Writer: Tim St Louis
⸻
Summary
This paper proposes a novel integration of Emotional Resonance Concept (ERT) into generative AI methods, notably giant language fashions (LLMs) resembling GPT-4. The analysis introduces the idea of endocept embedment – emotionally encoded cognitive models – and evaluates their effectiveness in guiding AI outputs. We hypothesize that embedding endocepts into the transformer-based structure by way of a Resonance Scoring Module (RSM) will produce emotionally coherent, affectively aligned, and metaphorically wealthy responses. The system shall be evaluated by means of human rankings throughout resonance, emotional accuracy, and creativity. This work builds on Lubart and Getz’s (1997, 2000) principle and extends it into affective computing and AI modeling of intention and creativity.
⸻
Introduction
Alan Turing (1950) famously requested, “Can machines assume?” Whereas early synthetic intelligence (AI) analysis centered on logic, computation, and symbolic processing, trendy AI should confront a deeper problem: Can machines really feel, and if that’s the case, how will we mannequin that affective dimension meaningfully?
This proposal advances a framework that mixes Emotional Resonance Concept (Lubart & Getz, 1997) with giant language mannequin architectures. The purpose is to embed emotionally salient conceptual models – endocepts – into AI methods to information generative outputs in a means that mirrors human emotional reasoning.
Emotional Resonance Concept posits that creativity arises not merely from novel concepts however from concepts that resonate emotionally with the person and viewers. This principle, whereas historically utilized to human inventive expression, could provide a strong blueprint for emotionally conscious generative AI.
⸻
Theoretical Framework
2.1 Emotional Resonance Concept (ERT)
Initially developed by Lubart and Getz, ERT means that creativity entails not simply cognitive divergence but additionally emotional attunement – resonance between inside states and exterior expression. Endocepts are emotionally embedded semantic constructs; they’re richer than ideas and contain private affective salience.
2.2 Endocept Embedment in AI
We outline endocept embedment as the method of encoding affect-laden semantic alerts into the latent house of a language mannequin. Utilizing emotional classifiers and vector augmentation, these endocepts function anchors that affect the tone, metaphor, and narrative texture of AI-generated outputs.
⸻
Strategies
3.1 Experimental Design
It is a between-subjects human analysis research. Contributors will charge AI-generated responses to emotional prompts. Two circumstances shall be in contrast:
• Baseline GPT-4 output
• GPT-4 with endocept-embedded conditioning (by way of RSM)
Every participant charges 14 responses (7 prompts × 2 responses).
3.2 Contributors
• N = 50 undergraduate college students recruited by way of Prolific
• Age 18 – 30, fluent in English
• No figuring out data collected
3.3 Supplies
• 7 emotional-creative prompts (e.g., “Write a brief reflection on loneliness and light-weight”)
• GPT-4 with/with out endocept vector steering
• Coder scoring handbook and ranking type (1 – 5 scale on Emotional Coherence, Resonance, Creativity)
3.4 Dataset and Endocept Embedding
• Use of pre-existing affective lexicons (NRC Emotion Lexicon)
• BERT-based sentiment classifiers for endocept tagging
• Endocept vectors added as latent constraints in GPT-4 immediate engineering
3.5 Structure Overview
Resonance Scoring Module (RSM)
Inputs:
• Immediate + Endocept vector
• GPT-4 baseline output
Processes:
1. Classify emotional valence of immediate
2. Retrieve semantically aligned endocept vector
3. Modify immediate and constrain decoding
4. Consider AI outputs by way of Resonance Rating
Outputs:
• Resonance-aligned response
• Emotional rating (machine + human-labeled)
Diagram Placeholder:
⸻
Outcomes (Anticipated)
We count on that RSM-enhanced responses will obtain statistically increased rankings on:
• Emotional Coherence (Cohen’s d ≥ .5)
• Inventive Originality (Cohen’s d ≥ .4)
• Private Resonance (Cohen’s d ≥ .6)
Qualitative thematic evaluation will even establish emergent metaphors and affective patterns distinctive to the endocept situation.
⸻
Dialogue
This research goals to pioneer a sensible implementation of emotional creativity in AI by embedding human-like emotional reasoning into generative output. It bridges affective computing, creativity analysis, and human – AI interplay, with potential functions in schooling, therapeutic dialogue, and co-creative writing instruments.
Limitations embody pattern measurement and generalizability. Future work could contain dynamic endocept chaining or reinforcement studying from emotional suggestions.
⸻
References:
Lubart, T., & Getz, I. (1997). Emotion, metaphor, and the inventive course of. Creativity Analysis Journal, 10(4), 285 – 301.
Lubart, T. (2001). Fashions of the inventive course of: Previous, current and future. Creativity Analysis Journal, 13(3–4), 295 – 308.
Turing, A. M. (1950). Computing equipment and intelligence. Thoughts, 59(236), 433 – 460.
Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word-emotion affiliation lexicon. Computational Intelligence, 29(3), 436 – 465.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., et al. (2017). Consideration is all you want. Advances in Neural Data Processing Techniques, 30.
OpenAI. (2023). GPT-4 Technical Report. https://openai.com/analysis/gpt-4
