lack of police , International enforcer

citizen and their unrest has become a force in which they are un-ignorable

mainstreaming of social media, easier communications

reflects legislative actions in congress

what would this mean ffor something like the UN building in NY

future reform of UN or just its ways of operation?

ukraine war, gaza conflict

archaic structure: outdated, unrelaible

explains previous note: lack of ability to change in geopolitics... veto powers today show the mahor power held when proposing change

use of force by who's authority; who's the real police?

meaning solving problems once they arrive instead of laying groundwork to prevent such issues?

is the UN authority inexistent? (sort of)

lack of admission of states, lack of organization

multiple governments/ambassadors working together

Would this page be better suited for the Healthy Lifestyles chapter?

Levels of prevention are covered in Healthy Lifestyles. Does it need to be covered here as well?

The reader have no context for this -include more set up.

This last paraphrase needs to do justice to the strong claim that ends the Ruberg quotation. I get no sense of "a colonizing of affect: an invasion, occupation, and subjugation of others’ experiences"

interesting word choice

This last sentence needs to be in the positive--too may "nots". What does GH show us about the limits of empathy?

This summary does some good work but I think it elides Ruberg's point about how the rhetoric of empathy "minimizes the lives and identities of those who are seen as 'different' or 'other.'" That active negation or "minimizing" is really key to their claim. It also might be harnessed it terms of thinking about Gone Home

Suggest adding unmet needs. Might be helpful to incorporate Maslow's Hierarchy of Needs somewhere in this chapter.

This should probably be explained. How does it work?

Perhaps add "as approved by the client" since permission would be needed.

Suggest "this can lead to" to align with how the others are worded.

can probably remove one instance of "provided"

Not always

Present tense

I dont see an average Tf value, which would be good to have. You should probably explain how exactly to rearrange eq 1 to get the slope in your methods. You have a good discussion about what the error and precision mean, but it can be expanded on a little bit. Otherwise its a solid results and discussion section

The calculations section is a little hard to follow, and there is an instance of a present tense word in your methods. (paragraph 1, is). Other than that, it needs a little bit of shortening/taking out redundant words. But it is pretty solid.

i is not 1 for weak electrolytes, because they still dissociate just not fully. We cant calculate i for weak electrolytes yet. Your introduction is pretty solid, but it needs a bit of succinctness. There are a couple types that need to be fixed too, such as "complexly"

Cant use you.

Cant use "our." its too close to "us"

Option 5 is blank in the last set of questions on the page

Consider moving this step to after "Create Issues". After the issues are created and defined, there should be a step for assigning the issues to devs and reviewing as a group to confirm that everything is clearly defined, before moving on to Phase 2. The GitHub views that you're describing here seem to me to be part of that step...how to visualize and review the scope of work for the release as a team to ensure balanced workload.

Explain what the Milestone represents

Before diving into how GitHub is used during the Planning Phase, please add a brief description of the purpose of this phase, what needs to be accomplished and the desired outputs or end state of the first week. Same goes for the other phases as well.

This is a nice visualization. We'll want to consider if there are weeks that should be skipped. For example, holiday weeks where there are only 3 work days or where staff are likely to be out of office. We can revisit this as we decide how and when to launch this cycle.

The doc is nicely structured by the 3 Phases, though the introduction is missing a clear explanation of how the phases map to the weeks. For increased clarity, in this "Cycle Length" section, I suggest adding the names of the phases here alongside the weeks, and in the table below.

Which week does merging take place? It's mentioned in Step 5 of the Development Phase above as well...which is confusing. Merging is a separate step from communications...currently it's not clear when the merging happens. Suggest making merging its own step to increase clarity, since it's an important part of the release process.

Project management is a very vague title. In reality, this entire SOP is a project management tool. Can you give this step a more specific title? These steps seem to describe closing out the GitHub items to prepare and reset for the next cycle...so maybe something like "cycle wrap-up" or "cycle close-out"??

This is a bit confusing. I'd suggest that unfinished issues should go into the backlog instead -- and then during the first week of the new cycle, the process is restarted to select which issues will go into that cycle.

Is the reviewing step part of the 2 week dev period? If so, this should be stated clearly in the initial description of the Development Phase. Reviewing is an additional task on top of development, and currently this reads as a bit of a hidden surprise at the end of this section. It's important to call it out up front.

What is the process for determining who will review (if only 1 reviewer is needed)?

Avoid using "I" so that this is written as a document that reflects processes used by the entire team. Suggest either using "we" or 3rd person.

Suggest taking out gender throughout this paragraph "They are"

change to client

Shouldn't the client be included in the process?

Perhaps add knowledge about the topic?

PV=nRT

Would this flow better to have this on the same page as where the domains are discussed?

Perhaps the cone of learning should be briefly mentioned so they understand how information is retained.

I think this wording should match how it is described in both spots in this chapter. I like this wording better as it describes it better. It initial break down of the domains is not as specific. Again I think this should all be on one page.

This is used many times in this chapter. If keeping it as is I would suggest defining what this means.

What does this mean? Perhaps just as necessary or as needed?

Suggest adding "and expected versus unexpected changes related to aging"

What about teaching the child as well? They should be included.

paper factories save money for paper now can up their production for other goods since they can just buy paper and recycle it can use the trees they still buy for other lucrative products

Idea is great but outcome of reality is not sweet and happy

Both lying to the public, hiding the truth of how the corporation is doing both to blind the consumer or worker from the truth that they are benefiting and making more money while lying to you about the effects of their production, environment still losing resources and workers getting paid poorly even with the company saving money.

The topic is introduced, is recycling really in the end conserving natural recourse and preserving our world

The call to action from the writer

Its presented as 1.4 C, but the calculated class average is 2.4 C

Good job explaining exactly what the % error and r squared value mean.

The methods are decent. The chronological order and some of the wording should be checked on a part or two, but otherwise its good.

Strong introduction, I like how you jump right into explaining exactly what the reader should know.

in paragraph 2, the first sentence says 8 grams of lauric acid and benzoic acid were used, but 8 grams of benzoic acid were not used.

Good introdduction section, except it is a little scattered and it might be better off with some shortening/ making it more concise.

All tables figures and eqns check out.

This report is pretty strong, but can use a little bit of work. Mainly just shortening up sentences to be more concise. Another thing to work on is to explain which points of data were important on the labquest. Otherwise good job.

This report does a good job of explaining the results of the experiment, and thoroughly introduces what exactly a colligative property is. Good job.

Two instances of the word "average" are unnecessary. you can remove one or the other and the sentence will have the same meaning

This is a run on sentence and I would shorten it a bit. But it does make a good point

I would re-word this so that you use the words "freezing point depression" less. Maybe say "Tf represents the change in temperature from the initial freezing point"

this is an important passage that hiflights the experience of migration and borders specifically from mexico to california

Interesting perspective.

Talks about something more about the 21st century.

connection to climate change.

Gendered job.

craftmanship

Introduction of the contractor means that the personal touch is now gone.

Details.

Shows the migration story.

BAcking for heritage sites.

Provides dignity to people.

Context setting

Introdcution to the poeple who the story is based on- nutfggarf.

Scene setting lede- Even as a person who isn't from Mumabi I'm able to visialise the space very nicely. Perfectly in lighn with the title. The picture helps in providing context.

renin-angiotensin --- influences TPR and therefore systemic arterial BP

eLife Assessment

This study provides important findings on the nature of eye movement choices by human subjects. The study uses a novel approach and provides relatively clear and convincing results of the relationship between pupil size and saccade production. The results should be of interest to a broad audience interested in sensorimotor integration and sensory-guided decision-making.

Reviewer #3 (Public review):

Summary:

This manuscript extends previous research by this group by relating variation in pupil size to the endpoints of saccades produced by human participants under various conditions including trial-based choices between pairs of spots and search for small items in natural scenes. Based on the premise that pupil size is a reliable proxy of "effort", the authors conclude that less costly saccade targets are preferred. Finding that this preference was influenced by the performance of a non-visual, attention-demanding task, the authors conclude that a common source of effort animates gaze behavior and other cognitive tasks.

Strengths:

Strengths of the manuscript include the novelty of the approach, the clarity of the findings, and the community interest in the problem.

Weaknesses:

Enthusiasm for this manuscript is reduced by the following weaknesses:

(1) A relationship between pupil size and saccade production seems clear based on the authors' previous and current work. What is at issue is the interpretation. The authors test one, preferred hypothesis, and the narrative of the manuscript treats the hypothesis that pupil size is a proxy of effort as beyond dispute or question. The stated elements of their argument seem to go like this:<br /> PROPOSITION 1: Pupil size varies systematically across task conditions, being larger when tasks are more demanding.<br /> PROPOSITION 2: Pupil size is related to the locus coeruleus.<br /> PROPOSITION 3: The locus coeruleus NE system modulates neural activity and interactions.<br /> CONCLUSION: Therefore, pupil size indexes the resource demand or "effort" associated with task conditions.<br /> How the conclusion follows from the propositions is not self-evident. Proposition 3, in particular, fails to establish the link that is supposed to lead to the conclusion.

(2) The authors test one, preferred hypothesis and do not consider plausible alternatives. Is "cost" the only conceivable hypothesis? The hypothesis is framed in very narrow terms. For example, the cholinergic and dopamine systems that have been featured in other researchers' consideration of pupil size modulation are missing here. Thus, because the authors do not rule out plausible alternative hypotheses, the logical structure of this manuscript can be criticized as committing the fallacy of affirming the consequent.

(3) The authors cite particular publications in support of the claim that saccade selection is influenced by an assessment of effort. Given the extensive work by others on this general topic, the skeptic could regard the theoretical perspective of this manuscript as too impoverished. Their work may be enhanced by consideration of other work on this general topic, e.g, (i) Shenhav A, Botvinick MM, Cohen JD. (2013) The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron. 2013 Jul 24;79(2):217-40. (ii) Müller T, Husain M, Apps MAJ. (2022) Preferences for seeking effort or reward information bias the willingness to work. Sci Rep. 2022 Nov 14;12(1):19486. (iii) Bustamante LA, Oshinowo T, Lee JR, Tong E, Burton AR, Shenhav A, Cohen JD, Daw ND. (2023) Effort Foraging Task reveals a positive correlation between individual differences in the cost of cognitive and physical effort in humans. Proc Natl Acad Sci U S A. 2023 Dec 12;120(50):e2221510120.

(4) What is the source of cost in saccade production? What is the currency of that cost? The authors state (page 13), "... oblique saccades require more complex oculomotor programs than horizontal eye movements because more neuronal populations in the superior colliculus (SC) and frontal eye fields (FEF) [76-79], and more muscles are necessary to plan and execute the saccade [76, 80, 81]." This statement raises questions and concerns. First, the basis of the claim that more neurons in FEF and SC are needed for oblique versus cardinal saccades is not established in any of the publications cited. Second, the authors may be referring to the fact that oblique saccades require coordination between pontine and midbrain circuits. This must be clarified. Second, the cost is unlikely to originate in extraocular muscle fatigue because the muscle fibers are so different from skeletal muscles, being fundamentally less fatigable. Third, if net muscle contraction is the cost, then why are upward saccades, which require the eyelid, not more expensive than downward? Thus, just how some saccades are more effortful than others is not clear.

(5) The authors do not consider observations about variation in pupil size that seem to be incompatible with the preferred hypothesis. For example, at least two studies have described systematically larger pupil dilation associated with faster relative to accurate performance in manual and saccade tasks (e.g., Naber M, Murphy P. Pupillometric investigation into the speed-accuracy trade-off in a visuo-motor aiming task. Psychophysiology. 2020 Mar;57(3):e13499; Reppert TR, Heitz RP, Schall JD. Neural mechanisms for executive control of speed-accuracy trade-off. Cell Rep. 2023 Nov 28;42(11):113422). Is the fast relative to the accurate option necessarily more costly?

(6) The authors draw conclusions based on trends across participants, but they should be more transparent about variation that contradicts these trends. In Figures 3 and 4 we see many participants producing behavior unlike most others. Who are they? Why do they look so different? Is it just noise, or do different participants adopt different policies?

Comments on revisions:

The authors have addressed the concerns and questions raised in the original review.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Vision is a highly active process. Humans move their eyes 3-4 times per second to sample information with high visual acuity from our environment, and where eye movements are directed is critical to our understanding of active vision. Here, the authors propose that the cost of making a saccade contributes critically to saccade selection (i.e., whether and where to move the eyes). The authors build on their own recent work that the effort (as measured by pupil size) that comes with planning and generating an eye movement varies with saccade direction. To do this, the authors first measured pupil size for different saccade directions for each participant. They then correlated the variations in pupil size obtained in the mapping task with the saccade decision in a free-choice task. The authors observed a striking correlation: pupil size in the mapping task predicted the decision of where to move the eyes in the free choice task. In this study, the authors provide a number of additional insightful analyses (e.g., based on saccade curvature, and saccade latency) and experiments that further support their claim that the decision to move the eyes is influenced by the effort to move the eyes in a particular direction. One experiment showed that the same influence of assumed saccade costs on saccade selection is observed during visual search in natural scenes. Moreover, increasing the cognitive load by adding an auditory counting task reduced the number of saccades, and in particular reduced the costly saccades. In sum, these experiments form a nice package that convincingly establishes the association between pupil size and saccade selection.

We thank the reviewer for highlighting the novelty and cogency of our findings.

In my opinion, the causal structure underlying the observed results is not so clear. While the relationship between pupil size and saccade selection is compelling, it is not clear that saccade-related effort (i.e., the cost of a saccade) really drives saccade selection. Given the correlational nature of this relationship, there are other alternatives that could explain the finding. For example, saccade latency and the variance in landing positions also vary across saccade directions. This can be interpreted for instance that there are variations in oculomotor noise across saccade directions, and maybe the oculomotor system seeks to minimize that noise in a free-choice task. In fact, given such a correlational result, many other alternative mechanisms are possible. While I think the authors' approach of systematically exploring what we can learn about saccade selection using pupil size is interesting, it would be important to know what exactly pupil size can add that was not previously known by simply analyzing saccade latency. For example, saccade latency anisotropies across saccade directions are well known, and the authors also show here that saccade costs are related to saccade latency. An important question would be to compare how pupil size and saccade latency uniquely contribute to saccade selection. That is, the authors could apply the exact same logic to their analysis by first determining how saccade latencies (or variations in saccade landing positions; see Greenwood et al., 2017 PNAS) vary across saccade directions and how this saccade latency map explains saccade selection in subsequent tasks. Is it more advantageous to use one or the other saccade metric, and how well does a saccade latency map correlate with a pupil size map?

We thank the reviewer for the detailed comment. 1) The reviewer first points out the correlational nature of many of our results. Thereafter, 2), the reviewer asks whether saccade latencies and landing precision also predict saccade selection, and could be these potential predictors be considered alternative explanations to the idea of effort driving saccade selection? Moreover, what can pupil size add to what can be learned from saccade latency?

In brief, although we report a combination of correlational and causal findings, we do not know of a more parsimonious explanation for our findings than “effort drives saccade selection”. Moreover, we demonstrate that oculomotor noise cannot be construed as an alternative explanation for our findings.

(1) Correlational nature of many findings.

We acknowledge that many of our findings are predominantly correlational in nature. In our first tasks, we correlated pupil size during saccade planning to saccade preferences in a subsequent task. Although the link between across tasks was correlational, the observed relationship clearly followed our previously specified directed hypothesis. Moreover, experiments 1 and 2 of the visual search data replicated and extended this relationship. We also directly manipulated cognitive demand in the second visual search experiment. In line with the hypothesis that effort affects saccade selection, participants executed less saccades overall when performing a (primary) auditory dual task, and even cut the costly saccades most – which actually constitutes causal evidence for our hypothesis. A minimal oculomotor noise account would not directly predict a reduction in saccade rate under higher cognitive demand. To summarize, we have a combination of correlational and causal findings, although mediators cannot be ruled out fully for the latter. That said, we do not know of a more fitting and parsimonious explanation for our findings than effort predicting saccade selection (see following points for saccade latencies). We now address causality in the discussion for transparency and point more explicitly to the second visual search experiment for causal evidence.

“We report a combination of correlational and causal findings. Despite the correlational nature of some of our results, they consistently support the hypothesis that saccade costs predicts saccade selection [which we predicted previously, 33]. Causal evidence was provided by the dual-task experiment as saccade frequencies - and especially costly saccades were reduced under additional cognitive demand. Only a cost account predicts 1) a link between pupil size and saccade preferences, 2) a cardinal saccade bias, 3) reduced saccade frequency under additional cognitive demand, and 4) disproportional cutting of especially those directions associated with more pupil dilation. Together, our findings converge upon the conclusion that effort drives saccade selection.”

(2) Do anisotropies in saccade latencies constitute an alternative explanation?

First of all, we would like to to first stress that differences in saccade latencies are indeed thought to reflect oculomotor effort (Shadmehr et al., 2019; TINS). For example, saccades with larger amplitudes and saccades where distractors need to be ignored are associated with longer latencies. Therefore, even if saccade latencies would predict saccade selection, this would not contrast the idea that effort drives saccade selection. Instead, this would provide convergent evidence for our main novel conclusion: effort drives saccade selection. There are several reasons why pupil size can be used as a more general marker of effort (see responses to R2), but ultimately, our conclusions do not hinge on the employed measure of effort per se. As stressed above in 1), we see no equally parsimonious explanation besides the cost account. Moreover, we predicted this relationship in our previous publication before running the currently reported experiments and analyses (Koevoet et al., 2023). That said, we are open to discuss further alternative options and would be looking forward to test these accounts in future work against each other – we are welcoming the reviewers’ (but also the reader’s) suggestions.

