用神经网络模拟短时记忆中发现事件规律的能力
A Neural Model of Rule Discovery with Relatively Short-Term Sequence Memory
- 基于短时记忆构建神经认知模型，捕捉事件序列中的规律
- 在延迟匹配任务中验证了模型对规律的识别能力
- 适合研究认知推理与机器学习中序列理解的交叉领域
本文提出一种神经认知模型，用于在相对短时记忆条件下发现事件序列中的规律。该模型旨在解释流体智力任务中对序列规律的发现能力。研究通过构建神经网络，在延迟匹配任务中进行实现与测试，验证了模型在有限记忆窗口下识别模式的有效性。实验结果表明，该模型能有效模拟人类在短时记忆约束下对事件序列规律的探索过程。
原文摘要 · Abstract (English)
This report proposes a neural cognitive model for discovering regularities in event sequences. In a fluid intelligence task, the subject is required to discover regularities from relatively short-term memory of the first-seen task. Some fluid intelligence tasks require discovering regularities in event sequences. Thus, a neural network model was constructed to explain fluid intelligence or regularity discovery in event sequences with relatively short-term memory. The model was implemented and tested with delayed match-to-sample tasks.
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