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Replay shapes abstract cognitive maps for efficient social navigation

Nature Human Behaviour (2024)Cite this article

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Abstract

To make adaptive social decisions, people must anticipate how information flows through their social network. While this requires knowledge of how people are connected, networks are too large to have first-hand experience with every possible route between individuals. How, then, are people able to accurately track information flow through social networks? Here we find that people immediately cache abstract knowledge about social network structure as they learn who is friends with whom, which enables the identification of efficient routes between remotely connected individuals. These cognitive maps of social networks, which are built while learning, are then reshaped through overnight rest. During these extended periods of rest, a replay-like mechanism helps to make these maps increasingly abstract, which privileges improvements in social navigation accuracy for the longest communication paths that span distinct communities within the network. Together, these findings provide mechanistic insight into the sophisticated mental representations humans use for social navigation.

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Fig. 1: Study design.
Fig. 2: Evidence of social navigation.
Fig. 3: Evidence for a replay-like mechanism.
Fig. 4: Evidence of cached structural knowledge.

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Data availability

All data needed to reproduce the analyses are available in a publicly accessible GitHub repository at https://github.com/feldmanhalllab/network-navigation-replay (ref. 57).

Code availability

All code needed to reproduce the analyses are available in a publicly accessible GitHub repository at https://github.com/feldmanhalllab/network-navigation-replay (ref. 57).

References

  1. Travers, J. & Milgram, S. An experimental study of the small world problem. Sociometry 32, 425–443 (1969).

    Article  Google Scholar 

  2. Christakis, N. A. & Fowler, J. H. Social contagion theory: examining dynamic social networks and human behavior. Stat. Med. 32, 556–577 (2013).

    Article  PubMed  Google Scholar 

  3. Son, J.-Y., Bhandari, A. & FeldmanHall, O. Cognitive maps of social features enable flexible inference in social networks. Proc. Natl Acad. Sci. USA 118, e2021699118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Son, J.-Y., Bhandari, A. & FeldmanHall, O. Abstract cognitive maps of social network structure aid adaptive inference. Proc. Natl Acad. Sci. USA 120, e2310801120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948).

    Article  CAS  PubMed  Google Scholar 

  6. O’Keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Clarendon, 1978).

  7. Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005).

    Article  CAS  PubMed  Google Scholar 

  8. Bellmund, J. L. S., Gärdenfors, P., Moser, E. I. & Doeller, C. F. Navigating cognition: spatial codes for human thinking. Science 362, eaat6766 (2018).

    Article  PubMed  Google Scholar 

  9. Behrens, T. E. J. et al. What is a cognitive map? Organizing knowledge for flexible behavior. Neuron 100, 490–509 (2018).

    Article  CAS  PubMed  Google Scholar 

  10. Constantinescu, A. O., O'Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464–1468 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Garvert, M. M., Dolan, R. J. & Behrens, T. E. J. A map of abstract relational knowledge in the human hippocampal–entorhinal cortex. eLife 6, e17086 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Tavares, R. M. et al. A map for social navigation in the human brain. Neuron 87, 231–243 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Park, S. A., Miller, D. S., Nili, H., Ranganath, C. & Boorman, E. D. Map making: constructing, combining, and inferring on abstract cognitive maps. Neuron 107, 1226–1238.e1228 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dayan, P. Improving generalization for temporal difference learning: the successor representation. Neural Comput. 5, 613–624 (1993).

    Article  Google Scholar 

  15. Momennejad, I. Learning structures: predictive representations, replay, and generalization. Curr. Opin. Behav. Sci. 32, 155–166 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Momennejad, I. et al. The successor representation in human reinforcement learning. Nat. Hum. Behav. 1, 680–692 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Russek, E. M., Momennejad, I., Botvinick, M. M., Gershman, S. J. & Daw, N. D. Predictive representations can link model-based reinforcement learning to model-free mechanisms. PLoS Comput. Biol. 13, e1005768 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. The hippocampus as a predictive map. Nat. Neurosci. 20, 1643–1653 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Lynn, C. W. & Bassett, D. S. How humans learn and represent networks. Proc. Natl Acad. Sci. USA. 117, 29407–29415 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lynn, C. W., Kahn, A. E., Nyema, N. & Bassett, D. S. Abstract representations of events arise from mental errors in learning and memory. Nat. Commun. 11, 2313 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Momennejad, I. & Howard, M. W. Predicting the future with multi-scale successor representations. Preprint at bioRxiv https://doi.org/10.1101/449470 (2018).

