Characterizing Human Semantic Navigation in Concept Production as Trajectories in Embedding Space

• URL: http://arxiv.org/abs/2602.05971v1
• Date: Thu, 05 Feb 2026 18:23:04 GMT
• Title: Characterizing Human Semantic Navigation in Concept Production as Trajectories in Embedding Space
• Authors: Felipe D. Toro-Hernández, Jesuino Vieira Filho, Rodrigo M. Cabral-Carvalho,
• Abstract summary: We introduce a framework that represents concept production as navigation through embedding space.<n>We construct participant-specific semantic trajectories based on cumulative embeddings and extract geometric and dynamical metrics.<n>We evaluate the framework on four datasets across different languages, spanning different property generation tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic representations can be framed as a structured, dynamic knowledge space through which humans navigate to retrieve and manipulate meaning. To investigate how humans traverse this geometry, we introduce a framework that represents concept production as navigation through embedding space. Using different transformer text embedding models, we construct participant-specific semantic trajectories based on cumulative embeddings and extract geometric and dynamical metrics, including distance to next, distance to centroid, entropy, velocity, and acceleration. These measures capture both scalar and directional aspects of semantic navigation, providing a computationally grounded view of semantic representation search as movement in a geometric space. We evaluate the framework on four datasets across different languages, spanning different property generation tasks: Neurodegenerative, Swear verbal fluency, Property listing task in Italian, and in German. Across these contexts, our approach distinguishes between clinical groups and concept types, offering a mathematical framework that requires minimal human intervention compared to typical labor-intensive linguistic pre-processing methods. Comparison with a non-cumulative approach reveals that cumulative embeddings work best for longer trajectories, whereas shorter ones may provide too little context, favoring the non-cumulative alternative. Critically, different embedding models yielded similar results, highlighting similarities between different learned representations despite different training pipelines. By framing semantic navigation as a structured trajectory through embedding space, bridging cognitive modeling with learned representation, thereby establishing a pipeline for quantifying semantic representation dynamics with applications in clinical research, cross-linguistic analysis, and the assessment of artificial cognition.

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