@article {68773,
	title = {Meta-learning in head fixed mice navigating in virtual reality: A Behavioral Analysis},
	journal = {bioRxiv},
	year = {2023},
	month = {2023 May 02},
	abstract = {<p>Animals can learn general task structures and use them to solve new problems with novel sensory specifics. This capacity of {\textquoteleft}learning to learn{\textquoteright}, or meta-learning, is difficult to achieve in artificial systems, and the mechanisms by which it is achieved in animals are unknown. As a step toward enabling mechanistic studies, we developed a behavioral paradigm that demonstrates meta-learning in head-fixed mice. We trained mice to perform a two-alternative forced-choice task in virtual reality (VR), and successively changed the visual cues that signaled reward location. Mice showed increased learning speed in both cue generalization and serial reversal tasks. During reversal learning, behavior exhibited sharp transitions, with the transition occurring earlier in each successive reversal. Analysis of motor patterns revealed that animals utilized similar motor programs to execute the same actions in response to different cues but modified the motor programs during reversal learning. Our study demonstrates that mice can perform meta-learning tasks in VR, thus opening up opportunities for future mechanistic studies.</p>
},
	doi = {10.1101/2023.05.01.538936},
	url = {https://doi.org/10.1101/2023.05.01.538936},
	author = {Xinyu Zhao and Rachel Gattoni and Andrea Kozlosky and Angela Jacobs and Colin Morrow and Sarah Lindo and Nelson Spruston}
}
