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A Mixture of Generative Models Strategy Helps Humans Generalize across Tasks
Herce Castañón, Santiago; CARDOSO-LEITE, Pedro; Altarelli, Irene et al.


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Abstract :
[en] 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.
Disciplines :
Neurosciences & behavior
Author, co-author :
Herce Castañón, Santiago
CARDOSO-LEITE, Pedro ;  University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS)
Altarelli, Irene
Green, C. Shawn
Schrater, Paul
Bavelier, Daphne
Language :
Title :
A Mixture of Generative Models Strategy Helps Humans Generalize across Tasks
Publication date :
FnR Project :
FNR11242114 - Scientifically Validated Digital Learning Environments, 2016 (01/06/2017-31/01/2023) - Pedro Cardoso-leite
Commentary :
Available on ORBilu :
since 04 March 2021


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