首页 /研究 /Exploration Behaviors, Body Representations, and Simulation Processes for the Development of Cognition in Artificial Agents
PERCEPTION

Exploration Behaviors, Body Representations, and Simulation Processes for the Development of Cognition in Artificial Agents

Guido Schillaci, Verena V. Hafner, Bruno Lara

发表年份
2016
引用次数
67
访问权限
开放获取

摘要

Sensorimotor control and learning are fundamental prerequisites for cognitive development in humans and animals. Evidence from behavioural sciences and neuroscience suggests that motor and brain development are strongly intertwined with the experiential process of \textit{exploration}, where internal body representations are formed and maintained over time. In order to guide our movements, our brain must hold an internal model of our body and constantly monitor its configuration state. How can sensorimotor control using such low-level body representations enable the development of more complex cognitive and motor capabilities? Although a clear answer has still not been found for this question, several studies suggest that processes of mental simulation of action-perception loops are likely to be executed in our brain and are dependent on internal body representations. Therefore, the capability to re-enact sensorimotor experience might represent a key mechanism behind the implementation of higher cognitive capabilities, such as behaviour recognition, arbitration and imitation, sense of agency and self-other distinction. Addressed mainly to researchers on autonomous motor and mental development in artificial agents, this work aims at gathering the latest development in the study on exploration behaviours, on internal body representations, on internal models, and on mechanisms for internal sensorimotor simulations. Relevant studies in human and animal sciences are discussed and a parallel to similar investigations in robotics is presented.

关键词

Internal modelImitationCognitionSense of agencyCognitive scienceComputer sciencePerceptionMechanism (biology)Motor imageryExperiential learning

相关论文

查看 PERCEPTION 分类全部论文