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Episodic memory facilitates flexible decision-making via access to detailed events

Nature Human Behaviour (2026)Cite this article

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Abstract

Our experiences contain countless details that may be important in the future, yet we rarely know which will matter and which will not. This uncertainty poses a difficult challenge for adaptive decision-making, as failing to preserve relevant information can prevent us from making good choices later on. One solution to this challenge is to store detailed memories of individual experiences that can be flexibly accessed whenever their details become relevant. By allowing us to store and recall specific events in vivid detail, the human episodic memory system provides exactly this capacity. Yet, whether and how this ability supports adaptive behaviour is poorly understood. Here we aimed to determine whether people use detailed episodic memories to make decisions when future task demands are uncertain. We hypothesized that the episodic memory system’s ability to store events in great detail allows us to reference any of these details if they later become relevant. We tested this hypothesis using a novel decision-making task in which participants encoded individual events with multiple features and later made decisions based on these features to maximize their earnings. Across 5 experiments (total n = 535), we found that participants referenced episodic memories during decisions in feature-rich environments and that they did so specifically when it was unclear at encoding which features would be needed in the future. Overall, these findings reveal a fundamental adaptive function of episodic memory, showing how its rich representational capacity enables flexible decision-making under uncertainty.

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Fig. 1: Task design.
Fig. 2: People use episodic memory to make decisions in multifeature environments.
Fig. 3: Episodic memory is primarily used for decisions when it is unclear what features are important.
Fig. 4: Episodic memory is used to make choices about unexpected offers.
Fig. 5: Realistic decision demands encourage participants to develop more efficient retrieval strategies.

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Data availability

The data are available via GitHub at https://github.com/jonathanicholas/nm2025_emdm.

Code availability

The code is available via GitHub at https://github.com/jonathanicholas/nm2025_emdm.

References

  1. Tulving, E. in Organization of Memory (eds Tulving, E. & Donaldson, W.) 381–403 (Academic Press, 1972).

  2. Eichenbaum, H. The hippocampus and mechanisms of declarative memory. Behav. Brain Res. 103, 123–133 (1999).

    Article  CAS  PubMed  Google Scholar 

  3. Bartlett, F. C. Remembering: A Study in Experimental and Social Psychology (Cambridge Univ. Press, 1932).

  4. Anderson, J. R. & Milson, R. Human memory: an adaptive perspective. Psychol. Rev. 96, 703–719 (1989).

    Article  Google Scholar 

  5. Schacter, D. L., Guerin, S. A. & Jacques, P. L. S. Memory distortion: an adaptive perspective. Trends Cogn. Sci. 15, 467–474 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Shohamy, D. & Adcock, R. A. Dopamine and adaptive memory. Trends Cogn. Sci. 14, 464–472 (2010).

    Article  CAS  PubMed  Google Scholar 

  7. Lengyel, M. & Dayan, P. Hippocampal contributions to control: the third way. In Proc. Advances in Neural Information Processing Systems (eds Platt, J. C. et al.) 889–896 (Curran Associates, 2008).

  8. Gershman, S. J. & Daw, N. D. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annu. Rev. Psycho. 68, 101–128 (2017).

    Article  Google Scholar 

  9. Nagy, D. G., Orbán, G. & Wu, C. M. Adaptive compression as a unifying framework for episodic and semantic memory. Nat. Rev. Psychol. 4, 484–498 (2025).

    Article  Google Scholar 

  10. Rescorla, R. A. & Wagner, A. R. in Classical Conditioning II: Current Research and Theory (eds Black, A. H. & Prokasy, W. F.) 64–99 (Appleton-Century-Crofts, 1972).

  11. Shohamy, D. & Daw, N. D. in The Cognitive Neurosciences 5th edn (eds Gazzaniga, M. S. & Mangun, G. R.) 591–603 (Boston Review, 2014).

  12. Knowlton, B. J., Mangels, J. A. & Squire, L. R. A neostriatal habit learning system in humans. Science 273, 1399–1402 (1996).

