Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

Covered by SCOPUS

CiteScore 2018: 2.17

SCImago Journal Rank (SJR) 2018: 0.336
Source Normalized Impact per Paper (SNIP) 2018: 1.707

ICV 2018: 120.52

Open Access
See all formats and pricing
More options …

Dynamic contextualization and comparison as the basis of biologically inspired action understanding

Laith Alkurdi
  • Corresponding author
  • Chair of Automatic Control Engineering, Department of Electrical, Electronic and Computer Engineering, Technische Universität München, München, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Christian Busch
  • Chair of Automatic Control Engineering, Department of Electrical, Electronic and Computer Engineering, Technische Universität München, München, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Angelika Peer
  • Bristol Robotics Laboratory, Faculty of Environment and Technology, Department of Engineering Design and Mathematics, University of the West of England, Bristol, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-03-16 | DOI: https://doi.org/10.1515/pjbr-2018-0003


People exhibit a robust ability to understand the actions of others around them. In this work, we identify two biologically inspired mechanisms that we hypothesize to be central in the function of action understanding. The first module is a contextual predictor of the observed action, given the goal-directed movement towards objects, and the actions that are allowed to be performed on the object. The second module is a kinematic trajectory parser that validates the previous prediction against a set of learned templates.We model both mechanisms and link them to the environment using the cognitive framework of Dynamic Field Theory and present our first steps into integrating the aforementioned modules into a consistent framework for the purpose of action understanding. The two modules and the combined architecture as awhole are experimentally validated using a recording of an actor performing a series of intentional actions testing the ability of the architecture to understand context and parse actions dynamically. Our initial qualitative results show that action understanding benefits from the combination of the two modules, while any module alone would be insufficient to resolve ambiguity in the perceived actions.

Keywords: dynamic field theory; action understanding; embodied embedded cognition; affordance theory; theory of mind


  • [1] D. Feil-Seifer, M. J. Mataric, Defining socially assistive robotics, 9th International Conference on Rehabilitation Robotics (ICORR), 2005, 465-468Google Scholar

  • [2] A. Avci, S. Bosch, M. Marin-Perianu, R. M. Perianu, P. Havinga, Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey, 23rd International Conference on Architecture of Computing Systems (ARCS), 2010, 1-10Google Scholar

  • [3] L. W. Barsalou, W. K. Simmons, A. K. Barbey, C. D. Wilson, Grounding conceptual knowledge in modality-specific systems, Trends in cognitive sciences, 2003, 7(2), 84-91 Google Scholar

  • [4] E. R. Smith, G. R. Semin, Socially situated cognition: Cognition in its social context, Advances in experimental social psychology, 2004, 36, 53-117Google Scholar

  • [5] G. Schöner, Dynamical systems approaches to cognition, Cambridge handbook of computational cognitive modeling, 2008, 101-126Google Scholar

  • [6] J. K. Aggarwal, M. S. Ryoo, Human activity analysis: A review, ACM Comput. Surv., 2011, 43(3), art. 16Google Scholar

  • [7] A. Bulling, U. Blanke, B. Schiele, A tutorial on human activity recognition using body-worn inertial sensors, ACM Computing Surveys (CSUR), 2014, 46(3), art. 33CrossrefGoogle Scholar

  • [8] D. Lobato, Y. Sandamirskaya, M. Richter, G. Schöner, Parsing of action sequences: A neural dynamics approach, Paladyn, Journal of Behavioral Robotics, 2015, 6(1)Google Scholar

  • [9] E. Bicho, L. Louro, W. Erlhagen, Integrating verbal and nonverbal communication in a dynamic neural field architecture for human-robot interaction, Frontiers in Neuro robotics, 2010, 4(5), 1-13Google Scholar

  • [10] J. J. Gibson, The Ecological Approach to Visual Perception, Boston: Houghton Mifflin, 1979Google Scholar

  • [11] G. Semin, J. Cacioppo, Grounding social cognition: Synchronization, coordination, and co-regulation, in G. R. Semin, E. R. Smith (Eds.), Embodied grounding: Social, cognitive, affective, and neuroscientific approaches, Cambridge University Press, 2008Google Scholar

  • [12] G. R. Semin, E. R. Smith, Socially situated cognition in perspective, Social Cognition, 2013, 31(2), 125-146CrossrefGoogle Scholar

