Semantic Compression of Episodic Memories

TL;DR

This paper proposes a theoretical framework for understanding how semantic memory facilitates the efficient compression of episodic memories using information theory, supported by machine learning approximations and comparisons with human memory errors.

Contribution

It introduces a normative information-theoretic model of semantic compression for episodic memories, linking semantic memory to memory efficiency and error patterns.

Findings

Semantic compression can be modeled as a distortion function in information theory.

Machine learning methods can approximate semantic compression in naturalistic settings.

Deviations in compressed episodes align with observed human memory errors.

Abstract

Storing knowledge of an agent's environment in the form of a probabilistic generative model has been established as a crucial ingredient in a multitude of cognitive tasks. Perception has been formalised as probabilistic inference over the state of latent variables, whereas in decision making the model of the environment is used to predict likely consequences of actions. Such generative models have earlier been proposed to underlie semantic memory but it remained unclear if this model also underlies the efficient storage of experiences in episodic memory. We formalise the compression of episodes in the normative framework of information theory and argue that semantic memory provides the distortion function for compression of experiences. Recent advances and insights from machine learning allow us to approximate semantic compression in naturalistic domains and contrast the resulting deviations in compressed episodes with memory errors observed in the experimental literature on human memory.