    Article  CAS  PubMed  Google Scholar 

  13. Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

    Article  CAS  PubMed  Google Scholar 

  14. Bayer, H. M. & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron 47, 129–141 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shohamy, D., Myers, C. E., Grossman, S., Sage, J. & Gluck, M. A. The role of dopamine in cognitive sequence learning: evidence from Parkinson’s disease. Behav. Brain Res. 156, 191–199 (2005).

    Article  CAS  PubMed  Google Scholar 

  16. Zaghloul, K. A. et al. Human substantia nigra neurons encode unexpected financial rewards. Science 323, 1496–1499 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction 2nd edn (MIT Press, 2018).

  18. Wilson, R. C. & Niv, Y. Inferring relevance in a changing world. Front. Hum. Neurosci. 5, 189 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Leong, Y. C., Radulescu, A., Daniel, R., DeWoskin, V. & Niv, Y. Dynamic interaction between reinforcement learning and attention in multidimensional environments. Neuron 93, 451–463 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Niv, Y. et al. Reinforcement learning in multidimensional environments relies on attention mechanisms. J. Neurosci. 35, 8145–8157 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Miller, G. A. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956).

    Article  CAS  PubMed  Google Scholar 

  22. Bornstein, A. M., Khaw, M. W., Shohamy, D. & Daw, N. D. Reminders of past choices bias decisions for reward in humans. Nat. Commun. 8, 15958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Duncan, K., Semmler, A. & Shohamy, D. Modulating the use of multiple memory systems in value-based decisions with contextual novelty. J. Cogn. Neurosci. 31, 1455–1467 (2019).

    Article  PubMed  Google Scholar 

  24. Mason, A., Madan, C. R., Simonsen, N., Spetch, M. L. & Ludvig, E. A. Biased confabulation in risky choice. Cognition 229, 105245 (2022).

  25. Nicholas, J., Daw, N. D. & Shohamy, D. Uncertainty alters the balance between incremental learning and episodic memory. eLife 11, e81679 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Montaser-Kouhsari, L., Nicholas, J., Gerraty, R. T. & Shohamy, D. Differentiating reinforcement learning and episodic memory in value-based decisions in Parkinson’s disease. J. Neurosci. 45, e0911242025 (2024).

    Article  Google Scholar 

  27. Plonsky, O., Teodorescu, K. & Erev, I. Reliance on small samples, the wavy recency effect, and similarity-based learning. Psychol. Rev. 122, 621–647 (2015).

    Article  PubMed  Google Scholar 

  28. Daw, N. D. & Dayan, P. The algorithmic anatomy of model-based evaluation. Philos. Trans. R. Soc. B 369, 20130478 (2014).

    Article  Google Scholar 

  29. Kool, W., Cushman, F. A. & Gershman, S. J. When does model-based control pay off? PLoS Comput. Biol. 12, e1005090 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kahana, M. J. Foundations of Human Memory (Oxford Univ. Press, 2012).

  31. Ratcliff, R. A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978).

    Article  Google Scholar 

  32. Sederberg, P. B., Howard, M. W. & Kahana, M. J. A context-based theory of recency and contiguity in free recall. Psychol. Rev. 115, 893–912 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  33. O’Reilly, R. C. & McClelland, J. L. Hippocampal conjunctive encoding, storage, and recall: avoiding a trade-off. Hippocampus 4, 661–682 (1994).

    Article  PubMed  Google Scholar 

  34. O'Reilly, R. C. & Rudy, J. W. Conjunctive representations in learning and memory: principles of cortical and hippocampal function. Psychol. Rev. 108, 311–345 (2001).

