Using Workflows and Libcal, you will accurately check in and out equipment including video cameras, audio recorders, and other creative materials. You will accurately assist patrons to check in and out specialized cameras and lights and correctly account for all included accessories. You will accurately scan materials, account for all accessories, and return them to the appropriate place on the shelf.

For this Job Task, change first bullet to studios instead of spaces. for the third bullet, change to "Consultants refer complex questions to full-time staff."

RMC and DML Desks Change pic Change card to: Work at the RMC and DML Desks: As a Consultant, you will welcome, orient, and help patrons access Library resources, services, and studios.

Job Task #1: Change to work at the Desk.

RMC Consultant's

change to reflect the take a guess language above.

Recall test: I had trouble with this fill in the blank. I'd prefer a multiple choice that was less finicky to answer.

Take a Guess section - * change intro picture to RMC related pic * T/F change to "At the desk, Robertson Media Center Consultants mostly help visitors to check out equipment from the Library" The No not exactly true answer should change to reflect that students "check out equipment, provide help using audio and video equipment and studios, troubleshoot equipment, and more!" * change final bubble to "As a RMC Consultant, you are the person who can best help students with their creative projects. Your work is a key part of our Library services!"

this should reflect content that has already been delivered and shouldn't be a trick question. maybe it should be bullet 1 above?

Robertson Media Center Consultant

Library staff members

Robertson Media Center Consultant's

this is a small thing but in addition to changing this photo can we reorder to say "such as working in the Media Center, assisting patrons at the reference desk, assisting in the makerspace, and much more." or similar.

Photo - update to RMC photo

Update photo and caption

Robertson Media Center Consultant

Capitalize Library

supervisor (singular)

Robertson Media Center Consultant's

Add a bullet here about headphones: Per Desk policy, do not wear headphones while at the desk.

Photo - change to RMC photo

Robertson Media Center Consultants

Robertson Media Center Consultants

Dark patterns are the result of deceptive practices in retail, nudging, and growth hacking. Nudging and retail practices both utilize psychological bias tactics, for example using countdown timers for 'limited deals' or holding items in a cart for a limited time to create a sense of urgency and scarcity. This way, users feel pressured to make decisions quickly because of fear of missing out. Practices like this are very common and many shoppers are aware of how they are being manipulated, but will fall into the trap anyways.

a lot of this just feels like people not reading the fine print or skimming the rules. if you read carefully and take things with a pinch of salt, you won't get screwed over.

I am not too surprised that companies and businesses use different types of tactics to try to make more money from the consumer. I think its a smart idea for them to do these thing, if its in a legal way, because doing things like A/B Testing and using different tactics to make people spend money and that is ultimately the goal is to make people spend money. In terms of dark patterns I think personally that the consumer should be more aware if possible. I do also believe that it should be regulated in some way.

Another example I always disliked as an Amazon prime user, is when Amazon displays that they only have a certain number of items left in stock. I sometimes feel like I'm being pressured to make a quick decision on actually purchasing the item. However, Amazon might not be doing anything wrong, but I sometimes feel like they should also add content like we expect another shipment in another couple of days/months.

Its interesting how I've seen this pattern of nudging everywhere from actual games, online shopping stores and even in person shopping. I've seen it especially in stores where they'll have a buy one get one half of where even the cashier will nudge you to get a another item to retrieve the sale or even in online shopping where it'll nudge you to buy another item just to get free shipping. ultimately they result in you spending more money on something you dont need.

It's really surprising to hear that stores can make such false claims. I've encountered a few stores closing and having big closeouts and they did in fact close. I wonder what the repercussions would be if a store is caught with false claiming to close down and how it would affect the customer base.

This is pretty interesting in the sense that as a developer, you would like to refine the user experience as best as you can. But in doing this, you can create a predatory environment even when you did not directly try to. The Dark patterns that can be created through A/B testing like changing the color or size of a button is directly related to creating dark patterns within your designs. This is something that surprises me as I did not think this type of testing could lead to these bad or intrusive design decisions.

"Growth hacking is not inherently deceptive or manipulative but often is in practice."

The most interesting concept on this page to me is growth hacking. The idea of spreading awareness of your product through subtle features such as the Hotmail ad allows a designer to hold impactful power over a user's decision making. Another thing that makes that method interesting, yet concerning, is that false statistics and illegal actions are not required to deceive or impact the user.

The examples in the first paragraph expanded on strategies used by businesses to manipulate users for financial gain. Although, this article does mention it to have "dark patterns", I believe it to be smart as a business decision but wrong morally. Next, I found interesting the anchoring effect example which can add to the tone of this article that points to a particular persons ability to understand certain facts, trends, and psychological biases to make an educated decision. These companies may have been morally wrong to some, but with the right education the users will understand what is trying to be achieved. I believe when private personal information is exposed is when an issue arises.

I think this shows how normalized manipulation in retail has become, even when consumers are aware of it. Though it's legal, psychological pricing is a tool that can affect customers decisions without them knowing. For instance, if a product is on offer for $9.99, instead of $10, it makes one feel that the product is less expensive, although it is just one cent less expensive. I believe this ties into the way businesses use human psychology to boost sales numbers and wonder if these are ethical or just a form of sound business practice.

Reading the article I think I've come to the conclusion that majority of these dark patterns comes from the initial idea that it's actually marketers pushing for these dark patterns. The reason why they are so prevalent is because it works in bringing more money for the business.

Something that really stood out to me from this article was how companies use “dark patterns” as a form of deception to get people to agree to actions they typically would not, such as signing up for subscriptions or giving up private information. What surprised me the most was how websites will enlarge the “accept” button and make it very easy to click on, while shrinking the “decline” option below it or hiding it. Design can really affect peoples’ decisions without them fully realizing it until later. This was very insightful and something I had never considered before how often this occurs.

This is a particularly predatory pattern because it exploits a user’s desire for security to increase corporate revenue. It forces users into a "privacy tax" where they have to choose between protecting their account and protecting their personal data

I was very surprised by the example about companies using phone numbers from two factor authentiation for ads.. If people are giving their number for security reasons, they probably expect it to only be used to protect their account, not for other reasons. It makes companies seem less trustworthy because users don’t fully know how their information is actually being used and becausee lot of websites do this because it helps them make more money through ads

When reading this part in particular, I found it not only sickly but also felt like a sort of twisted attempt to get more money. TurboTax brands itself as free for everyone but when it hides its from side from low income user for the purpose to getting more money reveals that no matter how good it seems from a corporation that there is always something underlying factor, The factor here and in most other places is greed which is a common dark pattern for large companies who advertise being "free" as nothing is truly free in business.

I believe dark patterns should be banned and if a company continues to use this then it should be fined and have legal actions taken. Designers and tech companies have a responsibility to the people to make good products without manipulating the people. The count down tactic gets a lot of people and its just alot of misinformation.

I wonder if this had any serious legal repercussions or if companies are just getting away with it! especially the Facebook one, it makes you realize many of us arent as educated as we need to be regarding rules surrounding our data privacy.

I think this is seen more and more in today's world. Apps such as TikTok, Instagram, and YouTube are purposely making their apps addicting. I think this is unethical because these companies are promoting their profits at the expense of people's well being.

A common tactic of many stores is to also make "fake sales". Sometimes, stores will artificially increase the price of a product and then put it on "sale" to it's original price, so that it seems like a good deal when you are actually just paying full price.

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Referee #4

Evidence, reproducibility and clarity

RNAi is remarkably efficient in planaria, yet no mechanism for the amplification of the RNAi signal has thus far been observed. In this manuscript, the authors analyse the mechanisms of RNAi spread in planaria. Starting from some basic observations on the identity of the Dicer and Argonaute proteins required for RNAi, the authors performed a set of elegant experiments to conclude that cycling stem cells likely take up dsRNA and excrete Ago-siRNA complexes, which are then taken up by other cells to mediate RNAi. In addition, the authors provide compelling evidence that RNAi is indeed independent of an amplification mechanism.