We now discuss this in the manuscript as follows:

“We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort [100], whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Supplemen- tary Materials). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established as marker of effort, and is sensitive to effort more broadly than only in the context of saccades [36–45, 48]. Pupil size therefore allows to capture not only the costs of saccades, but also of covert attentional shifts [33], or shifts with other effectors such as head or arm movements [54, 101]. Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost.

Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.”

Second, we followed the reviewer’s recommendation in testing whether other oculomotor metrics would predict saccade selection. To this end, we conducted a linear regression across directions. We calculated pupil size, saccade latencies, landing precision and peak velocities maps from the saccade planning task. We then used AICbased backward model selection to determine the ‘best’ model model to determine which factor would predict saccade selection best. The best model included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences ~ pupil size + saccade latency + landing precision). Pupil size (b \=-42.853, t \= 4.791, p < .001) and saccade latency (b \=-.377, t \= 2.106, p \= .043; see Author response image 1) predicted saccade preferences significantly. In contrast, landing precision did not reach significance (b \= 23.631, t \= 1.675, p \= .104). This analysis shows that although saccade latency also predicts saccade preferences, pupil size remains a robust predictor of saccade selection. These findings demonstrate that minimizing oculomotor noise cannot fully explain the pattern of results.

Author response image 1.

The relationship between saccade latency (from the saccade planning task) and saccade preferences averaged across participants. Individual points reflect directions and shading represents bootstrapped 95% confidence intervals.

We have added this argument into the manuscript, and discuss the analysis in the discussion. Details of the analysis have been added to the Supporting Information for transparency and further detail.

“A control analysis ruled out that the correlation between pupil size and saccade preferences was driven by other oculomotor metrics such as saccade latency and landing precision (see Supporting Information).”

“To ascertain whether pupil size or other oculomotor metrics predict saccade preferences, we conducted a multiple regression analysis. We calculated average pupil size, saccade latency, landing precision and peak velocity maps across all 36 directions. The model, determined using AIC-based backward selection, included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences  pupil size + saccade latency + landing precision). The analysis re- vealed that pupil size (β = -42.853, t = 4.791, p < .001) and saccade latency (β = -.377, t = 2.106, p = .043) predicted saccade preferences. Landing precision did not reach significance (β = 23.631, t = 1.675, p = .104). Together, this demonstrates that although other oculomotor metrics such as saccade latency contribute to saccade selection, pupil size remains a robust marker of saccade selection.”

In addition to eye-movement-related anisotropies across the visual field, there are of course many studies reporting visual field anisotropies (see Himmelberg, Winawer & Carrasco, 2023, Trends in Neuroscience for a review). It would be interesting to understand how the authors think about visual field anisotropies in the context of their own study. Do they think that their results are (in)dependent on such visual field variations (see Greenwood et al., 2017, PNAS; Ohl, Kroell, & Rolfs, 2024, JEP:Gen for a similar discussion)?

We agree that established visual field anisotropies are fascinating to be discussed in context of our own results. At the reviewer’s suggestion, we now expanded this discussion.

The observed anisotropies in terms of saccade costs are likely related to established anisotropies in perception and early visual cortex. However, the exact way that these anisotropies may be linked remains elusive (i.e. what is cause, what is effect, are links causal?), and more research is necessary to understand how these are related.

“The observed differences in saccade costs across directions could be linked to established anisotropies in perception [80–86], attention [87–92], saccade charac- teristics [87, 88, 92, 93], and (early) visual cortex [94–98] [also see 99]. For example, downward saccades are more costly than upward saccades, which mimics a similar asymmetry in early visual areas wherein the upper visual field is relatively under- represented [94–98]; similarly stronger presaccadic benefits are found for down- compared with upward saccades [87, 88]. Moreover, upward saccades are more pre- cise than downward saccades [93]. Future work should elucidate where saccade cost or the aforementioned anisotropies originate from and how they are related - something that pupil size alone cannot address.”

We also added that the finding that more precise saccades are coupled with worse performance in a crowding task might be attributed to the increased effort associated with more precise saccades (Greenwood et al., 2017).

“Adaptive resource allocation from, and to the oculomotor system parsimoniously explains a number of empirical observations. For example, higher cognitive demand is accompanied by smooth pursuits deviating more from to-be tracked targets [137], reduced (micro)saccade frequencies [Figure 4; 63, 64, 138, 139], and slower peak saccade velocities [140–142]. Relatedly, more precise saccades are accompanied with worse performance in a crowding task [93].”

Finally, the authors conclude that their results "suggests that the eye-movement system and other cognitive operations consume similar resources that are flexibly allocated among each other as cognitive demand changes. The authors should speculate what these similar resources could mean? What are the specific operations of the auditory task that overlap in terms of resources with the eye movement system?

We agree that the nature of joint resources is an interesting question. Our previous discussion was likely too simplistic here (see also responses to R3). We here specifically refer to the cognitive resources that one can flexibly distribute between tasks.

Our data do not directly speak to the question of what the shared resources between the auditory and oculomotor tasks are. Nevertheless, both tasks charge working memory as saccade targets are mandatorily encoded into working memory prior to saccade onset (Van der Stigchel & Hollingworth, 2018), and the counting task clearly engages working memory. This may indicate some domain-generality between visual and auditory working memory during natural viewing (see Nozari & Martin, 2024 for a recent review), but this remains speculative. Another possibility is that not the working memory encoding associated with saccades per se, but that the execution of overt motor actions itself also requires cognitive processing as suggested by Beatty (1982): “the organization of an overt motor act places additional demands on informationprocessing resources that are reflected in the task-evoked pupillary response”.

We have added upon this in more detail in the results and discussion sections.

“Besides the costs of increased neural activity when exerting more effort, effort should be considered costly for a second reason: Cognitive resources are limited. Therefore, any unnecessary resource expenditure reduces cognitive and behavioral flexibility [22, 31, 36, 116]. As a result, the brain needs to distribute resources between cognitive operations and the oculomotor system. We found evidence for the idea that such resource distribution is adaptive to the general level of cognitive demand and available resources: Increasing cognitive demand through an additional pri- mary auditory dual task led to a lower saccade frequency, and especially costly sac- cades were cut. In this case, it is important to consider that the auditory task was the primary task, which should cause participants to distribute resources from the ocu- lomotor system to the counting task. In other situations, more resources could be distributed to the oculomotor system instead, for example to discover new sources of reward [22, 136]. Adaptive resource allocation from, and to the oculomotor system parsimoniously explains a number of empirical observations. For example, higher cognitive demand is accompanied by smooth pursuits deviating more from to-be tracked targets [137], reduced (micro)saccade frequencies [Figure 4; 63, 64, 138, 139], and slower peak saccade velocities [140–142]. Relatedly, more precise saccades are accompanied with worse performance in a crowding task [93]. Furthermore, it has been proposed that saccade costs are weighed against other cognitive operations such as using working memory [33, 143–146]. How would the resources between the oculomotor system and cognitive tasks (like the auditory counting task) be related? One possibility is that both consume from limited working memory resources [147, 148]. Saccades are thought to encode target objects in a mandatory fashion into (vi- sual) working memory [79], and the counting task requires participants to keep track of the auditory stream and maintain count of the instructed digit in working mem- ory. However, the exact nature of which resources overlap between tasks remain open for future investigation [also see 149]. Together, we propose that cognitive re- sources are flexibly (dis)allocated to and from the oculomotor system based on the current demands to establish an optimal balance between performance and cost minimization.”

Reviewer #2 (Public Review):

The authors attempt to establish presaccadic pupil size as an index of 'saccade effort' and propose this index as one new predictor of saccade target selection. They only partially achieved their aim: When choosing between two saccade directions, the less costly direction, according to preceding pupil size, is preferred. However, the claim that with increased cognitive demand participants would especially cut costly directions is not supported by the data. I would have expected to see a negative correlation between saccade effort and saccade direction 'change' under increased load. Yet participants mostly cut upwards saccades, but not other directions that, according to pupil size, are equally or even more costly (e.g. oblique saccades).

Strengths:

The paper is well-written, easy to understand, and nicely illustrated.

The sample size seems appropriate, and the data were collected and analyzed using solid and validated methodology.

Overall, I find the topic of investigating factors that drive saccade choices highly interesting and relevant.

We thank the reviewer for pointing out the strengths of our paper.

Weaknesses:

The authors obtain pupil size and saccade preference measures in two separate tasks. Relating these two measures is problematic because the computations that underly saccade preparation differ. In Experiment 1, the saccade is cued centrally, and has to be delayed until a "go-signal" is presented; In Experiment 2, an immediate saccade is executed to an exogenously cued peripheral target. The 'costs' in Experiment 1 (computing the saccade target location from a central cue; withholding the saccade) do not relate to Experiment 2. It is unfortunate, that measuring presaccadic pupil size directly in the comparatively more 'natural' Experiment 2 (where saccades did not have to be artificially withheld) does not seem to be possible. This questions the practical application of pupil size as an index of saccade effort

This is an important point raised by the reviewer and we agree that a discussion on these points improves the manuscript. We reply in two parts: 1) Although the underlying computations during saccade preparation might differ, and are therefore unlikely to be fully similar (we agree), we can still predict saccade selection between (Saccade planning to Saccade preference) and within tasks (Visual search). 2) Pupil size is a sluggish physiological signal, but this is outweighed by the advantages of using pupil size as a general marker of effort, also in the context of visual selection compared with saccade latencies.

(1) Are delayed saccades (cost task) and the much faster saccades (preference task) linked?

As the reviewer notes the underlying ‘type’ of oculomotor program may differ between voluntarily delayed-saccades and those in the saccade preference task. There are, however, also considerable overlaps between the oculomotor programs as the directions and amplitudes are identical. Moreover, the different types of saccades have considerable overlap in their underlying neural circuitry. Nevertheless, the underlying oculomotor programs likely still differ in some regard. Even despite these differences, we were able to measure differences across directions in both tasks, and costs and preferences were negatively and highly correlated between tasks. The finding itself therefore indicates that the costs of saccades measured during the saccade planning task generalize to those in the saccade preference task. Note also that we predicted this finding and idea already in a previous publication before starting the present study (Koevoet et al., 2023).

We now address this interesting point in the discussion as follows:

“We observed that aOordable saccades were preferred over costly ones. This is especially remarkable given that the delayed saccades in the planning task likely differ in their oculomotor program from the immediate saccades in the preference task in some regard.”

(2) Is pupil size a sensible measure of saccade effort?

As the reviewer points out, the pupillary signal is indeed relatively sluggish and therefore relatively slow and more artifical tasks are preferred to quantify saccade costs. This does not preclude pupil size from being applied in more natural settings, as we demonstrate in the search experiments – but a lot of care has to be taken to control for many possible confounding factors and many trials will be needed.

That said, as saccade latencies may also capture differences in oculomotor effort (Shadmehr et al., 2019) they are a possible alternative option to assess effort in some oculomotor tasks (see below on why saccade latencies do not provide evidence for an alternative to effort driving saccade selection, but converging evidence). Whilst we do maintain that pupil size is an established and versatile physiological marker of effort, saccade latencies provide converging evidence for our conclusion that effort drives saccade selection.

As for the saccade preference task, we are not able to analyze the data in a similar manner as in the visual search task for two reasons. First, the number of saccades is much lower than in the natural search experiments. Second, in the saccade preference task, there were always two possible saccade targets. Therefore, even if we were able to isolate an effort signal, this signal could index a multitude of factors such as deciding between two possible saccade targets. Even simple binary decisions go hand in hand with reliable pupil dilations as they require effort (e.g. de Gee et al., 2014).

There are three major reasons why pupil size is a more versatile marker of saccade costs than saccade latencies (although as mentioned, latencies may constitute another valuable tool to study oculomotor effort). First, pupil size is able to quantify the cost of attentional shifts more generally, including covert attention as well as other effector systems such as head and hand movements. This circumvents the issue of different latencies of different effector systems and also allows to study attentional processes that are not associated with overt motor movements. Second, saccade latencies are difficult to interpret in natural viewing data, as fixation duration and saccade latencies are inherently confounded by one another. This makes it very difficult to separate oculomotor processes and the extraction of perceptual information from a fixated target. Thus, pupil size is a versatile marker of attentional costs in a variety of settings, and can measure costs that saccade latencies cannot (i.e. covert attention). Lastly, pupil size is highly established as a marker of effort which has been demonstrated across wide range of cognitive tasks and therefore not bound to eye movements alone (Bumke, 1911; Koevoet et al., 2024; Laeng et al., 2012; Loewenfeld, 1958; Mathôt, 2018; Robison & Unsworth, 2019; Sirois & Brisson, 2014; Strauch et al., 2022; van der Wel & van Steenbergen, 2018).

We now discuss this as follows:

“We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort [100], whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Supplemen- tary Materials). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established as marker of effort, and is sensitive to effort more broadly than only in the context of saccades [36–45, 48]. Pupil size therefore allows to capture not only the costs of saccades, but also of covert attentional shifts [33], or shifts with other effectors such as head or arm movements [54, 101]. Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost. Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.”

The authors claim that the observed direction-specific 'saccade costs' obtained in Experiment 1 "were not mediated by differences in saccade properties, such as duration, amplitude, peak velocity, and landing precision (Figure 1e,f)". Saccade latency, however, was not taken into account here but is discussed for Experiment 2.

The final model that was used to test for the observed anisotropies in pupil size across directions indeed did not include saccade latencies as a predictor. However, we did consider saccade latencies as a potential predictor originally. As we performed AICbased backward model selection, however, this predictor was removed due to the marginal predictive contribution of saccade latency beyond other predictors explaining pupil size.

For completeness, we here report the outcome of a linear mixed-effects that does include saccade latency as a predictor. Here, saccade latencies did not predict pupil size (b \= 1.859e-03, t \= .138, p \= .889). The asymmetry effects remained qualitatively unchanged: preparing oblique compared with cardinal saccades resulted in a larger pupil size (b \= 7.635, t \= 3.969, p < .001), and preparing downward compared with upward saccades also led to a larger pupil size (b \= 3.344, t \= 3.334, p \= .003).

The apparent similarity of saccade latencies and pupil size, however, is striking. Previous work shows shorter latencies for cardinal than oblique saccades, and shorter latencies for horizontal and upward saccades than downward saccades - directly reflecting the pupil sizes obtained in Experiment 1 as well as in the authors' previous study (Koevoet et al., 2023, PsychScience).

As the reviewer notes, there are substantial asymmetries across the visual field in saccade latencies. These assymetries in saccade latency could also predict saccade preferences. We will reply to this in three points: 1) even if saccade latency is a predictor of saccade preferences, this would not constitute as an alternative explanation to the conclusion of effort driving saccade selection, 2) saccade latencies show an up-down asymmetry but oblique-cardinal effects in latency may not be generalizable across saccade tasks, 3) pupil size remains a robust predictor of saccade preferences even when saccade latencies are considered as a predictor of saccade preferences.

(1) We want to first stress that saccade latencies are thought to reflect oculomotor effort (Shadmehr et al., 2019). For example, saccades with larger amplitudes and saccades where distractors need to be ignored are associated with longer latencies. Therefore, even if saccade latencies predict saccade selection, this would not contrast the idea that effort drives saccade selection. Instead, this would provide convergent evidence for our main conclusion – effort predicting saccade selection (rather than pupil size predicting saccade selection per se).

“We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort [100], whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Supplemen- tary Materials). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established as marker of effort, and is sensitive to effort more broadly than only in the context of saccades [36–45, 48]. Pupil size therefore allows to capture not only the costs of saccades, but also of covert attentional shifts [33], or shifts with other effectors such as head or arm movements [54, 101]. Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost. Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.”

(2) We first tested anisotropies in saccade latency in the saccade planning task (Wilkinson notation: latency ~ obliqueness + updownness + leftrightness + saccade duration + saccade amplitude + saccade velocity + landing error + (1+obliqueness + updownness|participant)). We found upward latencies to be shorter than downward saccade latencies (b \= -.535, t \= 3.421, p \= .003). In addition, oblique saccades showed shorter latencies than cardinal saccades (b \= -1.083, t \= 3.096, p \= .002) – the opposite of what previous work has demonstrated.

We then also tested these latency anisotropies in another dataset wherein participants (n \= 20) saccaded toward a single peripheral target as fast as possible (Koevoet et al., submitted; same amplitude and eccentricity as in the present manuscript). There we did not find a difference in saccade latency between cardinal and oblique targets, but we did observe shorter latencies for up- compared with downward saccades. We are therefore not sure in which situations oblique saccades do, or do not differ from cardinal saccades in terms of latency, and even in which direction the effect occurs.

In contrast, we have now demonstrated a larger pupil size prior to oblique compared with cardinal saccades in two experiments. This indicates that pupil size may be a more reliable and generalizable marker of saccade costs than saccade latency. However, this remains to be investigated further.

(3) To gain further insights into which oculomotor metrics would predict saccade selection, we conducted a linear regression across directions. We created pupil size, saccade latencies, landing precision and peak velocities maps from the saccade planning task. We then used AIC-based model selection to determine the ‘best’ model to determine which factor would predict saccade selection best. The selected model included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences ~ pupil size + saccade latency + landing precision). Pupil size (b \=-42.853, t \= 4.791, p < .001) and saccade latency (b \=-.377, t \= 2.106, p \= .043) predicted saccade preferences significantly. In contrast, landing precision did not reach significance (b \= 23.631, t \= 1.675, p \= .104). This analysis shows that although saccade latency predicts saccade preferences, pupil size remains a robust predictor of saccade selection.

