Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

Junpei Zhong, Angelo Cangelosi, Stefan Wermter

    Research output: Contribution to journalArticlepeer-review

    16 Citations (Scopus)
    35 Downloads (Pure)

    Abstract

    The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.

    Original languageEnglish
    Article number22
    JournalFrontiers in Behavioral Neuroscience
    Volume8
    Issue numberFEB
    DOIs
    Publication statusPublished - 4 Feb 2014

    Keywords

    • Horizontal product
    • Parametric biases
    • Pre-symbolic communication
    • Recurrent neural networks
    • Sensorimotor integration

    Fingerprint

    Dive into the research topics of 'Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives'. Together they form a unique fingerprint.

    Cite this