Overall, I found the experiments and results compelling and the manuscript a pleasure to read. I have only a few suggestions for consideration, none of which are essential to support the main conclusions:

• What does the arrest of stem cell proliferation do to the expression of RNAi genes (with and without dsRNA stimulation)?
• Page 9: top panel. Is there a control that the dsRNA generated by RNaseIII is functional? I.e. that the defect is indeed due to an uptake effect and not the quality of the siRNA preparation itself? (In our hands silencing of siRNA prepared with bacterial RNaseIII has not been efficient at all). As a side note: no method is provided for the RNaseIII treatment.
• Have the authors analyzed which of the Argonautes are present in the preparations generated with Q-sepharose?

Data presentation:

• For all figure legends: please make sure to state animals, number of repeats, define boxplots and what the individual data points represent. Please provide statistics where quantitative statements are made.

Minor points:

• First paragraph results: The statement that Ago1 and 3 were "closer to the nematode-specific WAGOs" does not seem correct, (horizontal distance to the miRNA-AGOs is still lower than the the WAGOs). I suggest removing the statement.
• Use of checkmarks: please define when a checkmark vs cross was indicated? E.g., does a checkmark indicate that 100% of the animals showed efficient RNAi, or a majority of animals?
• Many of the legends contain conclusions. While this may be a matter of taste/style, I would suggest to introduce conclusions only sparingly, if at all, in the legends
• Some of the font sizes are rather small on print size (e.g. Fig 1A, S4i). In Fig 1A the black font on dark blue background is hard to distinguish.

Textual suggestion:

• Abstract "that rely on dsRNA intermediates, such as viruses" > ".. such as those from viruses..."
• Materials and Methods: The lowerscript numbers for the ion show as squares in my pdf.

Significance

Strength/weaknesses:

I found the experimental support robust and well supported and I did not find weaknesses that jeopardize the conclusions.

Significance:

One of the most intriguing features of RNAi is the systemic spread of a silencing signal across an organism's body. This has received significant attention in C. elegans and plants, but for other organisms, this is much less well explored. Planaria have a very efficient RNAi response, which the authors propose is due to uptake of an initial dsRNA by stem cell and excretion of an Argonaute-siRNA complex, which is then taken up by distal cells in an endocytic mechanism. I find this an intriguing mechanism that to differ from mechanisms for RNAi spread observed in other organisms.

The work will be of interest to those interested in small RNA pathways (and RNA biology in general) and has practical implications for scientists working on planaria. The fact that small RNAs spread in an Argonaute-siRNA complex in an organism should also be of interest for cell biologists.

My field of expertise: Small RNA pathways and antiviral defense in insects. No experience working with planaria.

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Referee #3

Evidence, reproducibility and clarity

In RNA interference (RNAi), double-stranded RNA (dsRNA) is processed into small interfering RNAs (siRNAs), which can function locally or act as mobile RNA species that spread between cells. In organisms such as nematodes and plants, the underlying mechanisms and key factors involved in this process, including transporters such as SID-1, have been well characterized. While systemic RNAi has also been reported in other animals, the underlying mechanisms remain largely unclear. In this context, the authors focus on planarians as one such model to investigate these processes. In the planarian S. mediterranea, gene knockdown by dsRNA injection is commonly employed, and the RNAi effect is known to spread rapidly throughout the organism. However, given the absence of RNA-dependent RNA polymerase (RdRP), the mechanism by which RNAi signals are efficiently propagated remains unclear. In this study, the authors provide several important insights into this question.

First, the authors carefully evaluated the duration of the RNAi effect. In addition, they systematically examined the involvement of known RNAi-related factors and demonstrated that this process depends on ago1 and ago3. Second, interestingly, the authors find that initiation of systemic RNAi depends on neoblasts. Third, Argonaute-siRNA complexes play a crucial role in systemic RNAi. This differs markedly from the nematode system, in which dsRNA itself is transported, highlighting an intriguing mechanistic distinction. Finally, the authors suggest that distinct Argonaute proteins may function at different stages of RNAi propagation. Ago1 + Ago3 play essential roles in the initial phase of systemic RNAi in neoblast, Ago3 but not Ago1 silences the target in the differentiated cells. While the phenomenon described here is highly interesting, the underlying mechanism remains to be fully elucidated. In particular, how different Argonaute proteins functionally coordinate with each other, especially with respect to the transfer of siRNAs between Argonaute complexes, is still unclear and represents an important direction for future studies.

The study is supported by well-designed control experiments, and the results are consistent with and support the authors' conclusions.

I have no major concerns about this manuscript. The study is well conducted, and I only have minor comments that could further improve the manuscript.

(Minor) While the authors have examined the effects of irradiation on the donor, it would be interesting to test the reciprocal experiment in which the recipient is irradiated. In particular, assessing whether the addition of donor lysate to irradiated recipients can recapitulate the observed RNAi propagation would further strengthen the proposed model.

(Minor) The purity of the AGO complexes obtained via the TraPR anion-exchange procedure is not entirely clear. The authors may consider providing additional evidence of purity (e.g., visualization of small RNAs with T4-PNK), which would strengthen the conclusions.

(Minor) Figure 4H is not referred to in the main text. The authors may consider incorporating a description of this panel into the Results section for clarity.

Significance

Overall, given the substantial amount of data and the overall high quality of the present study, further mechanistic dissection would likely be beyond the scope of the current manuscript. I look forward to future work from the authors addressing these mechanistic questions in more detail. RNAi has been widely used in stem cell research in planarians. In light of the findings presented in this study, however, previous studies that combine UV irradiation and RNAi may warrant careful re-evaluation. In this regard, the present work has important implications and is likely to have a broad impact on the field.

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Referee #2

Evidence, reproducibility and clarity

In this paper, the authors set out to understand how dsRNA elicits a system-wide RNAi effect using planarians as a model system. This is an important question, because it gets at evolution of these processes in different animal models and because knowing more about how RNAi works can allow scientists to tweak their approach for a better knock down efficiency. Importantly, though the system-wide mechanism of RNAi is fairly well understood in C. elegans and in some plants, it isn't clear how conserved these mechanisms are. Some aspects of this paper are quite convincing, including identification of the responsible Argonaute and Dicer proteins. Further, the identification of potential Sid-1 homologs that may allow for import of dsRNA is new. However, the role for Ago-3 was recently reported in Sasidharan, et al (Science Advances, 2026), which is not cited in this manuscript. Perhaps more importantly, several key aspects of the argument set up in this paper are not adequately supported and there are key gaps in the mechanism proposed that prevent its publication in this form. Major and minor suggestions follow:

Major issues:

1. The argument that siRNAs must be generated in stem cells that are cycling is not well supported.

a. The authors only use one approach to reduce stem cell numbers, lethal irradiation. In addition to causing loss of stem cells, lethal irradiation causes wide-spread DNA damage and organismal/cellular stress responses. By 6 days after lethal irradiation, other progenitor cells are lost as well. Epidermal progenitors are known to be very abundant and to play signaling and/or metabolic roles in planarian physiology, so their loss may also be impactful. The authors should consider other orthogonal approaches to eliminate stem cells and to rule out other potential mechanisms.

b. The authors use camptothecin in planarians and claim that it reduces cell divisions of stem cells. To my knowledge, this drug has not been shown to work in planarians before. The concentration used is also higher than in published studies. The authors should show whether stem cells are lost after this drug treatment (through levels of stem cell markers or stem cell counting) and should clarify the timing of the treatment relative to the RNAi, which is not clear from the figure legend or methods section. The authors should also discuss possible alternative interpretations of this piece of data (e.g. potential off-target effects). Without more information, it is hard to interpret the data relative to the irradiation results. The authors also do not provide any insight into how or why dividing/cycling stem cells would be important for the systemic RNAi mechanism they propose.

c. ago-1, ago-3, and dcr-2 were shown to be enriched in stem cells (Fig. 3C), but these genes are also expressed in differentiated cells in single-cell sequencing data. Therefore, it isn't intuitive that non-stem cells would lack the capacity to generate siRNAs.

