In accordance with internal clock models 1, 9, 10, researchers have attributed these misjudgments of time to cognitive modules added to the clock module as part of a wider temporal information processing. Numerous neuropsychological studies which have assessed the cognitive abilities of children of different ages have shown that misjudgments of time increase in line with a decrease in children's abilities, mainly in terms of working memory and attention 5, 6, 7, 8. These misjudgments of time have been found in particular among individuals with limited cognitive abilities, such as young children 3, 4. However, despite having this internal clock system, evidence has accumulated that humans vary in their estimation of time and are prone to the under- and over-estimation of time. 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.įor decades there has been a consensus that humans are equipped with an internal clock system for accurate time measurement 1, 2. The simulation successfully predicted 90% of temporal errors. 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. 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. 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.
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