A cognitive robotic model of mental rotation

Abstract

Mental rotation processes allow an agent to mentally rotate an image of an object in order to solve a given task, for example to make a decision on whether two objects presented with different rotational orientation are same or different. This article proposes a bio-constrained neural network model that accounts for the mental rotation processes based on neural mechanisms involving not only visual imagery but also affordance encoding, motor simulation, and the anticipation of the visual consequences of actions. The proposed model is in agreement with the theoretical and empirical research on mental rotation. The model is validated with a simulated humanoid robot (iCub) engaged in solving a typical mental rotation task. The results of the simulations show that the model is able to solve a mental rotation task and, in agreement with data from psychology experiments, they also show response times linearly dependent on the angular disparity between the objects. The model represents a novel account of the brain sensorimotor mechanisms that might underlie mental rotation.