Hi! Thank you very much for such a detailed and thoughtful review — I really appreciate the time and attention you gave it. Your feedback is exactly the kind of constructive perspective that helps strengthen interdisciplinary work like this.

Let me address your main points:

1 On the Free Energy Principle. You are absolutely right that part of the neuroscience community sees FEP as overly broad and sometimes unnecessarily complex. In ICT, the model does not rely on FEP as a foundation — we use it only as an illustrative special case where dI/dT can be interpreted in terms of prediction error and the energetic cost of updating internal states. In other words, FEP is not a basis for ICT, but rather a local projection of a more general temporal structure. We will make this explicit in ICT 2.0 to avoid any confusion.

2 Citations and connection to existing research. You’re right: in several places the preprint assumes familiarity with background work — entropy metrics, temporal integration, information-theoretic models, etc. The next version will include a more structured “background and context” section with clear references throughout. Your comment here is very helpful and will definitely make the next edition stronger and more accessible.

3 Empirical testability and neuroscientific methods. Thank you for highlighting this. Section 8 already outlines specific paradigms (oddball / novelty detection, LZ-complexity, entropy rate, γ-coupling, prediction-error energetics, etc.), and I agree that for readers outside neuro-metrics these connections should be made more explicit. ICT 2.0 will expand this section with clearer explanations of applicable methods, their benefits, and limitations as used in practice.

4 On quantitative scales of dI/dT and metabolic effects. A key clarification is this: the magnitude of measurable effects varies greatly depending on the type of cognitive process. Simple, fast sensory events do produce very small changes — but the paradigms we propose are specifically chosen to target conditions where dI/dT varies much more strongly, such as:

disrupted temporal sequences,

violated expectations over time,

high-level predictive mismatch,

integration over multi-step patterns.

These are precisely the contexts where entropy, γ-coherence, and prediction-error timing produce the strongest and most reliable signals. We are formalizing these estimates for ICT 2.0 and will include the corresponding references.

You are right that basic stimuli produce minimal metabolic signatures — but the ICT experiments are deliberately focused on scenarios where the temporal structure is perturbed more significantly, and where the relevant methods are most sensitive.

And most importantly — thank you for your kind words. I’m an independent researcher, and your tone and careful attention truly mean a lot. The aim of ICT is not to bypass academic standards, but to offer something genuinely testable and conceptually consistent. Your comments help sharpen that aim.

Thanks again for your thoughtful, precise, and generous critique.

P.S. And yes… you’re not the first one to mention the formatting issue on Zenodo. We’ll fix that as well, but only in the next version, because Zenodo doesn’t allow replacing a file without deleting the entire record and creating a new version, which would reset the metadata and links. So I’m unable to change the extension there at the moment. If you’d like to download the paper as a PDF, you can do so via Academia: https://www.academia.edu/s/8924eff666