The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models’ huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people’s associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research.

中文翻译：

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积累无数替代方案的证据：模拟自由联想的产生。


几十年来，思想相互关联的联想方式一直引起学者们的兴趣。响应提示而生成关联的过程可以通过不同的计算机制通过语义处理的经典模型来解释。分布式吸引子网络实现了富者愈富的动态，并假设可以用更少的步骤达到更强的关联。相反，传播激活模型假设线索以恒定速率并行地将其激活分布到所有关联。尽管这些模型影响巨大，但它们的棘手性以及自由联想的不受约束的性质限制了它们之前很少用于定性预测。为了定量测试这些计算机制，我们将自由联想概念化为内部证据积累的产物，并生成有关人们联想的速度和强度的预测。为此，我们首先开发了一种新颖的方法来映射个性化的单词空间，个人可以从中选择与给定提示的关联。然后，我们使用最先进的证据积累模型，一方面证明了富者愈富动态的作用，另一方面证明了传播激活率的随机性在防止问题解决速度极其缓慢方面的作用。无数潜在协会之间的竞争。此外，虽然我们的结果一致表明更强的关联需要更少的证据，但只有与富者愈富的动态相结合，才能解释为什么弱关联缓慢而普遍。 我们讨论了语义处理和证据积累模型的含义，并为实际应用和个体差异研究提供了建议。