SpreadPy: A Python tool for modelling spreading activation and superdiffusion in cognitive multiplex networks

2025-07-13

AI Summary

This study addresses the lack of flexible, scalable computational tools for modeling structure–function relationships in cognitive networks. We introduce SpreadPy, an open-source Python toolkit that uniquely unifies activation spreading and hyper-spreading dynamics across both single-layer and multilayer cognitive networks, integrating empirically grounded knowledge networks (e.g., lexical networks) with theory-driven hierarchical architectures. Its key innovation is the incorporation of hyper-spreading—a mechanism capturing cross-module cooperative activation—to enhance explanatory power for higher-order cognitive processes. We validate SpreadPy through three empirical case studies: (1) quantifying reorganization of conceptual networks associated with individual differences in math anxiety; (2) characterizing how cognitive workload modulates lexical retrieval pathways; and (3) replicating topological distributions of naming errors in aphasia patients. Results demonstrate that SpreadPy effectively elucidates the network dynamical underpinnings of inter-individual variability and neurocognitive disorders.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Models for Web evolution

Abstract

We introduce SpreadPy as a Python library for simulating spreading activation in cognitive single-layer and multiplex networks. Our tool is designed to perform numerical simulations testing structure-function relationships in cognitive processes. By comparing simulation results with grounded theories in knowledge modelling, SpreadPy enables systematic investigations of how activation dynamics reflect cognitive, psychological and clinical phenomena. We demonstrate the library's utility through three case studies: (1) Spreading activation on associative knowledge networks distinguishes students with high versus low math anxiety, revealing anxiety-related structural differences in conceptual organization; (2) Simulations of a creativity task show that activation trajectories vary with task difficulty, exposing how cognitive load modulates lexical access; (3) In individuals with aphasia, simulated activation patterns on lexical networks correlate with empirical error types (semantic vs. phonological) during picture-naming tasks, linking network structure to clinical impairments. SpreadPy's flexible framework allows researchers to model these processes using empirically derived or theoretical networks, providing mechanistic insights into individual differences and cognitive impairments. The library is openly available, supporting reproducible research in psychology, neuroscience, and education research.

Problem

• Model spreading activation in cognitive networks
• Test structure-function relationships in cognition
• Link network dynamics to clinical impairments

Innovation

• Python library for cognitive network simulations
• Simulates spreading activation in multiplex networks
• Links network structure to clinical impairments