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

• Main Authors: Flora Bouchacourt, Stefano Palminteri, Etienne Koechlin, Srdjan Ostojic
• Format: Article
• Language: English
• Published: eLife Sciences Publications Ltd 2020-03-01
• Series: eLife
• Subjects: computational neuroscience; cognitive neuroscience; neural networks
• Online Access: https://elifesciences.org/articles/50469

Depending on environmental demands, humans can learn and exploit multiple concurrent sets of stimulus-response associations. Mechanisms underlying the learning of such task-sets 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 subject-by-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.