“To ascertain whether pupil size or other oculomotor metrics predict saccade preferences, we conducted a multiple regression analysis. We calculated average pupil size, saccade latency, landing precision and peak velocity maps across all 36 directions. The model, determined using AIC-based backward selection, included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences  pupil size + saccade latency + landing precision). The analysis re- vealed that pupil size (β = -42.853, t = 4.791, p < .001) and saccade latency (β = -.377, t = 2.106, p = .043) predicted saccade preferences. Landing precision did not reach significance (β = 23.631, t = 1.675, p = .104). Together, this demonstrates that although other oculomotor metrics such as saccade latency contribute to saccade selection, pupil size remains a robust marker of saccade selection.”

The authors state that "from a costs-perspective, it should be eOicient to not only adjust the number of saccades (non-specific), but also by cutting especially expensive directions the most (specific)". However, saccade targets should be selected based on the maximum expected information gain. If cognitive load increases (due to an additional task) an effective strategy seems to be to perform less - but still meaningful - saccades. How would it help natural orienting to selectively cut saccades in certain (effortful) directions? Choosing saccade targets based on comfort, over information gain, would result in overall more saccades to be made - which is non-optimal, also from a cost perspective.

We thank the reviewer for this comment. Although we do not fully agree, the logic is quite close to our rationale and it is worth adding a point of discussion here. A vital part of the current interpretation is the instruction given to participants. In our second natural visual search task, participants were performing a dual task, where the auditory task was the primary task, whilst the search task was secondary. Therefore, participants are likely to adjust their resources to optimize performance on the primary task – at the expense of the secondary task. Therefore, less resources are made available and used to searching in the dual than in the single task, because these resources are needed for the auditory task. Cutting expensive directions does not help search in terms of search performance, but it does reduce the cost of search, so that more resources are available for the prioritized auditory task. Also note that the search task was rather difficult – participants did it, but it was tough (see the original description of the dataset for more details), which provides another reason to go full in on the auditory task at expense of the visual task. This, however, opens up a nice point of discussion: If one would emphasize the importance of search (maybe with punishment or reward), we would indeed expect participants to perform whichever eye movements are getting them to their goal fastest – thus reducing the relative influence of costs on saccade behavior. This remains to be tested however - we are working on this and are looking forward to discussing such findings in the future.

Together, we propose that there is a trade-off between distributing resources either towards cognitive tasks or the oculomotor system (also see Ballard et al., 1995; Van der Stigchel, 2020). How these resources are distributed depends highly on the current task demands (also see Sahakian et al., 2023). This allows for adaptive behavior in a wide range of contexts.

We now added these considerations to the manuscript as follows (also see our previous replies):

“Do cognitive operations and eye movements consume from a similar pool of resources [44]? If so, increasing cognitive demand for non-oculomotor processes should result in decreasing available resources for the oculomotor system. In line with this idea, previous work indeed shows altered eye-movement behavior un- der effort as induced by dual tasks, for example by making less saccades under increased cognitive demand [62–64]. We therefore investigated whether less sac- cades were made as soon as participants had to count the occurrence of a specific digit in the auditory number stream in comparison to ignoring the stream (in Exp. 2; Figure 4a). Participants were instructed to prioritize the auditory digit-counting task over finding the visual search target. Therefore, resources should be shifted from the oculomotor system to the primary auditory counting task. The additional cognitive demand of the dual task indeed led to a decreased saccade frequency (t(24) = 7.224, p < .001, Cohen’s d = 1.445; Figure 4h).”

I would have expected to see a negative correlation between saccade effort and saccade direction 'change' under increased load. Yet participants mostly cut upwards saccades, but not other directions that, according to pupil size, are equally or even more costly (e.g. oblique saccades).

The reviewer’s point is taken from the initial comment, which we will address here. First, we’d like to point out that is it not established that saccade costs in different directions are always the same. Instead, it is possible that saccade costs could be different in natural viewing compared with our delayed-saccade task. Therefore, we used pupil size during natural viewing for the search experiments. Second, the reviewer correctly notes that oblique saccades are hardly cut when under additional cognitive demand. However, participants already hardly execute oblique saccades when not confronted with the additional auditory task (Figure 4b, d), making it difficult to reduce those further (i.e. floor effect). Participants chose to cut vertical saccades, possibly because these are more costly than horizontal saccades.

We incorporated these point in our manuscript as follows:

“To test this, we analyzed data from two existing datasets [63] wherein participants (total n = 41) searched for small targets (’Z’ or ’H’) in natural scenes (Figure 4a; [64]). Again, we tested whether pupil size prior to saccades negatively linked with saccade preferences across directions. Because saccade costs and preferences across directions could differ for different situations (i.e. natural viewing vs. saccade preference task), but should always be negatively linked, we established both cost and preferences independently in each dataset.”

“We calculated a saccade-adjustment map (Figure 4g) by subtracting the saccade preference map in the single task (Figure 4f) from the dual task map (Fig- ure 4d). Participants seemingly cut vertical saccades in particular, and made more saccades to the top right direction. This pattern may have emerged as vertical saccades are more costly than horizontal saccades (also see Figure 1d). Oblique saccades may not have been cut because there were very little oblique saccades in the single condition to begin with (Figure 4d), making it difficult to observe a further reduction of such saccades under additional cognitive demand (i.e. a floor effect).”

Overall, I am not sure what practical relevance the relation between pupil size (measured in a separate experiment) and saccade decisions has for eye movement research/vision science. Pupil size does not seem to be a straightforward measure of saccade effort. Saccade latency, instead, can be easily extracted in any eye movement experiment (no need to conduct a separate, delayed saccade task to measure pupil dilation), and seems to be an equally good index.

There are two points here.

(1) What is the practical relevance of a link between effort and saccade selection for eyemovement research and vision science?

We see plenty – think of changing eye movement patterns under effort (be it smooth pursuits, saccade rates, distributions of gaze positions to images etc.) which have substantial implications for human factors research, but also neuropsychology. With a cost account, one may predict (rather than just observe) how eye movement changes as soon as resources are reduced/ non-visual demand increases. With a cost account, we can explain such effects (e.g. lower saccade rates under effort, cardinal bias, perhaps also central bias) parsimoniously that cannot be explained by what is so far referred to as the three core drivers of eye movement behavior (saliency, selection history, goals, e.g., Awh et al., 2012). Conversely, one must wonder why eye-movement research/vision science simply accepts/dismisses these phenomena as such, without seeking overarching explanations.

(2) What is the usefulness of using pupil size to measure effort?

We hope that our replies to the comments above illustrate why pupil size is a sensible, robust and versatile marker of attentional costs. We briefly summarize our most important points here.

- Pupil size is an established measure of effort irrespective of context, as demonstrated by hundreds of original works (e.g. working memory load, multiple object tracking, individual differences in cognitive ability). This allows pupil size to be a versatile marker of the effort, and therefore costs, of non-saccadic attentional shifts such as covert attention or those realized by other effector systems (i.e. head or hand movements).

- Our new analysis indicates that pupil size remains a strong and robust predictor of saccade preference, even when considering saccade latency.

- Pupil size allows to study saccade costs in natural viewing. In contrast, saccade latencies are difficult to assess in natural viewing as fixation durations and saccade latencies are intrinsically linked and very difficult to disentangle.

- Note however, that we think that it is interesting and useful so study effects of effort/cost on eye movement behavior. Whichever index is used to do so, we see plenty potential in this line of research, this paper is a starting point to do so.

Reviewer #3 (Public Review):

This manuscript extends previous research by this group by relating variation in pupil size to the endpoints of saccades produced by human participants under various conditions including trial-based choices between pairs of spots and search for small items in natural scenes. Based on the premise that pupil size is a reliable proxy of "effort", the authors conclude that less costly saccade targets are preferred. Finding that this preference was influenced by the performance of a non-visual, attentiondemanding task, the authors conclude that a common source of effort animates gaze behavior and other cognitive tasks.

Strengths:

Strengths of the manuscript include the novelty of the approach, the clarity of the findings, and the community interest in the problem.

We thank the reviewer for pointing out the strengths of our paper.

Weaknesses:

Enthusiasm for this manuscript is reduced by the following weaknesses:

(1) A relationship between pupil size and saccade production seems clear based on the authors' previous and current work. What is at issue is the interpretation. The authors test one, preferred hypothesis, and the narrative of the manuscript treats the hypothesis that pupil size is a proxy of effort as beyond dispute or question. The stated elements of their argument seem to go like this:

PROPOSITION 1: Pupil size varies systematically across task conditions, being larger when tasks are more demanding.

PROPOSITION 2: Pupil size is related to the locus coeruleus.

PROPOSITION 3: The locus coeruleus NE system modulates neural activity and interactions.

CONCLUSION: Therefore, pupil size indexes the resource demand or "effort" associated with task conditions.

How the conclusion follows from the propositions is not self-evident. Proposition 3, in particular, fails to establish the link that is supposed to lead to the conclusion.

We inadvertently laid out this rationale as described above, and we thank the reviewer for pointing out this initial suboptimal structure of argumentation. The notion that the link between pupil size and effort is established in the literature because of its neural underpinnings is inaccurate. Instead, the tight link between effort and pupil size is established based on covariations of pupil diameter and cognition across a wide variety of tasks and domains. In line with this, we now introduce this tight link predominantly based on the relationships between pupil size and cognition instead of focusing on putative neural correlates of this relationship.

As reviewed previously (Beatty, 1982; Bumke, 1911; Kahneman, 1973; Kahneman & Beatty, 1966; Koevoet et al., 2024; Laeng et al., 2012; Mathôt, 2018; Sirois & Brisson, 2014; Strauch et al., 2022; van der Wel & van Steenbergen, 2018), any increase in effort is consistently associated with an increase in pupil size. For instance, the pupil dilates when increasing load in working memory or multiple object tracking tasks, and such pupillary effects robustly explain individual differences in cognitive ability and fluctuations in performance across trials (Alnæs et al., 2014; Koevoet et al., 2024; Robison & Brewer, 2020; Robison & Unsworth, 2019; Unsworth & Miller, 2021). This extends to the planning of movements as pupil dilations are observed prior to the execution of (eye) movements (Koevoet et al., 2023; Richer & Beatty, 1985). The link between pupil size and effort has thus been firmly established for a long time, irrespective of the neural correlates of these effort-linked pupil size changes.

We again thank the reviewer for spotting this logical mistake, and now revised the paragraph where we introduce pupil size as an established marker of effort as follows:

“We recently demonstrated that the effort of saccade planning can be measured with pupil size, which allows for a physiological quantification of saccade costs as long as low-level visual factors are controlled for [33]. Pupil size is an established marker of effort [36–44]. For instance, loading more in working memory or tracking more objects results in stronger pupil dilation [44–52]. Pupil size not only reflects cognitive (or mental) effort but also the effort of planning and executing movements [37, 53, 54]. We leveraged this to demonstrate that saccade costs can be captured with pupil size, and are higher for oblique compared with cardinal directions [33]. Here, we addressed whether saccade costs predict where to saccade.”

We now mention the neural correlates of pupil size only in the discussion. Where we took care to also mention roles for other neurotransmitter systems:

“Throughout this paper, we have used cost in the limited context of saccades.

However, cost-based decision-making may be a more general property of the brain [31, 36, 114–116]. Every action, be it physical or cognitive, is associated with an in- trinsic cost, and pupil size is likely a general marker of this [44]. Note, however, that pupil dilation does not always reflect cost, as the pupil dilates in response to many sensory and cognitive factors which should be controlled for, or at least considered, when interpreting pupillometric data [e.g., see 39, 40, 42, 117]. Effort-linked pupil dilations are thought to be, at least in part, driven by activity in the brainstem locus coeruleus (LC) [40, 118–120] [but other neurotransmitters also affect pupil size, e.g. 121, 122]. Activity in LC with its widespread connections throughout the brain [120, 123–127] is considered to be crucial for the communication within and between neu- ral populations and modulates global neural gain [128–132]. Neural firing is costly [22, 133], and therefore LC activity and pupil size are (neuro)physiologically plausible markers of cost [40]. Tentative evidence even suggests that continued exertion of effort (accompanied by altered pupil dilation) is linked to the accumulation of glutamate in the lateral prefrontal cortex [134], which may be a metabolic marker of cost [also see 116, 134, 135]. “

(2) The authors test one, preferred hypothesis and do not consider plausible alternatives. Is "cost" the only conceivable hypothesis? The hypothesis is framed in very narrow terms. For example, the cholinergic and dopamine systems that have been featured in other researchers' consideration of pupil size modulation are missing here. Thus, because the authors do not rule out plausible alternative hypotheses, the logical structure of this manuscript can be criticized as committing the fallacy of aOirming the consequent.

As we have noted in the response to the reviewer’s first point, we did not motivate our use of pupil size as an index of effort clearly enough. For the current purpose, the neural correlates of pupil size are less relevant than the cognitive correlates (see previous point). We reiterate that the neuromodulatory underpinnings of the observed pupil size effects (which indeed possibly include effects of the cholinergic, dopaminergic and serotonergic systems), while interesting for the discussion on the neural origin of effects, are not crucial to our conclusion. We hope the new rationale (without focusing too much on the (irrelevant) exact neural underpinnings) convinces the reviewer and reader.

Our changes to the manuscript are shown in our reply to the previous comment.

The reviewer notes that other plausible alternative hypotheses could explain the currently reported results. However, we did not find a more parsimonuous explanation for our data than ‘Effort Drives Saccade Selection’. Effort explains why participants prefer saccading toward specific directions in (1) highly controlled and (2) more natural settings. Note that we also predicted this effect previously (Koevoet et al., 2023). Moreover, this account explains (3) why participants make less saccades under additional cognitive demand, and (4) why especially costly saccades are reduced under additional cognitive demand. We are very open to the reviewer presenting other possible interpretations of our data so these can be discussed to be put to test in future work.

(3) The authors cite particular publications in support of the claim that saccade selection is influenced by an assessment of effort. Given the extensive work by others on this general topic, the skeptic could regard the theoretical perspective of this manuscript as too impoverished. Their work may be enhanced by consideration of other work on this general topic, e.g, (i) Shenhav A, Botvinick MM, Cohen JD. (2013) The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron. 2013 Jul 24;79(2):217-40. (ii) Müller T, Husain M, Apps MAJ. (2022) Preferences for seeking effort or reward information bias the willingness to work. Sci Rep. 2022 Nov 14;12(1):19486. (iii) Bustamante LA, Oshinowo T, Lee JR, Tong E, Burton AR, Shenhav A, Cohen JD, Daw ND. (2023) Effort Foraging Task reveals a positive correlation between individual differences in the cost of cognitive and physical effort in humans. Proc Natl Acad Sci U S A. 2023 Dec 12;120(50):e2221510120.

We thank the reviewer for pointing us toward this literature. These papers are indeed relevant for our manuscript, and we have now incorporated them. Specifically, we now discuss how the costs of effort are weighed in relation to possible rewards during decision-making. We have also incorporated work that has investigated how the biomechanical costs of arm movements contribute to action selection.

“Our findings are in line with established effort-based models that assume costs to be weighed against rewards during decision-making [102–107]. In such studies, reward and cognitive/physical effort are often parametrically manipulated to as- sess how much effort participants are willing to exert to acquire a given (monetary) reward [e.g. 108, 109]. Whereas this line of work manipulated the extrinsic costs and/or rewards of decision options (e.g. perceptual consequences of saccades [110, 111] or consequences associated with decision options), we here focus on the intrin- sic costs of the movement itself (in terms of cognitive and physical effort). Relatedly, the intrinsic costs of arm movements are also considered during decision-making: biomechanically aOordable movements are generally preferred over more costly ones [26–28]. We here extend these findings in two important ways. First, until now, the intrinsic costs of saccades and other movements have been inferred from gaze behavior itself or by using computational modelling [23, 25–28, 34, 35, 112]. In con- trast, we directly measured cost physiologically using pupil size. Secondly, we show that physiologically measured saccade costs predict where saccades are directed in a controlled binary preference task, and even during natural viewing. Our findings could unite state-of-the-art computational models [e.g. 23, 25, 34, 35, 113] with physiological data, to directly test the role of saccade costs and ultimately further our understanding of saccade selection.”

(4) What is the source of cost in saccade production? What is the currency of that cost? The authors state (page 13), "... oblique saccades require more complex oculomotor programs than horizontal eye movements because more neuronal populations in the superior colliculus (SC) and frontal eye fields (FEF) [76-79], and more muscles are necessary to plan and execute the saccade [76, 80, 81]." This statement raises questions and concerns. First, the basis of the claim that more neurons in FEF and SC are needed for oblique versus cardinal saccades is not established in any of the publications cited. Second, the authors may be referring to the fact that oblique saccades require coordination between pontine and midbrain circuits. This must be clarified. Second, the cost is unlikely to originate in extraocular muscle fatigue because the muscle fibers are so different from skeletal muscles, being fundamentally less fatigable. Third, if net muscle contraction is the cost, then why are upward saccades, which require the eyelid, not more expensive than downward? Thus, just how some saccades are more effortful than others is not clear.

Unfortunately, our current data do not allow for the specification of what the source is of differences in saccade production, nor what the currency is. We want to explicitly state that while pupil size is a sensitive measure of saccade costs, pupil size cannot directly inform what underlying mechanisms are causing differences in saccade costs across conditions (e.g. directions). Nevertheless, we do speculate about these issues because they are important to consider. We thank the reviewer for pointing out the shortcomings in our initial speculations.

Broadly, we agree with the reviewer that a neural source of differences in costs between different types of saccades is more likely than a purely muscular account (also see Koevoet et al., 2023). Furthermore, we think that the observed differences in saccade costs for oblique vs. cardinal and up vs. down could be due to different underlying mechanisms. While we caution against overinterpreting single directions, tentative evidence for this may also be drawn by the different time course of effects for up/down versus cardinal/oblique, Figure 1c.