  • [13] R. E. Shaw, E. Kadar, M. Sim, D. W. Repperger, The intentional spring: A strategy for modeling systems that learn to perform intentional acts, Journal of Motor Behavior, 1992, 24(1), 3-28Google Scholar

  • [14] M. A. Umilta, E. Kohler, V. Gallese, L. Fogassi, L. Fadiga, C. Keysers, G. Rizzolatti, I know what you are doing: a neurophysiological study, Neuron, 2001, 31(1), 155-165CrossrefGoogle Scholar

  • [15] E. Kohler, C. Keysers, M. A. Umilta, L. Fogassi, V. Gallese, G. Rizzolatti, Hearing sounds, understanding actions: action representation in mirror neurons, Science, 2002, 297(5582), 846-848Google Scholar

  • [16] L. Fogassi, P. F. Ferrari, B. Gesierich, S. Rozzi, F. Chersi, G. Rizzolatti, Parietal lobe: from action organization to intention understanding, Science, 2005, 308(5722), 662-667Google Scholar

  • [17] N. Sebanz, H. Bekkering, G. Knoblich, Joint action: bodies and minds moving together, Trends in cognitive sciences, 2006, 10(2), 70-76Google Scholar

  • [18] J. J. Gibson, The Senses Considered as Perceptual Systems, Boston: Houghton Mifflin., 1966Google Scholar

  • [19] J. J. Gibson, E. S. Reed, R. Jones, Reasons for Realism: Selected Essays of J. J. Gibson, Resources for Ecological Psychology, L. Erlbaum, 1982Google Scholar

  • [20] A. Chemero, An outline of a theory of affordances, Ecological psychology, 2003, 15(2), 181-195CrossrefGoogle Scholar

  • [21] K. S. Jones, What is an affordance? Ecological psychology, 2003, 15(2), 107-114CrossrefGoogle Scholar

  • [22] T. A. Stoffregen, Affordances as properties of the animal environment system, Ecological Psychology, 2003, 15(2), 115-134Google Scholar

  • [23] C. F. Michaels, Affordances: Four points of debate, Ecological Psychology, 2003, 15(2), 135-148CrossrefGoogle Scholar

  • [24] H. Heft, Affordances, dynamic experience, and the challenge of reification, Ecological Psychology, 2003, 15(2), 149-180Google Scholar

  • [25] W. H. Warren, Perceiving affordances: visual guidance of stair climbing, Journal of Experimental Psychology: Human Perception and Performance, 1984, 10(5), 683-703CrossrefGoogle Scholar

  • [26] E.-J. Marey, Analyse cinématique de la marche, Comptes Rendus des Séances de lAcadémie des Sciences, Paris, XCVIII, 1884Google Scholar

  • [27] G. Johansson, Visual perception of biological motion and a model for its analysis, Perception & psychophysics, 1973, 14(2), 201-211CrossrefGoogle Scholar

  • [28] W. H. Dittrich, Action categories and the perception of biological motion, Perception, 1993, 22(1), 15-22CrossrefGoogle Scholar

  • [29] J. Lange, M. Lappe, A model of biological motion perception from configural form cues, The Journal of Neuroscience, 2006, 26(11), 2894-2906Google Scholar

  • [30] M. A. Giese, T. Poggio, Neural mechanisms for the recognition of biological movements, Nature Reviews Neuroscience, 2003, 4(3), 179-192CrossrefGoogle Scholar

  • [31] M. A. Giese, Computational Principles for the Recognition of Biological Movements: Model-based versus feature-based approaches, Oxford University Press, 2005Google Scholar

  • [32] M. A. Giese, Biological and body motion perception, in J.Wagemans (Ed.), Oxford Handbook of Perceptual Organization, Oxford University Press, 2014Google Scholar

  • [33] M. A. Giese, Biological and body motion perception, Oxford Handbook of Perceptual Organization, 2014Google Scholar

  • [34] M. A. Giese, T. Poggio, Neural mechanisms for the recognition of biological movements, Nature Reviews Neuroscience, 2003, 4, 179-192CrossrefGoogle Scholar

  • [35] R. Blake, M. Shiffrar, Perception of human motion, Annu. Rev. Psychol., 2007, 58, 47-73CrossrefGoogle Scholar