    Article  CAS  PubMed  Google Scholar 

  35. Rudy, J. W. & O’Reilly, R. C. Conjunctive representations, the hippocampus, and contextual fear conditioning. Cogn. Affect. Behav. Neurosci. 1, 66–82 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. Lee, S. W., O’Doherty, J. P. & Shimojo, S. Neural computations mediating one-shot learning in the human brain. PLoS Biol. 13, e1002137 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Packard, M. G. & McGaugh, J. L. Inactivation of hippocampus or caudate nucleus with lidocaine differentially affects expression of place and response learning. Neurobiol. Learn. Mem. 65, 65–72 (1996).

    Article  CAS  PubMed  Google Scholar 

  38. Poldrack, R. A. & Packard, M. G. Competition among multiple memory systems: converging evidence from animal and human brain studies. Neuropsychologia 41, 245–251 (2003).

    Article  PubMed  Google Scholar 

  39. Stewart, N., Chater, N. & Brown, G. D. Decision by sampling. Cogn. Psychol. 53, 1–26 (2006).

    Article  PubMed  Google Scholar 

  40. Biele, G., Erev, I. & Ert, E. Learning, risk attitude and hot stoves in restless bandit problems. J. Math. Psychol. 53, 155–167 (2009).

    Article  Google Scholar 

  41. Shadlen, M. N. & Shohamy, D. Decision making and sequential sampling from memory. Neuron 90, 927–939 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wang, S., Feng, S. F. & Bornstein, A. M. Mixing memory and desire: how memory reactivation supports deliberative decision-making. WIREs Cogn. Sci. 13, e1581 (2022).

    Article  Google Scholar 

  43. Hertwig, R. & Erev, I. The description–experience gap in risky choice. Trends Cogn. Sci. 13, 517–523 (2009).

    Article  PubMed  Google Scholar 

  44. Schlichting, M. L. & Preston, A. R. in The Hippocampus from Cells to Systems: Structure, Connectivity, and Functional Contributions to Memory and Flexible Cognition (eds Hannula, D. E. & Duff, M. C.) 405–437 (Springer, 2017).

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

    Article  Google Scholar 

  46. Biderman, N., Bakkour, A. & Shohamy, D. What are memories for? The hippocampus bridges past experience with future decisions. Trends Cogn. Sci. 24, 542–556 (2020).

    Article  PubMed  Google Scholar 

  47. Squire, L. R. & Zola, S. M. Structure and function of declarative and nondeclarative memory systems. Proc. Natl Acad. Sci. USA 93, 13515–13522 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Poldrack, R. A. et al. Interactive memory systems in the human brain. Nature 414, 546 (2001).

    Article  CAS  PubMed  Google Scholar 

  49. Daw, N. D., Niv, Y. & Dayan, P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8, 1704–1711 (2005).

    Article  CAS  PubMed  Google Scholar 

  50. Lee, S. W., Shimojo, S. & O’Doherty, J. P. Neural computations underlying arbitration between model-based and model-free learning. Neuron 81, 687–699 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Greve, A., Cooper, E., Tibon, R. & Henson, R. N. Knowledge is power: prior knowledge aids memory for both congruent and incongruent events, but in different ways. J. Exp. Psychol. Gen. 148, 325–341 (2019).

    Article  PubMed  Google Scholar 

  52. van Kesteren, M. T. R., Ruiter, D. J., Fernández, G. & Henson, R. N. How schema and novelty augment memory formation. Trends Neurosci. 35, 211–219 (2012).

    Article  PubMed  Google Scholar 

  53. Rouhani, N. & Niv, Y. Signed and unsigned reward prediction errors dynamically enhance learning and memory. eLife 10, e61077 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Jang, A. I., Nassar, M. R., Dillon, D. G. & Frank, M. J. Positive reward prediction errors during decision-making strengthen memory encoding. Nat. Hum. Behav. 3, 719–732 (2019).

  55. Howard, M. W. & Kahana, M. J. A distributed representation of temporal context. J. Math. Psychol. 46, 269–299 (2002).

    Article  Google Scholar 

  56. Polyn, S. M., Norman, K. A. & Kahana, M. J. A context maintenance and retrieval model of organizational processes in free recall. Psychol. Rev. 116, 129–156 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Kahana, M. J. Computational models of memory search. Annu. Rev. Psychol. 71, 107–138 (2020).

