Personalized Emotional Intelligence in Generative AI through Symbolic Affective Reasoning
2026-07-12 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created a new AI system called EROS that combines deep learning and symbolic reasoning to change images in ways that influence how people feel. It learns from large datasets of images and emotions to find patterns and adjust pictures while keeping their meaning. EROS can also remember individual people’s preferences to personalize emotional effects without needing to retrain the model. In tests with humans, EROS worked better at making people feel certain emotions compared to other AI models. This approach could help in areas like mental health and education by understanding and shaping human emotions.
Emotional intelligenceDeep learningSymbolic reasoningAffective computingImage-emotion datasetsPersonalizationPsychophysicsMultimodal modelsHuman-computer interactionMemory bank
Authors
Qing Lin, Mengmi Zhang
Abstract
Emotional intelligence enables humans to recognize emotions, infer their causes, reason about interventions, and modify their environment to achieve desired affective states. Despite recent advances in artificial intelligence (AI), current models remain largely limited to generating realistic content or performing semantic reasoning, with little capacity for understanding, predicting, and personalizing human emotional responses. Here we introduce Emotion-augmented geneRatiOn System (EROS), a hybrid AI framework that integrates symbolic reasoning with deep learning to enable personalized emotion augmentation through visual content. Leveraging large-scale image-emotion datasets, EROS discovers generalizable affective rules, identifies emotion-relevant image regions, and predicts context-aware visual modifications that preserve scene semantics while steering emotional responses toward desired targets. To account for individual variability, EROS incorporates an expandable memory bank that supports inference-time personalization without model fine-tuning, yielding interpretable emotional profiles and rapid adaptation to new users. Across extensive human psychophysics experiments, EROS elicits target emotional responses more effectively than state-of-the-art large multimodal models while adapting to individual affective preferences. Beyond affective computing, EROS provides a foundation for AI systems that can understand, reason about, and augment human cognitive states, with potential applications in mental health, adaptive media, education, and human-computer interaction.