  22. Schapiro, A. C., Rogers, T. T., Cordova, N. I., Turk-Browne, N. B. & Botvinick, M. M. Neural representations of events arise from temporal community structure. Nat. Neurosci. 16, 486–492 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pudhiyidath, A. et al. Representations of temporal community structure in hippocampus and precuneus predict inductive reasoning decisions. J. Cogn. Neurosci. 34, 1736–1760 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Foster, D. J. Replay comes of age. Annu. Rev. Neurosci. 40, 581–602 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Schapiro, A. C., McDevitt, E. A., Rogers, T. T., Mednick, S. C. & Norman, K. A. Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance. Nat. Commun. 9, 3920 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Sun, W., Advani, M., Spruston, N., Saxe, A. & Fitzgerald, J. E. Organizing memories for generalization in complementary learning systems. Nat. Neurosci. 26, 1438–1448 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Liu, Y., Mattar, M. G., Behrens, T. E. J., Daw, N. D. & Dolan, R. J. Experience replay is associated with efficient nonlocal learning. Science 372, eabf1357 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Foster, D. J. & Wilson, M. A. Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature 440, 680–683 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Igata, H., Ikegaya, Y. & Sasaki, T. Prioritized experience replays on a hippocampal predictive map for learning. Proc. Natl Acad. Sci. USA 118, e2011266118 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. Zhenglong, Z., Michael, J. K. & Anna, C. S. Replay as context-driven memory reactivation. Preprint at bioRxiv https://doi.org/10.1101/2023.03.22.533833 (2023).

  31. Ellenbogen, J. M., Hu, P. T., Payne, J. D., Titone, D. & Walker, M. P. Human relational memory requires time and sleep. Proc. Natl Acad. Sci. USA 104, 7723–7728 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lewis, P. A. & Durrant, S. J. Overlapping memory replay during sleep builds cognitive schemata. Trends Cogn. Sci. 15, 343–351 (2011).

    Article  PubMed  Google Scholar 

  33. Lutz, N. D., Diekelmann, S., Hinse-Stern, P., Born, J. & Rauss, K. Sleep supports the slow abstraction of gist from visual perceptual memories. Sci. Rep. 7, 42950 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Feld, G. B., Bernard, M., Rawson, A. B. & Spiers, H. J. Sleep targets highly connected global and local nodes to aid consolidation of learned graph networks. Sci. Rep. 12, 15086 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Klinzing, J. G., Niethard, N. & Born, J. Mechanisms of systems memory consolidation during sleep. Nat. Neurosci. 22, 1598–1610 (2019).

    Article  CAS  PubMed  Google Scholar 

  36. Correa, C. G., Ho, M. K., Callaway, F., Daw, N. D. & Griffiths, T. L. Humans decompose tasks by trading off utility and computational cost. PLoS Comput. Biol. 19, e1011087 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Callaway, F. et al. Rational use of cognitive resources in human planning. Nat. Hum. Behav. 6, 1112–1125 (2022).

    Article  PubMed  Google Scholar 

  38. Rigoux, L., Stephan, K. E., Friston, K. J. & Daunizeau, J. Bayesian model selection for group studies—revisited. NeuroImage 84, 971–985 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. Wagenmakers, E.-J. & Farrell, S. AIC model selection using Akaike weights. Psychon. Bull. Rev. 11, 192–196 (2004).

    Article  PubMed  Google Scholar 

  40. Kurth-Nelson, Z., Economides, M., Dolan, R. J. & Dayan, P. Fast sequences of non-spatial state representations in humans. Neuron 91, 194–204 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Schuck, N. W. & Niv, Y. Sequential replay of non-spatial task states in the human hippocampus. Science 364, eaaw5181 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gómez, R. L., Bootzin, R. R. & Nadel, L. Naps promote abstraction in language-learning infants. Psychol. Sci. 17, 670–674 (2006).

    Article  PubMed  Google Scholar 

  43. Lau, H., Alger, S. E. & Fishbein, W. Relational memory: a daytime nap facilitates the abstraction of general concepts. PLoS ONE 6, e27139 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pereira, S. I. R. et al. Rule abstraction is facilitated by auditory cuing in REM sleep. J. Neurosci. 43, 3838–3848 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. St Clair, M. C. & Monaghan, P. Language abstraction: consolidation of language structure during sleep. In Proc. of the Annual Meeting of the Cognitive Science Society 30 (Cognitive Science Society, 2008).

  46. Walker, M. P. & Stickgold, R. Overnight alchemy: sleep-dependent memory evolution. Nat. Rev. Neurosci. 11, 218 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wittkuhn, L., Krippner, L. M. & Schuck, N. W. Replay in human visual cortex is linked to the formation of successor representations and independent of consciousness. Preprint at bioRxiv https://doi.org/10.1101/2022.02.02.478787 (2022) .

  48. Jadhav, S. P., Kemere, C., German, P. W. & Frank, L. M. Awake hippocampal sharp-wave ripples support spatial memory. Science 336, 1454–1458 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Stoianov, I., Maisto, D. & Pezzulo, G. The hippocampal formation as a hierarchical generative model supporting generative replay and continual learning. Prog. Neurobiol. 217, 102329 (2022).

    Article  PubMed  Google Scholar 

  50. Gershman, S. J., Moore, C. D., Todd, M. T., Norman, K. A. & Sederberg, P. B. The successor representation and temporal context. Neural Comput. 24, 1553–1568 (2012).

    Article  PubMed  Google Scholar 

  51. Lau, T., Gershman, S. J. & Cikara, M. Social structure learning in human anterior insula. Elife 9, e53162 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Whittington, J. C. R. et al. The Tolman–Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation. Cell 183, 1249–1263.e1223 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wu, C. M., Schulz, E. & Gershman, S. J. Inference and search on graph-structured spaces. Comput. Brain Behav. 4, 125–147 (2021).

    Article  Google Scholar 

  54. Ma, D. S., Correll, J. & Wittenbrink, B. The Chicago face database: a free stimulus set of faces and norming data. Behav. Res. Methods 47, 1122–1135 (2015).

    Article  PubMed  Google Scholar 

  55. Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).

    Article  Google Scholar 

  56. Wilson, R. C. & Collins, A. G. E. Ten simple rules for the computational modeling of behavioral data. Elife 8, e49547 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Network-navigation-replay. GitHub https://github.com/feldmanhalllab/network-navigation-replay (2024).

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Acknowledgements

We thank I. Aslarus, K. Danowski, E. Duchan, Y.-F. Jerry Hu, A. Lawrence, J. Palfy, V. Poyraz, M. Malhotra, M. Mazumder, S. Shulman, A. Stein, S. Vaca Narvaja and J. Wang for assisting with data collection. We thank A. Maddah for developing some of the task code used in these studies and Y. Yang Teoh for helpful computational modelling advice. Part of this research was conducted using computational resources and services at the Centre for Computation and Visualization, Brown University. Advanced access to these computing resources was supported by NIH award 1S10OD025181. This work is supported by the National Science Foundation award 2123469 (O.F.H. and A.B.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

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Author notes

  1. These authors contributed equally: Apoorva Bhandari, Oriel FeldmanHall.

Authors and Affiliations

  1. Department of Cognitive and Psychological Sciences, Brown University, Providence, RI, USA

    Jae-Young Son, Apoorva Bhandari & Oriel FeldmanHall

  2. Institute of Psychology, Leiden University, Leiden, The Netherlands

    Marc-Lluís Vives

  3. Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA

    Oriel FeldmanHall

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  1. Jae-Young Son
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  2. Marc-Lluís Vives
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Contributions

J.Y.S. and M.-L.V. contributed equally to this work. A.B. and O.F.H. contributed equally to this work. Conceptualization: M.-L.V., J.Y.S., A.B. and O.F.H. Formal analysis: J.Y.S. and M.-L.V. Funding acquisition: O.F.H. Investigation: M.-L.V. Methodology: M.-L.V., J.Y.S., A.B. and O.F.H. Supervision: O.F.H. and A.B., Writing: J.Y.S., M.-L.V., A.B. and O.F.H.

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Correspondence to Apoorva Bhandari or Oriel FeldmanHall.

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Son, JY., Vives, ML., Bhandari, A. et al. Replay shapes abstract cognitive maps for efficient social navigation. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01990-w

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