Learning to Abstract Visuomotor Mappings using Meta-Reinforcement Learning
This addresses the challenge of efficiently acquiring de novo skills with multiple mappings, which is incremental as it builds on existing meta-reinforcement learning and human learning research.
The study tackled the problem of learning multiple visuomotor mappings for new skills, finding that contextual cues significantly improve task performance in both humans and meta-reinforcement learning agents, with contextual information enabling separate representations and providing a computational advantage in learning more mappings.
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.