Author Response:

I appreciate the time and effort of both Reviewers, who have raised important points that I would like to briefly discuss before I start working on a full revision of the paper.

Generality. First, there is the question of how much these conclusions broadly apply across experimental paradigms and subjects, which could give rise to potentially very different TGMs. As the Reviewers mention, I have focussed on one specific TGM that I assumed prototypical, and it could be that these conclusions fit other TGMs less well. Further, the model has quite a few hyperparameters so that it can flexibly accommodate a broad span of scenarios. This flexibility comes at a price, as pointed out by Reviewer 1: that “a different selection of parameters could lead to similar results”, i.e. that other configurations could fit this specific TGM just as well. This is related to the next point, so I will address them jointly.

Lack of quantitative evaluation, “making it hard to draw firm conclusions”. Indeed, I have not explicitly quantified the fit of the hyperparameters to this empirical TGM using a specific measure, and (related to the previous point) I have not made a systematic search through the space of model configurations based on such measure.

There is here a trade-off between generality and specificity. In fact, it is intentional that I did not optimise the hyperparameters to this specific TGM, and that I chose not to show a quantitative measure of fitness. This is because the TGM that I show in the paper is only meant as an example. Instead of focussing on fitting a specific TGM, I aimed at characterising some prominent general features that we often see throughout the literature, which this specific TGM shows in its own specific way. That is, if the paper was meant to focus on a specific paradigm (e.g. passive vision), then the use of a specific metric to fit the model to one or various empirical TGMs would have perhaps been more adequate, but this was not the case here. In future work, when focussing on specific paradigms, I will adapt methods of Bayesian optimisation (Lorenz et al., 2017) for this purpose, as mentioned in the Discussion. Note that doing this right is not trivial and would complicate the paper significantly; for this reason, I feel it should belong to a different piece of work.

I would also like to note that evaluating the different features of the data one by one (“in a stepwise manner”) was necessary for interpretation. One can loosely think of it as a sort of F-test: one is showing how important a feature is by comparing the full model vs. a nested model that does not have that feature. While the Reviewer is right that there might be interactions between the features that we can only unveil through a joint evaluation, my approach is at least valid as a first approximation. I will discuss this limitation in an updated version of the paper in more detail.

In a future revision of the paper, I will argue more specifically why and how these model configurations are, in general terms, necessary to produce these main effects in the TGM, and why other alternative configurations could not easily generate them.

Practical guidelines for researchers. It was suggested to make it clearer how researchers could leverage this model in their own studies to understand their data better and to help relating their TGMs to specific neurobiological mechanisms.

In a future revision of the paper, I will introduce a new section explaining how to use genephys practically, emphasising both opportunities and current limitations.

Neurobiological interpretation. It was criticised that the results were a mere characterisation of sensor space data, and that these were not related clearly to any neurobiological aspect.

In a future revision, I will work toward relating the main findings to existing literature in order to strengthen the neurobiological interpretation of the results, and toward a better justification of how genephys can help shed light on specific brain mechanisms.

Above and beyond these specific points, I intend to restructure the text so that the main goals of the study become clearer. This includes clarifying in the Introduction more unambiguously what is the gap of knowledge this work is specifically tackling.

Again, I would like to thank the Reviewers for helping me realise the limitations of the current version of the paper.

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