Below we speculate about why some specific saccade directions may be more costly than others:

Why would oblique saccades be more costly than cardinal saccades? We thank the reviewer for pointing out that oblique saccades additionally require coordination between pontine and midbrain circuits (Curthoys et al., 1984; King & Fuchs, 1979; Sparks, 2002). This point warrants more revised discussion compared to our initial version. We have incorporated this as follows:

“The complexity of an oculomotor program is arguably shaped by its neural underpinnings. For example, oblique but not cardinal saccades require communication between pontine and midbrain circuits [73–75]. Such differences in neural complexity may underlie the additional costs of oblique compared with cardinal saccades. Besides saccade direction, other properties of the ensuing saccade such as its speed, distance, curvature, and accuracy may contribute to a saccade’s total cost [22, 33, 53, 76, 77] but this remains to be investigated directly.”

Why would downward saccades be more costly than upward saccades? As the reviewer points out: from a net muscular contraction account of cost, one would expect the opposite pattern due to the movement of the eyelid. Instead, we speculate that our findings may be associated with the well-established anisotropy in early visual cortex along the vertical meridian. Specifically, the upper vertical meridian is represented at substantially less detail than the lower vertical meridian (Himmelberg et al., 2023; Silva et al., 2018). Prior to a saccade, attention is deployed towards the intended saccadic endpoint (Deubel & Schneider, 1996; Kowler et al., 1995). Attention tunes neurons to preferentially process the attended location over non-attended locations. Due to the fact that the lower visual field is represented at higher detail than the upper visual field, attention may tune neuronal responses differently when preparing up- compared with downward saccades (Hanning et al., 2024; Himmelberg et al., 2023). Thus, it may be more costly to prepare down- compared with upward saccades. This proposition, however, does not account for the lower costs associated horizontal compared with up- and downward saccades as the horizontal meridian is represented at a higher acuity than the vertical merdian. This makes it unlikely that this explains the pattern of results completely. Again, at this point we can only speculate why costs differ, yet we demonstrate that these differences in cost are decisive for oculomotor behavior. We now explicitly state the speculative nature of these ideas that would all need to be tested directly.

We have updated our discussion of this issue as follows:

“The observed differences in saccade costs across directions could be linked to established anisotropies in perception [80–86], attention [87–92], saccade charac- teristics [87, 88, 92, 93], and (early) visual cortex [94–98] [also see 99]. For example, downward saccades are more costly than upward saccades, which mimics a similar asymmetry in early visual areas wherein the upper visual field is relatively under- represented [94–98]; similarly stronger presaccadic benefits are found for down- compared with upward saccades [87, 88]. Moreover, upward saccades are more pre- cise than downward saccades [93]. Future work should elucidate where saccade cost or the aforementioned anisotropies originate from and how they are related - something that pupil size alone cannot address.”

(5) The authors do not consider observations about variation in pupil size that seem to be incompatible with the preferred hypothesis. For example, at least two studies have described systematically larger pupil dilation associated with faster relative to accurate performance in manual and saccade tasks (e.g., Naber M, Murphy P. Pupillometric investigation into the speed-accuracy trade-off in a visuo-motor aiming task. Psychophysiology. 2020 Mar;57(3):e13499; Reppert TR, Heitz RP, Schall JD. Neural mechanisms for executive control of speed-accuracy trade-off. Cell Rep. 2023 Nov 28;42(11):113422). Is the fast relative to the accurate option necessarily more costly?

We thank the reviewer for this interesting point that we will answer in two ways. First, we discuss the main point: the link between pupil size, effort, and cost. Second, we discuss the findings described specifically in these two papers and how we interpret these from a pupillometric account.

First, one may generally ask whether 1) any effort results in pupil dilation, 2) whether any effort is costly, and 3) whether this means that pupil dilation always reflects effort and cost respectively. Indeed, it has been argued repeatedly, prominently, and independently (e.g., Bumke, 1911; Mathôt, 2018) that any change in effort (no matter the specific origin) is associated with an evoked pupil dilation. Effort, in turn, is consistently and widely experienced as aversive, both across tasks and cultures (David et al., 2024). Effort minimization may therefore be seen as an universal law of human cognition and behavior with effort as a to-be minimized cost (Shadmehr et al., 2019; Hull 1943, Tsai 1932). However, this does not imply that any pupil dilation necessarily reflects effort or that, as a consequence thereof, any pupil dilation is always signaling cost. For instance, the pupil dark response, the pupil far response and changes in baseline pupil size are not associated with effort. Baseline and task-evoked pupil dilation responses have to be interpreted differently (see below), moreover, the pupil also changes (and dilates) due to other factors (see Strauch et al., 2022; Mathôt, 2018, Bumke 1911, Loewenfeld, 1999 for reviews).

Second, as for Naber & Murphy (2020) & Reppert at al. (2023) specifically: Both Reppert et al. (2023) and Naber & Murphy (2020) indeed demonstrate a larger baseline pupil size when participants made faster, less accurate responses. However, baseline pupil size is not an index of effort per-se, but task-evoked pupil dilation responses are (as studied in the present manuscript) (Strauch et al., 2022). For work on differences between baseline pupil diameter and task-evoked pupil responses, and their respective links with exploration and exploitation please see Jepma & Nieuwenhuis (2011). Indeed, the link between effort and larger pupil size holds for task evoked responses, but not baseline pupil size per se (also see Koevoet et al., 2023).

Still, Naber (third author of the current paper) & Murphy (2020) also demonstrated larger task-evoked pupil dilation responses when participants were instructed to make faster, less accurate responses compared with making accurate and relatively slow responses. However, this difference in task-evoked response gains significance only after the onset of the movement itself, and peaks substantially later than response offset. Whilst pupil dilation may be sluggish, it isn’t extremely sluggish either. As feedback to the performance of the participant was displayed 1.25s after performing the movement and clicking (taking about 630ms), we deem it possible that this effect may in part result from appraising the feedback to the participant rather than the speed of the response itself (in fact, Naber and Murphy also discuss this option). In addition to not measuring saccades but mouse movements, it is therefore possible that the observed evoked pupil effects in Naber & Murphy (2020) are not purely linked to motor preparation and execution per se. Therefore, future work that aims to investigate the costs of movements should isolate the effects of feedback and other potential factors that may drive changes in pupil size. This will help clarify whether fast or more accurate movements could be linked to the underlying costs of the movements.

Relatedly, we do not find evidence that pupil size during saccade planning predicts the onset latency of the ensuing saccade (please refer to our second response to Reviewer 2 for a detailed discussion).

Together, we therefore do not see the results from Reppert et al. (2023) and Naber & Murphy (2020) to be at odds with our interpretation of evoked pupil size reflecting effort and cost in the context of planning saccades.

We think that these are considerations important to the reader, which is why we now added them to the discussion as follows:

“Throughout this paper, we have used cost in the limited context of saccades.

However, cost-based decision-making may be a more general property of the brain [31, 36, 114–116]. Every action, be it physical or cognitive, is associated with an in- trinsic cost, and pupil size is likely a general marker of this [44]. Note, however, that pupil dilation does not always reflect cost, as the pupil dilates in response to many sensory and cognitive factors which should be controlled for, or at least considered, when interpreting pupillometric data [e.g., see 39, 40, 42, 117].”

(6) The authors draw conclusions based on trends across participants, but they should be more transparent about variation that contradicts these trends. In Figures 3 and 4 we see many participants producing behavior unlike most others. Who are they? Why do they look so different? Is it just noise, or do different participants adopt different policies?

We disagree with the transparency point of the reviewer. Note that we deviated from the norm here by being more transparent than common: we added individual data points and relationships rather than showing pooled effects across participants with error bars alone (see Figures 2c, 3b,c, 4c,e,f).

Moreover, our effects are consistent and stable across participants and are highly significant. To illustrate, for the classification analysis based on cost (Figure 2E) 16/20 participants showed an effect. As for the natural viewing experiments (total > 250,000 fixations), we also find that a majority of participants show the observed effects: Experiment 1: 15/16 participants; Experiment 2: 16/25 participants; Experiment 2 – adjustment: 22/25 participants.

We fully agree that it’s interesting to understand where interindividual variation may originate from. We currently have too little data to allow robust analyses across individuals and zooming in on individual differences in cost maps, preference maps, or potential personalized strategies of saccade selection. That said, future work could study this further. We would recommend to hereby reduce the number of directions to gain more pupil size data per direction and therefore cleaner signals that may be more informative on the individual level. With such stronger signals, studying (differences in) links on an individual level may be feasible and would be interesting to consider – and will be a future direction in our own work too. Nonetheless, we again stress that the reported effects are robust and consistent across participants, and that interindividual differences are therefore not extensive. Moreover, our results from four experiments consistently support our conclusion that effort drives saccade selection.

Recommendations for the authors:  

Reviewer #1 (Recommendations For The Authors):

- Based on the public review, I would recommend that the authors carefully review and correct the manuscript with regard to the causal conclusions. The study is largely correlational (i.e. the pupil was only observed, not manipulated) and therefore does not allow causal conclusions to be drawn about the relationship between pupil size and saccade selection. These causal conclusions become even more confusing when pupil size is equated with effort and saccade cost. As a consequence, an actual correlation between pupil size and saccade selection has led to the title that effort drives saccade selection. It would also be helpful for the reader to summarize in an additional section of the discussion what they consider to be a causal or correlational link based on their results.

We agree with the reviewer, and we have indeed included more explicitly which findings are correlational and which causal in detail now. As outlined before we do not see a more parimanious explanation for our findings than our title, but we fully agree that the paper benefits from making the correlational/causal nature of evidence for this idea explicitly transparent.

“We report a combination of correlational and causal findings. Despite the correlational nature of some of our results, they consistently support the hypothesis that saccade costs predicts saccade selection [which we predicted previously, 33]. Causal evidence was provided by the dual-task experiment as saccade frequencies - and especially costly saccades were reduced under additional cognitive demand. Only a cost account predicts 1) a link between pupil size and saccade preferences, 2) a cardinal saccade bias, 3) reduced saccade frequency under additional cognitive demand, and 4) disproportional cutting of especially those directions associated with more pupil dilation. Together, our findings converge upon the conclusion that effort drives saccade selection.”

- Can the authors please elaborate in more detail on how they transformed the predictors of their linear mixed model for the visualization in Figure 1f? It is difficult to see how the coeOicients in the table and the figure match.

We used the ‘effectsize’ package to provide effect sizes of for each predictor of the linear mixed-effects model (https://cran.r-project.org/web/packages/effectsize/index.html). We report absolute effect sizes to make it visually easier to compare different predictors. These details have now been included in the Methods section to be more transparent about how these effect sizes were computed.

“Absolute effect sizes (i.e. r) and their corresponding 95% confidence intervals for the linear mixed-effects models were calculated using t and df values with the ’effectsize’ package (v0.8.8) in R.”

- Could the authors please explain in more detail why they think that a trial-by-trial analysis in the free choice task adds something new to their conclusions? In fact, a trialby-trial analysis somehow suggests that the pupil size data would enter the analysis at a single trial level. If I understand correctly, the pupil size data come from their initial mapping task. So there is only one mean pupil size for a given participant and direction that goes into their analysis to predict free choice in a single trial. If this is the case, I don't see the point of doing this additional analysis given the results shown in Figure 2c.

The reviewer understands correctly that pupil size data is taken from the initial mapping task. We then used these mean values to predict which saccade target would be selected on a trial-by-trial basis. While showing the same conceptual result as the correlation analysis, we opted to include this analysis to show the robustness of the results across individuals. Therefore we have chosen to keep the analysis in the manuscript but now write more clearly that this shows the same conceptual finding as the correlation analysis.

“As another test of the robustness of the effect, we analyzed whether saccade costs predicted saccade selection on a trial-by-trial basis. To this end, we first determined the more aOordable option for each trial using the established saccade cost map (Figure 1d). We predicted that participants would select the more aOordable option. Complementing the above analyses, the more aOordable option was chosen above chance level across participants (M = 56.64%, 95%-CI = [52.75%-60.52%], one-sample t-test against 50%: t(19) = 3.26, p = .004, Cohen’s d = .729; Figure 2e). Together, these analyses established that saccade costs robustly predict saccade preferences.”

Reviewer #2 (Recommendations For The Authors):

The authors report that "Whenever the difference in pupil size between the two options was larger, saccades curved away more from the non-selected option (β = .004, SE = .001, t = 4.448, p < .001; Figure 3b), and their latencies slowed (β = .050, SE = .013, t = 4.323, p < .001; Figure 3c)". I suspect this effect might not be driven by the difference but by a correlation between pupil size and latency.

The authors correlate differences in pupil size (Exp1) with saccade latencies (Exp2), I recommend correlating pupil size with the latency directly, in either task. This would show if it is actually the difference between choices or simply the pupil size of the respective individual option that is linked to latency/effort. Same for curvature.

The reviewer raises a good point. Please see the previous analyses concerning the possible correlations between pupil size and saccade latency, and how they jointly predict saccade selection.

Our data show that saccade curvature and latencies are linked with the difference in pupil size between the selected and non-selected options. Are these effects driven by a difference in pupil size or by the pupil size associated with the chosen option?

To assess this, we conducted two linear mixed-effects models. We predicted saccade curvature and latency using pupil size (from the planning task) of the selected and nonselected options while controlling for the chosen direction (Wilkinson notation: saccade curvature/latency ~ selected pupil size + non-selected pupil size + obliqueness + vertical + horizontal + (1+ selected pupil size + non-selected pupil size|participant). We found that saccades curved away more from costlier the non-selected targets (β \=1.534, t \= 8.151, p < .001), and saccades curved away from the non-selected target less when the selected target was cheaper (β \=-2.571, t \= -6.602, p < .001). As the costs of the selected and non-selected show opposite effects on saccade curvature, this indicates that the difference between the two options drives oculomotor conflict.

As for saccade latencies, we found saccade onsets to slow when the cost of the selected target was higher (b \= .068, t \= 2.844, p \= .004). In contrast, saccade latencies were not significantly affected by the cost of the non-selected target (β \= -.018, t \= 1.457, p \= .145), although numerically the effect was in the opposite direction. This shows that latencies were primarily driven by the cost of the selected target but a difference account cannot be fully ruled out.

Together, these analyses demonstrate that the difference in costs between two alternatives reliably affects oculomotor conflict as indicated by the curvature analysis. However, saccade latencies are predominantly affected by the cost of the selected target – even when controlling for the obliqueness, updownness and leftrightness of the ensuing saccade. We have added these analyses here for completeness, but because the findings seem inconclusive for saccade latency we have chosen to not include these analyses in the current paper. We are open to including these analyses in the supplementary materials if the reviewer and/or editor would like us to, but have chosen not to do so due to conciseness and to keep the paper focused.

I was wondering why the authors haven't analyzed the pupil size in Experiment 2. If the pupil size can be assessed during a free viewing task (Experiment 3), shouldn't it be possible to also evaluate it in the saccade choice task?

We did not analyze the pupil size data from the saccade preference task for two reasons. First, the number of saccades is much lower than in the natural search experiments (~14.000 vs. ~250.000). Second, in the saccade preference task, there were always two possible saccade targets. Therefore, even if we were able to isolate an effort signal, this signal could index a multitude of factors such as deciding between two possible saccade targets (de Gee et al., 2014), and has the possibility of two oculomotor programs being realized instead of only a single one (Van der Stigchel, 2010).

Discussion: "due to stronger presaccadic benefits for upward compared with downward saccades [93,94]". I think this should be the other way around.

We thank the reviewer for pointing this out. We have corrected our mistake in the revised manuscript.

Saccade latencies differ around the visual field; to account for that, results / pupil size should be (additionally) evaluated relative to saccade onset (rather than cue offset). It is interesting that latencies were not accounted for here (Exp1), since they are considered for Exp2 (where they correlate with a pupil size difference). I suspect that latencies not only correlate with the difference in pupil size, but directly with pupil size itself.

We agree with the reviewer that locking the pupil size signal to saccade onset instead of cue offset may be informative. We included an analysis in the supporting information that investigates this (see Figure S1). The results of the analysis were conceptually identical.

The reviewer writes that latencies were not accounted for in Experiment 1. Although saccade latency was not included in the final model reported in the paper, it was considered during AIC-based backward model selection. As saccade latency did not predict meaningful variance in pupil size, it was ultimately not included in the analysis as a predictor. For completeness, we here report the outcome of a linear mixed-effects that does include saccade latency as a predictor. Here, saccade latencies did not predict pupil size (β \= 1.859e-03, t \= .138, p \= .889). The assymetry effects remained qualitatively unchanged: preparing oblique compared with cardinal saccades resulted in a larger pupil size (β \= 7.635, t \= 3.969, p < .001), and preparing downward compared with upward saccades also led to a larger pupil size (β \= 3.344, t \= 3.334, p \= .003).

In addition, we have included a new analysis in the supporting information that directly addresses this issue. We will reiterate the main results here:

“To ascertain whether pupil size or other oculomotor metrics predict saccade preferences, we conducted a multiple regression analysis. We calculated average pupil size, saccade latency, landing precision and peak velocity maps across all 36 directions. The model, determined using AIC-based backward selection, included pupil size, latency and landing precision as predictors (Wilkinson notation: saccade preferences  pupil size + saccade latency + landing precision). The analysis re- vealed that pupil size (β = -42.853, t = 4.791, p < .001) and saccade latency (β = -.377, t = 2.106, p = .043) predicted saccade preferences. Landing precision did not reach significance (β = 23.631, t = 1.675, p = .104). Together, this demonstrates that although other oculomotor metrics such as saccade latency contribute to saccade selection, pupil size remains a robust marker of saccade selection.”

