A naukri.com initiative
Medium
1d
117
Proposed Study: Integrating Emotional Resonance Theory into AI : An Endocept-Driven Architecture
- This paper proposes integrating Emotional Resonance Theory (ERT) into AI systems, specifically large language models like GPT-4, through endocept embedment, aiming to improve emotional coherence and creativity in AI-generated outputs.
- The research seeks to introduce emotionally encoded cognitive units known as endocepts into transformer-based architectures using a Resonance Scoring Module (RSM) to produce affectively aligned and metaphorically rich responses.
- The study combines Lubart and Getz's Emotional Resonance Theory with AI modeling to enhance generative AI's emotional reasoning capabilities and creativity by embedding emotionally salient conceptual units — endocepts.
- Endocept embedment involves encoding emotional semantic signals into language models' latent space to influence AI-generated outputs' tone, metaphor, and narrative texture.
- The experimental design includes a human evaluation study comparing AI-generated responses to emotional prompts under two conditions: Baseline GPT-4 output and GPT-4 with endocept-embedded conditioning via RSM.
- Expected results anticipate higher ratings for emotionally coherent, creatively original, and personally resonant responses with endocept embedding in AI systems.
- The study aims to implement emotional creativity in AI, merging affective computing, creativity research, and human-AI interaction, with potential applications in education, therapy, and co-creative writing tools.
- Limitations include sample size and generalizability concerns, with future work potentially exploring dynamic endocept chaining or reinforcement learning from emotional feedback.
Read Full Article
7 Likes
For uninterrupted reading, download the app