Abstract
Mirror neurons in monkey premotor cortex of are active during the motor planning and the visual observation of actions. These neurons have recently received a vast amount of interest in cognitive neuroscience and have been discussed as potential basis of imitation learning and the understanding of actions. We present a model that explains visual properties of mirror neurons without a reconstruction of the three-dimensional structure of action and object. The proposed model is based on a small number of physiologically well-established principles. In addition, it postulates novel neural mechanisms for the integration of information about object and effector movement, which can be tested in electrophysiological experiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., Rizzolatti, G.: Understanding motor events: a neurophysiological study. Exp. Brain Res. 91(1), 176–180 (1992)
Rizzolatti, G., Craighero, L.: The mirror-neuron system. Annu. Rev. Neurosci. 27, 169–192 (2004)
Oztop, E., Kawato, M., Arbib, M.: Mirror neurons and imitation: a computationally guided review. Neural Netw 19(3), 254–271 (2006)
Gallese, V., Fadiga, L., Fogassi, L., Rizzolatti, G.: Action recognition in the premotor cortex. Brain 119(Pt 2), 593–609 (1996)
Rizzolatti, G., Fadiga, L., Gallese, V., Fogassi, L.: Premotor cortex and the recognition of motor actions. Cognitive Brain Research 3, 131–141 (1996)
Logothetis, N.K., Pauls, J., Poggio, T.: Shape representation in the inferior temporal cortex of monkeys. Curr. Biol. 5(5), 552–563 (1995)
Edelman, S.: Representation and Recognition in Vision. MIT Press, Cambridge (1999)
Tarr, M.J., Bülthoff, H.H.: Image-based object recognition in man, monkey and machine. Cognition 67(1-2), 1–20 (1998)
Wolpert, D.M., Doya, K., Kawato, M.: A unifying computational framework for motor control and social interaction. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 358(1431), 593–602 (2003)
Tani, J., Ito, M., Sugita, Y.: Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using rnnpb. Neural Netw. 17(8-9), 1273–1289 (2004)
Oztop, E., Wolpert, D., Kawato, M.: Mental state inference using visual control parameters. Brain Res. Cogn. Brain Res. 22(2), 129–151 (2005)
Demiris, Y., Simmons, G.: Perceiving the unusual: temporal properties of hierarchical motor representations for action perception. Neural Netw. 19(3), 272–284 (2006)
Fagg, A.H., Arbib, M.A.: Modeling parietal-premotor interactions in primate control of grasping. Neural Netw. 11(7-8), 1277–1303 (1998)
Oztop, E., Arbib, M.A.: Schema design and implementation of the grasp-related mirror neuron system. Biol. Cybern. 87(2), 116–140 (2002)
Erlhagen, W., Mukovskiy, A., Bicho, E.: A dynamic model for action understanding and goal-directed imitation. Brain Research 1083(1), 174–188 (2006)
Metta, G., Sandini, G., Natale, L., Craighero, L., Fadiga, L.: Understanding mirror neurons: a bio-robotic approach. Interaction Studies, special issue on Epigenetic Robotica 7(2), 197–232 (2006)
Bonaiuto, J., Rosta, E., Arbib, M.: Extending the mirror neuron system model, i. audible actions and invisible grasps. Biol. Cybern. 96(1), 9–38 (2007)
Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 358(1431), 537–547 (2003)
Sauser, E.L., Billard, A.G.: Parallel and distributed neural models of the ideomotor principle: an investigation of imitative cortical pathways. Neural Netw. 19(3), 285–298 (2006)
Billard, A., Mataric, M.: Learning human arm movements by imitation: Evaluation of a biologically-inspired connectionist architecture. Robotics and Autonomous Systems 941, 1–16 (2001)
Perrett, D., Oram, M.: Neurophysiology of shape processing. IVC 11(6), 317–333 (1993)
Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019–1025 (1999)
Mel, B.W., Fiser, J.W.: Minimizing binding errors using learned conjunctive features. Neural. Comput. 12, 731–762 (2000)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 411–426 (2007)
Giese, M.A., Poggio, T.: Neural mechanisms for the recognition of biological movements. Nat Rev Neurosci 4(3), 179–192 (2003)
Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nat. Rev. Neurosci. 1(2), 125–132 (2000)
DiCarlo, J.J., Maunsell, J.H.: Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. J. Neurophysiol. 89(6), 3264–3278 (2003)
Jastorff, J., Kourtzi, Z., Giese, M.A.: Learning to discriminate complex movements: Biological versus artificial trajectories. J. Vis. 6, 791–804 (2006)
Perrett, D.I., Harries, M.H., Bevan, R., Thomas, S., Benson, P.J., Mistlin, A.J., Chitty, A.J., Hietanen, J.K., Ortega, J.E.: Frameworks of analysis for the neural representation of animate objects and actions. J. Exp. Biol. 146, 87–113 (1989)
Murata, A., Gallese, V., Luppino, G., Kaseda, M., Sakata, H.: Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area aip. J. Neurophysiol. 83(5), 2580–2601 (2000)
Verfaillie, K., Daems, A.: Predicting point-light actions in real-time. Visual Cognition 9, 217–232 (2002)
Graf, M., Reitzner, B., Corves, C., Casile, A., Giese, M., Prinz, W.: Predicting point-light actions in real-time. Neuroimage 36(Suppl. 2), T22–32 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fleischer, F., Casile, A., Giese, M.A. (2008). Neural Model for the Visual Recognition of Goal-Directed Movements. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_97
Download citation
DOI: https://doi.org/10.1007/978-3-540-87559-8_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87558-1
Online ISBN: 978-3-540-87559-8
eBook Packages: Computer ScienceComputer Science (R0)