A Neural Network model for spatial mental imagery investigation: A study with the humanoid robot platform iCub

Abstract

Understanding the process behind the human ability of creating mental images of events and experiences is a still crucial issue for psychologists. Mental imagery may be considered a multimodal biological simulation that activates the same, or very similar, sensorial and motor modalities that are activated when we interact with the environment in real time. Neuro-psychological studies show that neural mechanisms underlying real-time visual perception and mental visualization are the same when a task is mentally recalled. Nevertheless, the neural mechanisms involved in the active elaboration of mental images might be different from those involved in passive elaborations. The enhancement of this active and creative imagery is the aim of most psychological and educational processes, although, more empirical effort is needed in order to understand the mechanisms and the role of active mental imagery in human cognition. In this work we present some results of on ongoing investigation about mental imagery using cognitive robotics. Here we focus on the capability to estimate, from proprioceptive and visual information, the position into a soccer field when the robot acquires the goal. Results of simulation with the iCub platform are given to show that the computational model is able to efficiently estimate the robot's position. The final objective of our work is to replicate with a cognitive robotics model the mental imagery when it is used during the training phase of athletes that are allowed to imaginary practice to score a goal.