Cognitive MRI of AI Conversations: Analyzing AI Interactions through Semantic Embedding Networks
Published on December 9, 2025
Authors:
Abstract
Through a single-user case study of 449 ChatGPT conversations, we introduce a cognitive MRI applying network analysis to reveal thought topology hidden in linear conversation logs. We construct semantic similarity networks with user-weighted embeddings to identify knowledge communities and bridge conversations that enable cross-domain flow. Our analysis reveals heterogeneous topology: theoretical domains exhibit hub-and-spoke structures while practical domains show tree-like hierarchies. We identify three distinct bridge types that facilitate knowledge integration across communities.
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@article{towell2025,
title = {"Cognitive MRI of AI Conversations: Analyzing AI Interactions through Semantic Embedding Networks"},
author = {"Alex Towell, John Matta"},
year = {2025},
url = {"https://metafunctor.com/publications/cognitive-mri/"},
abstract = {"Through a single-user case study of 449 ChatGPT conversations, we introduce a cognitive MRI applying network analysis to reveal thought topology hidden in linear conversation logs. We construct semantic similarity networks with user-weighted embeddings to identify knowledge communities and bridge conversations that enable cross-domain flow. Our analysis reveals heterogeneous topology: theoretical domains exhibit hub-and-spoke structures while practical domains show tree-like hierarchies. We identify three distinct bridge types that facilitate knowledge integration across communities.\n"}
}