GENESIS: A Generative Model of Episodic-Semantic Interaction

2025-10-17

AI Summary

Existing cognitive models lack a unified computational framework to explain the dynamic interplay between semantic and episodic memory—and their synergistic roles in learning, recall, and imagination. To address this, we propose GENESIS: a neurobiologically grounded, dual-system generative architecture wherein semantic memory is modeled by a cortical variational autoencoder (VAE) for compression and generalization, while episodic memory is instantiated by a hippocampal retrieval-augmented generation (RAG) module supporting contextual binding and replay. GENESIS formalizes memory as constructive, active, and resource-constrained. It successfully reproduces core empirical phenomena—including semantic generalization, sequential recall, episodic distortion, and creative simulation—and quantifies how capacity limits degrade memory fidelity. By unifying semantic and episodic memory within a single generative framework, GENESIS advances a computationally precise, biologically plausible theory of declarative memory.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

A central challenge in cognitive neuroscience is to explain how semantic and episodic memory, two major forms of declarative memory, typically associated with cortical and hippocampal processing, interact to support learning, recall, and imagination. Despite significant advances, we still lack a unified computational framework that jointly accounts for core empirical phenomena across both semantic and episodic processing domains. Here, we introduce the Generative Episodic-Semantic Integration System (GENESIS), a computational model that formalizes memory as the interaction between two limited-capacity generative systems: a Cortical-VAE, supporting semantic learning and generalization, and a Hippocampal-VAE, supporting episodic encoding and retrieval within a retrieval-augmented generation (RAG) architecture. GENESIS reproduces hallmark behavioral findings, including generalization in semantic memory, recognition, serial recall effects and gist-based distortions in episodic memory, and constructive episodic simulation, while capturing their dynamic interactions. The model elucidates how capacity constraints shape the fidelity and memorability of experiences, how semantic processing introduces systematic distortions in episodic recall, and how episodic replay can recombine previous experiences. Together, these results provide a principled account of memory as an active, constructive, and resource-bounded process. GENESIS thus advances a unified theoretical framework that bridges semantic and episodic memory, offering new insights into the generative foundations of human cognition.

Problem

• Modeling interaction between semantic and episodic memory systems
• Unifying computational framework for memory phenomena across domains
• Explaining capacity constraints and distortions in memory processes

Innovation

• Combines cortical and hippocampal generative systems
• Uses dual VAE architecture for semantic-episodic integration
• Implements retrieval-augmented generation for memory interactions