d. Is there a way to directly test the hypothesis that stem cells are only generated in stem cells, potentially by blocking transport in some way and then visualizing new siRNAs with a miRNA/siRNA version of ISH OR FACS and sequencing? If the Ago-1/3-siRNA complexes are indeed transported by EVs as per Sasidharan, et al then the ESCRT(RNAi) approach might be useful in blocking movement of siRNAs? Or, could the authors show that dsRNAs are preferentially taken up by stem cells using the type of experiment shown in Fig. S5H? 2. It isn't clear from the manuscript how the authors believe that Ago-1/3-siRNA complexes exit and enter cells. The diagram in Fig. 5F describes the complex as moving between cells either through vesicles or extracellularly. How do the authors propose that Ago-siRNA complexes pass through the plasma membrane given that they are not known to go through the secretory pathway? Or once endocytosed, how do they exit the vesicle? Uncertainty on these points makes the molecular mechanism proposed here seem poorly supported by the data provided in the paper. 3. One key result in the paper is the transplant of "AGO complexes" that are purified from lysate. The authors writing about this experiment implies that they are transplanting a fairly pure material representing these RNPs and no others. However, the approach described is unlikely to result in purification of highly specific protein complexes. At a minimum, gels that illustrate protein/complex purity should be provided. Preferably, though, mass spectrometry and sequencing would be provided to detail siRNAs and proteins in this sample. 4. The Sasidharan, et al (Science Advances, 2026) paper should be cited and also the findings of this paper should be put in the context of that work.

Minor changes:

1. In several experiments, quantitative assessment of impacts (e.g. eye size or ovo/opsin transcript levels) rather than subjective eye scoring would be preferable for rigor and for statistical analysis of changes rather than check/X (e.g. 4F-H).
2. F1 and F2 terminology for regenerates is probably not accurate since F in those terms stands for "filial" and is used to denote offspring.
3. The images in Figure 2 (A, C) are quite hard to see on the printed page. Using white for fluorescence might improve contrast and visibility.
4. The element of time seems to be very important for transmission of the RNAi effect in sexual offspring. Instead of the claim that hatchlings from RNAi crosses have no effect (Fig. 2H), the detail provided in the results section seems to indicate that there is a time-limited effect. These findings should be clarified with progeny sorted by time of egg laying and with a better sense of time between RNAi injection and hatch. Further, even in animals that do regenerate eyes, it would be nice to see a quantification of transcript as a clearer readout of whether some knockdown persists.
5. This is more of a curiosity question, but it would be interesting to know how the differences in Ago1/2/3 protein structure might relate to their function, particularly in terms of the PAZ and MID domains.

Significance

This paper provides some new insight into mechanisms underlying systemic RNAi in planarians. Some of the results are quite preliminary and the overall interpretation of data is not yet well founded. However, there are some highlights, including the potential identification of dsRNA transporters that will be interesting to those in the planarian field.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this manuscript, the authors characterized the molecular mechanism of systemic RNAi in planarian Schmidtea mediterranea (Sme) through loss-of-function genetic perturbations. They genetically identified key protein factors involved in the siRNA pathway and assessed the systemic RNAi efficacy at the molecular level. Notably, they find that the proliferating stem cells (neoblasts) are specifically required for systemic RNAi in Sme. They further propose that the requirement of neoblasts in systemic RNAi is mediated by spreading of the RISC RNP to differentiated cells.

Major Comments

1. The authors show that in Sme systemic RNAi strictly relies on the presence of neoblasts, which is one of the most interesting finding. It is important to understand the mechanism, specifically whether neoblasts are generally required for dsRNA processing or for conferring mRNA slicing activity. Although the authors claimed in Figure 5 that neoblasts are required for siRNA biogenesis, the results provided do not directly support this claim. An alternative scenario is that dsRNA can still be processed into siRNA in the absence of neoblasts, but the resulting siRNA subsequently fails to function without the neoblast AGOs or other signals. To directly confirm that neoblasts are required for dsRNA processing, one additional experiment should be performed in which irradiated worms are injected with dsRNA, followed by small RNA cloning and sequencing to detect whether processed siRNAs are present.
2. An alternative mechanism to interpret the role of neoblasts is that, instead of processing dsRNA and/or spreading RISC RNP, the neoblasts may function as regulatory cells that provide signals licensing the dsRNA processing and target slicing in differentiated cells. Under this scenario, the requirement for sid-1 and vha-16 could instead be interpreted as necessitate the dsRNA transfer from the initial uptake tissues (parenchyma for be injection or intestine for feeding) to the target tissues. To rule out this possibility, isolated neoblasts from naive donors could be transplanted into irradiated recipient worms who have been injected with dsRNA, and whether such transplantation can restore the systemic RNAi in the recipient animals could then be tested phenotypically. One caveat is that any positive result may be due to the proliferation of the donor neoblasts in the recipient. This can be addressed by performing the same transplantation but using neoblasts isolated from camptothecin-treated worms, which would limit the proliferative contribution.
3. In Figure 5E, the authors show that recipient ago-3 is required for systemic RNAi, and they suggest in the Discussion a plausible model in which recipient AGO-3 is required for nuclear RNAi for transcriptional target repression. However, this result appears inconsistent with the results in Figure 1C-D, where ago-3 KD did not abolish systemic RNAi. This contradiction should be acknowledged in text and further investigated. One possible interpretation is that the presence of the neoblast ago-3 from the donor lysate may have an antimorphic effect and interferes with the recipient AGO(s) (presumably ago-1 in this case) during target silencing , implying that homogeneity of AGO(s), or at least homogeneity of ago-1, is required for such systemic RNAi. Although the underlying mechanism remains difficult to interpret, such hypothesis could be tested by injecting lysate from ago-3 KD donor into ago-3 KD recipient. If AGO homogeneity is indeed required, such transfer treatment should no longer abolish systemic RNAi in the recipient in Figure 5D. Additionally, the target genes used for the systemic RNAi in Figure 1C/D and Figure 5E are different. To exclude the possibility that this discrepancy is target-specific, either six1/2 should be tested in the whole worm RNAi assay in Figure 1 or opsin should be used in the transfer assay in Figure 5.
4. The authors claim in Figure 4 that the systemic RNAi is mediated by secreted RISC. This claim is not unexpected, because naked siRNA generally suffers poor half-life in vivo and therefore must be stabilized by bounding to AGO to evade the endogenous ribonucleases. Nevertheless, the alternative hypothesis that the transferred RNAi is mediated by the spread of naked RNA, though unlikely, should be experimentally excluded. Specifically, the isolated RNA and lysate with protease in Figure 4F (which failed to induce RNAi in the host worm) should be tested to confirm whether they contain siRNAs. This can be done by cloning and sequencing the sRNA in the lysate.
5. The authors assigned ago-1 and ago-3 to the siRNA pathway and ago-2 to the miRNA pathway. This is an important conclusion for subsequent sRNA studies in planarians. However, the evidence provided in the current manuscript is insufficient to exclude ago-2 from the siRNA pathway, especially given that DDH catalytic triad is present in AGO-2. The observed redundancy between ago-1 and ago-3 to maintain functional RNAi can only support the involvement of these two AGO genes in the siRNA pathway but does not exclude AGO-2. To more rigorously test whether ago-2 should be excluded from the siRNA pathway, double RNAi of ago-2 and ago-1, as well as of ago-2 and ago-3, should be performed, and ago-2 should only be excluded from the siRNA-pathway if such double KD do not further reduce the RNAi efficacy compared with individual KD.
6. The results shown in Figure 1F, where exposure to exogenous dsRNA can enhance the endogenous transcription of ago-1 and ago-3 in Sme, are particularly interesting. The authors should discuss whether this phenomenon is related to nuclear RNAi. In addition, it has been reported that exposure to exogenous dsRNA can increase the AGO/DICER protein levels without increasing the mRNA level (PMID32194567), and this should be compared with the present findings. Importantly, the result also suggests a potential strategy to improve the Sme RNAi efficiency. Accordingly, it would be valuable to test whether the increased ago-1/3 transcript levels caused by introducing exogenous dsRNA can lead to higher RNAi efficacy, both in terms of target silencing depth and the duration of RNAi effectiveness.
7. Figure 2I-J provides remarkable evidence that the systemic RNAi in Sme is independent of RdRP. This result should be highlighted in the final paragraphs of the Introduction and mentioned in the Abstract.