We have also added this point in our discussion:

“We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort [100], whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Supplemen- tary Materials). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established as marker of effort, and is sensitive to effort more broadly than only in the context of saccades [36–45, 48]. Pupil size therefore allows to capture not only the costs of saccades, but also of covert attentional shifts [33], or shifts with other effectors such as head or arm movements [54, 101]. Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost. Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.”

References

Alnæs, D., Sneve, M. H., Espeseth, T., Endestad, T., van de Pavert, S. H. P., & Laeng, B. (2014). Pupil size signals mental eFort deployed during multiple object tracking and predicts brain activity in the dorsal attention network and the locus coeruleus. Journal of Vision, 14(4), 1. https://doi.org/10.1167/14.4.1

Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Sciences, 16(8), 437–443. https://doi.org/10.1016/j.tics.2012.06.010

Ballard, D. H., Hayhoe, M. M., & Pelz, J. B. (1995). Memory Representations in Natural Tasks. Journal of Cognitive Neuroscience, 7(1), 66–80. https://doi.org/10.1162/jocn.1995.7.1.66

Beatty, J. (1982). Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin, 91(2), 276–292. https://doi.org/10.1037/0033-2909.91.2.276

Bumke, O. (1911). Die Pupillenstörungen bei Geistes-und Nervenkrankheiten (2nd ed.). Fischer.

Curthoys, I. S., Markham, C. H., & Furuya, N. (1984). Direct projection of pause neurons to nystagmusrelated excitatory burst neurons in the cat pontine reticular formation. Experimental Neurology, 83(2), 414–422. https://doi.org/10.1016/S0014-4886(84)90109-2

David, L., Vassena, E., & Bijleveld, E. (2024). The unpleasantness of thinking: A meta-analytic review of the association between mental eFort and negative aFect. Psychological Bulletin, 150(9), 1070–1093. https://doi.org/10.1037/bul0000443

de Gee, J. W., Knapen, T., & Donner, T. H. (2014). Decision-related pupil dilation reflects upcoming choice and individual bias. Proceedings of the National Academy of Sciences, 111(5), E618–E625. https://doi.org/10.1073/pnas.1317557111

Deubel, H., & Schneider, W. X. (1996). Saccade target selection and object recognition: Evidence for a common attentional mechanism. Vision Research, 36(12), 1827–1837. https://doi.org/10.1016/0042-6989(95)00294-4

Greenwood, J. A., Szinte, M., Sayim, B., & Cavanagh, P. (2017). Variations in crowding, saccadic precision, and spatial localization reveal the shared topology of spatial vision. Proceedings of the National Academy of Sciences, 114(17), E3573–E3582. https://doi.org/10.1073/pnas.1615504114

Hanning, N. M., Himmelberg, M. M., & Carrasco, M. (2024). Presaccadic Attention Depends on Eye Movement Direction and Is Related to V1 Cortical Magnification. Journal of Neuroscience, 44(12). https://doi.org/10.1523/JNEUROSCI.1023-23.2023

Himmelberg, M. M., Winawer, J., & Carrasco, M. (2023). Polar angle asymmetries in visual perception and neural architecture. Trends in Neurosciences, 46(6), 445–458. https://doi.org/10.1016/j.tins.2023.03.006

Jepma, M., & Nieuwenhuis, S. (2011). Pupil Diameter Predicts Changes in the Exploration–Exploitation Trade-oF: Evidence for the Adaptive Gain Theory. Journal of Cognitive Neuroscience, 23(7), 1587– 1596. https://doi.org/10.1162/jocn.2010.21548

Kahneman, D. (1973). Attention and Effort. Prentice-Hall.

Kahneman, D., & Beatty, J. (1966). Pupil diameter and load on memory. Science (New York, N.Y.), 154(3756), 1583–1585. https://doi.org/10.1126/science.154.3756.1583

King, W. M., & Fuchs, A. F. (1979). Reticular control of vertical saccadic eye movements by mesencephalic burst neurons. Journal of Neurophysiology, 42(3), 861–876. https://doi.org/10.1152/jn.1979.42.3.861

Koevoet, D., Strauch, C., Naber, M., & Van der Stigchel, S. (2023). The Costs of Paying Overt and Covert Attention Assessed With Pupillometry. Psychological Science, 34(8), 887–898. https://doi.org/10.1177/09567976231179378

Koevoet, D., Strauch, C., Van der Stigchel, S., Mathôt, S., & Naber, M. (2024). Revealing visual working memory operations with pupillometry: Encoding, maintenance, and prioritization. WIREs Cognitive Science, e1668. https://doi.org/10.1002/wcs.1668

Kowler, E., Anderson, E., Dosher, B., & Blaser, E. (1995). The role of attention in the programming of saccades. Vision Research, 35(13), 1897–1916. https://doi.org/10.1016/0042-6989(94)00279-U

Laeng, B., Sirois, S., & Gredebäck, G. (2012). Pupillometry: A Window to the Preconscious? Perspectives on Psychological Science, 7(1), 18–27. https://doi.org/10.1177/1745691611427305

Loewenfeld, I. E. (1958). Mechanisms of reflex dilatation of the pupil. Documenta Ophthalmologica, 12(1), 185–448. https://doi.org/10.1007/BF00913471

Mathôt, S. (2018). Pupillometry: Psychology, Physiology, and Function. Journal of Cognition, 1(1), 16. https://doi.org/10.5334/joc.18

Naber, M., & Murphy, P. (2020). Pupillometric investigation into the speed-accuracy trade-oF in a visuomotor aiming task. Psychophysiology, 57(3), e13499. https://doi.org/10.1111/psyp.13499

Nozari, N., & Martin, R. C. (2024). Is working memory domain-general or domain-specific? Trends in Cognitive Sciences, 0(0). https://doi.org/10.1016/j.tics.2024.06.006

Reppert, T. R., Heitz, R. P., & Schall, J. D. (2023). Neural mechanisms for executive control of speedaccuracy trade-oF. Cell Reports, 42(11). https://doi.org/10.1016/j.celrep.2023.113422

Richer, F., & Beatty, J. (1985). Pupillary Dilations in Movement Preparation and Execution. Psychophysiology, 22(2), 204–207. https://doi.org/10.1111/j.1469-8986.1985.tb01587.x

Robison, M. K., & Brewer, G. A. (2020). Individual diFerences in working memory capacity and the regulation of arousal. Attention, Perception, & Psychophysics, 82(7), 3273–3290. https://doi.org/10.3758/s13414-020-02077-0

Robison, M. K., & Unsworth, N. (2019). Pupillometry tracks fluctuations in working memory performance. Attention, Perception, & Psychophysics, 81(2), 407–419. https://doi.org/10.3758/s13414-0181618-4

Sahakian, A., Gayet, S., PaFen, C. L. E., & Van der Stigchel, S. (2023). Mountains of memory in a sea of uncertainty: Sampling the external world despite useful information in visual working memory. Cognition, 234, 105381. https://doi.org/10.1016/j.cognition.2023.105381

Shadmehr, R., Reppert, T. R., Summerside, E. M., Yoon, T., & Ahmed, A. A. (2019). Movement Vigor as a Reflection of Subjective Economic Utility. Trends in Neurosciences, 42(5), 323–336. https://doi.org/10.1016/j.tins.2019.02.003

Silva, M. F., Brascamp, J. W., Ferreira, S., Castelo-Branco, M., Dumoulin, S. O., & Harvey, B. M. (2018). Radial asymmetries in population receptive field size and cortical magnification factor in early visual cortex. NeuroImage, 167, 41–52. https://doi.org/10.1016/j.neuroimage.2017.11.021

Sirois, S., & Brisson, J. (2014). Pupillometry. WIREs Cognitive Science, 5(6), 679–692. https://doi.org/10.1002/wcs.1323

Sparks, D. L. (2002). The brainstem control of saccadic eye movements. Nature Reviews Neuroscience, 3(12), Article 12. https://doi.org/10.1038/nrn986

Strauch, C., Wang, C.-A., Einhäuser, W., Van der Stigchel, S., & Naber, M. (2022). Pupillometry as an integrated readout of distinct attentional networks. Trends in Neurosciences, 45(8), 635–647. https://doi.org/10.1016/j.tins.2022.05.003

Unsworth, N., & Miller, A. L. (2021). Individual DiFerences in the Intensity and Consistency of Attention. Current Directions in Psychological Science, 30(5), 391–400. https://doi.org/10.1177/09637214211030266

Van der Stigchel, S. (2010). Recent advances in the study of saccade trajectory deviations. Vision Research, 50(17), 1619–1627. https://doi.org/10.1016/j.visres.2010.05.028

Van der Stigchel, S. (2020). An embodied account of visual working memory. Visual Cognition, 28(5–8), 414–419. https://doi.org/10.1080/13506285.2020.1742827

Van der Stigchel, S., & Hollingworth, A. (2018). Visuospatial Working Memory as a Fundamental Component of the Eye Movement System. Current Directions in Psychological Science, 27(2), 136–143. https://doi.org/10.1177/0963721417741710

van der Wel, P., & van Steenbergen, H. (2018). Pupil dilation as an index of eFort in cognitive control tasks: A review. Psychonomic Bulletin & Review, 25(6), 2005–2015. https://doi.org/10.3758/s13423-018-1432-y

clarity of expression through the use of precise images.

Sound level: Let's flee from rhyme, please. Listen, though, for the texture on the fabric of each word you put in my mouth. Sour starburst makes rain. Grit of clay and straw the crunch. https://en.wikipedia.org/wiki/Phonaesthetics

"Poetry is the right word in the right place." - Don Welch (Nebraska poet, deceased)

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Uttley et al., describes the identification of a candidate sequence for enhancing craniofacial sox9 expression in Neanderthals and offers functional genomics evidence towards identification of candidate sequence variants in a cis regulatory element (CRE) responsible for jaw morphology variation in hominin evolution. They generated a transgenic zebrafish model for testing the activity of a previously characterised regulatory element in human, which when mutated causes Pierre Robin developmental disorder and its neanderthal counterpart which has been identified as a candidate enhancer by sequence similarity and by being a DMR in the Neanderthal genome. They show that the Neanderthal CRE is active similarly in distribution to its human counterpart but with elevated activity in anatomically loosely or unspecified cell types in zebrafish cartilaginous neural crest candidates, which they argue are matching the cells where the same enhancer is active in mammalian development. They then show by single cell transcriptomics the cell distribution for the enhancer activity in relation to neural crest subpopulations and trasncription factors involved in craniofacial development. Finally they carry out overexpression of SOX9 with the human enhancer variant in zebrafish and demonstrate morphology changes which they interpret as evidence towards the capacity of the enhancer to broaden mesenchymal condensations leading to change in jaw morphology.<br /> Taken together, the paper provides evidence for a predicted neanderthal regulatory element candidate to function as enhancer in a zebrafish model and evidence for this enhancer to carry sequence variation which can lead to overactivation in craniofacial cell types relevant to jaw morphology, which the authors interpret as the source of the cis regulatory mechanism for jaw morphology evolution in hominin evolution.

Main comments:

I found the conclusion on the functional divergence of sequence variants of Neanderthal v human enhancer convincing as they were provided by an elegant double reporter approach which offers internal control for variant comparison. However, i found the argument about the role of the sequence variant in craniofacial development less convincing

1. Setting the aims I found the introduction to the topic and the setting of aims somewhat sketchy. It is not clear from the introduction, why the Neanderthal element was chosen for further study and why the SNVs in this one element were worth pursuing in the lack of broader understanding of the potentially complex regulatory element complexity at the Neanderthal Sox9 locus. While it is a very reasonable assumption, that a key CRE found and well characterised in human (by the authors in their seminal paper) is a worthy candidate for functional assessment, without better understanding of the overall locus conservation between human and Neanderthal this element may be one of many functionally redundant elements.
2. Justification of the fish model in hominin gene regulation

2.1. For the neanderthal element function to be compared to human in a valuable and informative fashion, one would expect that the host system i.e. the zebrafish is sufficiently conserved by offering a similar developmental context both in terms of gene regulation and in terms of anatomy. From the gene regulation perspective, i would expect that the analysis of the EC1.45 is based on expectation of similar regulatory information content to that in the fish homolog thus one can expect similar TF network activities on them and as a result one an test sequence variation effects relevant to endogenous regulatory interactions both in fish and hominins. However, there is no data shown for the relevance of fish regulatory background as a test system. No information is provided on the fish sox9 locus and its activity, or whether the fish homolog enhancer (or any sox9 enhancer that is expressed in the expected domains of craniofacial lineages and structures) has been identified and how it compares to the hominins. One expects that the hominin enhancers are active in domains of the zebrafish sox9 for the anatomical structures to give relevant readout. I would expect a comparison and match of the EC1.45 activity to ether endogenous sox9 by WISH or (although less accurate) a cross to one of the several sox9 reporter transgenic lines available on ZFIN.

2.2. There is an argument about the regulatory networks being conserved (without references), this would need more arguments particularly in the context of Sox9/SOX9 regulation. 3. Further to the justification of the fish model, from the anatomical perspective, the assessment of the parallels of zebrafish and mammalian craniofacial development need strengthening.

3.1. While indeed transparency and external development helps the reporter transgenesis and argues for the fish model, but the generation time is actually comparable to mouse (in contrast to the statement in the introduction), however the understanding of zebrafish craniofacial development and its similarity to human is not well argued, and indeed very superficially compared in the manuscript. I found the anatomical analyses to be rather imprecise and difficult to compare. In the lack of direct comparisons and diagrams comparing mammalian and fish developmental structures and their origins, the statement of 'EC1.45 activity matches expression domains from mammalian development' or 'broadly recapitulate' to be an oversimplification and overstatement. The lineage tracing is an important evidence but again the anatomical homologies need to be more clearly visualized and the lineage history better explained.

3.2. In a similar vein, direct comparison of human and Neanderthal adult morphologies (Figure 1B) would be very helpful.

3.3. I was also confused why the sox10 reporter is used as reference (with no direct overlap of activity to the SOX9 associated EC1.45 reporter) rather than or alongside a sox9a reporter line or even comparison to endogenous sox9a activity by WISH (Figure 2). The anatomical details in Figure 2 would need to be extended with more precisely describing the cell types, where the transgene is active and how the homology to mammalian anatomies are established.

3.4. Overall, the use of the fluorescence reporter is helpful for initial assessments but accurate enhancer activity profiling and comparison should be done by WISH, as mRNA is far more likely to follow the temporal activation dynamics and may explain fluorescence signal intensity differences, the latter important for correct interpretation of sequence variant effects (e.g. is the perceived higher expression by the Ne element is perhaps due to longer expression or earlier activation). 4. Single cell transcriptomics This experiment was not only used to characterise transgenic reporter active cell types, but to establish transcription factor candidates relevant to neural crest differentiation regulated by EC1.45. What is somewhat confusing, is that the EC1.45 element activity domain is only partially and not predominantly overlapping with the twist1a expressing cells. The authors previously established Twist1 as key regulator of EC1.45 in craniofacial development. How do the authors explain the apparent little relevance of twist1a in regulating the enhancer in fish? Overall the lack of any attempt to link the SNVs to TFBS (including, if available that of the fish homolog sequences) is making the interpretation of the sequence variation harder. BTW, even of the fish elements are not directly identifiable by direct sequence alignment it may be possible to identify the fish homolog through phylogenetic footprinting with stepping stone species such as the non-duplicated paddlefish. 5. Sox9 overexpression This experiment seems not to add too much to the main claim of the paper. While not essential, for this data to add more value, a comparison to that using the Neanderthal element would be more interesting and not a difficult experiment to carry out. 6. Throughout the paper there is a lack of data on reproducibility of reporter activities. As random integration often leads to position effects, it is expected that more than one lines showing the same patterns is used to identify cell type and tissue specificities. This is lacking in the paper and is a concern, as for example, the human element activity in Fig. 1 appears to be different from that by in the dual reporter shown in Fig. 3.

Minor points

A request to the editor as much as the authors: please make sure that legends are on the same page with figures, it is very hard to follow manuscripts when one needs to scroll between 3 pages at the same time (text, figure, legend). This archaic separation inherited from decades ago when physical prints used to be submitted has no justification in the digital era but continues to make reviewer's life difficult. Similarly, there should be no limit, and it should be encouraged to label anatomical structures directly on panels to point out expression domains, highlight expression variation, or to make a panel more self-explanatory, while making sure that clarity is not lost.

Figure 1A does not support the statement it is referenced to

Figure 1B should include human anatomy in comparison and perhaps a schematic diagram of the hypothesized developmental morphogenesis divergence modelled in this paper

Figure 1D should show why the authors argue the neanderthal is not the ancestral state (BTW, what does the fish homolog look like?)

Figure 4A,B are better suited in Supplemental

Significance

Conceptual: identifying sequence variants in Neanderthal cis-regulatory element as potential source of evolutionary change in morphology.

Technologically mostly following prior art, use of single cell in reporter analysis is technologically improvement on current standards, albeit somewhat rudimentary.

The use of a tractable embryo model to explore a regulatory sequence change leading to morphology change has often been applied for carious aspects of evolutionary changes during development pioneering examples include the shh ZRA enhancer in fin/limb morphogenesis, or balean fin evolution (PMID: 9860988) or human versus ape hand evolution (PMID: 18772437), but this is the first for applying it to hominin evolution. This will be of interest to human geneticists, evolutionary geneticists and developmental geneticists.

My expertise is in developmental gene regulation with the zebrafish model.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

The authors provide evidence that nucleotide sequence variants in a remote enhancer, E1.45, which is located 1.45 Mb upstream of the Sox9 promoter, probably contributed to subtle morphological differences in the lower jaws of Neanderthals and modern humans. The study employs the use of a cleverly-designed dual reporter gene for directly comparing the activities of the Neanderthal and modern human enhancers in transgenic zebrafish. The results are clear and convincing: the Neanderthal enhancer is significantly more active than the modern human enhancer.

