Temporal chunking as a mechanism for unsupervised learning of task-sets

Flora Bouchacourt (1) , Stefano Palminteri (1) , Etienne Koechlin (1) , Srdjan Ostojic (1)

1 ENS-PSL - École normale supérieure - Paris

Depending on environmental demands, humans can learn and exploit multiple concurrent sets of stimulus-response associations. Mechanisms underlying the learning of such tasksets remain unknown. Here we investigate the hypothesis that task-set learning relies on unsupervised chunking of stimulus-response associations that occur in temporal proximity. We examine behavioral and neural data from a task-set learning experiment using a network model. We first show that task-set learning can be achieved provided the timescale of chunking is slower than the timescale of stimulus-response learning. Fitting the model to behavioral data on a subjectby-subject basis confirmed this expectation and led to specific predictions linking chunking and task-set retrieval that were borne out by behavioral performance and reaction times. Comparing the model activity with BOLD signal allowed us to identify neural correlates of task-set retrieval in a functional network involving ventral and dorsal prefrontal cortex, with the dorsal system preferentially engaged when retrievals are used to improve performance.

Domaines: Neurosciences [q-bio.NC]

Flora Bouchacourt, Stefano Palminteri, Etienne Koechlin, Srdjan Ostojic. Temporal chunking as a mechanism for unsupervised learning of task-sets. eLife, 2020, 9, ⟨10.7554/eLife.50469⟩. ⟨hal-03013614⟩