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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为无数选择积累证据：模拟自由联想的产生。

几十年来，思想相互联系的联想方式一直吸引着学者们的兴趣。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实现更强的关联。相反，传播激活模型假设一个提示以恒定速率将其激活并行分布到所有关联。尽管这些模型具有巨大的影响力，但它们的难处理性和自由联想的不受约束的性质限制了它们以前很少用于定性预测。为了定量地测试这些计算机制，我们将自由联想概念化为内部证据积累的产物，并生成关于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来证明一方面是富人越来越富的动态功能，另一方面是传播激活率的随机性，以防止问题的解决极其缓慢。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，但只有结合富人更富的动态才能解释为什么弱关联缓慢但普遍存在。