Here are a few minor recommendations that might help clarify aspects of the study:

1. Is it possible to quantify the different enhancer activities in the zebrafish assays? Is it strictly a question of levels or are there also subtle differences in the timing and/or sites of expression during development?
2. Is the Neanderthal form of the E1.45 enhancer ancestral for the hominids? If so, then reduced expression in modern humans is a derived trait. This could be stated more clearly.
3. Are there potential transcription factor binding motifs associated with the SNVs?

Significance

The authors address one of the most compelling problems in biology: the evolutionary origins of modern humans. This study addresses the role of regulatory DNAs in the divergence of Neanderthals and modern humans. Sox9 is a good focus of study since it has been implicated in the development of craniofacial features in humans. The authors identified three SNVs (single nucleotide variants) in Neanderthal vs. modern human E1.45 enhancer sequences. Direct comparison of these enhancers provide compelling evidence that these SNVs cause upregulation of the Sox9 in Neanderthals. I think this is a very interesting finding and strongly endorse publication.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

This is an interesting paper that is logical continuation of authors previous work characterizing a human enhancer mutation implicated in Pierre Robin malformations that alters Sox9 expression. Here using zebrafish as a convenient model organism, the authors test the activity of the human enhancer compared to its Neanderthal ortholog. The results show that both enhancers drive reporter expression in the vicinity of forming cartilage condensations of the jaw. While both enhancers mediate reporter expression in neural crest derived cells, the Neanderthal sequence drives quantitatively higher expression than the orthologous human enhancer. Consistent with this, overexpression of Sox9 using the human enhancer caused an increase in cartilage volume. Altogether, this is a nicely done study that would be appropriate for publication after some revisions as detailed below.

Major Revisions:

1. The introduction seems overly long and a bit rambling so diminishes from the excitement of the work. It should be half the length and focus on the novelty of this question and findings.
2. The authors should demonstrate that that human EC1.45 activity overlaps with Sox9 expression. This should be included in Figure 2.
3. There are differences in level of enhancer activity signal between figures (e.g. seems lower in Fig. 3 than Fig. 2). Does enhancer activity vary between embryos or was the imaging protocol different?
4. Some co-staining should be performed to show whether or not the enhancers are active in the same cells but at different levels or if they are actually in different cells.
5. There is an important issue with the single cell RNA seq. Given that the cells were FACS sorted for +GFP and +Cherry, there seem to be many negative cells in their scRNAseq data. Perhaps the FACS gates (figure 4B) were not conservative enough? Did negative cells get included? Authors should verify that their clusters express both GFP and Cherry transcripts.
6. From their scRNAseq data, they talk about enhancer activity in PA1, but this isn't discussed/shown in the enhancer reporter embryos. It would be appropriate to annotate PA1 in figures 2 and 3.
7. Authors should quantify how many Sox9+ cells also have enhancer activity. Looking at the UMAPs in figure 4E and 4F, it actually looks like there is less enhancer activity in the Sox9 dense regions of the clusters.
8. For the over-expression of Sox9 driven by EC1.45, it is important to first establish that EC1.45 activity does indeed overlap with Sox9 gene expression. Does Sox9 itself drive EC1.45?
9. Importantly the authors do not discuss if the Neanderthal SNVs lie in TF binding sites? Which TF motifs? Are they conserved? Are those TF's expressed in the same cells as both enhancers?
10. If you introduce the Neanderthal SNVs into the human sequence, do you gain enhancer activity?
11. The over-expression experiments are tricky as they cause major developmental defects. Would it be possible to drive Sox9 expression at levels that better reflect those driven endogenously by the human versus Neanderthal enhancer?

Minor Revisions:

1. Figure 1 - authors should highlight that panel C is a zoom in of panel A.
2. Figure 3 - Why does Human EC1.45 activity looks weaker here than it does in Figure 2.
3. The first sentence of the last paragraph in the Introduction is unclear: "spatiotemporal developmental expression patterns for the human EC1.45 cluster during zebrafish development". Instead should read "reporter expression driven by the human EC1.45 enhancer over developmental time"

Significance

This is a nice paper that advances understanding of jaw development and has disease relevance as well as some evolutionary implications. Thus it is novel and would appeal to developmental biologist, the craniofacial community, and to some extent to evolutionary biologists.

Aren't these specific to the cognitive domain?

Perhaps add teaching strategies for each domain?

add values?

suggest removing here as well

Is this always true? Perhaps "manage" instead of heal

More willing implies I am not willing if I can not immediately apply it which I don't think is true. Perhaps "better able"

Short URL

解释很好

有意思，不仅仅是物理、生物，而且是复杂社会。

HTDRTBYNBY

Will

are they

the

ou "Parliament" plutôt, vu qu'il s'agit des deux chambres.

chain gang

"chain gang".

ou juste "at a master's whim"

punishment

italics

je mettrais "punishment" autrement c'est trompeur.

peut-être: "while idle"?

italics

Je dirais plutôt" "This is about men... this is about interests"

delete

You never want to write something that can be taken out of context and lead to injury of the user.

Usually with furniture instructions, the first few pages will be a parts list, warranty (if applicable), tools needed to assemble, and sometimes they'll tell you how long it should take to assemble as well as how many people it may take.

For example, if you were writing instructions about power tools, you would write aiming at your general audience of people who already use power tools, or have some knowledge about them. You wouldn't be writing this manual for a child with colorful, bright pictures and fun word layouts. It would be aimed at the audience intended on using it.

Making the instructions clear for the reader to understand is always important. You don't know what background knowledge the instruction user has, so it's best to write the instructions in a way that everyone can understand. It's descriptive and has enough details for any errors along the way.

Kinnell is saying that some things that we love and believe in can be setting us back, holding us from keeping on.

luttes

Should these align with the state criteria? * Identify client learning needs and potential barriers. *Contribute to a teaching plan. * Choose the appropriate teaching method. * Apply principles of teaching and learning. * Determine if learning was achieved.

thebaine and morphine needed to make oxycontin

FDA neoliberal deregulation (and privitisation) and less funding, neoliberal government didn't care enough about public health, FDA revolving door with pharmaceutical companies

Public opinion of a business is very important. If a business isn't acting ethically, the public may start boycotting them, demanding change, or even taking legal action against them. Businesses must act ethically not only because it's the right thing to do, but also because they could do more harm to their reputation permanently.

This is a great idea because sometimes when people are in positions of power at a company for extended periods they can loose sight of whats really important or caring for the employees. Something as simple as asking a question about who this is affecting can give them a wake up call and reevaluate their decisions.

It's important to cite your courses and give credit to others in your documents if you weren't the one who either said it or researched it. If you don't, it hurts your credibility as well. Someone could read something from your paper that is identical to someone else's they read, and if you didn't cite them, the reader would create a distrust with you and not find you credible anymore.

In this example, it is your responsibility to include all research and evidence collected about vaccines for the parents. It could be seen as a bias if you were to leave out the medical evidence, if you were against the vaccines.

When writing for an audience that may not have the same knowledge, you want to present the critical information in the beginning and ending, not in the middle where it could be skipped over or deemed unimportant as in the example above.

It's important when working for any organization or company to review their code of ethics because they will vary from company to company.

A lot of people tend to be followers rather than leaders because of their inability to speak up for either themselves or what is right/ethical.

Everyone thinks that could never happen to them or they would never act like that. But the reality is, once you're in that situation, some people freeze up and will just follow along with others even if it is unethical. This is why it's so important to be able to stand up for yourself and your beliefs.

This is a great way to look at acting ethically. It's important to note that acting ethically won't get you any congratulations or awards because it's something we should be doing. Following your intuition and being true to yourself and your values is a good way to stay ethical.

In Sliding window technique, it helps prevent starvation because it checks at every point how many requests are there in the queue. So at the 1st second if there are 100 requests made and limit is also 100, at the next second it will check again how many active requests are still there. if it is less than 100 it will allow the next requests to be processed, while in leaky bucket it would have completely blocked everything for 1 minute.

for - annotate

IED CH1 SUB

a exclusão do crédito tributário não importa em descumprimento das obrigações acessórias

Lindbergh frequently claims that both he and the American First Committee speak for the majority of average, hard-working citizens (see above paragraph) who don't have a "newspaper, newsreel, or radio station at their command." Again evoking the same conspiracy about a Jewish-controlled media.

Annotation #3:

I think this is a really interesting approach to thinking about this. Can new, novel technologies really reduce emission reduction costs? This reminds me of the argument on whether electric cars really do help the environment at all, or if they are just shifting the source of pollution from a point source (the car) to a nonpoint source (factories, byproducts of production, etc.)

https://greenly.earth/en-us/blog/industries/electric-cars-are-they-really-ecological

Annotation #2:

Can the lowered GDP compare with the increased production costs, as natural resources become scarcer from pollution and over-extraction?

Annotation #1:

The author introduces the claim that man-made climate change is a negative externality of production, and will increase the marginal social cost for society, impacting many generations to come. This gives us another nuance to consider when thinking about the inquiry question: even if economic growth exceeds the deadweight loss from climate change, does it take into account that this deadweight loss may multiply and carry over onto future generations?

Version 2 of this preprint has been peer-reviewed and recommended by Peer Community in Ecology.<br /> See the peer reviews and the recommendation.

don't be afraid to exaggerate on your bullets a little bit, you can probably add another bullet talking about streamlining data from csv files. even if it's just importing a csv as a game, really emphasize how useful this feature is

if you received some sort of certificate for this, add a certifications section to your resume instead, and then mention how you used Python for this role

don't rly need to specify personal project here, consider replacing this with the tech stack instead. same for other projects

i recommend you keep the game jam project since hackathon projects check off a lot of benchmarks. collaboratively working on code, working in fast-paced environment, playing different roles in a team , etc...

be more specific here again and action statement-ify. i found this pretty helpful: https://www.linkedin.com/posts/kshitijdhyani_job-resume-softwaredevelopment-activity-7091234601506918400-vUiE/?utm_source=share&utm_medium=member_android although completely different projects, just realized i stole the name for one of my projects from this guys resume lmao.

action statement-ify this more. Developed X sign-up pages using SAP UI5, ensuring responsiveness across all devices and .... something along the lines of that. if you handled auth for these pages you could also mention that. Implemented secure user authentication using .... you get the point

same thing as the Python one, if you received any certification add it as a seperate section.

again be more specific here, what front-end and back-end frameworks/tools?

try quantifying some information here, and be more specific. how many products? how many clients? what programming languages and frameworks?

can just be "Experience"

eLife Assessment

This manuscript presents valuable findings showing that rapamycin directly activates the cool-sensing ion channel, TRPM8, acting through a different binding site than other small-molecule cooling agents such as menthol. The use of Ca2+-imaging, electrophysiology, and computational biology provides solid evidence to support the finding. The authors also present a novel NMR-based method to help identify details of the binding site interactions. In this revised version, some analysis and the presentation have been corrected and improved. Their findings provide insights into TRP channel pharmacology and may indicate previously unknown physiological effects or therapeutic mechanisms of the immunosuppressant, rapamycin.

Reviewer #1 (Public review):

Summary:

In this valuable study, the authors found that the macrolide drug rapamycin, which is an important pharmacological tool in the clinic and the research lab, is less specific than previously thought. They provide solid functional evidence that rapamycin activates TRPM8 and begin to develop an NMR method to measure the specific binding of a ligand to a membrane protein.

Strengths:

The authors use a variety of complementary experimental techniques in several different systems, and their results support the conclusions drawn.

Weaknesses:

The proposed location of the rapamycin binding pocket within the membrane means that molecular docking approaches designed for soluble proteins alone do not provide solid evidence for a rapamycin binding pocket location in TRPM8, but the authors are appropriately careful in stating that the model is consistent with their functional experiments. The novel STTD method is intriguing and supportive of the functional results and docking predictions, but further validation of this method is needed.

Impact:

This work provides still more evidence for the polymodality of TRP channels, reminding both TRP channel researchers and those who use rapamycin in other contexts that the adjective "specific" is only meaningful in the context of what else has been explicitly tested.

Comments on revisions:

The authors have addressed my major concerns from the previous round of revision, and I agree that those things that remain un-done are outside the scope of this manuscript.

Reviewer #2 (Public review):

Summary:

Tóth and Bazeli et al. find rapamycin activates heterologously-expressed TRPM8 and dissociated sensory neurons in a TRPM8-dependent way with Ca2+-imaging. With electrophysiology and STTD-NMR, they confirmed the activation is through direct interaction with TRPM8. Using mutants and computational modeling, the authored localized the binding site to the groove between S4 and S5, different than the binding pocket of cooling agents such as menthol. The hydroxyl group on carbon 40 within the cyclohexane ring in rapamycin is indispensable for activation, while other rapalogs with its replacement, such as everolimus, still bind but cannot activate TRPM8. Overall, the findings provide new insights into TRPM8 functions and may indicate previously-unknown physiological effects or therapeutic mechanisms of rapamycin.

Strengths:

The authors spent extensive effort on demonstration that the interaction between TRPM8 and rapamycin is direct. The evidence is solid. In probing the binding site and the structural-function relationship, the authors combined computational simulation and functional experiments. It is very impressive to see that "within" a rapamycin molecule, the portion shared with everolimus is for "binding", while the hydroxyl group in the cyclohexane ring is for activation. Such detailed dissection represents a successful trial in computational biology-facilitated, functional experiment-validated study of TRP channel structural-activity relationship. The research draws the attention of scientists, including those outside the TRP channel field, to previously-neglected effects of rapamycin, and therefore the manuscript deserves broad readership.

Weaknesses:

The significance of the research could be improved by showing or discussing whether a similar binding pocket is present in other TRP channels, and hence rapalogs might bind to or activate these TRP channels. Additionally, while the finding on TRPM8 is novel, it is worthwhile to perform more comprehensive pharmacological characterization, including single-channel recording and a few more mutant studies to offer further insight into the mechanism of rapamycin binding to S4~S5 pocket driving channel opening. It is also necessary to know if rapalogs have independent or synergistic effects on top of other activators, including cooling agents and lower temperature, and its dependence on regulators such as PIP2.

Additional discussion that might be helpful:

The authors did confirm that rapamycin does not activate TRPV1, TRPA1 and TRPM3. But other TRP channels, particularly other structurally-similar TRPM channels, should be discussed or tested. Alignment of the amino acid sequences or structures at the predicted binding pocket might predict some possible outcomes. In particular, rapamycin is known to activate TRPML1 in a PI(3,5)P2-dependent manner, which should be highlighted in comparison among TRP channels (PMID: 35131932, 31112550).

After revision:

I acknowledge that the authors have addressed some of the questions in their revised version. They have explained that additional experiments might be beyond the scope of the current study. I appreciate their effort in doing their best to improve the manuscript and to leave the rest in discussion.

Reviewer #3 (Public review):

Summary:

Rapamycin is a macrolide of immunologic therapeutic importance, proposed as a ligand of mTOR. It is also employed as in essays to probe protein-protein interactions.<br /> The authors serendipitously found that the drug rapamycin and some related compounds, potently activate the cationic channel TRPM8, which is the main mediator of cold sensation in mammals. The authors show that rapamycin might bind to a novel binding site that is different from the binding site for menthol, the prototypical activator of TRPM8. These convincing results are important to a wide audience, since rapamycin is a widely used drug and is also employed in essays to probe protein-protein interactions, which could be affected by potential specific interactions of rapamycin with other membrane proteins, as illustrated herein.

Strengths:

The authors employ several experimental approaches to convincingly show that rapamycin activates directly the TRPM8 cation channel and not an accessory protein or the surrounding membrane. In general, the electrophysiological, mutational and fluorescence imaging experiments are adequately carried out and cautiously interpreted, presenting a clear picture of the direct interaction with TRPM8. In particular, the authors convincingly show that the interactions of rapamycin with TRPM8 are distinct from interactions of menthol with the same ion channel.

Weaknesses:

The main weakness of the manuscript was the NMR method employed to show that rapamycin binds to TRPM8. The authors developed and deployed a novel signal processing approach based on subtraction of several independent NMR spectra to show that rapamycin binds to the TRPM8 protein and not to the surrounding membrane or other proteins. In this revised version the authors have strengthened the evidence that the method gives solid results and have improved the clarity of the presentation.

Comments on revisions:

The authors have greatly improved the quality of the presentation of the NMR data and have answered my concerns regarding the new methodology. The manuscript is improved and represents an important contribution.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this valuable study, the authors found that the macrolide drug rapamycin, which is an important pharmacological tool in the clinic and the research lab, is less specific than previously thought. They provide solid functional evidence that rapamycin activates TRPM8 and develop an NMR method to measure the specific binding of a ligand to a membrane protein.

Strengths:

The authors use a variety of complementary experimental techniques in several different systems, and their results support the conclusions drawn.

Weaknesses:

Controls are not shown in all cases, and a lack of unity across the figures makes the flow of the paper disjointed. The proposed location of the rapamycin binding pocket within the membrane means that molecular docking approaches designed for soluble proteins alone do not provide solid evidence for a rapamycin binding pocket location in TRPM8, but the authors are appropriately careful in stating that the model is consistent with their functional experiments.

Impact:

This work provides still more evidence for the polymodality of TRP channels, reminding both TRP channel researchers and those who use rapamycin in other contexts that the adjective "specific" is only meaningful in the context of what else has been explicitly tested.

