A dynamic model for action understanding and goal-directed imitation

Abstract

The understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills this mapping very often cannot be direct. In this paper we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network.The modeling results are discussed with respect to recent experimental findings in action observation/execution studies. Theme: Neural basis of behaviorTopic: Cognition

Measuring Human-Robot Interaction Through Motor Resonance

“…Though traditionally the goal of the observed action was proposed to play a dominant role during action imitation [16,88,89], some recent works focused on motion kinematic features attributing them a determinant function in automatic imitation. As example, Bove et al [90] ascertained whether the frequency of self-paced finger movements was modified by prior observation of motion performed at different frequencies.…”

Abstraction Levels for Robotic Imitation: Overview and Computational Approaches

“…The former decouples each of the action primitives and is thus able to deal with a larger variety of tasks, while the latter is able to combine several primitives and deal with complex motor skills in a robust way. In [43,44] dynamic neural networks are used to recognize actions goals taking into account task restrictions. A single neural network able to encode several behaviors was introduced in [137] and performs similar computations.…”

“…Dynamical systems have also been used for goal directed imitation in [43,44]. The proposed architecture contains three interconnected layers corresponding to different brain areas responsible for the observed action, the action primitives and the goal.…”

Elements for a Neural Theory of the Processing of Dynamic Faces

“…An important hypothesis in this context is that the visual recognition of actions might be accomplished by an internal simulation of the underlying motor behavior (Prinz, 1997;Rizzolatti et al, 2001). A number of computational models in robotics and neuroscience have tried to implement this principle (e.g., Erlhagen, Mukovskiy, & Bicho, 2006;Miall, 2003;Oztop, Kawato, & Arbib, 2006;Wolpert, Doya, & Kawato, 2003).…”