Free association among words is a fundamental and ubiquitous memory task. Although distributed semantics (DS) models can predict the association between pairs of words, and semantic network (SN) models can describe transition probabilities in free association data, there have been few attempts to apply established cognitive process models of memory search to free association data. Thus, researchers are currently unable to explain the dynamics of free association using memory mechanisms known to be at play in other retrieval tasks, such as free recall from lists. We address this issue using a popular neural network model of free recall, the context maintenance and retrieval (CMR) model, which we fit using stochastic gradient descent on a large data set of free association norms. Special cases of CMR mimic existing DS and SN models of free association, and we find that CMR outperforms these models on out-of-sample free association data. We also show that training CMR on free association data generates improved predictions for free recall from lists, demonstrating the value of free association for the study of many different types of memory phenomena. Overall, our analysis provides a new account of the dynamics of free association, predicts free association with increased accuracy, integrates theories of free association with established models of memory, and shows how large data sets and neural network training methods can be used to model complex cognitive processes that operate over thousands of representations.

中文翻译：

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神经网络中的自由联想。

单词之间的自由联想是一项基本且普遍存在的记忆任务。尽管分布式语义（DS）模型可以预测单词对之间的关​​联，并且语义网络（SN）模型可以描述自由联想数据中的转移概率，但很少有人尝试将已建立的记忆搜索认知过程模型应用于自由联想数据。因此，研究人员目前无法使用已知在其他检索任务中发挥作用的记忆机制（例如从列表中自由回忆）来解释自由联想的动态。我们使用流行的自由回忆神经网络模型、上下文维护和检索（CMR）模型来解决这个问题，我们使用随机梯度下降对自由关联规范的大数据集进行拟合。CMR 的特殊情况模仿现有的自由关联 DS 和 SN 模型，我们发现 CMR 在样本外自由关联数据上优于这些模型。我们还表明，在自由联想数据上训练 CMR 可以改进列表中自由回忆的预测，证明自由联想对于研究许多不同类型的记忆现象的价值。总的来说，我们的分析提供了对自由联想动态的新解释，以更高的准确性预测自由联想，将自由联想理论与已建立的记忆模型相结合，并展示了如何使用大数据集和神经网络训练方法来建模复杂的模型。运作超过数千种表征的认知过程。