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Adaptive compression as a unifying framework for episodic and semantic memory

Nature Reviews Psychology volume 4, pages 484–498 (2025)Cite this article

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Abstract

Sensory experiences are encoded as memories, not as verbatim copies, but through interpretation and transformation. Rate distortion theory frames this process as compression in which irrelevant details are discarded. Despite the successes of approaches based on rate–distortion theory in aligning with empirical findings, these approaches assume that environmental regularities are known and unchanging and that surprising experiences are dismissed. However, the brain’s model of environmental regularities (semantic memory) is continually learned and refined, and surprising events have a pivotal role in this learning. In this Perspective, we offer a normative framework that addresses the interplay between semantic and episodic memory in the context of this computational problem that encompasses memory distortions, curriculum effects and prioritized replay. We propose to consider memory as solving an online structure learning problem, with semantic and episodic memory each having a role. We argue that semantic memory must learn the regularities that enable the efficient encoding of experience and that episodic memory supports this process by preserving surprising experiences in a relatively raw format for later interpretation. This framework opens up avenues towards understanding how adaptive compression and surprise shape the trajectory of learning and memory distortions.

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Fig. 1: Compression in memory.
Fig. 2: Online structure learning in the coffee-brewing example.
Fig. 3: Interactions between semantic and episodic memory.
Fig. 4: The Neurath’s ship analogy for human learning.
Fig. 5: The episodic ‘life raft’.

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Acknowledgements

The authors thank N. R. Bramley for insightful comments and suggestions, which helped to improve this manuscript, and P. Dayan for valuable discussions and extensive comments on earlier drafts. Additionally, the authors thank C. Frater, R. Uchiyama and M. Banyai for helpful feedback on the manuscript and B. Meszena for help with figures. This work is supported by the Humboldt Foundation, the German Federal Ministry of Education and Research (BMBF), by the Tübingen AI Center (FKZ 01IS18039A) funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy (EXC2064/1–390727645), and by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy (EXC 2117 422037984). G.O. was supported by a grant from the National Research, Development and Innovation Office (grant ADVANCED 150361) and by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory in Hungary.

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  1. These authors contributed equally: Gergő Orbán, Charley M. Wu.

Authors and Affiliations

  1. Human and Machine Cognition Lab, University of Tübingen, Tübingen, Germany

    David G. Nagy & Charley M. Wu

  2. Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    David G. Nagy & Charley M. Wu

  3. Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary

    Gergő Orbán

  4. Center for Cognitive Computation, Central European University, Budapest, Hungary

    Gergő Orbán

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D.G.N. and G.O. researched data for the article. All authors contributed substantially to discussion of the content. D.G.N. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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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). https://doi.org/10.1038/s44159-025-00458-6

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