Curriculum learning in humans and neural networks
Paper in proceeding, 2025

The sequencing of training trials can significantly influence learning outcomes in humans and neural networks. However, studies comparing the effects of training curricula between the two have typically focused on the acquisition of multiple tasks. Here, we investigate curriculum learning in a single perceptual decision-making task, examining whether the behavior of a parsimonious network trained on different curricula would be replicated in human participants. Our results show that progressively increasing task difficulty during training facilitates learning compared to training at a fixed level of difficulty or at random. Furthermore, a sequences designed to hamper learning in a parsimonious neural network network impair learning in humans. As such, our findings indicate strong qualitative similarities between neural networks and humans in curriculum learning for perceptual decision-making, suggesting the former can serve as a viable computational model of the latter.

Perceptual Decision Making

Curriculum Learning

Human Learning

Neural Networks

Cognitive Science

Author

Younes Strittmatter

Princeton University

Stefano Sarao Mannelli

Data Science and AI 3

University of Gothenburg

Miguel Ruiz-Garcia

Complutense University

Sebastian Musslick

Osnabrück University

Brown University

Markus Spitzer

Martin-Luther-Universität Halle-Wittenberg

Proceedings of the Annual Meeting of the Cognitive Science Society

1069-7977 (eISSN)

Vol. 47

Annual Meeting of the Cognitive Science Society
San Franscisco, USA,

Areas of Advance

Information and Communication Technology

Subject Categories (SSIF 2025)

Computer Sciences

Psychology

Neurosciences

Related datasets

Curriculum learning in humans and neural networks [dataset]

More information

Latest update

12/22/2025