| name | quantum-like-benchmark-context-sensitive-memory |
| description | Quantum-like benchmark framework for context-sensitive associative memory with adaptive plasticity. Provides controlled methodology for comparing quantum-like vs classical associative memory models under weak structural support, order-sensitive recall, and staged task conditions. |
| version | 1 |
| created | 2026-06-12T00:00:00.000Z |
| arxiv_id | 2606.12449 |
| authors | Yashine H. Goolam Hossen, Lea Gassab, Travis J. A. Craddock |
| paper_title | A quantum-like benchmark for context-sensitive associative memory with adaptive plasticity |
| doi | 10.48550/arXiv.2606.12449 |
| activation_keywords | ["quantum-like memory","associative memory benchmark","context-sensitive memory","adaptive plasticity","order-sensitive recall","staged recall","homeostatic stabilization","weak structural support","temporal organization","memory dynamics"] |
| related_domains | ["quantum cognition","associative memory","computational neuroscience","memory models","neural plasticity"] |
Quantum-like Benchmark for Context-Sensitive Associative Memory
Core Insight
No universal quantum-like advantage — model classes distinguished by multi-objective profile (recall, temporal organization, context sensitivity) rather than single recall score. Quantum-like models preserve order sensitivity better; classical Markov-rate models achieve stronger raw recall.
Key Methodology
Order-Sensitive Adaptive-Plasticity Benchmark
- Staged Associative Recall: Sequential task presentation testing memory across phases
- Weak Structural Support Screening: Identify narrow non-monotonic useful regime
- Factorial Comparison: Quantum-like vs matched real-valued no-phase vs Markov-rate controls
- Conservative Operating Point: Fixed settings for fair comparison
Model Classes Compared
- Quantum-like: Complex-valued associative memory with phase information
- No-phase (real-valued): Same architecture without complex phase
- Markov-rate: Classical probabilistic model matched for comparison
Plasticity Mechanisms Tested
- Adaptive plasticity: Dynamic weight updates during learning
- Homeostatic stabilization: Self-regulating synaptic strength
- No-plasticity ablation: Control condition to isolate structural support effects
Key Findings
-
Weak Structural Support Regime:
- Useful regime is narrow and non-monotonic
- Weak structure alone does not rescue recall without plasticity
-
Plasticity Contributions:
- Most useful recall gains from adaptive plasticity
- Homeostatic stabilization particularly important
- Plasticity more impactful than background structure
-
Model Comparison Profile:
- Markov-rate: Stronger raw recall scores
- Quantum-like: Better order sensitivity preservation
- Quantum-like: More consistent stage-dependent organization
-
Multi-Objective Evaluation:
