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.

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Data availability

The data sets from the APS, analysed during the current study, are available in the APS Data Sets for Research repository, under request: https://journals.aps.org/datasets. The data sets of the USPTO, analysed during the current study, are available in the NBER repository: http://www.nber.org/patents/. The data sets for songs, movies and biographies generated and analysed during the current study are available from the corresponding authors upon reasonable request.

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  1. 1.

    Halbwachs, M. La Mémoire Collective (Albin Michel, Paris, 1997).

  2. 2.

    Assmann, J. Collective memory and cultural identity. New Ger. Crit. 65, 125–133 (1995).

  3. 3.

    Assmann, J. Das kulturelle Gedächtnis. Schrift, Erinnerung und politische Identität in frühen Hochkulturen (Verlag C. H. Beck, Munich, 2007).

  4. 4.

    Assmann, J. in Cultural Memories (eds Meusburger, P. et al.) 15–27 (Springer, Heidelberg, 2011).

  5. 5.

    Wertsch, J. V. Voices of Collective Remembering (Cambridge Univ. Press, Cambridge, 2002).

  6. 6.

    Goldhammer, A., Nora, P. & Kritzman, L. D. Realms of Memory: The Construction of the French Past (Columbia Univ. Press, New York, 1998).

  7. 7.

    Roediger, H. & DeSoto, K. Forgetting the presidents. Science 346, 1106–1109 (2014).

  8. 8.

    Roediger, H. L. III, Zaromb, F. M. & Butler, A. C. in Memory in Mind and Culture (eds Boyer, P. & Wertsch, J. V.) 138–170 (Cambridge Univ. Press, Cambridge, 2009).

  9. 9.

    Zaromb, F., Butler, A. C., Agarwal, P. K. & Roediger, H. L. III. Collective memories of three wars in United States history in younger and older adults. Mem. Cognit. 42, 383–399 (2014).

  10. 10.

    Rubin, D. C. How quickly we forget. Science 346, 1058–1059 (2014).

  11. 11.

    García-Gavilanes, R., MollgaardA., Tsvetkova, M. & Yasseri, T. The memory remains: understanding collective memory in the digital age. Sci. Adv. 3, e1602368 (2017).

  12. 12.

    Wang, D., Song, C. & Barabási, A.-L. Quantifying long-term scientific impact. Science 342, 127–132 (2013).

  13. 13.

    Higham, K. W., Governale, M., Jaffe, A. B. & Zülicke, U. Fame and obsolescence: disentangling growth and aging dynamics of patent citations. Phys. Rev. E 95, 042309 (2017).

  14. 14.

    Higham, K., Governale, M., Jaffe, A. & Zülicke, U. Unraveling the dynamics of growth, aging and ination for citations to scientific articles from specific research fields. J. Informetr. 11, 1190–1200 (2017).

  15. 15.

    Valverde, S., Solé, R. V., Bedau, M. A. & Packard, N. Topology and evolution of technology innovation networks. Phys. Rev. E 76, 056118 (2007).

  16. 16.

    Csárdi, G., Strandburg, K. J., Zalányi, L., Tobochnik, J. & Érdi, P. Modeling innovation by a kinetic description of the patent citation system. Physica A 374, 783–793 (2007).

  17. 17.

    Golosovsky, M. & Solomon, S. Stochastic dynamical model of a growing citation network based on a self-exciting point process. Phys. Rev. Lett. 109, 098701 (2012).

  18. 18.

    Dorogovtsev, S. N. & Mendes, J. F. F. Evolution of networks with aging of sites. Phys. Rev. E 62, 1842–1845 (2000).

  19. 19.

    Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

  20. 20.

    Albert, R. & Barabási, A.-L. Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002).

  21. 21.

    Price, D. D. S. A general theory of bibliometric and other cumulative advantage processes. J. Am. Soc. Inf. Sci. 27, 292–306 (1976).

  22. 22.

    Yule, G. U. A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S. Phil. Trans. R. Soc. Lond. B 213, 21–87 (1925).

  23. 23.

    Merton, R. K. The Matthew effect in science, II: cumulative advantage and the symbolism of intellectual property. Isis 79, 606–623 (1988).

  24. 24.

    Assmann, J. in Cultural Memory Studies. An International and Interdisciplinary Handbook (eds Erll, A. & Nünning, A.) 109–118 (Walter de Gruyter, Berlin, 2008).

  25. 25.

    Hirst, W., Yamashiro, J. K. & Coman, A. Collective memory from a psychological perspective. Trends. Cogn. Sci. 22, 438–451 (2018).

  26. 26.

    Roediger, H. L. & DeSoto, K. A. Recognizing the presidents: was Alexander Hamilton president? Psychol. Sci. 27, 644–650 (2016).

  27. 27.

    Rubin, D. C. Memory in Oral Traditions: the Cognitive Psychology of Epic, Ballads, and Counting-Out Rhymes (Oxford Univ. Press, Oxford, 1995).

  28. 28.

    Hammack, P. L. Narrative and the politics of meaning. Narrat. Inq. 21, 311–318 (2011).

  29. 29.

    Sperber, D. & Hirschfeld, L. A. The cognitive foundations of cultural stability and diversity. Trends Cogn. Sci. 8, 40–46 (2004).

  30. 30.

    Buskell, A. What are cultural attractors? Biol. Philos. 32, 377–394 (2017).

  31. 31.

    Richerson, P. J. & Boyd, R. Not by Genes Alone: How Culture Transformed Human Evolution (Univ. Chicago Press, Chicago, 2005).

  32. 32.

    Storm, B. C., Bjork, E. L. & Bjork, R. A. On the durability of retrieval-induced forgetting. J. Cogn. Psychol. 24, 617–629 (2012).

  33. 33.

    Garcia-Bajos, E., Migueles, M. & Anderson, M. Script knowledge modulates retrieval-induced forgetting for eyewitness events. Memory 17, 92–103 (2009).

  34. 34.

    Cuc, A., Koppel, J. & Hirst, W. Silence is not golden: a case for socially shared retrieval-induced forgetting. Psychol. Sci. 18, 727–733 (2007).

  35. 35.

    Echterhoff, G., Higgins, E. T. & Levine, J. M. Shared reality: experiencing commonality with others’ inner states about the world. Perspect. Psychol. Sci. 4, 496–521 (2009).

  36. 36.

    Coman, A. & Hirst, W. Social identity and socially shared retrieval-induced forgetting: the effects of group membership. J. Exp. Psychol. Gen. 144, 717–722 (2015).

  37. 37.

    Coman, A., Stone, C. B., Castano, E. & Hirst, W. Justifying atrocities: the effect of moral-disengagement strategies on socially shared retrieval-induced forgetting. Psychol. Sci. 25, 1281–1285 (2014).

  38. 38.

    Stone, C. B., Barnier, A. J., Sutton, J. & Hirst, W. Building consensus about the past: schema consistency and convergence in socially shared retrieval-induced forgetting. Memory 18, 170–184 (2010).

  39. 39.

    Kurzban, R., Tooby, J. & Cosmides, L. Can race be erased? Coalitional computation and social categorization. Proc. Natl Acad. Sci. USA 98, 15387–15392 (2001).

  40. 40.

    Yu, A. Z., Ronen, S., Hu, K., Lu, T. & Hidalgo, C. A. Pantheon 1.0, a manually verified dataset of globally famous biographies. Sci. Data 3, 150075 (2016).

  41. 41.

    Jara-Figueroa, C., Yu, A. Z. & Hidalgo, C. A. How the medium shapes the message: printing and the rise of the arts and sciences. PLoS ONE (in the press).

  42. 42.

    Skiena, S. & Ward, C. B. Who’s Bigger? Where Historical Figures Really Rank (Cambridge Univ. Press, Cambridge, 2014).

  43. 43.

    Kanhabua, N., Nguyen, T. N. & Niederée, C. What triggers human remembering of events? A large-scale analysis of catalysts for collective memory in Wikipedia. In Proc. 14th ACM/IEEE-CS Joint Conference on Digital Libraries 341–350 (IEEE, 2014).

  44. 44.

    Uzzi, B., Mukherjee, S., Stringer, M. & Jones, B. Atypical combinations and scientific impact. Science 342, 468–472 (2013).

  45. 45.

    Mukherjee, S., Romero, D. M., Jones, B. & Uzzi, B. The nearly universal link between the age of past knowledge and tomorrow's breakthroughs in science and technology: the hotspot. Sci. Adv. 3, e1601315 (2017).

  46. 46.

    Ronen, S. et al. Links that speak: the global language network and its association with global fame. Proc. Natl Acad. Sci. USA 111, E5616–E5622 (2014).

  47. 47.

    Ferron, M. & Massa, P. Beyond the encyclopedia: collective memories in Wikipedia. Mem. Stud. 7, 22–45 (2014).

  48. 48.

    Yucesoy, B. & Barabási, A.-L. Untangling performance from success. EPJ Data Sci. 5, 17 (2016).

  49. 49.

    Halbwachs, M. On Collective Memory (Univ. Chicago Press, Chicago, 1992).

  50. 50.

    Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).

  51. 51.

    Radicchi, F., Fortunato, S. & Castellano, C. Universality of citation distributions: toward an objective measure of scientific impact. Proc. Natl Acad. Sci. USA 105, 17268–17272 (2008).

  52. 52.

    Kuhn, T., Perc, M. & Helbing, D. Inheritance patterns in citation networks reveal scientific memes. Phys. Rev. X 4, 041036 (2014).

  53. 53.

    Sinatra, R., Wang, D., Deville, P., Song, C. & Barabasi, A.-L. Quantifying the evolution of individual scientific impact. Science 354, aaf5239 (2016).

  54. 54.

    King, M. M., Bergstrom, C. T., Correll, S. J., Jacquet, J. & West, J. D. Men set their own cites high: gender and self-citation across fields and over time. Socius 3, 1–22 (2017).

  55. 55.

    Chu, J. S. G. & Evans, J. A. Too many papers? Slowed canonical progress in large fields of science. Preprint at SocArXiv https://doi.org/10.31235/osf.io/jk63c (2018).

  56. 56.

    Yook, S.-H., Radicchi, F. & Meyer-Ortmanns, H. Self-similar scale-free networks and disassortativity. Phys. Rev. E 72, 045105 (2005).

  57. 57.

    Mukherjee, S., Uzzi, B., Jones, B. F. & Stringer, M. in Knowledge and Networks (eds Glückler, J. et al.) 243–267 (Springer, Cham, 2017).

  58. 58.

    Shen, H.-W. & Barabasi, A.-L. Collective credit allocation in science. Proc. Natl Acad. Sci. USA 111, 12325–12330 (2014).

  59. 59.

    Hall, B. H., Jaffe, A. B. & Trajtenberg, M. The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools (National Bureau of Economic Research, 2001); www.nber.org/papers/w8498

  60. 60.

    Jaffe, A. B., Trajtenberg, M. & Henderson, R. Geographic localization of knowledge spillovers as evidenced by patent citations. Q. J. Econ. 108, 577–598 (1993).

  61. 61.

    The Hot 100 Ranking Billboard https://www.billboard.com/charts/hot-100 (2016).

  62. 62.

    Spotify web API Spotify https://developer.spotify.com/documentation/web-api/ (2017).

  63. 63.

    Last.fm web services last.fm https://www.last.fm/api (2017).

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C.C. and C.R.-S. acknowledge financial support from Centro de Investigación en Complejidad Social and Universidad del Desarrollo. C.J.-F. and C.A.H. acknowledge support from the MIT Media Lab Consortia. The authors thank F. Pinheiro, T. Roukny, G. Castro-Dominguez, the Centro de Investigación en Complejidad Social, the Collective Learning Group at the MIT Media Lab and the Center for Complex Network Research at Northeastern University for the helpful insights and discussions. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information


  1. Collective Learning Group, The MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Cristian Candia
    • , C. Jara-Figueroa
    •  & César A. Hidalgo
  2. Network Science Institute, Northeastern University, Boston, MA, USA

    • Cristian Candia
    •  & Albert-László Barabási
  3. Centro de Investigación en Complejidad Social (CICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile

    • Cristian Candia
    •  & Carlos Rodriguez-Sickert


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C.C., C.A.H. and A.-L.B. contributed to the study conception and design, interpretation of data and drafting of the manuscript. C.C. and C.J.-F. contributed to the acquisition of data, data analysis, modelling and drafting of the manuscript. C.R.-S. contributed to study conception and design, and interpretation of data.

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The authors declare no competing interests.

Corresponding authors

Correspondence to Cristian Candia or César A. Hidalgo.

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