ArticleRoyal Society open science2025
Artificial intelligence can emulate human normative judgments on emotional visual scenes.
Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Morphological Buffering in Neuro-Architecture: Interactive Effects of Color, Shape, and Area Proportion on Affective Processing.Behavioral sciences (Basel, Switzerland) · 2026Article
- Error-related memory biases are specific to social stimuli for socially anxious individuals.Cognitive, affective & behavioral neuroscience · 2026Article
- An Operational Framework for Affect-Adjacent Structure in Plant-Environment Interaction.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence Approximates Human Affect Ratings of Cannabis Images.medRxiv : the preprint server for health sciences · 2025Article
- Artificial intelligence can emulate human normative judgments on emotional visual scenes.Royal Society open science · 2025Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Affective reactions have deep biological foundations; however, in humans, the development of emotion concepts is also shaped by language and higher-order cognition. A recent breakthrough in artificial intelligence (AI) has been the creation of multimodal language models that exhibit impressive intellectual capabilities, but their responses to affective stimuli have not been investigated. Here, we study whether state-of-the-art multimodal systems can emulate human emotional ratings on a standardized set of images, in terms of affective dimensions and basic discrete emotions. The AI judgements correlate surprisingly well with the average human ratings: given that these systems were not explicitly trained to match human affective reactions, this suggests that the ability to visually judge emotional content can emerge from statistical learning over large-scale databases of images paired with linguistic descriptions. Besides showing that language can support the development of rich emotion concepts in AI, these findings have broad implications for sensitive use of multimodal AI technology.
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