The role of synergies within generative models of action execution and recognition: A computational perspective. Comment on "Grasping synergies: A motor-control approach to the mirror neuron mechanism" by A. D'Ausilio et al

Haris Dindo, Pierpaolo Iodice, Francesco Donnarumma, Giovanni Pezzulo, Roberto Prevete

Risultato della ricerca: Articlepeer review

4 Citazioni (Scopus)

Abstract

Controlling the body – given its huge number of degrees of freedom – poses severe computational challenges. Mounting evidence suggests that the brain alleviates this problem by exploiting “synergies”, or patterns of muscle activities (and/or movement dynamics and kinematics) that can be combined to control action, rather than controlling individual muscles of joints [1–10]. D’Ausilio et al. [11] explain how this view of motor organization based on synergies can profoundly change the way we interpret studies of action recognition in humans and monkeys, and in particular the controversy on the “granularity” of the mirror neuron system (MNs): whether it encodes either (lower) kinematic aspects of movements, or (higher) goal representations, or both but at different hierarchical levels [12]. Here we offer a complementary, computational perspective on the role of synergies for action recognition and the MNs. In computational modeling and robotics, it is widely assumed that a control scheme using synergies simplifies movement planning and execution. This scheme permits to use elemental behaviors or primitives as “building blocks” to be composed (e.g., combined linearly, sequenced) to produce more complex behaviors, thus controlling relatively few degrees of freedom [13–15]. Do synergies yield equivalent benefits for action recognition? To answer this question from a computational viewpoint, we frame the concept of synergies within generative architectures of action execution and recognition [16–20]. According to two leading theories of motor control, optimal feedback control [21] and active inference [22], the motor system can be conceptualized as a (hierarchical) generative model, which encodes a (probabilistic) mapping between “task goals” specified at a higher level (e.g., grasping a cup) and states of the “plant” to be controlled (i.e., (Less)
Lingua originaleEnglish
pagine (da-a)114-117
Numero di pagine4
RivistaPhysics of Life Reviews
Volume12
Stato di pubblicazionePublished - 2015

All Science Journal Classification (ASJC) codes

  • Agricultural and Biological Sciences(all)
  • Physics and Astronomy(all)
  • Artificial Intelligence

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