Cute or Not: Validating Lorenz's Kindchenschema With Text-To-Image Generation

This study validates Lorenz’s Kindchenschema features by using a commercial text-to-image generator (Midjourney v.5.2) to generate stimulus material that depicted either animals or objects with or without the text prompts for “cute” and “anthropomorphic”. In an online study human participants evaluated the presence of Kindchenschema features in said images. Our results show their increased presence and perception in AI-generated images created with the prompt “cute” compared to those explicitly excluding it, as well as the systematically prioritisation of features such as “large, deep-set eye” and “rounded body shapes” in cute images. Whereas “round, protruding cheeks”, traditionally considered essential by Lorenz, ranks lower. Furthermore, the anthropomorphisation of subjects reduces the likelihood of Kindchenschema features, highlighting constraints in AI-generated stimuli. This research demonstrates the potential of AI-tools to objectively quantify and test theoretical concepts as represented in popular culture.

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Publication Details

Published
2024-08-26
DOI
https://doi.org/10.1145/3670653.3677512
Primary Topic
Digital Imaging in Medicine
Type
article
Field-Weighted Citation Impact
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article

Cute or Not: Validating Lorenz's Kindchenschema With Text-To-Image Generation

Josef Haupt, Lewis L. Chuang, Lena Marcella Nischwitz, Aurora-Zoe Weigelt
Digital Imaging in Medicine
article

Cute or Not: Validating Lorenz's Kindchenschema With Text-To-Image Generation

Josef Haupt, Lewis L. Chuang, Lena Marcella Nischwitz, Aurora-Zoe Weigelt
article en

Abstract

This study validates Lorenz’s Kindchenschema features by using a commercial text-to-image generator (Midjourney v.5.2) to generate stimulus material that depicted either animals or objects with or without the text prompts for “cute” and “anthropomorphic”. In an online study human participants evaluated the presence of Kindchenschema features in said images. Our results show their increased presence and perception in AI-generated images created with the prompt “cute” compared to those explicitly excluding it, as well as the systematically prioritisation of features such as “large, deep-set eye” and “rounded body shapes” in cute images. Whereas “round, protruding cheeks”, traditionally considered essential by Lorenz, ranks lower. Furthermore, the anthropomorphisation of subjects reduces the likelihood of Kindchenschema features, highlighting constraints in AI-generated stimuli. This research demonstrates the potential of AI-tools to objectively quantify and test theoretical concepts as represented in popular culture.

Chemnitz University of Technology (DE)
Openalex Percentile: Top 79%
Digital Imaging in Medicine
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