  • [36] J. M. Zacks, S. Kumar, R. A. Abrams, R. Mehta, Using movement and intentions to understand human activity, Cognition, 2009, 112(2), 201-216Google Scholar

  • [37] M. Iacoboni, I. Molnar-Szakacs, V. Gallese, G. Buccino, J. C. Mazziotta, G. Rizzolatti, Grasping the intentions of others with one’s own mirror neuron system, PLOS Biology, 2005, https://doi.org/10.1371/journal.pbio.0030079CrossrefGoogle Scholar

  • [38] V. Gallese, A. Goldman, Mirror neurons and the simulation theory of mind-reading, Trends in cognitive sciences, 1998, 2(12), 493-501Google Scholar

  • [39] G. Rizzolatti, L. Fogassi, V. Gallese, Neurophysiological mechanisms underlying the understanding and imitation of action, Nature Reviews Neuroscience, 2001, 2(9), 661-670Google Scholar

  • [40] V. Gallese, C. Keysers, G. Rizzolatti, A unifying view of the basis of social cognition, Trends in cognitive sciences, 2004, 8(9), 396-403CrossrefGoogle Scholar

  • [41] E. Oztop, M. Kawato, M. A. Arbib, Mirror neurons: functions, mechanisms and models, Neuroscience letters, 2013, 540, 43-55Google Scholar

  • [42] V. Gallese, L. Fadiga, L. Fogassi, G. Rizzolatti, Action recognition in the premotor cortex, Brain, 1996, 119(2), 593-609Google Scholar

  • [43] S. T. Grafton, L. Fadiga, M. A. Arbib, G. Rizzolatti, Premotor cortex activation during observation and naming of familiar tools, Neuroimage, 1997, 6(4), 231-236CrossrefGoogle Scholar

  • [44] L. Fadiga, L. Fogassi, V. Gallese, G. Rizzolatti, Visuomotor neurons: Ambiguity of the discharge or motor perception?, International Journal of Psychophysiology, 2000, 35(2), 165-177CrossrefGoogle Scholar

  • [45] M. Kellenbach, M. Brett, K. Patterson, Actions speak louder than functions: the importance of manipulability and action in tool representation, Journal of Cognitive Neuroscience, 2003, 15(1), 30-46CrossrefGoogle Scholar

  • [46] C. B. Boronat, L. J. Buxbaum, H. B. Coslett, K. Tang, E. M. Saffran, D. Y. Kimberg, J. A. Detre, Distinctions between manipulation and function knowledge of objects: evidence from functionalmagnetic resonance imaging, Cognitive Brain Research 2005, 23(2) 361-373CrossrefGoogle Scholar

  • [47] M. A. Arbib, G. Rizzolatti, Neural expectations: A possible evolutionary path frommanual skills to language, Communication & Cognition, 1996Google Scholar

  • [48] S. Amari, Dynamics of pattern formation in lateral-inhibition type neural fields, Biological Cybernetics, 1977, 27(2), 77-87CrossrefGoogle Scholar

  • [49] S. A. Ellias, S. Grossberg, Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off-surround networks, Biological Cybernetics, 1975, 20(2), 69-98CrossrefGoogle Scholar

  • [50] Y. Sandamirskaya, S. K. U. Zibner, S. Schneegans, G. Schöner, Using dynamic field theory to extend the embodiment stance toward higher cognition, New Ideas in Psychology, 2013, 31(3), 322-339CrossrefGoogle Scholar

  • [51] A. Bastian, G. Schöner, A. Riehle, Preshaping and continuous evolution of motor cortical representations during movement preparation, European Journal of Neuroscience, 2003, 18(7), 2047-2058CrossrefGoogle Scholar

  • [52] Y. Sandamirskaya, G. Schöner, An embodied account of serial order: How instabilities drive sequence generation, Neural Networks, 2010, 23(10), 1164-1179Google Scholar

  • [53] Y. Sandamirskaya, G. Schöner, Serial order in an acting system: A multidimensional dynamic neural fields implementation, IEEE 9th International Conference on Development and Learning (ICDL), 2010, 251-256Google Scholar

  • [54] Y. Sandamirskaya, M. Richter, G. Schöner, A neuraldynamic architecture for behavioral organization of an embodied agent, IEEE International Conference on Development and Learning (ICDL), 2011, 2, 1-7Google Scholar