    Article  PubMed  Google Scholar 

  58. Bornstein, A. M. & Norman, K. A. Reinstated episodic context guides sampling-based decisions for reward. Nat. Neurosci. 20, 997–1003 (2017).

    Article  CAS  PubMed  Google Scholar 

  59. Aka, A. & Bhatia, S. What I like is what I remember: memory modulation and preferential choice. J. Exp. Psychol. Gen. 150, 2175–2184 (2021).

    Article  PubMed  Google Scholar 

  60. Michelmann, S., Staresina, B. P., Bowman, H. & Hanslmayr, S. Speed of time-compressed forward replay flexibly changes in human episodic memory. Nat. Hum. Behav. 3, 143–154 (2019).

    Article  PubMed  Google Scholar 

  61. Wimmer, G. E., Liu, Y., Vehar, N., Behrens, T. E. J. & Dolan, R. J. Episodic memory retrieval success is associated with rapid replay of episode content. Nat. Neurosci. 23, 1025–1033 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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 

  63. McFadyen, J., Liu, Y. & Dolan, R. J. Differential replay of reward and punishment paths predicts approach and avoidance. Nat. Neurosci. 26, 627–637 (2023).

    Article  CAS  PubMed  Google Scholar 

  64. Wimmer, G. E., Liu, Y., McNamee, D. C. & Dolan, R. J. Distinct replay signatures for prospective decision-making and memory preservation. Proc. Natl Acad. Sci. USA 120, e2205211120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Carmichael, L., Hogan, H. P. & Walter, A. A. An experimental study of the effect of language on the reproduction of visually perceived form. J. Exp. Psychol. 15, 73–86 (1932).

    Article  Google Scholar 

  66. Nagy, D. G., Török, B. & Orbán, G. Semantic compression of episodic memories. Preprint at https://arxiv.org/abs/1806.07990 (2018).

  67. Zhou, D. et al. A compressed code for memory discrimination. Preprint at bioRxiv https://doi.org/10.1101/2025.10.12.681901 (2025).

  68. Kerrén, C., Reznik, D., Doeller, C. F. & Griffiths, B. J. Exploring the role of dimensionality transformation in episodic memory. Trends Cogn. Sci. 29, 614–626 (2025).

    Article  PubMed  Google Scholar 

  69. Nicholas, J., Daw, N. D. & Shohamy, D. Proactive and reactive construction of memory-based preferences. Nat. Commun. 16, 1618 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Hoffman, M. D. & Gelman, A. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15, 1593–1623 (2014).

    Google Scholar 

  71. Bürkner, P.-C. Brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).

    Article  Google Scholar 

  72. Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).

    Article  Google Scholar 

  73. Sivula, T., Magnusson, M., Matamoros, A. A. & Vehtari, A. Uncertainty in Bayesian leave-one-out cross-validation based model comparison. Preprint at https://arxiv.org/abs/2008.10296 (2023).

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Acknowledgements

We thank members of the Mattar Lab as well as K. Jensen, Q. Lu, N. Biderman and D. Shohamy for their helpful comments and feedback on the project. This work was supported by the National Science Foundation SBE Postdoctoral Research Fellowship under grant no. 2507527.

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  1. Department of Psychology, New York University, New York, NY, USA

    Jonathan Nicholas & Marcelo G. Mattar

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  1. Jonathan Nicholas
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  2. Marcelo G. Mattar
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Contributions

J.N. and M.G.M. conceived the research and study design. J.N. conducted the data collection and analysis and drafted the paper. J.N. and M.G.M. reviewed and edited the paper.

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Correspondence to Jonathan Nicholas.

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Nicholas, J., Mattar, M.G. Episodic memory facilitates flexible decision-making via access to detailed events. Nat Hum Behav (2026). https://doi.org/10.1038/s41562-025-02383-3

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