Minor Comments:

1. The authors show that ago-1 + ago-3 KD only slightly perturbed the miRNA levels. However, this observation can be interpreted in at least two ways: (a) these two AGO genes are not involved in the miRNA pathway; or (b) these two genes are expressed at low abundance (which was mentioned later in the paper), such that their KD only mildly perturb their associated miRNAs, especially if these miRNAs are also associated with AGO-2. Scenario (a) seems less likely to be true because ago-2 is enriched in neoblasts (Figure 3C), whereas many conserved miRNAs have been reported to be enriched in Xins in Sme (Sasidharan et al 2013). This issue should be therefore discussed. In addition, the gene expression levels of the three ago genes from previously published bulk RNAseq datasets should be included in the figure.
2. The illustration in Figure 1A is not fully accurate. In the miRNA pathway, target repression also includes mRNA degradation (which is conventionally referred to as mRNA decay or mRNA destabilization), which is in fact the dominant mode of miRNA-mediated repression. Therefore, "mRNA decay" should be added in addition to "translational inhibition". In the siRNA pathway, mRNA degradation is not directly mediated by RISC itself, but by the downstream exonucleases (i.e., XRN-1); therefore, the term "mRNA slicing" should be used instead for the siRNA part. Additionally, it has been shown that C. elegans RDE-1 is also associated with miRNAs (PMID 36790166), so the functional assignment in the model should be adjusted accordingly.
3. In Figure S1C, the authors claimed that ago-1 and ago-3 exhibit more divergent PAZ and MID domains according to the AF modeling. However, this divergence may simply reflect the lower sequence conservation of AGO-1 and AGO-3 relative to AGO-2, which is shown in the phylogeny in Figure S1A. To address this caveat, Robetta modeling should be performed for both the full-length proteins in the comparative modeling mode due to the length of AGO proteins, or de novo modeling of the PAZ and MID domain. Structural the alignment in reference to solved AGO structures such as 4W5Q or 6N4O should be shown. If the MID/PAZ domains divergency remains evident, it should be quantified using backbone RMSD relative to known AGO structures.
4. In Figure S1C, a second structural view should also be included to better illustrate the AGO architecture. The duplex channel within the PIWI-MID lobe should be clearly visible in one of the views. The L2 domain, or at least helix-7, should be labeled. If possible, the relative position of helix-7 to the guide RNA should also be shown. All the predicted structural models should be included in the supplemental files.
5. The authors suggest that the spread of functional RISC from neoblasts depends on EVs. The evidence involving vha-16 is convincing, but to directly validate the presence of EVs that cargo RISC, CsCl ultracentrifugation would be informative. Although this experiment is beyond the scope of the current manuscript, the need for direct EV validation should be discussed.
6. In Figure 2G, the authors show that although zfp-1 restores the homeostatic mRNA level at week 5, its downstream target prog-1 and agat-3 fail to recover. It remains unclear whether this is due to the delay of newly translated zfp-1 to activate the downstream targets, or due to translational suppression of zfp-1. Therefore, the mRNA levels of prog-1 and agat-3 should be further monitored beyond week 5.
7. In Figure 3, the authors use co-expression by in situ hybridization to demonstrate the expression of ago in neoblasts. To provide the whole-animal context, co-expression of smedwi-1 and ago genes should also be confirmed using the current Sme scRNAseq datasets.
8. The authors proposed in the Discussion that AGO-1 may sponge unwound RNA duplex and this facilitates the dsRNA transfer. This interpretation seems unlikely, because the ago-1 single KD, which would abolish such dsRNA transfer, did not show phenotypes in terms of systemic RNAi defect. Also, such scenario suggests that loss-of-function of ago-1 may be antimorphic since the sponged dsRNA were released, and thus co-KD of ago-1 and ago-3 should result in more efficient RNAi. These concern should be discussed.
9. The Discussion states that AGO-1 is required in the donor, but this is inconsistent with the results in Figure 5D, where ago-1 KD in the donor did not abolish RNAi in the recipients. This inconsistency between results and text should be corrected.
10. In Figure S5I, the authors show that short dsRNA generated by RNase III digestion failed to induce systemic RNAi in sid-1 loss-of-function condition. However, the alternative explanation is that RNase III digestion produced short dsRNAs that may result in siRNA with suboptimal length for AGO loading or functioning. This caveat should be mentioned, and the length profile of the RNase III digestion products should be shown by high density urea gel electrophoresis or HPLC.
11. In all the transfer assays, one concern is the lysate may contain viable neoblasts, so that any observed results could be attributed to the proliferation of the donor-derived neoblasts rather than the transfer of RNAi materials. Therefore, a cell viability test using Calcein AM or other equivalent assay should be conducted to confirm the absence of live cells in the lysate preparation protocol.
12. In the second paragraph of the introduction, when comparing the siRNA and miRNA pathways, the difference in base-pairing configuration with the target site should be introduced with appropriate reference.
13. In the last paragraph of the introduction, the claim that the results may have implications for the design of effective RNAi-based therapies is too vague. Given that the current therapeutic siRNA delivery methods are already robust in clinical applications, the authors should more specifically explain how their findings in Sme might inform therapeutic development.
14. In the last sentence of the second last paragraph of the introduction and Figure S5I, "RNAse" should be corrected to "RNase".
15. In the first Results subsection, the second last paragraph, first sentence, one left parenthesis is missing.
16. Throughout the Discussion, the term "AGO-RNA". If the authors intend to express a distinction from RISC, how this terminology differs from RISC should be justified. Otherwise, RISC would be more appropriate.
17. Statistical significance should be shown in Figure 2E, 2G, 3A, 3C, S2A, S2D, S2G, S3D, S5D, S5G, S5J.
18. Molecular weight should be labeled in Figure S5L.
19. In Figure 2J, where the y-axis indicates % prevalence, the down-facing bars (antisense reads) should also be labeled as positive values on the y-axis. Displaying them as negative percentages (-20%) is incorrect.
20. The small RNA cloning procedure should be described in the Methods. Basic information of sRNA sequencing, including read numbers, biotype distribution, proportion mapping to the triggering dsRNA, should be included too.
21. The methods used to measure RNA and protein concentrations should be included in the Methods section.
22. The irradiation protocol, including dosage, should be included in the Methods.
23. In the Methods section, subscripts of chemical formulas are rendered as squares throughout the text. This formatting issue should be corrected.
24. In the Results section, first subsection, second paragraph and first sentence. The cited data should be Suppl Figure S2D, not the current S2A-C.
25. The manuscript uses inconsistent formatting for supplemental figures (for example, "Suppl Figure S1B,C" versus "Suppl Figure 2A-C"). The formatting should be standardized.

Significance

Planarians have long been appreciated as a robust model organisms for studying gene function in animal regeneration, and one major advantage of this system is its highly efficient systemic RNAi. However, the molecular basis of the RNAi machinery has not been thoroughly investigated, and detailed RNAi efficacy hasn't been evaluated. This study therefore provides important value by characterizing the molecular components underlying systemic RNAi in Sme, which contributes to both fundamental understanding and to potential optimization of RNAi-based experiments in Sme.

In addition, the manuscript reports that stem cells are required for systemic RNAi in differentiated cells in Sme, a finding that has not been described in other organisms. Although the underlying mechanism remains unresolved, this observation offers potentially important implications for both RNA biology and stem cell biology.

We don't have this but if we can getb this from PubChem then why not.

I don't think we have this but maybe it is easy to recreate it with existing data.

We separated targets and mutations. In this specific case KRAS would be the target and KRAS G12C the mutation.

