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A mixture of generative models strategy helps humans generalize across tasks

Santiago Herce Castañón, Pedro Cardoso-Leite, Irene Altarelli, C. Shawn Green, Paul Schrater, Daphne Bavelier
doi: https://doi.org/10.1101/2021.02.16.431506

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doi 
https://doi.org/10.1101/2021.02.16.431506
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  • February 25, 2021.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Author Information

  1. Santiago Herce Castañón1,*,
  2. Pedro Cardoso-Leite2,
  3. Irene Altarelli3,
  4. C. Shawn Green4,
  5. Paul Schrater5 and
  6. Daphne Bavelier1
  1. 1Department of Psychology and Educational Sciences, University of Geneva, Switzerland
  2. 2Department of Behavioural and Cognitive Science, University of Luxembourg, Luxembourg
  3. 3Université de Paris, LaPsyDÉ, CNRS, France
  4. 4Department of Psychology, University of Wisconsin-Madison, USA
  5. 5Department of Computer Science, University of Minnesota, USA
  1. ↵* Correspondence: s.hercecastanon{at}gmail.com
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Posted February 25, 2021.
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A mixture of generative models strategy helps humans generalize across tasks
Santiago Herce Castañón, Pedro Cardoso-Leite, Irene Altarelli, C. Shawn Green, Paul Schrater, Daphne Bavelier
bioRxiv 2021.02.16.431506; doi: https://doi.org/10.1101/2021.02.16.431506
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