From the represenationalist stance to conceptual blending in AI-generated images

This paper examines how AI-generated images reconfigure key aesthetic and epistemological categories within contemporary visual culture. Drawing on Nelson Goodman’s distinction between autographic and allographic art forms, we argue that AI image generation occupies a hybrid position, functioning as a “mixed case” where linguistic prompts serve as notational structures and generative outputs as autographic instantiations. Through the notion of semantic attractors, we describe how lexical inputs act as gravitational forces within the model’s latent space, guiding the emergence of visual and affective configurations. This dynamic is interpreted through conceptual blending theory, revealing how AI systems integrate heterogeneous domains into novel perceptual and semantic syntheses. Finally, we introduce the concept of the image-ideogram to analyze the cultural impact of deepfakes and synthetic imagery, suggesting that photorealistic synthesis now operates less as an epistemic index and more as a visual and linguistic resource within the accelerated economy of contemporary visual discourse.

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Publication Details

Journal
Itinera
Published
2025-11-26
DOI
https://doi.org/10.54103/2039-9251/30244
Primary Topic
Aesthetic Perception and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

From the represenationalist stance to conceptual blending in AI-generated images

Lorenzo Manera, Francesco D'Isa
Itinera
Aesthetic Perception and Analysis
article

From the represenationalist stance to conceptual blending in AI-generated images

Lorenzo Manera, Francesco D'Isa
article en

Abstract

This paper examines how AI-generated images reconfigure key aesthetic and epistemological categories within contemporary visual culture. Drawing on Nelson Goodman’s distinction between autographic and allographic art forms, we argue that AI image generation occupies a hybrid position, functioning as a “mixed case” where linguistic prompts serve as notational structures and generative outputs as autographic instantiations. Through the notion of semantic attractors, we describe how lexical inputs act as gravitational forces within the model’s latent space, guiding the emergence of visual and affective configurations. This dynamic is interpreted through conceptual blending theory, revealing how AI systems integrate heterogeneous domains into novel perceptual and semantic syntheses. Finally, we introduce the concept of the image-ideogram to analyze the cultural impact of deepfakes and synthetic imagery, suggesting that photorealistic synthesis now operates less as an epistemic index and more as a visual and linguistic resource within the accelerated economy of contemporary visual discourse.

Itinera(30)
University of Modena and Reggio Emilia (IT)
Openalex Percentile: Top 63%
Aesthetic Perception and Analysis
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