tensor-network-emotional-memory-modeling

name	tensor-network-emotional-memory-modeling
description	Tensor network modeling for order-dependent emotional memory in children (arXiv:2606.28470)
category	quantum-cognition

Tensor Network Modeling for Emotional Memory

Methodology from arXiv:2606.28470 (June 2026). Quantum-inspired tensor network approach to modeling order-dependent emotional memory in children.

Core Pattern

Tensor networks capture order-dependent structure in recognition memory that standard models miss:

• Correct recall depends not just on item valence, but on valence of preceding and following items
• Classical tensor network with valence factoring achieves 77.98% accuracy
• Massive accuracy increase over standard psychological models
• Novel task protocol for exploring emotional temporal memory

Key Findings

• Order-dependence differs across events (sequences of items)
• Standard models confirm order-dependence but have low accuracy
• Standard models don't reflect how memory for emotional objects influences others
• Tensor network captures inter-item influence through valence factoring
• Task protocol enables real-world tool for emotional temporal memory analysis

Implementation Steps

1. Design sequence presentation task with emotionally-valenced items
2. Record recall accuracy and order errors
3. Build tensor network model factoring in valence (positive/negative/neutral)
4. Train on sequence recall data with order-sensitive objective
5. Compare against standard psychological models and ablation controls
6. Analyze how memory for one item influences recall of adjacent items

When to Use

• Modeling order-dependent cognitive phenomena
• Emotional memory research and assessment
• Quantum-inspired modeling of psychological processes
• When standard models fail to capture sequential dependencies

References

• arXiv: 2606.28470v1
• Authors: Henry Groves, Lucia F. Jackson, Barbara-Anne Robertson