Using free association networks to extract characteristic patterns of affect dynamics

Yaniv Dover*, Zohar Moore

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The dynamics of human affect in day-to-day life are an intrinsic part of human behaviour. Yet, it is difficult to observe and objectively measure how affect evolves over time with sufficient resolution. Here, we suggest an approach that combines free association networks with affect mapping, to gain insight into basic patterns of affect dynamics. This approach exploits the established connection in the literature between association networks and behaviour. Using extant rich data, we find consistent patterns of the dynamics of the valence and arousal dimensions of affect. First, we find that the individuals represented by the data tend to feel a constant pull towards an affect-neutral global equilibrium point in the valence- arousal space. The farther the affect is from that point, the stronger the pull. We find that the drift of affect exhibits high inertia, i.e. is slow-changing, but with occasional discontinuous jumps of valence.We further find that, under certain conditions, anothermetastable equilibrium point emerges on the network, but one which represents a much more negative and agitated state of affect. Finally, we demonstrate how the affectcoded association network can be used to identify useful or harmful trajectories of associative thoughts that otherwise are hard to extract.

Original languageAmerican English
Article number20190647
JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2236
StatePublished - 1 Apr 2020

Bibliographical note

Funding Information:
Funding. This research was funded partially by the Israel Science Foundation grant no. 1124/16. Acknowledgements. We thank Neta Livneh for her useful suggestions and Prof. Shaul Oreg for support and direction. Also, we thank the anonymous reviewers who greatly helped to shape this paper.

Publisher Copyright:
© 2020 The Author(s) Published by the Royal Society. All rights reserved.


  • Affect dynamics
  • Association networks
  • Complex networks


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