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

eLife

Etienne Koechlin¹  Flora Bouchacourt¹  Stefano Palminteri²  Srdjan Ostojic² 

[1] Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Sante et de la Recherche Medicale, Paris, France;Departement d’Etudes Cognitives, Ecole Normale Superieure, Paris, France;Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Sante et de la Recherche Medicale, Paris, France;Departement d’Etudes Cognitives, Ecole Normale Superieure, Paris, France;Institut d’Etudes de la Cognition, Universite de Recherche Paris Sciences et Lettres, Paris, France;

关键词: computational neuroscience;    cognitive neuroscience;    neural networks;    Human;   

DOI  :  10.7554/eLife.50469

来源: publisher

【 摘 要 】

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.

【 授权许可】

CC BY