We also have categories like Action type(s): covalent, irreversible, non-covalent, reversible Target - Action Specificity: Mutation Specific, HLA-restricted Binding Site - allosteric site, active site HLA Allele - a bunch of HLA subtypes as values

Additionally we also capture if a drug is brain penetrant.

Top Targets as a section doesn't go well with Drugs because most have only 1-2 targets. I would remove this.

I am not sure we need this here. Some drugs has 30+ indications and they wouldn't fit here.

Change Owner to Organization and add Geography next to the Organization with values like Worldwide, US, Ex-US, Europe, Japan, China, Greater China A lot of drugs have multiple organizations in different geographies. We also need to add a "Partner" between Organization and Geography. For example Company A partner with Company B in US and Company C sells it Ex-US.

We need a "Development Status" somewhere in a prominent place to show if the drug is: In Development Discontinued Unknown

Change Development Phase to Highest Development Phase

Change Phases to: Pre-Clinical, Phase 1, Phase 2, Phase 3, Approved There is also some drugs which are "Withdrawn"

Additionally we could also add: IND Submission - which happens between Pre-clinical and Phase 1 NDA Submission - between Phase 3 and Approved I just don't know if we have this data consistently

!

Good example of accessibility, the headline is straight to the point and a clearness that helps those with assistive technologies quickly navigate and understand the article.

To have an another individual article that further expands that topic is great because it adds valuable information but by not having it integrated with this article, it makes it cluster-free and it invites those who wish to read more about it to do so, while those who just want an basic knowledge of the news to only read this short article.

While having a built-in preview of a video can make it very accessible and less of a hassle for the readers, it also adds figures that now become possibly too many considering the length of the article.

While it is great to have a structure that isn't filled with paragraphs, the spacing between all these texts can possibly make one feel like overwhelmed and some of these could possibly be combined into one paragraph that actually makes it easier to read instead of these broken down ones.

A built-in listening aid is great for accessibility and it doesn't require external apps or aids, which makes it very simple for the any reader to have that option as they wish.

длььдл

ыыыыыыы

This entry should follow the same format as the dictionary entry for Dilettante

Where did you get access to this annotated copy? This would be a great resource to link to and should be included in your Bibliography.

Good argument, but this last clause dangles. Consider cutting it.

She may be creating allegorical figures here, using everyday objects and symbols to represent both real people and abstract ideas.

Not only satirical but very bawdy, scatalogical, and explicit. Lots of references to body parts, especially ones with sexual/erotic functions!

This zodiac sign also evokes two other symbolic traditions:

1. the Blazon, the catalog of female body parts that appears in lyric poetry from Petrarch on ("her eyes are like diamonds, lips like cherries...)
2. The St. Sebastian motif, a saint who was killed by a thousand arrows and who, Richard Kaye argues, became a homosexual icon by the late 19th century.

intriguing!

good insight into Barnes' feminist strategy

This sentence seems to repeat the idea from the first sentence of the previous paragraph.

echoing (and parodying) a cultural tendency to either denigrate or idealize women, with nothing in between.

good connection

Interesting medley of lamentation, bawdy doggerel, and storytelling suggests a wide range of affective moods and lesbian experiences.

Excellent point! As with her evocations of Christianity, she is subverting -- or even inverting -- traditional moral values. And remember "sexual inversion" was consider a valid theory of homosexuality at the time.

Can you transcribe them?

Seems important that this conception occurred without a man or male angel!

caricatured or characterized

Might be helpful to have an image of the 2-column format?

use an m-dash (command/shift/hyphen) rather than a hyphen.

This phrase dangles and the next sentence is cut off.

quote and cite the Afterword for this phrasing.

a more generous quotation might help untangle the syntax here. Sounds like she's being paired like to like, which doesn't make sense if she's straight.

What is the intention? Isn't she just following the conventions of the calendar year and the almanack form?

Barnes describes January as... (avoid passive voice)

irreverence? Is she mocking the women for their reverence? for their irreverence? It's not quite clear what she's mocking, but my sense is that she evokes Christian imagery to create an ironic contrast, since their behaviors are what would be deemed sinful in a traditional Christian theology.

to be enjoyed and read as a communal experience?

This sentence is vague. Why not just take it out?

seems almost like a warning as much as an invitation.

The foreword is fascinating. She starts by minimizing the project as a small, flawed, and obscure affair. To set it before "the compound public eye," she then shrouds it in Latinate rhetoric that obscures as much as it reveals, casting the Almanack as a dangerous mythological creature.

It feels as if, almost 50 years later, she's still trying to preserve the secrecy and self-mythologizing that strengthened and sustained the Lesbian community of the 1920s. They were considered monstrous and unnatural by society, and in some ways, Barnes embraces and recuperates the sense of being an outcast, grotesque monstrosity, much in the way subsequent activists recuperated the term "queer" and staged elaborate gay parades.

difficult? particularly for readers who are not "in the know"!

"is composed of" or "comprises"

I've never come across this view of Barnes as a dilettante, so unless you found it in a more scholarly source than Mirriam-Webster, I would leave it out. She was a serious writer, as well as a witty, ironic, and sometimes outright hilarious one.

I'm not sure she used these movements as much as participated in them. You could simplify to: She combined avant garde aesthetics with decadent, bohemian, "art for art's sake" sensibility.

I think these two sections might be combined in one concise bio of Barnes that leads to the Paris and the Almanac.

Simple, elegant title page tells us exactly what we need to know and entices us to enter, without minimizing your project.

I'd love to know more detail here. Who requested it? Also this information seems to require a citation for its source.

I am wondering if we could introduce a box somewhere on the top which shows the study outcome as positive, negative, not available?

Could we also show here the Number of Publications?

Could we add after publications a new section called "Trial History". We run this for few trials and maybe we could do for a selected number of trials. See an example in the data lake. <br /> https://datalake.bioloupe.com/admin/clinical_trials/36511#history-timeline

I would add Diseases, Disease Stage, Line of therapy, Biomarker and Value A trial might have multiple diseases with each has their own Disease Stage, Line of therapy, Biomarker and Value therefore we need to structure this box to accommodate this.

Sometimes the trial has additional Other Study ID Numbers, which could be listed here.

I can't highlight and annotate "Overview" therefore I leave comments here. We need the following under Overview: Official Title, Brief Summary, Detailed Description, Conditions, Interventions , Sponsors

I would like to see here: Study Start Date (Actual or Estimated) Primary Completion Date (Actual or Estimated) Study Completion Date (Actual or Estimated)

I think this is too prominent here. It could be somewhere else with smaller text size

We show Phase II, Completed, 2 sponsors on the top of the page. Do we need those? They don't bother me but it is a duplication.

The Phases should be: Early Phase 1, Phase 1, Phase 2, Phase 3, Phase 4.

Sometimes the trials is Phase 1/Phase 2 or Phase 2/Phase 3, in these cases we should highlight both.

Brilliant connection and excellent point about the setting, but do you mean that the setting is as much a part of her work as the language of the poem itself? Also, might this be a place where you can gesture to the Paris connection. Loy's poem, if it's Three Moments in Paris, connects this NY scene to its Paris "sister city" in modernist decadence.

I'm not the author of this work. See the about page to create a correct citation.

Carolyn

Actually periodical studies is a very established field with a methodology that these students adopt! They didn't invent it.

As a design rule, use left alignment for any text longer than a single line. It's much harder to read centered text.

page #?

I don't think she published a drawing in Rogue, did she? Clara Tice published drawings that seemed to illustrate her work.

Do you need a citation for this information? It may be considered common knowledge, but if you had to look it up, cite your source.

Stein may be looming a bit too large in this project. Her salon was an important gathering place, but not the only one. Natalie Barney's salon, Sylvia Beach's bookstore, and the many cafes and clubs provided other nodes in the network.

awkward syntax. Was she the American facing literature?

Maybe clear to say "About Mina Loy," so it doesn't seem like this is about you, the author of this essay!

Good, clear, engaging opening.

