Accumulating Evidence for Myriad Alternatives: Modeling the Generation of Free Association

Isaac Fradkin*, Eran Eldar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageAmerican English
JournalPsychological Review
StateAccepted/In press - 2022

Bibliographical note

Funding Information:
This work has been made possible by National Institutes of Health Grants R01MH124092 and R01MH125564, Israel Science Foundation Grant 1094/20, and United States—Israel Binational Science Foundation Grant 2019801 to Eran Eldar. The authors have no conflict of interest to disclose.

Publisher Copyright:
© 2022 American Psychological Association


  • Attractor networks
  • Evidence accumulation
  • Free association
  • Semantic memory
  • Spreading activation


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