 Comput Biol 6(9):e1000919PubMedCentralPubMedGoogle Scholar
  18. Martin V, Scholz JP, Schöner G (2009) Redundancy, self-motion and motor control. Neural Comput 21(5):1371–1414PubMedCentralPubMedGoogle Scholar
  19. McClelland JL, Rogers TT (2003) The parallel distributed processing approach to semantic cognition. Nat Rev Neurosci 4(4):310–322PubMedGoogle Scholar
  20. Perone S, Spencer JP (2013) Autonomy in action: linking the act of looking to memory formation in infancy via dynamic neural fields. Cognit Sci 37(1):1–60Google Scholar
  21. Riegler A (2002) When is a cognitive system embodied? Cognit Syst Res 3:339–348Google Scholar
  22. Sandamirskaya Y (2014) Dynamic neural fields as a step toward cognitive neuromorphic architectures. Front Neurosci 7Google Scholar
  23. Sandamirskaya Y, Schöner G (2010) An embodied account of serial order: how instabilities drive sequence generation. Neural Netw 23(10):1164–1179PubMedGoogle Scholar
  24. Sandamirskaya Y, Zibner SK, Schneegans S, Schöner G (2013) Using dynamic field theory to extend the embodiment stance toward higher cognition. New Ideas Psychol 31(3):322–339Google Scholar
  25. Schneegans S, Schöner G (2008) Dynamic field theory as a framework for understanding embodied cognition. In: Calvo P, Gomila T (eds) Handbook of cognitive science: an embodied approach. Elsevier, Amsterdam/Boston/London, pp 241–271Google Scholar
  26. Schöner G, Thelen E (2006) Using dynamic field theory to rethink infant habituation. Psychol Rev 113(2):273–299PubMedGoogle Scholar
  27. Simons DJ, Levin DT (1997) Change blindness. Trends Cogn Sci 1(7):261–267PubMedGoogle Scholar
  28. Spencer JP, Schöner G (2003) Bridging the representational gap in the dynamical systems approach to development. Dev Sci 6:392–412Google Scholar
  29. Spencer JP, Simmering VR, Schutte AR (2006) Toward a formal theory of flexible spatial behavior: geometric category biases generalize across pointing and verbal response types. J Exp Psychol Hum Percept Perform 32(2):473–490PubMedGoogle Scholar
  30. Spencer JP, Perone S, Johnson JS (2009) Dynamic field theory and embodied cognitive dynamics. In: Spencer J, Thomas M, McClelland J (eds) Toward a unified theory of development: connectionism and dynamic systems theory re-considered. Oxford University Press, Oxford, pp 86–118Google Scholar
  31. Thelen E, Schöner G, Scheier C, Smith L (2001) The dynamics of embodiment: a field theory of infant perseverative reaching. Brain Behav Sci 24:1–33Google Scholar
  32. Trappenberg T (2008) Decision making and population decoding with strongly inhibitory neural field models. In: Heinke D, Mavritsak E (eds) Computational modelling in behavioural neuroscience: closing the gap between neurophysiology and behaviour. Psychology Press, London, pp 1–19Google Scholar
  33. Trappenberg T, Dorris MC, Munoz DP, Klein RM (2001) A model of saccade initiation based on the competitive integration of exogenous and endogenous signals in the superior colliculus. J Cogn Neurosci 13(2):256–271PubMedGoogle Scholar
  34. Zibner SKU, Faubel C, Iossifidis I, Schöner G (2011) Dynamic neural fields as building blocks for a cortex-inspired architecture of robotic scene representation. IEEE Trans Auton Ment Dev 3(1):74–91Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Institut für NeuroinformatikRuhr-Universität BochumBochumGermany