Reviewer #2 (Public Review):

Summary:

Tóth and Bazeli et al. find rapamycin activates heterologously-expressed TRPM8 and dissociated sensory neurons in a TRPM8-dependent way with Ca2+-imaging. With electrophysiology and STTD-NMR, they confirmed the activation is through direct interaction with TRPM8. Using mutants and computational modeling, the authored localized the binding site to the groove between S4 and S5, different than the binding pocket of cooling agents such as menthol. The hydroxyl group on carbon 40 within the cyclohexane ring in rapamycin is indispensable for activation, while other rapalogs with its replacement, such as everolimus, still bind but cannot activate TRPM8. Overall, the findings provide new insights into TRPM8 functions and may indicate previously unknown physiological effects or therapeutic mechanisms of rapamycin.

Strengths:

The authors spent extensive effort on demonstrating that the interaction between TRPM8 and rapamycin is direct. The evidence is solid. In probing the binding site and the structural-function relationship, the authors combined computational simulation and functional experiments. It is very impressive to see that "within" a rapamycin molecule, the portion shared with everolimus is for "binding", while the hydroxyl group in the cyclohexane ring is for activation. Such detailed dissection represents a successful trial in the computational biology-facilitated, functional experiment-validated study of TRP channel structuralactivity relationship. The research draws the attention of scientists, including those outside the TRP channel field, to previously neglected effects of rapamycin, and therefore the manuscript deserves broad readership.

Weaknesses:

The significance of the research could be improved by showing or discussing whether a similar binding pocket is present in other TRP channels, and hence rapalogs might bind to or activate these TRP channels. Additionally, while the finding on TRPM8 is novel, it is worthwhile to perform more comprehensive pharmacological characterization, including single-channel recording and a few more mutant studies to offer further insight into the mechanism of rapamycin binding to S4~S5 pocket driving channel opening. It is also necessary to know if rapalogs have independent or synergistic effects on top of other activators, including cooling agents and lower temperature, and their dependence on regulators such as PIP2.

Additional discussion that might be helpful:

The authors did confirm that rapamycin does not activate TRPV1, TRPA1 and TRPM3. But other TRP channels, particularly other structurally similar TRPM channels, should be discussed or tested. Alignment of the amino acid sequences or structures at the predicted binding pocket might predict some possible outcomes. In particular, rapamycin is known to activate TRPML1 in a PI(3,5)P2-dependent manner, which should be highlighted in comparison among TRP channels (PMID: 35131932, 31112550).

Reviewer #3 (Public Review):

Summary:

Rapamycin is a macrolide of immunologic therapeutic importance, proposed as a ligand of mTOR. It is also employed as in essays to probe protein-protein interactions.

The authors serendipitously found that the drug rapamycin and some related compounds, potently activate the cationic channel TRPM8, which is the main mediator of cold sensation in mammals. The authors show that rapamycin might bind to a novel binding site that is different from the binding site for menthol, the prototypical activator of TRPM8. These solid results are important to a wide audience since rapamycin is a widely used drug and is also employed in essays to probe protein-protein interactions, which could be affected by potential specific interactions of rapamycin with other membrane proteins, as illustrated herein.

Strengths:

The authors employ several experimental approaches to convincingly show that rapamycin activates directly the TRPM8 cation channel and not an accessory protein or the surrounding membrane. In general, the electrophysiological, mutational and fluorescence imaging experiments are adequately carried out and cautiously interpreted, presenting a clear picture of the direct interaction with TRPM8. In particular, the authors convincingly show that the interactions of rapamycin with TRPM8 are distinct from interactions of menthol with the same ion channel.

Weaknesses:

The main weakness of the manuscript is the NMR method employed to show that rapamycin binds to TRPM8. The authors developed and deployed a novel signal processing approach based on subtraction of several independent NMR spectra to show that rapamycin binds to the TRPM8 protein and not to the surrounding membrane or other proteins. While interesting and potentially useful, the method is not well developed (several positive controls are missing) and is not presented in a clear manner, such that the quality of data can be assessed and the reliability and pertinence of the subtraction procedure evaluated.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points

(1) Given the novelty of the STTD NMR approach, please provide more details and data for the reader.

• I would like to see all of the collected spectra so that readers can see and judge the effect sizes for themselves, perhaps as an additional supplementary figure.

We agree with the reviewer that the data transparency of the NMR measurements should be improved. We changed panel C of Figure 2 in the main text and provided all the STD and the computed STDD and STTD spectra recorded on one set of experiments. We carried out additional experimental replicas on new samples and addressed the variability of cell samples by rescaling the STD effects based on reference ¹H measurements. We provided supplementary spectra of the reference experiments without saturation (Figure S5) and the obtained STTD spectra from the three parallel NMR sessions (Figure S6).

• I appreciate the labels for STDD-1, STDD-2, and STTD on the lower two spectra of Figure 2C. Is the top spectrum from STD-1 or is it prior to saturation? In Figure 2C, what do the x1 and x2 notations on the right-hand side of the spectra indicate?

We showed the top spectrum as an overview and a demonstration of the spectral complexity of the samples. ¹H experiments were run before the STD measurements to assess the sample quality and stability. The demonstrated spectrum on sample 1 (TRPM8 with rapamycin in HEK cells) was recorded with more transients than the corresponding STDs, thus it is only visually comparable with the difference spectra after scaling (2x). Figure 2 was changed and all the spectra were replaced as mentioned before. All the recorded ¹H-experiments without saturation including the one removed are now available in the supplementary information (Figure S5).

• The STTD NMR results with WT TRPM8 are consistent with rapamycin binding directly to the channel. Testing whether rapamycin binding observed with STTD NMR is disrupted by one of the most compelling mutations (D796A, D802A, G805A, or Q861A) would be a further test of this direct interaction.

We thank the reviewer for the suggestion and agree that testing the most compelling mutants would be a promising next step. These mutations were generated in plasmid vectors and only transiently transfected into HEK cells. For NMR analysis we would need a high amount of cells stably overexpressing the mutant channels which were not available for experimentation.

• Given that this is not a methods paper, it is probably outside the scope to further validate the STTD NMR measurements by performing parallel ITC, SPR, MST, or radiolabeled ligand experiments. Nevertheless, I would be excited to see such a comparison since STTD NMR appears to have promise as an experimental technique for assessing ligand binding to membrane proteins that does not require large amounts of purified protein or radioactive isotopes.

We agree with the reviewer that additional independent biophysical measurements on the interactions are necessary to further validate the STTD methodology. This paper is a preliminary demonstration of the STTD concept and our group is currently working on the challenges of on-cell NMR (e.g., sample and spectral complexity) and the standardization of the proposed workflow.     

(2) Please clarify the methods used to model of rapamycin binding. Docking can be imprecise in TRP channels, even with a sophisticated docking scheme (Hughes et al., 2019, doi: https://doi.org/10.7554/eLife.49572.001).  

Thank you for mentioning this point and providing the reference. We have further clarified our methods and included the reference in our discussion, indicating the limitations of our approach.

• As a positive control, does the docking strategy accurately predict binding of known compounds (menthol, icilin, etc.) to TRPM8 consistent with cryo-EM structures?  

Yes, the binding site for menthol, based on a similar docking strategy as for rapamycin, is also presented, and matches with predictions from other publications. This is now clarified in the revised manuscript.

• Why was homology modeling to the human sequence used with the mouse structure but not the avian structure?  

At this onset of the project, only the avian structure was available, and it was used in the primary docking. Later, to get more precise docking relevant for human TRPM8 pharmacology, we did revert to the then available structure of the mouse ortholog.  

• How many rapamycin structural clusters were built, and how many structures were there in each cluster? How many were used? "most populated" is unspecific.  

Thank you for your comment. We have added the following highlighted information to the methods section to address your comment:

“Representative conformations of rapamycin were identified by clustering of the 1000-membered pools, having the macrocycle backbone atoms compared with 1.0 Å RMSD cut-off. Middle structures of the ten most populated clusters, accounting for more than 90% of the total conformational ensemble generated by simulated annealing, were used for further docking studies. To refine initial docking results and to identify plausible binding sites, the above selected rapamycin structures were docked again, following the same protocol as above, except for the grid spacing which was set to 0.375 Å in the second pass. The resultant rapamycin-TRPM8 complexes were, again, clustered and ranked according to the corresponding binding free energies. Selected binding poses were subjected to further refinement. The three most populated and plausible binding poses were further refined by a third pass of docking, where amino acid side chains of TRPM8, identified in the previous pass to be in close contact with rapamycin (< 4 Å), were kept flexible. Grid volumes were reduced to these putative binding sites including all flexible amino acid side chains (21.0-26.2 Å x 26.2-31.5 Å x 24.8-29.2 Å).”

However, it is important to clarify that the clusters are not built and their number is not specified by the user. The number of clusters found depends on how similar the structures are in the structural ensemble analyzed by clustering. A high number of clusters indicates a diverse, whereas a low number suggests a uniform structural ensemble. Furthermore, it is arbitrarily controlled by the similarity cutoff specified by the user. If the cutoff is selected well, then the number of structures is different in each cluster. There are some highly populated clusters and a few which only have one structure. The selection of how many cluster representatives are used is usually based on the decision of whether or not the sum of the population of selected clusters sufficiently covers the mapped conformational space.

• Additionally, the rapamycin poses were generated using a continuum solvent model that is unlikely to replicate the conditions existing in the lipid bilayer or in a lipid-exposed binding pocket as is predicted here. It is therefore possible that the rapamycin poses chosen for docking do not represent the physiological rapamycin binding pose, hampering the ability of the docking algorithm to find an appropriate docking pocket.  

• Furthermore, accurately docking that may bind to membrane-exposed pockets is a challenging problem, particularly because many scoring algorithms, including those employed by Autodock, do not distinguish between solvent-exposed and membrane-exposed faces of the protein. This affects the predicted binding energies.  

We appreciate the reviewer's insightful comments. We add a note in discussion part, mentioning these important limitations.  

• In Figure 4, it appears that the proposed rapamycin binding pocket is located at the interface between two subunits, but only one is shown. Is there any contact with residues in the neighboring subunit? Based on Figure S4, I assume not, but am unsure.

Based on the estimated distances, we do not think that there are any relevant interactions with residues from neighboring subunits. This is now indicated in the results section.

• Consider uploading the rapamycin-docked model to a public repository such as Zenodo for readers to examine and manipulate themselves  

As suggested, the model will be uploaded in a public repository. A link to the file on Zenodo is now included.

(3) Please discuss the spatial location of the proposed rapamycin binding pocket relative to the vanilloid binding pocket in TRPV1.

• The mutagenesis indicates that D745, D802, G805, and Q861 are most important for rapamycin sensitivity in TRPM8. Interestingly, the proposed rapamycin binding pocket appears to overlap spatially with the vanilloid binding pocket in TRPV1. Consistent with this, Q861 aligns with E570 in TRPV1, which is a critical residue for resiniferatoxin sensitivity. Indeed, similar to Q861's modeled proximity to the cyclohexyl ring, the hydroxyl group of the vanillyl moity of capsaicin (4DY in 7LR0, for example) is in proximity to E750 in TRPV1. Additionally, searching PubChem by structural similarity suggests that vanillyl head group of the TRP channel modulators capsaicin and eugenol are similar structurally to the trans-2Methoxycyclohexan-1-ol ring. Without overlaying the two structures myself, it is difficult to say more than that, but I encourage the authors to comment on any similarities and differences they observe.

• If the proposed rapamycin pocket is indeed similar to the location of the vanilloid binding site, the authors may wish to discuss other TRPM channel structures that show ligands and lipids bound to this pocket because this provides evidence that this pocket influences TRPM channel function. For example, how does the proposed rapamycin binding pocket compare to TRPM8 bound to agonist AITC (PDBID 8e4l), TRPM5 bound to inhibitor NDNA (7mbv), and TRPM2 bound to phosphatidylcholine (6co7)?

• Other TRP channel structures with ligands or lipids modeled in this region include TRPV1 bound to resiniferatoxin, capsaicin, or phosphatidylinositol (7l2j, 7l24, 7l2s, 7l2t, 7l2u, 7lp9, 7lpc, 7lqy, 7mz6, 7mz9, 7mza); TRPV3 bound to phosphatidylcholine (7mij, 7mik, 7mim, 7min, 7ugg); TRPV5 bound to econazole (6b5v) or ZINC9155 (6pbf); TRPV6 bound to piperazine (7d2k, 7k4b, 7k4c, 7k4d, 7k4e, 7k4f) or cholesterol hemisuccinate (7s8c); TRPC6 bound to BTDM (7dxf) or phosphatidylcholine (6uza); and TRP1 bound to PIP2 (6pw5).

We thank the reviewer for these valuable insights. We have included some additional discussion highlighting the similarities between the proposed rapamycin binding site and some of the other ligandchannel interactions in the TRP superfamily, in particular the well-known vanilloid binding site in TRPV1. However, to keep the discussion focused, we have not fully discussed all the indicated interactions, to best serve the clarity and scope of the manuscript.  

(4) I would like to see negative control calcium imaging and electrophysiology data with untransfected HEK cells to confirm that the observed activation is mediated by TRPM8 to parallel the TRPM8 KO sensory neuron experiments.  

This important information is now included in the revised manuscript (Figure S2).

(5) The DM-nitrophen Ca uncaging experiments are an interesting method to test Ca sensitivity of rapamycin, but the results make these experiments more complex to interpret. Ca has been shown to be an obligate cofactor for icilin sensitivity in TRPM8 under conditions where both the internal and external Ca concentrations are tightly controlled (Kuhn et al., 2009, doi: https://doi.org/10.1074/jbc.M806651200), which is necessary because TRPM8 allows Ca permeation through the pore when open. The large icilin-evoked currents in Figure 5A and 5B indicate that the effective intracellular calcium concentration is not zero prior to calcium uncaging, which may be high enough to mask any Ca-dependence of rapamycin that occurs at low Ca concentrations. Given this ambiguity, the inside-out patch clamp configuration would provide more control over the internal and external Ca concentration than is achieved in the Ca uncaging experiments. Because the authors have already demonstrated their ability to perform such experiments (Figure 2 panel B), it would be nice to see tests of Ca dependence using inside-out patch clamp.

As was already shown in Figure 2, Rapamycin activates TRPM8 in inside-out patches, and these experiments were performed using calcium-free cytosolic and extracellular solutions. Note that earlier studies have already shown that icilin activates outward TRPM8 currents in the full absence of calcium: see e.g. Janssens et al. eLife, 2016. Chuang et al. 2004. In the case of Icilin, increased calcium further potentiates the current, which is more prominent for the inward current.

In the Ca uncaging experiments, considering the Kd of DM-nitrophen of 5 nM, we expect that the intracellular calcium concentration before the UV flash would be approximately 15 nM. Taken together, both the inside-out experiments and the flash uncaging experiments confirm that rapamycin responses are not directly regulated by intracellular calcium, contrary to icilin.

(6) Sequence conservation within TRPM channels could be used in combination with the binding pocket model and mutagenesis to predict rapamycin selectivity for TRPM8 over other TRPMs. For example, some important residues, specifically G805 and Q861, are not conserved in TRPM3, which agrees with the lack of rapamycin sensitivity observed in TRPM3 (Figure S1). Further sequence comparison would provide testable hypotheses for future exploration of rapamycin sensitivity in other TRPMs that could validate the proposed binding pocket.

Thank you for the suggestion. We now indicate in the discussion that only some of the key residues are conserved and make suggestions for future studies.  

(7) Please unify the color scheme across the figures to improve clarity.

• The authors frequently use the colors blue, red, and green to represent menthol and rapamycin in the figures, but they are inconsistent in which one represents menthol and which represents rapamycin. It would be clearer for the audience if, for example, rapamycin is always represented with red and menthol is always represented with blue.  

Thank you for pointing this out. We have made the coloring schemes more uniform.

• In Figure 1, panel E, the coloring for Menthol and Pregnenolone Sulfate changes between the TRPM8+/+ and TRPM8-/- panels.  

Thank you for pointing this out. We have updated the coloring schemes to ensure consistency between the TRPM8+/+ and TRPM8-/- panels.

• Figure 3 B and E, perhaps color the plot background as a 3-color gradient (blue to white to red) rather than yellow and aqua. Center the white at the WT ratio, keeping the dashed line, with diverging gradients to, for example, blue for mutations that selectively affect menthol sensitivity and red for rapamycin.

Thank you for the suggestion – we have changed the figure accordingly.  

• Figure 4 panels A and B use the same color (green) to show two different things (menthol molecule and mutated residues that affect rapamycin sensitivity). It would be clearer for readers to change these colors to agree with a unified color scheme such that, for example, the menthol molecule is colored blue and the rapamycin-neighboring residues are colored red.

Thank you for the suggestion. We have updated the figure to use a unified color scheme, with the menthol molecule now colored green and the rapamycin-neighboring residues colored cyan, to enhance clarity for readers.

• I recommend adding a figure or panel that shows side chains for all mutations, colored by menthol/rapamycin selectivity, as indicated by the functional data in Figure 3B and 3E. This will highlight spatial patterns of the selective residues that are discussed in the text.

Thank you for your suggestion, we added all the side residues in Figure S10.

Minor points

(1) It would be nice to have one more concentration data point in the middle of the dose response curve shown in Figure 1 panel B. The response is not saturating at the top or foot of the curve in Figure 1 panel D, precluding a confident fit to a two-state Boltzmann function.

Instead of adding a single data point to this figure, we performed independent measurements on a plate reader system, comparing concentration responses at room temperature and 37 degrees. These data are now included as Figure S1.   

(2) The cartoon in Figure 2 panel B should be made more accurate. For example, only the transmembrane helices should be depicted embedded in the membrane, not the whole protein including the intracellular domain. Because the experiment was performed with cells, change the orientation of TRPM8 in the cartoon to show the intracellular domain of the protein facing away from the extracellular side of the membrane where the rapamycin is applied.

