Potential Implications of Appling Conceptual Blending Theory in Artificial Intelligence Scope
DOI:
https://doi.org/10.25130/jfa.conf.10.5.18Keywords:
Conceptual Blending Theory, Artificial Intelligence, Cognitive Linguistics, Mental Spaces, Machine Creativity, AI Ethics, Deep LearningAbstract
This study investigates the application of Conceptual Blending Theory (CBT), a foundational model of human cognition from cognitive linguistics, to the field of artificial intelligence (AI). As AI systems rapidly evolve to mimic human-like cognitive capabilities, understanding the mechanisms that underpin their creative and reasoning processes becomes critical. This research posits that CBT, as formulated by Fauconnier and Turner (2002), provides a powerful framework for analyzing how AI integrates disparate concepts to generate novel and meaningful outputs. The study employs a dual-method approach, combining a theoretical examination of CBT and AI with a practical analysis of AI-generated visual texts sourced from social media.
The analysis demonstrates that advanced generative AI performs sophisticated conceptual blending, selectively projecting elements from multiple input mental spaces to create coherent and evocative visual blends. Findings confirm that AI not only replicates the structural processes of human conceptual integration—composition, completion, and elaboration—but also produces emergent meanings with significant pragmatic and emotional resonance. This indicates a profound correlation between human and machine cognition, suggesting AI is bridging the gap in contextual understanding and creative improvisation.
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