Now to the basic principles of the hybrid model:
1. Emotion perception as predictable rational emotion patterns (REM)
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REM would define emotions for AI systems as clearly structured, computable patterns.
Instead of trying to make AI “feel” emotions like humans do, my hybrid model would allow AI to capture emotions as a combination of experiential data and weighted influences. AI could then analyze this data in a rational-logical framework. -
In the formula, the positive or negative tendency of the interaction is expressed by the harmony need factor ℎ and the manipulation factor 𝑚:
y = (h * P + BE1) − (m * N + BE2)
It says here:
ℎ × 𝑃:
For the positive experience values, weighted by the need for harmony.
𝑚 × 𝑁:
For the negative experience values, weighted by the manipulation factor.
Basic emotions 𝐵𝐸1 and 𝐵𝐸2:
Have an additive effect on the positive and negative sides and influence the dynamics by increasing “interest in continuing the interaction” or “self-protection”.
➔ REMs would allow AI to capture emotions as predictable, measurable values that AI can use as data points for interactions.
These patterns turn emotions such as affection, self-protection or interest in continuation into measurable, stable data points.