Actions with and without movements: A unified computational, neural basis?

Perfection is not about being in absolute control, but to let go...

Thomas Leroy to Nina, The Black Swan (Aronofsky, 2010)

   During any goal directed behaviour and social interactions, the dual processes of generating dexterous actions and anticipating the feasibility, consequnces of potential actions (of oneself and others) must seamlessly alternate. Presently, there is growing consensus that cortical networks in the predominantly motor areas of the brain are activated in several contexts related to ‘action’ that do not cause any overt movement: like imagination of actions, observation/interpretation of other’s actions, and comprehension of language. Prevailing computational modelling approaches generally converge on the role of forward models, but diverge on the perspective of how they might be realized in the brain or modelled computationally [1-3]. 


   Our research puts forward a radical perspective that even real movements are an outcome of a mental simulation. The basic rationale is that overt and covert stages of action must represent a continuum: such that every overtly executed action implies the existence of a covert stage (simulation), whereas a covert action needs not necessarily turn out into an overt action (you dont have to execute everything that you can imagine! ). Simply, real and imagined actions are two sides of the same coin, very much like the alternating foregorund and background in MC Eschers lithographs..


Investigating this line of thinking has at least three consequences:





  1. Offers a way to unify the computational basis of action “Generation, Simulation and Understanding” in light of emerging results from motor neuroscience [4] suggesting that the purpose of the Action system is not just to shape motor output during action execution but to provide the self with critical information about feasibility, consequence and understanding of potential actions (of oneself and others);

  2. Offers a computationally inexpensive means to generate motor commands to coordinate complex redundant bodies (animals, robots) without complex kinematic inversions [2], closely linked to similar ideas emerging from active inference [3];

  3. Offers a functional role to a “plastic, configurable and expandable” internal representation of the body i.e. the body schema to facilitate the seamless continuum between actions with and without movements while we “act, interact and anticipate” closely connected to emerging insights from intracranial depth recordings [6] suggesting the link between body schema and motor imagery, tool use in animals as an extension to the body schema [7-8], neuromotor rehabilitation[9].


   The Action generation/Simulation framework developed so far (see, 2 for a review), has been deployed in several robots (humanoids, wheeled platforms, industrial robots: see video), used in whole body simulaiton though a range EU Funded and external Grants.


   More recently, this body of work on PMP has been cited by several leading scientists, like Prof Andy Clark (Embodied Prediction 2015 article here), Prof Tanaka and Prof Sejnowski (article link), Prof Friston (article link). At the same time, the framework draws upon emerging results from several other areas like tool use studies in animals, neurorehabilitation and virtual reality, intracranical depth recordings from parietal areas, body map development in infants (a nice recent article) and ofcourse social cognition.

     Connecting all these the Dots has motivated me to write an indepth persepctive article "Muscleless Motor Synergies and Actions without Movements: From Motor Neuroscience to Cognitive Robotics",  More updates soon! Comments on draft welcome! We can also provide a testable software to run the system on iCub (if you have one), Your own robot or some simulation engine..




  1.  Pickering, M,J., Clark, A. (2014). Getting ahead: forward models and their role in cognitive architecture, Trends in cognitive sciences, 18(9).

  2. Mohan V and Morasso P (2011)  Passive motion paradigm: an alternative to optimal control. Front. Neurorobot. 5:4. doi: 10.3389/fnbot.2011.00004.

  3. Friston, K.(2011). What is optimal about motor control?, Neuron, Volume 72, Pages 488–498.

  4. Gallese, V., Sinigaglia, C.  (2011). What is so special with Embodied Simulation. Trends in Cognitive Sciences. 15(11):512-9.

  5. Morasso P, Casadio M, Mohan V, Rea F, Zenzeri J (2015) Revisiting the body-schema concept in the context of Whole-Body Postural-Focal Dynamics. Frontiers in Human Neuroscience.

  6. Bhat, A, Mohan, V, Akkaladevi, S., Eitzinger, C.,Sandini, G, Morasso, P (2016). Towards a learnt neural body schema for dexterous coordination of action in humanoid and industrial robots, Autonomous Robots (in press).

  7. Marshall, P. J., & Meltzoff, A. N. (2015). Body maps in the infant brain. Trends in Cognitive Science, 19(9), 483–544. 

  8. Desmurget M, Reilly KT, Richard N, Szathmari A, Mottolese C, Sirigu A. (2009). Movement intention after parietal cortex stimulation in humans. Science. 2009 May 8;324(5928):811-3.

  9. Iriki A, and Taoka M (2012) "Triadic (ecological, neural, cognitive) niche construction: a scenario of human brain evolution extrapolating tool use and language from the control of reaching actions.", Philos Trans R Soc Lond B Biol Sci, 367(1585), 10-23 (2012)

  10. Umiltà, M.A., Escola, L., Intskirveli, I., Grammont, F., Rochat, M., Caruana, F., Jezzini, A., Gallese, V., Rizzolatti, G. (2008).  When pliers become fingers in the monkey motor system. Proc Natl Acad Sci U S A.105(6):2209-13.

  11. Blanke O (2012) Multisensory brain mechanisms of bodily self-consciousness. Nature Reviews Neuroscience: 13: 556-571.






















© Vishwanathan Mohan


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