Emergent Effector-Independent Internal Spaces: Adaptation and Intermanual Learning Transfer in Humans and Neural Networks

Psychological studies have shown immense behavioral flexibility in arm reaching tasks. Intermanual learning transfer (ILT) tasks have shown that both reaching movements adapt to distorted spaces rather rapidly and the adaptation generalizes to the behavior of other limbs. In this paper, we present an ILT experiment and replicate it with feedforward neural network (NN) architectures. We show that the NN architecture is the key to successfully replicating the experiments. Moreover, we show that dependent on the architecture and the initial training schedule applied, an internal space representation emerges that enables ILT. The results confirm that internal body spaces, identified in neuroscience and cognitive psychological research, can emerge solely due to an interdependence between different limb movements and the right neural architecture. We hypothesize that, in order to develop internal spatial representations observed in animals and humans, it might be sufficient to enforce the integration of multiple correlated sensory and motor information into one compact internal representation.