Cognitive and plastic recurrent neural network clock model for the judgment of time and its variations

• Published in: Scientific reports Vol. 13; no. 1; p. 3852
• Main Authors: Hallez, Quentin, Mermillod, Martial, Droit-Volet, Sylvie
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 08.03.2023; Nature Publishing Group UK; Nature Portfolio
• Subjects: Adult; Child; Cognition; Cognitive ability; Cognitive science; Computational neuroscience; Computer Simulation; Humanities and Social Sciences; Humans; Judgment; Learning; Neural networks; Neural Networks, Computer; Neural plasticity; Neuropsychological Tests; Neuropsychology; Psychology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-30894-4

The aim of this study in the field of computational neurosciences was to simulate and predict inter-individual variability in time judgements with different neuropsychological properties. We propose and test a Simple Recurrent Neural Network-based clock model that is able to account for inter-individual variability in time judgment by adding four new components into the clock system: the first relates to the plasticity of the neural system, the second to the attention allocated to time, the third to the memory of duration, and the fourth to the learning of duration by iteration. A simulation with this model explored its fit with participants' time estimates in a temporal reproduction task undertaken by both children and adults, whose varied cognitive abilities were assessed with neuropsychological tests. The simulation successfully predicted 90% of temporal errors. Our Cognitive and Plastic RNN-Clock model (CP-RNN-Clock), that takes into account the interference arising from a clock system grounded in cognition, was thus validated.