Department of Psychology, Hebrew University of Jerusalem.
Psychol Rev. 2023 Nov;130(6):1492-1520. doi: 10.1037/rev0000397. Epub 2022 Oct 3.
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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
思维之间的联想方式几十年来一直让学者们着迷。通过经典的语义处理模型可以解释联想是如何在对提示做出反应时产生的，这些模型通过不同的计算机制来实现。分布式吸引子网络实现了丰富性增加的动态，并假设更强的联想可以通过更少的步骤来实现。相反，扩散激活模型假设提示以恒定的速率将其激活平行地分配给所有联想。尽管这些模型具有巨大的影响力，但由于它们的不可计算性以及自由联想的不受限制的性质，它们之前的少数应用仅限于定性预测。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，并对人们联想的速度和强度做出预测。为此，我们首先开发了一种从个人选择联想的个性化单词空间映射到给定提示的新方法。然后，我们使用最先进的证据积累模型，一方面展示了丰富性增加的动态的作用，另一方面展示了扩散激活速率的随机性的作用，以防止在无数潜在联想的竞争中出现异常缓慢的解决。此外，尽管我们的结果一致表明，更强的联想需要更少的证据，但只有与丰富性增加的动态相结合，才能解释为什么弱联想缓慢但普遍存在。我们讨论了这些结果对语义处理和证据积累模型的影响，并为实际应用和个体差异研究提供了建议。（PsycInfo 数据库记录（c）2024 APA，保留所有权利）。
