Learning to Abstract Visuomotor Mappings using Meta-Reinforcement
  Learning
Carlos A. Velazquez-Vargas Isaac Ray Christian Jordan A. Taylor Sreejan Kumar
We investigated the human capacity to acquire multiple visuomotor mappings
for de novo skills. Using a grid navigation paradigm, we tested whether
contextual cues implemented as different "grid worlds", allow participants to
learn two distinct key-mappings more efficiently. Our results indicate that
when contextual information is provided, task performance is significantly
better. The same held true for meta-reinforcement learning agents that differed
in whether or not they receive contextual information when performing the task.
We evaluated their accuracy in predicting human performance in the task and
analyzed their internal representations. The results indicate that contextual
cues allow the formation of separate representations in space and time when
using different visuomotor mappings, whereas the absence of them favors sharing
one representation. While both strategies can allow learning of multiple
visuomotor mappings, we showed contextual cues provide a computational
advantage in terms of how many mappings can be learned.