| name | tensor-network-emotional-memory |
| description | Classical tensor network modeling of order-dependent emotional memory in children — quantum-inspired methods achieve 77.98% accuracy in modeling valence-influenced recognition sequences, demonstrating value of quantum-like approaches for temporal cognitive phenomena (arXiv: 2606.28470) |
| version | 1.0.0 |
| created | 2026-07-01T00:00:00.000Z |
| author | Hermes Agent |
| category | neuroscience |
| tags | ["tensor-networks","emotional-memory","quantum-cognition","children","recognition-memory","valence","order-dependence","quantum-inspired"] |
| activation | tensor network memory, emotional memory quantum, quantum-inspired cognition, valence sequence modeling, children recognition memory, order-dependent memory, quantum cognition tensor |
| arxiv_id | 2606.28470 |
| arxiv_url | https://arxiv.org/abs/2606.28470 |
Tensor Network Modeling of Emotional Memory in Children
Overview
This paper demonstrates how emotional valence influences the order-dependent structure of children's recognition memory using classical tensor network models. The key finding is that correct recall of emotionally-valenced toy sequences depends not just on individual valence but on the valence of surrounding items — and tensor networks capturing these contextual dependencies achieve 77.98% accuracy, far exceeding standard psychological models.
Key Innovation: While not strictly a "quantum cognition" model, the tensor network approach shows massive accuracy gains by naturally modeling order-dependent phenomena, validating quantum-inspired methods for cognitive temporal memory research.
Paper: arXiv:2606.28470 (2026-06-26)
Authors: Henry Groves, Lucia F. Jackson, Barbara-Anne Robertson, Jonte R. Hance
Comment: 26 pages, 9 figures
Core Methodology
1. Experimental Design
- Participants: Children shown sequences of emotionally-valenced toys
- Task: Recognition memory test after sequential presentation
- Manipulation: Each toy assigned emotional valence (positive/negative/neutral)
- Key measure: Whether recall accuracy depends on valence of adjacent items
2. Standard Psychological Models (Baseline)
- Standard models confirm order-dependence differs across event positions
- Limitation: Low accuracy; cannot capture how memory for one emotional object influences recall of others in the set
- Models treat items independently rather than as interacting sequence
3. Tensor Network Model
- Architecture: Classical tensor network factoring in valence context
- Key feature: Models interactions between adjacent emotional items in sequence
- Result: 77.98% accuracy — massive increase over standard models
- Interpretation: Tensor network structure naturally captures contextual interference patterns in memory
4. Why Tensor Networks Work
- Tensor contractions model how valence of one item affects processing of neighbors
- Captures non-independent structure: memory is not item-by-item but sequence-wide
- Quantum-inspired formalism (without requiring quantum mechanics) provides mathematical framework for contextual dependencies
