Temporal chunking as a mechanism for unsupervised learning of task-sets
- Ecole Normale Superieure Paris, France
Abstract
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.
Data availability
Code has been uploaded to https://github.com/florapython/TemporalChunkingTaskSets. Statistical maps corresponding to human subjects data have been uploadeed to Neurovault (https://neurovault.org/collections/6754/).
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Human behaviour datahttps://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001293.
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Human behaviour and BOLD imaging datahttps://science.sciencemag.org/content/344/6191/1481.abstract.
Article and author information
Author details
Stefano Palminteri
Funding
Ecole de Neuroscience de Paris (Doctoral Fellowship)
- Flora Bouchacourt
Agence Nationale de la Recherche (ANR-16-CE37- 0016-01)
- Srdjan Ostojic
Agence Nationale de la Recherche (ANR-17-ERC2-0005-01)
- Srdjan Ostojic
Inserm (R16069JS)
- Stefano Palminteri
Agence Nationale de la Recherche (ANR-16-NEUC-0004)
- Stefano Palminteri
Fondation Fyssen
- Stefano Palminteri
Fondation Schlumberger
- Stefano Palminteri
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Participants provided written informed consent approved by the French National Ethics Committee.
Copyright
© 2020, Bouchacourt et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Temporal chunking as a mechanism for unsupervised learning of task-sets
eLife 9:e50469.
https://doi.org/10.7554/eLife.50469
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