Thank you for this comment. We have corrected the cartoon accordingly

(3) Perhaps put the yellow circles under or around the carbon atoms to which the identified hydrogen atoms belong in Figure 2 panel E and Figure 4 panel C. I found it difficult to visualize and compare the STTD NMR results with the predicted binding pocket.

Thank you for the feedback. We have added yellow circles around the carbon atoms corresponding to the identified hydrogen atoms in Figure S9.  

(4) Regarding the sentence on p. 12 beginning "In agreement with this notion..."

• Include icilin, Cooling Agent-10, and WS-3 as other cooling agents whose sensitivity has been modulated by mutation of Y745

• Cryosim-3 responses were not tested in either of the two papers cited; please add citation to Yin et al., 2022, doi: https://doi.org/10.1126/science.add1268 .

• Other relevant papers include:

– Malkia et al., 2009, doi: https://doi.org/10.1186/1744-8069-5-62 which includes molecular docking showing the hydroxyl group of menthol interacting with Y745

– Beccari et al., 2017, doi: https://doi.org/10.1038/s41598-017-11194-0 Figure 5 shows disruption of icilin and Cooling Agent-10 sensitivity by Y745A

– Palchevskyi et al., 2023, doi: https://doi.org/10.1038/s42003-023-05425-6 Figure 3 shows disruption of icilin, cooling agent-10, WS-3, and menthol sensitivity by Y745A o Plaza-Cayon et al., 2022, https://doi.org/10.1002%2Fmed.21920 Review of TRPM8 mutations

• typo: Y754H should be Y745H

Thank you for these suggestions. We have added the above references to the text and corrected the typo.

(5) The authors use the competitive action of everolimus on rapamycin activation as evidence that the different macrolides are binding to the same binding pocket. In addition, prior work showed that Y745H and N799A mutations (which render TRPM8 insensitive to menthol and icilin, respectively) do not affect TRPM8 sensitivity to the structurally-related compound tacrolimus (Arcas et al., 2019). This is consistent with the docking and mutagenesis results presented here.

Thank you for this valuable suggestion. We discuss these data in the revised version.

(6) Rapamycin sensitivity has also been observed in TRPML1 (Zhang et al. 2019, doi: https://doi.org/10.1371/journal.pbio.3000252).

We added a short reference to this interesting finding in the discussion.

(7) The whole-cell currents are very large in several of the electrophysiology experiments (for example Figure 3 panel D and Figure S1), which could lead to artifacts of voltage errors as well as ion accumulation/depletion. However, because this paper is not relying on reversal potential measurements or trying to quantify V1/2, these errors are unlikely to affect the qualitative conclusions drawn.

This is a fair point, but indeed unlikely to affect our main conclusions. Note that we compensated between 70 and 90% of the series resistance, so we don’t expect voltage errors exceeding ~10 mV.

(8) Ligand sensitivity is frequently species-dependent in TRP channels, so it is interesting that multiple species were used here and that both human and mouse isoforms exhibit rapamycin sensitivity. It should be emphasized that human TRPM8 was used in the calcium imaging and electrophysiology experiments, as well as some docking models, while the mouse isoform was used in the sensory neuron experiments and a mutated avian isoform was used for some docking models.

This information is available in the Methods and we believe it is clear for the readers.

(9) Perhaps discuss the unclear mechanism of G805A action in icilin (but not menthol, cold, or praziquantel) sensitivity because it is not in direct contact with the ligand. For example, Yin et al., 2019 propose flexibility allowing Ca binding site and larger binding site for icilin.

Yin et al. (2019) suggests that the G805A mutation impacts icilin sensitivity by influencing the flexibility of the binding site and possibly affecting calcium binding. In our study, we found that G805A significantly reduces rapamycin sensitivity, likely due to its direct role in the rapamycin binding pocket rather than affecting calcium binding. This is now briefly mentioned in the results section.

(10) The Figure S1 legend indicates that n=5 for all panels, so please show normalized population IV curves rather than individual examples. Additionally, it would be interesting to see what happens when each agonist is co-applied with rapamycin. Does rapamycin potentiate or inhibit agonist activation in these channels and/or TRPM8?

We believe that normalized population IVs are not ideal for representing whole-cell currents, considering the substantial variation in current densities. We therefore prefer to show example traces in Figure S3 of the revised version but include mean values of current densities for all tested cells in the text.

While the effects of co-application of rapamycin with activating ligands could be of interest, we consider this somewhat outside the scope of the present manuscript. The combination of HEK293 cell experiments, along with results obtained in WT and TRPM8-deficient mice does, in our opinion, sufficiently describe the selectivity of rapamycin towards TRPM8 compared to other sensory TRP channels.

(11) Figure S1 panel A does not contain units for Rapamycin or AITC concentrations.

Thank you for pointing this out. The units were added to the figure.  

(12) It would be nice if the authors characterized the different mutations as predicted to contribute to site 1 (D796, H845, Q861, based on Figure S4), site 2 (D796, M801, F847, and R851), and/or site 3 (F847, V849, and R851).

The indicated mutants were all tested, as shown in Figure 3.

(13) The numbering scheme in Figure S4 does not appear to match the residue numbers in the rest of the paper for certain residues (HIS-844 rather than H845, PHE-846 rather than F847, VAL-848 rather than V849, ARG-850 rather than R851, and GLN-860 rather than Q861), and labels are often overlapping and difficult to see. I also find the transparent spheres very difficult to distinguish from the transparent background, which makes it difficult to appreciate the STTD NMR data overlay.

We apologize for the confusing numbering scheme. The lower numbers refer to the initial docking that was done using the avian TRPM8 ortholog. We have made a newer, clearer version of Figure S4 and inserted as Figure S9.  

(14) Please superpose the Ligplots in Figure S5 panels E and F as described in the LigPlus manual (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/manual/manual.html) to facilitate easier comparison.

Thank you for the suggestion. We followed the suggestion to superpose the Ligplots as described but found that the result was visually cluttered and difficult to interpret. To avoid confusion, we instead decided to remove panels E and F from Figure S5, as we believe that the visualization in panels A-D is clear and informative.

(15) Some n values are missing in figure legends.

We checked all legends, and added n numbers were missing.

(16) There is an inconsistent specification of error bars as SEM in the figure legends, though it is specified in methods.

A question for my own edification: Here, you have looked at ligand interactions with the protein by saturating the protein resonances and observing transfer to the ligand. Would it be possible to instead saturate lipid or solute resonances and observe transfer to a ligand? I am curious whether this would be one way to measure equilibrium partitioning of ligand into a membrane and/or determine the effective concentration of a ligand in the membrane. Additionally, could one determine whether the compound is fully partitioned into the center of the membrane or just sitting on the surface?

The reviewer highlights an interesting aspect. The widely used WaterLOGSY NMR experiment (doi: 10.1023/a:1013302231549) saturates water molecules then the magnetization is transferred to the ligand of interest. Characteristic changes in ligand resonances are observed in the case of a binding event with proteins. On the other hand, the selective saturation of lipids is -while theoretically possible –technically challenging mainly because of the inherent low signal-dispersion of lipids and peak overlapping with ligand resonances. Additionally, lipid systems are more dynamic compared to proteins and ligand-lipid interactions could be weaker and less specific, significantly affecting the sensitivity of STD experiments.

Reviewer #2 (Recommendations For The Authors):

Major:

• Is it feasible to test rapamycin on TRPM8 with single-channel recording? This will allow us to better probe the mechanism of rapamycin activation and compare it with menthol, with parameters of singlechannel conductance and maximal open probability.

In our experience, it is very difficult to obtain single-channel recordings from TRPM8. The channel expresses at high densities, typically leading to patches contain multiple channels, making a proper analysis of mean open and closed times very difficult. Therefore, we have decided not to include such measurements in the manuscript.

• The authors classified rapamycin as a type I agonist, the type that stabilizes the open conformation, same as menthol but more prominent. Does that indicate that rapamycin work synergistically (rather than independently) with menthol, because co-application of them can allow them to add to each other in stabilizing the open conformation? I wonder if the authors agree that this could be tested with experiments as in Figure S3, by showing a much more prolonged deactivation with co-application of menthol and rapamycin than applying each alone.

Thank you for the insightful suggestion. We conducted co-application experiments, and our results show that the deactivation time is indeed significantly prolonged when both compounds are applied together compared to each alone. In fact, very little deactivation is seen when both compounds are co-applied, which made it virtually impossible to perform reliable fits to the deactivation time course for the Menthol+Rapamycin condition. Instead, we have now included summary results showing the percentage of deactivation after 100 ms. We included these findings in FigureS8.  

• It could be tested whether rapamycin activation of TRPM8 requires or overrides the requirement of PIP2 with inside-out patch by briefly exposing the patch to poly-lysine to sequester PIP2.

This is certainly a good suggestion for further follow-up studies. However, we considered that examination of the (potential) interaction between ligands and PIP2 was outside the scope of the current manuscript.

• Figure 1C suggests that the authors test rapamycin when there is a relatively high baseline TRPM8 activation (prior to rapamycin) activation. This raises the possibility that rapamycin is more a potentiator than an activator. I wonder if the following two experiments could address it: (1) perfuse rapamycin while holding at different membrane potentials, wash-off rapamycin in the solution and quickly (in a few seconds) test the activated current magnitude (before rapamycin dissociation), to compare whether a more depolarized membrane potential (high baseline open probability) allows rapamycin to potentiate more. (2) Perform the experiment at a higher temperature (low baseline open probability) and test whether rapamycin EC50 shifts to the right.

Thank you for the thoughtful suggestion. Overall, we are not really in favor of making a distinction between a potentiator and an activator since it is not really feasible to create a situation where TRPM8 activity is zero. As suggested, we performed the dose response experiment at a higher temperature (37 °C) and observed that rapamycin’s EC₅₀ shifts to the right FigureS2. This is similar to what has been observed for menthol on TRPM8 and for many other ligands on other temperature-sensitive TRP channels.

Minor:

(1) The author should report hill coefficient together with EC50 when showing dose-responses.

We have added Hill coefficients for all the fits.

(2) In Figure 1 (E, F), it might be clearer to use Venn-diagram to show whether there is overlapping among rapamycin-, menthol-, and cinnamaldehyde-responsive neurons. According to the authors' explanation, we can predict that rapamycin-insensitive, menthol-sensitive neurons should predominantly be cinnamaldehyde-responsive.

Thank you for your suggestion. In these experiments, we applied several agonists and the combination of them would result in a visually crowded Venn diagram difficult to interpret. However, we agree, with the reviewer’s suggestion, and discuss the percentage of the cinnamaldehyde+ neurons in the rapa- menthol+ population in Trpm8^-/- neurons.

(3) In Figure 3(C), since F847 does not respond to either menthol or rapamycin, it should be excluded from (B). Otherwise it is misleading.

Thank you for pointing this out. To clarify, we have included a calcium imaging trace for the F847 mutant, demonstrating a clear response to rapamycin in FigureS9. This additional data highlights that F847 does respond to rapamycin, albeit with a more modest response amplitude. This is now also clarified in the results section.  

(4) The word "potency" in pharmacology usually refers to a smaller EC50 number in dose-dependent experiments. In "Effect of rapamycin analogs on TRPM8" session, the authors use "potency" to refer to response to a single-dose experiment of different compounds. The experiment does not measure potency.

Thank you for pointing out this mistake. We have corrected the text and replaced “potency” with “efficacy”.

(5)  "2-methoxyl-" is misspelled in the text body.

We have corrected the typo.

(6) It will be nice to include "vehicle" in Figure 6B, or alternatively normalize all individual traces to vehicle. In Figure 6C and D, everolimus has almost no effect with compared to vehicle, and should not be shown as if it had ~8% in Figure 6B.

We have added the vehicle values to Figure 6B from the same experiments.

Reviewer #3 (Recommendations For The Authors):

(1) The NMR method presented here as novel and employed to identify a proposed molecule bound to a membrane protein (TRPM8 in this case) is not well explained and presented. Since several spectra need to be subtracted, the authors should present the raw data and the results of the subtractions step by step. Also, it seems that the height of the peaks in each spectra will be highly variable and thus a reliable criterion employed to scale spectra before subtraction. None of these problems are discussed of described.

The reviewer is right, that the data transparency should be improved and due to the high molecular complexity of the samples the size of the STD effects should be carefully scaled. We carried out additional experimental replicas on new samples and addressed the inherent sample/peak height variability by rescaling the STD effects based on reference ¹H measurements. We provided supplementary spectra of the reference experiments without saturation (Figure S5) and the computed STTD spectra from three parallel NMR sessions (Figure S6). We changed panel C of Figure 2 in the main text and provided all the STD and the computed STDD and STTD spectra recorded on one set of NMR experiments. We added the following paragraph to the main text: “To address the effect of the inherent variability of cellular samples on peak heights, STD effects were normalized based on the comparison of independent ¹H experiments (Figure S5). Three STTD replicates were computed, unambiguously confirming direct binding to TRPM8 in two datasets (Figure S6 A,B)”.

Importantly since this signal subtraction method is proposed as a new development, control experiments employing well-established pairs of ligand and membrane protein receptor should be performed to demonstrate the reliability of the method.

We agree with the reviewer, that the STTD experiment as a new development needs further validation, however, this paper is a preliminary demonstration of a new strategy building on the well-established STD and STDD NMR methodologies. Our group is actively engaged in studying additional biological samples to enhance our understanding of the applicability of STTD NMR. These efforts also aim to address challenges such as sample and spectral complexity by refining and standardizing the proposed workflow.

(2) The tail currents shown in supplementary figure 3 are clearly not monoexponential. The fit to a single exponential can be seen to be inadequate and thus the comparison of kinetics of control, rapamycin and menthol is incorrect. At least two exponentials should be fitted and their values compared.

We agree that the decay in the (combined) presence of agonists deviates from a simple monoexponential behavior. While we agree that fitting with two (or more) exponentials would provide a better fit, this also comes with greater variations/uncertainties in the fit parameters. This is particularly the case when inactivation is very slow and incomplete, or when the difference between slow and fast exponential time constants is <5, as seen with rapamycin and rapamycin +menthol. Therefore, we decided to provide monoexponential time constants as a proxy to describe the clear slowing down of activation and deactivation time courses in the presence of Type I agonists.   

Also related to this aspect, recordings of TRPM8 currents can not be leak subtracted with a p/n protocol, thus a large fraction of the initial tail current must be the capacitive transient. There is no indication in the methods of how was this dealt with for the fitting of tail currents.

As explained in the methods, capacitive transients and series resistance were maximally compensated. Therefore, we do not agree that a large fraction of the initial tail current must be capacitive. This can also be clearly seen in experiment such as Figure 1C, where the inward tail current is fully abolished in the presence of a TRPM8 antagonist. Likewise, very small and rapidly inactivating tail currents can be seen during voltage steps under control conditions (e.g. Figure S7  and S8 in the revised version).  

(3) The docking procedure employed, as the authors show, is not appropriate for membrane proteins since it does not include a lipid membrane. It is not clear in the methods section if the MD minimization described applies only to the rapamycin molecule or to rapamycin bound to TRPM8.  

It is also not clear if the important residue Q861 (and other residues that are identified as interacting with rapamycin) were identified from dockings or proposed based on other evidence.

(4) Identifying amino acid residues that diminish the response to a ligand, does not uniquely imply that they form a binding site or even interact with said ligand. It is entirely possible that they can be involved in the allosteric networks involved in the activating conformational change. This caveat should be clearly posited by the authors when discussing their results.

In our study, we identified several residues that significantly reduce the response to rapamycin when mutated, while retaining robust responses to menthol, which indicates that these mutations do not affect crucial conformational changes leading to channel gating. While our cumulative data suggest that these residues may be involved in direct interaction with rapamycin, we recognize the alternative possibility that they allosterically affect rapamycin-induced channel gating. This is now clearly stated in the first paragraph of the discussion.

I found this part of the article interesting due to the statics leading all the way back to 2008.Also how Black americans who had the same income and credit scores as white families but didn't receive the same loan terms as them./'[/

nice model!

Did he legally change his name or just tell people to call him something else? Could he prove that he was this new person?

Was this open to the public or only to his officers? Was five pounds a lot of money for a reward back then

Véase el comentario extraído de de REST APIs must be hypertext-driven en las notas de lectura en mi bliki.

Participation is crucial for the majorty of the class and required to receive a good grade, I don't think there's a real way to altogether avoid participation andv that info for us to recive a passing grade we must actively engage with our peers on a class topic, or else it's not as engaging for us, and that not be benifical to our learnimg.

Will we be required to write it on paper? Or can we turn in our answers typed up?

I like that there's a schedule, A set schedule helps me stay on track and feel prepared for the next assignment. This lets me know how much time I will need for each class that day and prevents me from feeling overwhelmed.

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Share the same common ancestor

Before, scientists called animals different names. With this there was a universal name for species and animals to not cause confusion.

Family tree

Er dette krav ikke overflødigt i den forstand at det følger fra pkt. 3. og Prop. 5.2 pkt. 1.?

Opposite of selective selection

High mortality for babies who are under and over the mean

There is a loss in genetic variation because the mean is the favored phenotype.

Location of gene on a region of a chromosome

Has a spine/bones. evolved to flatten and as a result one eye is at the bottom of its body; swimming on its side.

No bones. Has evolved to live at the bottom of the sea. Has eyes similar to shark

Scheiding van de ouders kan interpersoonlijke traumatisering veroorzaken.

Broeders en zusters in de kerk, de Arisan groep.

Непонятно использование категории идеальное в рассуждении

Опрееделение величины предполагает части

Определение величины с необходимостью предполагает определения большего и меньшего

Переход к гегельянству

Идеализм молодого Маркса

Переход от фихтенанства к гегельянству