  • [55] F. L. da Silva, Neural mechanisms underlying brainwaves: from neural membranes to networks, Electroencephalography and clinical neurophysiology, 1991, 79(2), 81-83CrossrefGoogle Scholar

  • [56] J. M. Horschig, J. M. Zumer, A. Bahramisharif, Hypothesisdriven methods to augment human cognition by optimizing cortical oscillations, Frontiers in Systems Neuroscience, 2014, 8(119)Google Scholar

  • [57] R. Menzner, A. Steinhage, W. Erlhagen, Generating interactive robot behavior: Amathematical approach, From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior, MIT Press/Bradford Books, 2000, 135-144Google Scholar

  • [58] I. Iossifidis, A. Steinhage, Controlling an 8 dofmanipulator by means of neural fields, International Conference on Field and Service Robotics, 2001, 1-7Google Scholar

  • [59] Y. Lu, Y. Sato, S. Amari, Traveling bumps and their collisions in a two-dimensional neural field, Neural Computation, 2011, 23(5), 1248-1260CrossrefGoogle Scholar

  • [60] D. Roetenberg, H. Luinge, P. Slycke, Xsens mvn: Full 6dof human motion tracking using miniature inertial sensors, Technical report, XSENS TECHNOLOGIES, 2013Google Scholar

  • [61] A. M. Glenberg, What memory is for: Creating meaning in the service of action, Behavioral and Brain Sciences, Cambridge University Press, 1997, 20(1), 41-50CrossrefGoogle Scholar

  • [62] L. W. Barsalou, Language comprehension: Archival memory or preparation for situated action?, Discourse Processes, 1999, 28(1), 61-80CrossrefGoogle Scholar

  • [63] W. Prinz, A common coding approach to perception and action, Springer, 1990CrossrefGoogle Scholar

  • [64] D. I. Perrett, M. H. Harries, R. Bevan, S. Thomas, P. J. Benson, A. J. Mistlin, A. J. Chitty, J. K. Hietanen, J. E. Ortega, Frameworks of analysis for the neural representation of animate objects and actions, Journal of Experimental Biology, 1989, 146(1), 87-113Google Scholar

  • [65] N. F. Troje, Reference frames for orientation anisotropies in face recognition and biological-motion perception, Perception, 2003, 32(2), 201-210CrossrefGoogle Scholar

  • [66] V. Caggiano, L. Fogassi, G. Rizzolatti, J. K. Pomper, P. Thier, M. A. Giese, A. Casile, View-based encoding of actions in mirror neurons of area f5 in macaque premotor cortex, Current Biology, 2011, 21(2), 144-148CrossrefGoogle Scholar

  • [67] E. Marinoiu, D. Papava, C. Sminchisescu, Pictorial human spaces: How well do humans perceive a 3d articulated pose?, IEEE International Conference on Computer Vision (ICCV), 2013, 1289-1296Google Scholar

  • [68] I. Bülthoff, H. Bülthoff, P. Sinha, Topdown influences on stereoscopic depth-perception, Nature neuroscience, 1998, 1(3), 254-257Google Scholar

  • [69] S. Schneegans, G. Schöner, A neural mechanism for coordinate transformation predicts pre-saccadic remapping, Biological cybernetics, 2012, 106(2), 89-109Google Scholar

  • [70] R. L. Williams II, Engineering biomechanics of human motion, Technical report, Ohio University, 2013Google Scholar

  • [71] J. A. Feldman, Four frames suflce: A provisional model of vision and space, Behavioral and Brain Sciences, 1985, 8(02), 265-289CrossrefGoogle Scholar

  • [72] D. Marr, Vision: A computational investigation into the human representation and processing of visual information, WH San Francisco: Freeman and Company, 1982Google Scholar

  • [73] D.Marr, H. K. Nishihara, Representation and recognition of the spatial organization of three-dimensional shapes, Proceedings of the Royal Society of London, Series B, Biological Sciences, 1978, 200(1140), 269-294Google Scholar

  • [74] A. P. Georgopoulos, J. F. Kalaska, R. Caminiti, J. T. Massey, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex, Journal of Neuroscience, 1982, 2(11), 1527-1537CrossrefGoogle Scholar

