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Free associations as steady states in dynamic spaces

Kevin Shabahang
University of Melbourne ~ Psychology
Dr. Hyungwook Yim
Hanyang University ~ Department of Cognitive Sciences
Prof. Simon Dennis
University of Melbourne ~ University of Melbourne

The free association task provides a glimpse into the organizational structure of concepts in memory and has been used by theorists as a benchmark for computational models of semantic processing. While descriptive accounts like the Topics model and Latent Semantic Analysis have been shown to match free association data, to date no process model has been tested. We compared three descriptive models (Topics, LSA and word2vec, Mikolov et al., 2013) and two process models (Dynamic Eigen Network and BEAGLE; Jones & Mewhort, 2007). Overall, word2vec showed the best match to the South Florida free association norms. Of the process models, the DEN outperformed BEAGLE. When association pairs were characterized as either forward, backward, syntagmatic, paradigmatic, form-based or other, the profiles of performance of the models were remarkably similar. All models failed to capture form-based associations, as would be expected, and also performed best on paradigmatic associations.



Retrieval; Dynamic; Associative; Free associations; Semantic; Process model

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Cite this as:

Shabahang, K., Yim, H., & Dennis, S. (2021, July). Free associations as steady states in dynamic spaces. Paper presented at Virtual MathPsych/ICCM 2021. Via