This sentence has a misplaced modifier. The initial clause "through-reimposing..." attaches to the nearest subject noun, so that Three Moments in Paris is re-imposing itself.

You can make a better sentence and stronger anchor by saying something like: By examining Three Moments in its little magazine context, we can see...[and then insert a specific thesis/insight]

This source made me think of the Citizens United Supreme Court case that basically ruled that your money is an extension of your free speech. This ruling has put value to voices that are higher in monetary value. they can spread their message as they please while poorer people can't have the same reach a rich person can.

This article is about Anil tried to tell people to avoid reading negative comment like toxic comment about us. He explains that harmful comments, harassment, and hate speech can make online spaces feel unsafe, especially for women and minority groups. And he believe social media platforms can do better job by moderating content, try to make healthier online communities. I think this article makes a strong point because many people say “just ignore it,” but negative comments can still affect someone’s mental health. We can proyect ourselves by reporting toxic behavior while using it.

In this article, Mia Sato explains that YouTube received millions of incorrect copyright claims within a six-month period, showing how automated copyright systems often make mistakes when identifying content. The article highlights concerns about fairness and transparency, since false claims can negatively affect creators by removing videos or limiting their ability to earn revenue.

This article talks about how it plans to ban political moderators from posting certain content due to Trumps prevalent social media presence and ability to post at all times.

This Wikipedia source talks about spamming. It first gives us a definition. Next, it gives us it's direvation and roots/history. It also gives examples in different media forms, such as emails, messages, social network spam, and many more. Something that I also find interesting is the chart of where spam comes from with percentages for countries.

I found it interesting how Wikipedia administrators are painted less as “authorities” and more as trusted volunteers with extra technical tools. Conversely, the article shows how much power admins actually have, which makes the emphasis on neutrality, accountability, and consensus especially important in maintaining trust on the platform..

This article about how a woman had no wikipedia page until she was associated with a male is so sad to me. She literally won a Nobel Peace Prize and is not recognized onliine for it in any public matter. It is only until a man is beside her name that they rcognize it. This is extremley disappointing because id think that an accomplishment that big wouldn't go unrecognized because of gender.

I've noticed a common trend that these large tech companies do. When they feel they need to hire cheap work for controversial tasks, they outsource that work to separate their name from the controversy. We saw this with how big tech treats social media content moderators, and now we see this with how AI companies treat data workers and AI trainers. I see it as a way these companies are trying to manage their liability by hiring through a separate company.

This article talks about Facebook's moderation team, employees who are underpaid to look at disturbing content. The article highlights how these employees are not treated well, and issues such as PTSD from viewing this content is dismissed by the company. The article talks about lawsuits regarding the topic but explains that they have caused no change to how Facebook treats these employees.

I never knew people could get paid to make changes to wikipedia but it makes sense. Wikipedia being one of the largest free information databases in the world means that its data gets pulled for so much so changing that data by paying editors to make sure your pages are up to date and paint you favorably help influence those who might see the data without knowing their seeing it.

Content is moderated by the company that owns the social media platform. Which means there is an intrinsic bias within the algorithm. With censorship and misinformation rampant, it is hard to know what you're looking at on social media is truly accurate. I think there would be alot less open racisim online if everyone had power to change the algorithm and how people interact on social media.

The people in charge of social media platforms are typically tech executives who direct their moderation teams with legal council, and their individual viewpoints and business goals this influence the rules that get enforced (or not). As a direct result, moderation policies are more so often subject to political pressure and profit motives, or even the personal beliefs of platform leadership.

Ive reported one thing for violating rules because my friend was posted in something she did not consent too/ So me and my friends all reported it together hoping it would get taken down and she wouldn't have to do anything further to remove the post. It didn't necessarily break the platforms rules, but it violated her personal privacy.

I have reported a few posts before, especially ones related to school violence, sensitive or disturbing images, and posts that included suspicious links that seemed like they were trying to hack my account. I think reporting harmful content is important because it can protect us from any violation. These posts make me feel unsafe or spread harmful behavior online. I also report suspicious links because I do not want my personal information or account to be at risk. Or any content I do not want to see in future and report some fake account who tried to send me message or comment.

Personally, I never had but I've reported accounts or strange people online. Certain rules like this and the ability to report things are not often utilized.

I have reported posts not because they broke the terms of service (they might have, but I didn't check), but because I didn't like the post. Sometimes I do this so that I don't see content from the creator of the post anymore, or so that I don't get that type of content anymore, and sometimes I just do it as a troll. On TikTok, there are a couple of steps to take before you can block someone, but you can report them without going to their profile.

I think this passage shows how difficult content moderation is on large social media platforms like Facebook. Even though moderators are hired to review harmful posts, the platform’s algorithms may still promote controversial or inflammatory content because it increases engagement and keeps users active. After reading this, I realized that social media companies are not only influenced by ethics, but also by business goals and user activity. It made me think more critically about the type of content I interact with online and how algorithms shape what people see every day.

I think this passage shows how difficult content moderation is on large social media platforms like Facebook. Even though moderators are hired to review harmful posts, the platform’s algorithms may still promote controversial or inflammatory content because it increases engagement and keeps users active. After reading this, I realized that social media companies are not only influenced by ethics, but also by business goals and user activity. It made me think more critically about the type of content I interact with online and how algorithms shape what people see every day.

I think this passage shows how difficult content moderation is on large social media platforms like Facebook. Even though moderators are hired to review harmful posts, the platform’s algorithms may still promote controversial or inflammatory content because it increases engagement and keeps users active. After reading this, I realized that social media companies are not only influenced by ethics, but also by business goals and user activity. It made me think more critically about the type of content I interact with online and how algorithms shape what people see every day.

I think this passage shows how difficult content moderation is on large social media platforms like Facebook. Even though moderators are hired to review harmful posts, the platform’s algorithms may still promote controversial or inflammatory content because it increases engagement and keeps users active. After reading this, I realized that social media companies are not only influenced by ethics, but also by business goals and user activity. It made me think more critically about the type of content I interact with online and how algorithms shape what people see every day.

This passage is interesting because it shows that even platforms that strongly support free speech still place limits on certain types of content like spam. It highlights the idea that content moderation is often done for practical reasons, such as keeping users engaged and maintaining the platform’s usability. I think the passage effectively demonstrates that no online platform is completely unmoderated, since every site must balance freedom of expression with user experience.

I have read before about how horrible it is to work in content moderation at big tech. These employees are shown truly horrific things and need to determine what happens to the post. I imagine this job is very similar to certain AI Trainer jobs that exist today on platforms like Handshake. I have heard similar bad things about them. I wonder if content moderators and their decisions while moderating were used to train an AI to do it without potentially traumatizing employees. If so, I see that as a positive use of AI.

It is interesting to think how China is able to have such a robust censorship network blocking the news as well as a bunch of different sites but the US seems unable to block a lot of the things we censor such as CSAM. I wonder what sets China's censorship network apart from the US's.

Please read the published version of this paper at: https://proceedings.mlr.press/v311/haghani25a.html

Michigan Safe Drinking Water Act (Act 399 of 1976), Public Act 399 of 1976 (effective January 4, 1977), Safe Drinking Water Act PDF.

Michigan Safe Drinking Water Act (Act 399 of 1976), Public Act 399 of 1976 (effective January 4, 1977), Safe Drinking Water Act PDF.

comment via Joe Eisaman at https://www.facebook.com/groups/TypewriterCollectors/posts/10163611818554678/

in relation to a table of dimensions 32 x17 x28".

Floridi and Taddeo are optimistic here they see ethics and data science as compatible, not opposing forces. But that balance is easier to describe than to actually achieve in practice.

Mitch Fixit has some repair chops, so this is believe-able: Some of the colored platens were meant to have different harnesses for doing stencils.

via https://www.facebook.com/groups/TypewriterCollectors/posts/10163612704804678/

In the game, the added time pressure made it feel like answers needed to be instant. It also made looking into these sources feel like a waste of time, since it was impossible to do this for a few, let alone all of them. I believe that this can be shown in true moderation, as many things can slip past filters by using deception. This time crunch makes content moderation a practically impossible task.

