[2606.28470] Modelling Emotional Memory in Children with Tensor Networks
[Submitted on 26 Jun 2026]
Title:Modelling Emotional Memory in Children with Tensor Networks
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Abstract:We demonstrate how emotional valence influences the order-dependent structure of children's recognition memory: correct recall of a sequence of emotionally-valenced toys depended not just on the valence of a given toy itself, but also on the valence of the toys shown before and after it. Whilst standard psychological models confirm that order-dependence differs across an event (a set of toys shown in sequence), accuracy is low and the model does not reflect how memory for an emotional object influences others in the set. A classical tensor network model factoring in valence is able to achieve a 77.98\% accuracy in modelling the results of the study. While not strictly a ``quantum cognition'' model, this massive increase in accuracy shows the value of quantum-inspired methods for modelling order-dependent phenomena, such as emotional memory. Further, the task protocol we introduce presents a novel, real-world tool for exploring emotional temporal memory in children for analysis using classical and quantum-like models of cognition.
Comments: 26 pages, 9 figures
Subjects:
Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC); Quantum Physics (quant-ph)
Cite as: arXiv:2606.28470 [cs.LG]
(or arXiv:2606.28470v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.28470
arXiv-issued DOI via DataCite
Submission history
From: Jonte Hance [view email] [v1] Fri, 26 Jun 2026 14:31:09 UTC (3,441 KB)
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