@article{202796,
  keywords = {Motor learning, context learning, meta-learning, reinforcement learning, navigation},
  author = {Carlos Vel{\'a}zquez-Vargas and Isaac Christian and Jordan A. Taylor and Sreejan Kumar},
  title = {Learning to abstract visuomotor mappings using meta-reinforcement learning},
  abstract = {<p>We investigated the human capacity to acquire multiple visuomotor<br>mappings for de novo skills. Using a grid navigation<br>paradigm, we tested whether contextual cues implemented as<br>different {\textquotedblright}grid worlds{\textquotedblright}, allow participants to learn two distinct<br>key-mappings more efficiently. Our results indicate that when<br>contextual information is provided, task performance is significantly<br>better. The same held true for meta-reinforcement<br>learning agents that differed in whether or not they receive<br>contextual information when performing the task. We evaluated<br>their accuracy in predicting human performance in the<br>task and analyzed their internal representations. The results<br>indicate that contextual cues allow the formation of separate<br>representations in space and time when using different visuomotor<br>mappings, whereas the absence of them favors sharing<br>one representation. While both strategies can allow learning<br>of multiple visuomotor mappings, we showed contextual cues<br>provide a computational advantage in terms of how many mappings<br>can be learned.</p>},
  year = {2024},
  journal = {Proceedings of the Annual Meeting of the Cognitive Science Society},
  volume = {46},
  pages = {2240-2246},
  month = {07/2024},
  url = {https://escholarship.org/uc/item/4jj4q4df},
}