Via Brenton Beck at https://www.facebook.com/groups/TypewriterCollectors/posts/10163613494799678/

https://www.facebook.com/groups/4770669677/user/1183632210/

Janet Schwartz's boyfriend.

https://www.facebook.com/groups/TypewriterCollectors/posts/10163613988099678/

I feel like the trauma and violence these moderators are exposed to should be accounted for in both their salary and their accessibility to mental health resources as a bare minimum for social media companies to supply.

this should be in a good state already.

error recovery is lacking, we need to recover in the case of a dead node

rebase support is critical, we should be able to reuse it

most of this code should be reusable

this should be outside of the workflow

pr right now is opened exclusively via the api endpoint, and only updated asynchronously after a rebase. the workflow should also decouple this, and should allow user review

this is an entirely new agent decision making process

overlayfs will always be the best call here. fsx persists between node reboots that's why the worktree creation is actually very recoverable.

yes 100% they can and they will. each one may have different permutations. if they change their minds they should make another one.

it should have no extraneous external file changes and it should allow us to create a ref with the permissions at time of execution

rebase is critical

extraction step

don't ship patch contents between the activities hahaha we do this now I have a ticket for it

way out of scope, let's stick with the nebari default imo and move the pr template shit to a separate task

I would again add the plan generation in here so we're getting terminal states of everything. design for the ui! encode the steps! state machine!

this is when we rebase which we should be doing otherwise nothing will actually get accepted, if we don't then we end up with a pr that has many many unrelated changes and clients have complained many times

there should be some error recovery, I was told the disks aren't persisted

this isn't the pr open thing yet right? right now, we generate the plan, then the patch, then let user review patch, then iterate on patch, then open pr immediately. that system should be the same imo so it's consistent

again imo this is way out of scope for now, but there's a cache step before and after, and checking if we should invalidate

30 seconds is hopeful hahaha a cargo install can take that just to resolve the versions to download

what happens in between a deploy here? do we reclone? that's the main error case we currently see

for xai I'm not sure I believe this

sbom_remediation_id

getSbomPatchGenerationStatus

triggerSbomPatchGeneration

Sbom is overloaded already!

essa

Retirar vírgula.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors report the results of a tDCS brain stimulation study (verum vs sham stimulation of left DLPFC; between-subjects) in 46 participants, using an intense stimulation protocol over 2 weeks, combined with an experience-sampling approach, plus follow-up measures after 6 months.

Strengths:

The authors are studying a relevant and interesting research question using an intriguing design, following participants quite intensely over time and even at a follow-up time point. The use of an experience-sampling approach is another strength of the work.

Weaknesses:

There are quite a few weaknesses, some related to the actual study and some more strongly related to the reporting about the study in the manuscript. The concerns are listed roughly in the order in which they appear in the manuscript.

We truly appreciate your dedicating time and efforts to review our manuscript. Yes, we do perceive that those weaknesses you raised all make sense. We agree with you on almost all the suggestions that you detailed below, particularly in clarifying statistics and sample size determination. Please see specific responses below.

Major Comments

(1) In the introduction, the authors present procrastination nearly as if it were the most relevant and problematic issue there is in psychology. Surely, procrastination is a relevant and study-worthy topic, but that is also true if it is presented in more modest (and appropriate) terms. The manuscript mentions that procrastination is a main cause of psychopathology and bodily disease. These claims could possibly be described as 'sensationalized'. Also, the studies to support these claims seem to report associations, not causal mechanisms, as is implied in the manuscript.

Thank you for this very practical suggestion. We agree that the current statements to underline the importance of procrastination are somewhat overreaching. Upon revision, we have overall toned down such claims by explicitly stating them as “associative evidence”, and rewritten a portion of terms in a more modest and balanced style. Please see specific revisions in the main text below:

Introduction Section (Page 5, Line 64-81)

“Procrastination is increasingly becoming a prevalent behavioral problem around the world, which reflects the irrational voluntary postponement of scheduled tasks albeit being worse off for such delays (Blake, 2019; Steel, 2007). In the epidemiological investigations, more than 15% of adults were identified as having chronic procrastination problems, and the situation for students was worse as 70-80% of undergraduates engaged in procrastination (American College Health Association, 2022; Ferrari et al., 2005). Moreover, the behavioral genetic evidence indicates a certain heritability of procrastination in human beings as well (Gustavson et al., 2017; Gustavson et al., 2014, 2015). In addition to its prevalence, the undesirable associations between procrastination behavior and health also warrant cautions. There is cumulative evidence to show the close associations between procrastination behavior and working performance, financial status, interpersonal relationships, and subjective well-being (Ferrari, 1994; Pychyl & Sirois, 2016; Steel et al., 2021). Further, as the prospective cohort studies indicated, many mental health problems emerge alongside procrastination, particularly in sleep problems, depression, and anxiety (Hairston & Shpitalni, 2016; Johansson et al., 2023). Even worse, chronic procrastination behavior has been observed to impair general health, as manifested by the intimate associations with close system disruption, gastrointestinal disturbance, as well as a high risk of hypertension and cardiovascular disease (Sirois, 2015; Sirois, 2016). ... ”

(2) It is laudable that the study was pre-registered; however, the cited OSF repository cannot be accessed and therefore, the OSF materials cannot be used to (a) check the preregistration or to (b) fill in the gaps and uncertainties about the exact analyses the authors conducted (this is important because the description of the analyses is insufficiently detailed and it is often unclear how they analyzed the data).

We are sorry to encounter a serious technical barrier making our preregistration invisible and inaccessible. The OSF has disabled my OSF account, as it claimed to detect “suspicious user’s activities” in my account (please see the screenshot below). This results in no access to all materials already deposited in this OSF account, including this preregistration. We have contacted the OSF team, but received no valid technical solution to recover this preregistered report. We reckon that this may be triggered by my affiliation change to the Third Military Medical University of the People’s Liberation Army (PLA).

To address this unexpected circumstance and to ensure transparency, we have explicitly reported this case in the main text, and added the “Reconstructed Preregistration Statement” into the Supplemental Materials (SM). Also, as it has been out of best practices in preregistration, in addition to transparently reporting this case, we have removed this statement regarding preregistration elsewhere throughout the whole revised manuscript. Furthermore, we fully understand the gaps of comprehending the statistics of this study, resulting from inadequate methodological details in the reporting. Therefore, we have clearly reported extensive details in the Methods section to clarify how to conduct those analyses, favoring the smooth evaluations of our conclusions. Please see what we have added in the lines below (Comments #4-9).

Methods Section (Page 5, Line 186-191)

“This study fully adhered to CONSORT reporting guidelines, and was originally preregistered in the OSF repository (10.17605/OSF.IO/Y3EDT). However, due to the technical constraint related to OSF account service (see SM), this OSF page is no longer accessible. For transparency and best practices of open science, based on the original protocol documentations, a preregistration statement has been reconstructed to clarify aprior hypotheses, sample size determinations, and analysis plans for this study (Table S1).”

(3) Related to the previous point: I find it impossible to check the analyses with respect to their appropriateness because too little detail and/or explanation is given. Therefore, I find it impossible to evaluate whether the conclusions are valid and warranted.

Again, we apologize for confusing you because of inadequate statistical and methodological details. As you may know, this manuscript has ever been reviewed by Nature Human Behaviour, which editorially constrained the paper length. Thus, a substantial number of details had to be omitted or removed. As you kindly suggested, we have diligently added extensive descriptions to clarify how we carried out statistical analyses in the present study. Please see specific instances underneath.

(4) Why is a medium effect size chosen for the a priori power analysis? Is it reasonable to assume a medium effect size? This should be discussed/motivated. Related: 18 participants for a medium effect size in a between-subjects design strikes me as implausibly low; even for a within-subjects design, it would appear low (but perhaps I am just not fully understanding the details of the power analysis).

