Hostname: page-component-5db58dd55d-8mwbx Total loading time: 0 Render date: 2026-05-31T06:48:33.316Z Has data issue: false hasContentIssue false
  • English
  • Français

Mirror representations innate versus determined by experience: A viewpoint from learning theory

Published online by Cambridge University Press:  29 April 2014

Martin A. Giese
Martin A. Giese*
Affiliation:
Section for Computational Sensomotorics, Department of Cognitive Neurology, Hertie Institute for Clinical Brain Research, and Centre for Integrative Neuroscience, University Clinic Tübingen, D-72076 Tübingen, Germany. martin.giese@uni-tuebingen.de http://www.compsens.uni-tuebingen.de
Get access Rights & Permissions [Opens in a new window]

Abstract

From the viewpoint of pattern recognition and computational learning, mirror neurons form an interesting multimodal representation that links action perception and planning. While it seems unlikely that all details of such representations are specified by the genetic code, robust learning of such complex representations likely requires an appropriate interplay between plasticity, generalization, and anatomical constraints of the underlying neural architecture.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 37 , Issue 2 , April 2014 , pp. 201 - 202
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Arbib, M. (2008) From grasp to language: Embodied concepts and the challenge of abstraction. Journal of Physiology (Paris) 102(1–3):4–20.Google Scholar
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P. & Friston, K. J. (2012) Canonical microcircuits for predictive coding. Neuron 76(4):695–711.Google Scholar
Bengio, Y. & Le Cun, Y. (2007) Scaling learning algorithms towards AI. In: Large-scale kernel machines, ed. Bottou, L., Chapelle, O., DeCoste, D. & Weston, J., pp. 321–88. MIT Press.Google Scholar
Caggiano, V., Fogassi, L., Rizzolatti, G., Casile, A., Giese, M. A. & Thier, P. (2012) Mirror neurons encode the subjective value of an observed action. Proceedings of the National Academy of Sciences USA 109(29):11848–53.Google Scholar
Caggiano, V, Fogassi, L, Rizzolatti, G., Pomper, J. K., Thier, P., Giese, M. A. & Casile, A. (2011) View-based encoding of actions in mirror neurons of area F5 in macaque premotor cortex. Current Biology 21(2):144–48.Google Scholar
Caggiano, V., Fogassi, L., Rizzolatti, G., Thier, P. & Casile, A. (2009) Mirror neurons differentially encode the peripersonal and extrapersonal space of monkeys. Science 324(5925):403–406.CrossRefGoogle ScholarPubMed
Douglas, R. J. & Martin, K. A. C. (2004) Neuronal circuits of the neocortex. Annual Review of Neuroscience 27:419–51.Google Scholar
Fleischer, F., Caggiano, V., Thier, P. & Giese, M. A. (2013) Physiologically inspired model for the visual recognition of transitive hand actions. Journal of Neuroscience 33(15):6563–80.Google Scholar
Grafton, S. T. & Hamilton, A. F. (2007) Evidence for a distributed hierarchy of action representation in the brain. Human Movement Science 26(4):590–616.Google Scholar
Hinton, G. E. (2007) Learning multiple layers of representation. Trends in Cognitive Sciences 11:428–34.CrossRefGoogle ScholarPubMed
Kemmerer, D. & Gonzalez-Castillo, J. (2010) The two-level theory of verb meaning: An approach to integrating the semantics of action with the mirror neuron system. Brain and Language 112(1):54–76.Google Scholar
Kilner, J. M., Friston, K. J. & Frith, C. D. (2007a) Predictive coding: An account of the mirror neuron system. Cognitive Processing 8(3):159–66.CrossRefGoogle ScholarPubMed
Mukamel, R., Ekstrom, A. D., Kaplan, J., Iacoboni, M. & Fried, I. (2010) Single-neuron responses in humans during execution and observation of actions. Current Biology 20(8):750–56.Google Scholar
Olshausen, B. A. & Field, D. J. (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609.Google Scholar
Poggio, T. & Bizzi, E. (2004) Generalization in vision and motor control. Nature 431:768–74.CrossRefGoogle ScholarPubMed
Poggio, T. & Edelman, S. E. (1990) A network that learns to recognize 3D objects. Nature 343:263–66.Google Scholar
Prather, J. F., Peters, S., Nowicki, S. & Mooney, R. (2008) Precise auditory-vocal mirroring in neurons for learned vocal communication. Nature 451(7176):305–10.Google Scholar
Pulvermüller, F. (2005) Brain mechanisms linking language and action. Nature Reviews Neuroscience 6 (7):576–82.Google Scholar
Rizzolatti, G., Fogassi, L. & Gallese, V. (2001) Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience 2(9): 661–70.Google Scholar
Rizzolatti, G. & Sinigaglia, C. (2008) Mirrors in the brain. How our minds share actions and emotions. Oxford University Press.Google Scholar
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M. & Poggio, T. (2007) Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3):411–26.Google Scholar
Shepherd, S. V., Klein, J. T., Deaner, R. O. & Platt, M. L. (2009) Mirroring of attention by neurons in macaque parietal cortex. Proceedings of the National Academy of Sciences USA 106:9489–94.CrossRefGoogle ScholarPubMed
Tarr, M. J. & Bülthoff, H. H. (1998) Image based object recognition in man, monkey and machine. Cognition 67:1–20.CrossRefGoogle ScholarPubMed
Taylor, G. W., Hinton, G. & Roweis, S. (2011) Two distributed-state models for generating high-dimensional time series. Journal of Machine Learning Research 12:1025–68.Google Scholar
Tkach, D., Reimer, J. & Hatsopoulos, N. G. (2007) Congruent activity during action and action observation in motor cortex. Journal of Neuroscience 27(48):13241–50.CrossRefGoogle ScholarPubMed
Ullman, S. (1996) High-level vision: Object recognition and visual cognition. MIT Press.CrossRefGoogle Scholar
Ullman, S. (2007) Object recognition and segmentation by a fragment-based hierarchy. Trends in Cognitive Sciences 11(2):58–64.Google Scholar
Vapnik, V. N. (1998) Statistical learning theory. Wiley-Interscience.Google Scholar
Wolpert, D. M., Diedrichsen, J. & Flanagan, J. R. (2011) Principles of sensorimotor learning. Nature Reviews Neuroscience 12:739–51.CrossRefGoogle ScholarPubMed
Yildiz, I. B. & Kiebel, S. J. (2011) A hierarchical neuronal model for generation and online recognition of birdsongs. PLOS Computational Biology 7(12):e1002303.Google Scholar