用张量网络建模儿童情绪记忆，准确率达77.98%
Modelling Emotional Memory in Children with Tensor Networks
- 引入情绪效价的张量网络模型，捕捉记忆顺序依赖性
- 模型准确率77.98%，显著优于传统心理模型
- 适用于研究儿童情绪时间记忆，适合认知科学与量子类模型研究者
我们展示了情绪效价如何影响儿童识别记忆中的顺序依赖结构：正确回忆一组情绪化玩具的顺序，不仅取决于当前玩具的情绪效价，还受其前后玩具情绪效价的影响。尽管传统心理学模型确认事件内部顺序依赖性存在差异，但其准确率较低，且无法反映情绪对象对整体记忆的影响。采用情绪效价因子的经典张量网络模型，在该研究结果建模中达到了77.98%的准确率。虽非严格意义上的量子认知模型，但准确率的显著提升表明，量子启发方法在建模顺序依赖现象（如情绪记忆）中具有重要价值。此外，我们提出的新任务范式为探索儿童情绪时间记忆提供了真实世界工具，可用于经典及量子类认知模型分析。
原文摘要 · Abstract (English)
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.
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