PT  - JOURNAL ARTICLE
AU  - Herce Castañón, Santiago
AU  - Cardoso-Leite, Pedro
AU  - Altarelli, Irene
AU  - Green, C. Shawn
AU  - Schrater, Paul
AU  - Bavelier, Daphne
TI  - A mixture of generative models strategy helps humans generalize across tasks
AID  - 10.1101/2021.02.16.431506
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.16.431506
4099  - http://biorxiv.org/content/early/2021/02/25/2021.02.16.431506.short
4100  - http://biorxiv.org/content/early/2021/02/25/2021.02.16.431506.full
AB  - What role do generative models play in generalization of learning in humans? Our novel multi-task prediction paradigm—where participants complete four sequence learning tasks, each being a different instance of a common generative family—allows the separate study of within-task learning (i.e., finding the solution to each of the tasks), and across-task learning (i.e., learning a task differently because of past experiences). The very first responses participants make in each task are not yet affected by within-task learning and thus reflect their priors. Our results show that these priors change across successive tasks, increasingly resembling the underlying generative family. We conceptualize multi-task learning as arising from a mixture-of-generative-models learning strategy, whereby participants simultaneously entertain multiple candidate models which compete against each other to explain the experienced sequences. This framework predicts specific error patterns, as well as a gating mechanism for learning, both of which are observed in the data.Competing Interest StatementThe authors have declared no competing interest.