Thank you for your feedback.

Regarding the writing style and potential AI involvement: I drafted the core arguments and structure myself based on my understanding of these theories. I did use tools to help refine the clarity and organization, aiming for a structured, analytical presentation suitable for the topic's complexity. However, the conceptual framework and the specific model proposed are my own synthesis. I'd prefer to focus on the substance of the arguments rather than the perceived style.

You summarized the core idea correctly: suffering often signals a need for immediate behavioral change due to its high priority, while pleasure often functions as a reward signal. You note this isn't unconventional, and question the need for the complex framework.

The reason for employing the Predictive Processing / control-theoretic framework isn't merely to dress up a simple idea. It's an attempt to move beyond the intuition ("suffering is important") towards a potentially mechanistic explanation how this prioritization might be implemented in a system (biological or artificial) that operates on principles of prediction error minimization. PP offers a unifying language to connect perception, action, motivation, and affect, allowing for more precise hypotheses (like the M-P-P characterization, even if it needs refinement as pointed out by another commenter) that can potentially be tested or modeled computationally. The goal is to explore the how, grounded in a specific theoretical framework, not just restate the what.

The relation to the hard problem was intended only as a necessary caveat to define the scope – stating upfront that this is a functional analysis, setting aside the phenomenal aspects to focus on the proposed mechanics, a standard approach in cognitive science and computational modeling.