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Updated: Jun 12, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
The reinforcement metalearner as a biologically plausible meta-learning framework
Tim Vriens1, Mattias Horan2, Jacqueline Gottlieb3,4
1Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy Tim.Vriens@unicampus.it, massimo.silvetti@istc.cnr.ithttps://ctnlab.it/index.php/massimo-silvetti/, https://www.istc.cnr.it/en/people/massimo-silvetti.
Abstract:
We argue that the type of meta-learning proposed by Binz et al. generates models with low interpretability and falsifiability that have limited usefulness for neuroscience research. An alternative approach to meta-learning based on hyperparameter optimization obviates these concerns and can generate empirically testable hypotheses of biological computations.
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