  • [75] A. P. Georgopoulos, A. B. Schwartz, R. E. Kettner, Neuronal population coding of movement direction, Science, 1986, 233(4771), 1416-1419Google Scholar

  • [76] A. P. Georgopoulos, E. Karageorgiou, Understanding events: From perception to action, chapter Voluntary Arm Movements in the Motor Cortex, Oxford University Press, 2008, 229-254Google Scholar

  • [77] A. P. Georgopoulos, J. F. Kalaska, R. Caminiti, J. T. Massey, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex, Journal of Neuroscience, 1982, 2(11), 1527-1537Google Scholar

  • [78] P. Dayan, L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press, 2005Google Scholar

  • [79] D. I. Perrett, M.W. Oram, M. H. Harries, R. Bevan, J. K. Hietanen, P. J. Benson, S. Thomas, Viewer-centred and object-centred coding of heads in themacaque temporal cortex, Experimental Brain Research, 1991, 86(1), 159-173Google Scholar

  • [80] W. T. Newsome, C. D. Salzman, The neuronal basis of motion perception, Ciba Found Symposium, 1993, 174, 217-230Google Scholar

  • [81] M. Taira, S. Mine, A. P. Georgopoulos, A. Murata, H. Sakata, Parietal cortex neurons of the monkey related to the visual guidance of hand movement, Experimental brain research, 1990, 83(1), 29-36Google Scholar

  • [82] J. R. Flanagan, R. S. Johansson, Action plans used in action observation, Nature, 2003, 424(6950), 769-771Google Scholar

  • [83] C. L. Colby, J.-R. Duhamel, M. E. Goldberg, Ventral intraparietal area of the macaque: anatomic location and visual response properties, Journal of neurophysiology, 1993, 69, 902-902Google Scholar

  • [84] M. Oram, D. I. Perrett, Responses of anterior superior temporal polysensory (stpa) neurons to “biological motion” stimuli, Journal of Cognitive Neuroscience, 1994, 6(2), 99-116CrossrefGoogle Scholar

  • [85] G. Mather, K. Radford, S. West, Low level visual processing of biological motion, Proceedings of the Royal Society of London, Series B: Biological Sciences, 1992, 249(1325), 149-155Google Scholar

  • [86] M. V. Peelen, P. E. Downing, The neural basis of visual body perception, Nature Reviews Neuroscience, 2007, 8(8), 636-648Google Scholar

  • [87] W. W. Gaver, Technology affordances, in Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, 1991, 79-84Google Scholar

  • [88] O. Lomp, K. Terzić, C. Faubel, J. M. H. du Buf, G. Schöner, Instance-based object recognition with simultaneous pose estimation using keypoint maps and neural dynamics, in Artificial Neural Networks and Machine Learning - ICANN 2014, Springer, 2014, 451-458Google Scholar

  • [89] Cosivina - Compose, simulate, and visualize neurodynamic architectures, An open source toolbox forMatlab (accessed:May 27th 2015), https://bitbucket.org/sschneegans/cosivinaGoogle Scholar

  • [90] C. B. Holroyd, M. G. H. Coles, The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity, Psychological Review, 2002, 109(4), 679-709Google Scholar

  • [91] M. Haruno, D. M. Wolpert, M. Kawato, Mosaic model for sensorimotor learning and control, Neural Computation, 2001, 13(10), 2201-2220Google Scholar

  • [92] D. M. Wolpert, K. Doya, M. Kawato, A unifying computational framework for motor control and social interaction, Philosophical Transactions of the Royal Society of London, 2003, 358, 593-602Google Scholar

  • [93] J. Demiris, G. M. Hayes, Imitation as a dual-route process featuring predictive and learning components: a biologically plausible computational model, Imitation in animals and artifacts, 2002, 327-361Google Scholar

  • [94] Y. Demiris, M. Johnson, Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning, Connection Science, 2003, 15(4), 231-243CrossrefGoogle Scholar

  • [95] Y. Demiris, B. Khadhouri, Hierarchical attentive multiple models for execution and recognition of actions, Robotics and Autonomous Systems, 2006, 54(5), 361-369CrossrefGoogle Scholar

  • [96] Y. Demiris, G. Simmons, Perceiving the unusual: Temporal properties of hierarchical motor representations for action perception, Neural Networks, 2006, 19(3), 272-284CrossrefGoogle Scholar

