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The universal decay of collective memory and attention

Author

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  • Cristian Candia

    (Massachusetts Institute of Technology
    Northeastern University
    Universidad del Desarrollo)

  • C. Jara-Figueroa

    (Massachusetts Institute of Technology)

  • Carlos Rodriguez-Sickert

    (Universidad del Desarrollo)

  • Albert-László Barabási

    (Northeastern University)

  • César A. Hidalgo

    (Massachusetts Institute of Technology)

Abstract

Collective memory and attention are sustained by two channels: oral communication (communicative memory) and the physical recording of information (cultural memory). Here, we use data on the citation of academic articles and patents, and on the online attention received by songs, movies and biographies, to describe the temporal decay of the attention received by cultural products. We show that, once we isolate the temporal dimension of the decay, the attention received by cultural products decays following a universal biexponential function. We explain this universality by proposing a mathematical model based on communicative and cultural memory, which fits the data better than previously proposed log-normal and exponential models. Our results reveal that biographies remain in our communicative memory the longest (20–30 years) and music the shortest (about 5.6 years). These findings show that the average attention received by cultural products decays following a universal biexponential function.

Suggested Citation

  • Cristian Candia & C. Jara-Figueroa & Carlos Rodriguez-Sickert & Albert-László Barabási & César A. Hidalgo, 2019. "The universal decay of collective memory and attention," Nature Human Behaviour, Nature, vol. 3(1), pages 82-91, January.
    • Handle: RePEc:nat:nathum:v:3:y:2019:i:1:d:10.1038_s41562-018-0474-5
    DOI: 10.1038/s41562-018-0474-5
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    Cited by:

    1. Verluise, Cyril & Cristelli, Gabriele & Higham, Kyle & de Rassenfosse, Gaetan, 2020. "The Missing 15 Percent of Patent Citations," SocArXiv x78ys, Center for Open Science.
    2. Huimin Xu & Zhang Zhang & Lingfei Wu & Cheng-Jun Wang, 2019. "The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-18, November.
    3. Sidorov, Sergei & Mironov, Sergei, 2021. "Growth network models with random number of attached links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    4. Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2022. "Revealing mobility pattern of taxi movements with its travel trajectory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    5. Kobi Abayomi, 2024. "How & Why To Use Audience Segmentation to Maximize (Listener) Demand Across Digital Music Portfolio," Papers 2406.09226, arXiv.org.
    6. Matúš Medo & Manuel S. Mariani & Linyuan Lü, 2022. "The simple regularities in the dynamics of online news impact," Journal of Computational Social Science, Springer, vol. 5(1), pages 629-646, May.
    7. Cunico, Giovanni & Aivazidou, Eirini & Mollona, Edoardo, 2021. "Building a dynamic theory of citizens’ awareness of European Cohesion Policy interventions," European Journal of Operational Research, Elsevier, vol. 289(2), pages 758-773.
    8. Ma, Yin-Jie & Jiang, Zhi-Qiang & Podobnik, Boris, 2022. "Predictability of players’ actions as a mechanism to boost cooperation," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    9. Zhenpeng Li & Xijin Tang & Zhenjie Hong, 2022. "Collective attention dynamic induced by novelty decay," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(8), pages 1-11, August.
    10. Timur Gareev & Irina Peker, 2023. "Quantity versus quality in publication activity: knowledge production at the regional level," Papers 2311.08830, arXiv.org.

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