Neuroscience and Generative AI-Empowered Methodology in Emotional Design

Topic Information

Dear Colleagues,

Emotional design, the practice of creating products, interfaces, or experiences that evoke specific emotional responses, has gained significant attention in fields ranging from human–computer interaction to product design. Recent advancements in neuroscience and generative artificial intelligence (GenAI) have opened new avenues for understanding and leveraging emotional responses in design processes. The integration of neuroscience and GenAI has revolutionized emotional design, enabling a deeper understanding of human affect and the creation of products that resonate with users’ emotional needs. Neuroscience provides the biological foundation for emotional responses, while generative AI—including tools like ChatGPT and Midjourney—offers computational frameworks to translate neural insights into design solutions. This interdisciplinary approach bridges the gap between human cognition and artificial intelligence, fostering innovative methodologies in product design, user experience, and interactive systems. This topic synthesizes insights from studies on the integration of neuroscience techniques and GenAI methodologies to enhance emotional design, drawing on the relevant literature to highlight key findings, methodologies, and future directions. Neuroscience provides a robust framework for decoding the neural underpinnings of emotions, enabling designers to create emotionally resonant experiences grounded in biological insights. Techniques such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and eye-tracking have been instrumental in capturing real-time emotional responses. GenAI, encompassing models like generative adversarial networks (GANs) and diffusion models, has revolutionized design by enabling the creation of novel prototypes tailored to specific emotional goals. Unlike traditional design processes, which are often iterative and labor-intensive, generative AI can produce thousands of design variations in seconds, optimizing for emotional impact based on predefined criteria. The integration of neuroscience and generative AI creates a powerful methodology for emotional design. Neuroscience provides ground-truth data on emotional responses, while generative AI leverages this data to automate and scale the design process. A closed-loop system, where AI-generated designs are tested via neuroscience tools and the results are fed back to refine the model, has shown promise in recent research. This synergy also addresses limitations of each approach. Neuroscience methods, while precise, are costly and time-consuming, often limiting sample sizes. GenAI can mitigate this by simulating emotional responses based on large-scale datasets, reducing the need for extensive real-world testing. Conversely, AI models often lack interpretability, but neuroscience provides biological validation, ensuring that AI-generated designs align with human emotional processing.

Prof. Dr. Vincent G. Duffy
Dr. Jiang Shao
Dr. Qing-Xing Qu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Big Data and Cognitive Computing BDCC	4.4	9.8	2017	24.5 Days	CHF 1800	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit
Symmetry symmetry	2.2	5.3	2009	17.1 Days	CHF 2400	Submit

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