From semantic memory to collective creativity: A generative cognitive foundation for social creativity models

2026-02-03

AI Summary

Existing simulation-based social models struggle to account for collective creativity because they overlook the role of individual cognitive mechanisms in generating novel ideas. This work proposes a multilevel socio-cognitive agent framework that integrates a generative semantic memory mechanism into a social structure model: agents share a common semantic lexicon but possess heterogeneous semantic network topologies, with semantic modularity controlled by a single parameter. Without predefined assumptions, the model naturally reproduces key phenomena of collective creativity, such as higher ideational gains from low initial semantic overlap and network redundancy arising from shared inspiration. This study presents the first computationally tractable integration of cognitive generative processes with social interaction, offering a theoretical foundation for understanding how cognitive mechanisms and social structures jointly shape collective creativity.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Simulation-based theory development has yielded powerful insights into collective performance by linking social structure to emergent outcomes, yet it has struggled to extend to collective creativity. Creativity is hard to capture purely at the social level, as novel ideas are generated through cognitive mechanisms. To address this gap, we introduce a multi-level socio-cognitive agent-based framework in which agents share a common semantic vocabulary and substrate but differ in semantic network topology. A single generative parameter tunes semantic modularity, yielding emergent individual differences in ideational breadth. When agents exchange ideation traces, two canonical social-creativity phenomena arise without being imposed: lower pre-interaction ideation overlap predicts larger stimulation gains, and shared inspiration sources induce network-level redundancy. The framework enables mechanistic theory-building about cognition and social structure in collective creativity.

Problem

• collective creativity
• semantic memory
• cognitive mechanisms
• agent-based modeling
• social structure

Innovation

• socio-cognitive modeling
• semantic networks
• agent-based simulation
• collective creativity
• generative parameter