论文标题
迈向代表感觉运动原语的自组织的符号前神经模型
Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives
论文作者
论文摘要
感觉运动行为的符号和语言表征的获取是代理执行和/或观察自己和其他人的行为时执行的认知过程。根据Piaget的认知发展理论，这些表示在感觉运动阶段和术前阶段发展。我们提出了一个模型，将高级信息从视觉刺激与腹侧/背视觉流的发展相关联。该模型采用基于RNNPB（具有参数偏见的复发神经网络）和水平产品模型的神经网络体系结构，该神经网络体系结构结合了预测性感觉模块。我们通过一个机器人被动观察对象以学习其功能和运动来体现此模型。在观察感觉运动原语的学习过程中，即观察一组手臂运动及其定向对象特征的轨迹，前符号表示在参数单元中是自组织的。这些代表性单位充当分叉参数，引导机器人识别和预测各种学习的感觉运动原始素。符号前的表示还解释了在潜在学习环境中学习感觉运动原语的学习。
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.