  • [97] E. Oztop, D. M. Wolpert, M. Kawato, Mental state inference using visual control parameters, Cognitive Brain Research, 2005, 22(2), 129-151CrossrefGoogle Scholar

  • [98] J. Tani, Learning to generate articulated behavior through the bottom-up and the top-down interaction processes, Neural Networks, 2003, 16(1), 11-23CrossrefGoogle Scholar

  • [99] J. Tani, M. Ito, Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2003, 33(4), 481-488Google Scholar

  • [100] J. Tani, M. Ito, Y. Sugita, Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB, Neural Networks, 2004, 17(8-9), 1273-1289CrossrefGoogle Scholar

  • [101] J. Bonaiuto, E. Rosta, M. Arbib, Extending the mirror neuron system model, I, Biological Cybernetics, 2007, 96(1), 9-38Google Scholar

  • [102] E. Oztop, M. Kawato, M. Arbib, Mirror neurons and imitation: A computationally guided review, Neural Networks, 2006, 19(3), 254-271CrossrefGoogle Scholar

  • [103] B. Akgun, D. Tunaoglu, E. Sahin, Action recognition through an action generation mechanism, in International Conference on Epigenetic Robotics (EPIROB), 2010Google Scholar

  • [104] Y. Yang, C. Fermüller, Y. Aloimonos, A cognitive system for humanmanipulation action understanding, in the Proceedings of the Second Annual Conference on Advances in Cognitive Systems (ACS), 2013, 109-124Google Scholar

  • [105] E. E. Aksoy, M. Tamosiunaite, R. Vuga, A. Ude, C. Geib, M. Steedman, F. Worgotter, Structural bootstrapping at the sensorimotor level for the fast acquisition of action knowledge for cognitive robots, in IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2013, 1-8Google Scholar

  • [106] F. Fleischer, V. Caggiano, P. Thier, M. A. Giese, Physiologically inspired model for the visual recognition of transitive hand actions, Journal of Neuroscience, 2013, 33(15), 6563-6580CrossrefGoogle Scholar

  • [107] D. Newtson, Attribution and the unit of perception of ongoing behavior, Journal of Personality and Social Psychology, 1973, 28(1), 28-38CrossrefGoogle Scholar

  • [108] J. M. Zacks, B. Tversky, Event structure in perception and conception, Psychological Bulletin, 2001, 127(1), 3-21Google Scholar

  • [109] M. M. Saylor, D. A. Baldwin, J. A. Baird, J. LaBounty, Infants’ online segmentation of dynamic human action, Journal of Cognition and Development, 2007, 8(1), 113-128CrossrefGoogle Scholar

  • [110] D. A. Baldwin, J. A. Baird, M. M. Saylor, M. A. Clark, Infants parse dynamic action, Child Development, 2001, 72(3), 708-717Google Scholar

  • [111] P. de Leva, Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters, Journal of Biomechanics, 1996, 29(9), 1223-1230Google Scholar

  • [112] Y. Sandamirskaya, Dynamic neural fields as a step towardscognitive neuromorphic architectures, Frontiers in Neuroscience, 2014, DOI: 10.3389/fnins.2013.00276 CrossrefGoogle Scholar

  • [113] J. Zadny, H. B. Gerard, Attributed intentions and informational selectivity, Journal of Experimental Social Psychology, 1974, 10(1), 34-52CrossrefGoogle Scholar

  • [114] D. Baldwin, J. Loucks, M. Sabbagh, Pragmatics of human action, in T. F. Shipley, J. M. Zacks (Eds.), Understanding events: From perception to action, Oxford series in Visual Cognition, Oxford University Press, 2008, 96-129Google Scholar

  • [115] B. Duran, Y. Sandamirskaya, Neural dynamics of hierarchically organized sequences: A robotic implementation, in 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2012, 357-362Google Scholar

About the article

Received: 2016-02-29

Accepted: 2018-01-22

Published Online: 2018-03-16

Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 9, Issue 1, Pages 19–59, ISSN (Online) 2081-4836, DOI: https://doi.org/10.1515/pjbr-2018-0003.

Export Citation

© 2018 Laith Alkurdi et al. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Bardia Yousefi and Chu Kiong Loo
Electronics, 2019, Volume 8, Number 10, Page 1169

Comments (0)

Please log in or register to comment.
Log in