Thank you for raising this crucial question. We have determined this a priori effect size based on the existing work we published previously (Xu et al., 2023, J Exp Psychol Gen;152(4):1122-1133). In our pilot study (Xu et al., 2023), we identified a significant interaction effect between the single-session tDCS stimulation (active vs sham) and time (pre-test vs post-test) (t = 2.38, p = .02, n = 27; 95% CI [0.14, 1.49]) for changing procrastination willingness in the laboratory settings, indicating a medium effect size. Therefore, this pilot study provides supportive evidence to determine this effect size a priori. To clarify, we have explicitly justified the selection of this effect size in the Methods section.

Methods Section (Page 5, Line 206-215)

“A full randomized block design was used to assign participants to both groups (active neuromodulation group, NM; sham-control group, SC) (see Fig. 2C). As the pilot study probing into the effect of single-session tDCS stimulation to change procrastination willingness indicated (t = 2.38, p = .02, 95% CI [0.14, 1.49]; Xu et al., 2023), statistical power was predetermined by G*Power at a relatively medium effect size (1-β err prob = 0.80, f = 0.25), yielding the total sample size at 18 to reach acceptable power (see SM Methods and Fig. S1)....”

We fully understand that this sample size to reach a medium effect size is seemingly low, and that the18 participants for each group are apparently limited in any case. Upon double-checking these power analyses, we confirmed that this sample size requirement is indeed correct. Please see the G*Power outputs in Author response image 1.

Author response image 1.

Despite the absence of algorithmic errors in the power analysis here, we are aware that this limited sample size may hamper statistical robustness. To tackle this weakness, we have clearly warranted such cautions in the Limitation section:

Limitations Section (Page 12, Line 637-640)

“... In addition to technical limitations, given the apparently limited size of the sample (total N = 46), it warrants caution in generalizing these findings elsewhere, and necessitates further validations in a large-scale cohort.”

(5) It remains somewhat ambiguous whether the sham group had the same number of stimulation sessions as the verum stimulation group; please clarify: Did both groups come in the same number of times into the lab? I.e., were all procedures identical except whether the stimulation was verum or sham?

Yes, we fully followed the CONSORT pipeline to carry out this double-blind trial, and thus confirmed that all the participants in both groups had the same number of stimulation sessions in our lab. That is to say, except for the stimulation type (verum vs sham), all the procedures, equipment and even the room were identical for all the participants. For clarification, we have clearly stated this in the main text:

Results Section (Page 9, Line 419-423)

“In both groups, almost all participants (93.2%, 41/44) reported perceiving acceptable pain stemming from current stimulation, and believed they were receiving treatment (91.30% (21/23) for active neuromodulation group (NM), 86.95% (20/23) for sham control group (SC), x² = 0.224, p = .636). All the participants were engaged in the identical experimental procedures excepting to stimulation’s type (active vs sham). ...”

(6) The TDM analysis and hyperbolic discounting approach were unclear to me; this needs to be described in more detail, otherwise it cannot be evaluated.

We apologize for the inadequate details, which hindered a precise understanding of the TDM and the hyperbolic discounting model. The Temporal Decision Model (TDM) was originally proposed by our team (Xu et al., 2023; Zhang et al., 2019, 2020, 2021), which theoretically conceptualizes procrastination as the failure of trade-off between task outcome value (i.e., motivation to take actions now for pursuing task reward) and task aversiveness (i.e., motivations to take away from playing actions now for avoiding negative experiences). Once task aversiveness overrides the pursuit of task outcome values, the procrastination emerges. One overarching hypothesis in this theoretical model is that the task aversiveness is hyperbolically discounted when approaching the deadline: it would be discounted sharply when far from the deadline but discounted slowly when nearing the deadline (Zhang et al., 2019). Considering the nonlinear dynamics inherent in this hyperbolic discounting, we therefore employed a log-spaced temporal sampling scheme (Myerson et al., 2001) to strengthen curve-fitting performance (please see the schematic diagram (https://uen.pressbooks.pub/behavioraleconomics/chapter/the-reality-of-homo-sapiens, where each point indicates a sampling time)):

Specifically, based on the log-spaced temporal sampling rule, five time points were first selected to fulfill the statistical prerequisites for hyperbolic model fitting, with increasing sampling density toward the deadline (e.g., for a task due at 20:00: sampling occurred at 10:00, 16:00, 18:00, 19:30, 20:00). At each time point, participants reported task aversiveness (A) on a 0–100 Visual Analog Scale (VAS). Then, task aversiveness discounting was calculated as 1- (A_t / A_earliest), where t_earliest was the earliest sampling point (e.g., 10:00), serving as the reference for immediate execution. Subsequently, using the GraphPad Prisma software (v9, 525), we estimated the AUC from these five data points based on the Myerson algorithm (Myerson et al., 2001), which was computed as the trapezoidal integration of task aversiveness discounting over time. By this modelling method, a higher AUC reflects stronger temporal discounting of task aversiveness, which means that participants experience a faster decline in subjective aversiveness as execution is delayed, yielding lower effective aversiveness and reduced avoidance behavior. That is to say, if a participant showcases a greater discounting of task aversiveness as reflected by a higher AUC, she/he experiences a more pronounced reduction in subjective aversiveness upon postponement, plausibly yielding less procrastination. As you kindly suggested, we have added these details to explicitly clarify how to use the hyperbolic discounting approach for determining sampling time points and for calculating AUC of task aversiveness discounting.

Methods Section (Page 6, Line 268-283)

“On the Task day, we developed a mobile app to implement experience sampling method (ESM) for tracking one’s real-time evaluation of task aversiveness and task outcome value (see Fig. 1). The task aversiveness describes how disagreeable one perceives when performing a given real-life task to be, whereas outcome value refers to the subjective benefits of the task outcome brought about by completing the task before the deadline (Zhang & Feng, 2020). As theoretically conceptualized by the temporal decision model (TDM) of procrastination, the perceived task aversiveness is hyperbolically discounted when approaching deadline, showing sharply discounting when faring away from deadline but slowly discounting once nearing deadline (Zhang & Feng, 2020; Zhang et al., 2021). Thus, considering this nonlinear dynamics inherent in this hyperbolic discounting, the five recording moments of ESM were selected per task a priori by using a log-spaced temporal sampling scheme (Myerson et al., 2001), with increasing sampling density toward the deadline, such as moments of 10:00 (earliest), 16:00, 18:00, 19:30, 20:00 (deadline). The five sampling points could meet statistical prerequisite in the hyperbolic model fitting, requiring ≥ 4 points (Green & Myerson, 2004). To do so, recording moments of tasks were individually tailored for each task per participant in this ESM procedure.”

Methods Section (Page 7, Line 318-334)

“... As articulated temporal decision theoretical model above, the task aversiveness evoked by executing a task was temporally dynamic in a hyperbolic discounting pattern, with sharply discounting in faring away from deadline but slowly discounting in nearing deadline (Zhang & Feng, 2020). To quantitatively characterize the task aversiveness with consideration for its dynamics, the model-free area under the curve (AUC) was calculated. Specifically, based on the log-spaced temporal sampling rule, task aversiveness was measured by 100-point visual analog scale at the five sampling moments. Then, the task aversiveness discounting (A) was calculated as 1- (A(t) / A(earliest)), where t(earliest) was the earliest sampling point, serving as the reference for immediate execution. Subsequently, using the GraphPad Prisma software (v9, 525), the AUC was computed as the trapezoidal integration between task aversiveness discounting and time across five data points, basing on the Myerson algorithm (Myerson et al., 2001). By doing so, a higher AUC reflects stronger temporal discounting of task aversiveness along with nearing deadline, which means that participants experience a faster decline in subjective aversiveness as execution is delayed, yielding lower effective aversiveness and reduced avoidance behavior. As for the task outcome value, it was theoretically posited as a relatively stable evaluation of the task (Zhang & Feng, 2020; Zhang et al., 2021).”

References

Myerson, J., Green, L., & Warusawitharana, M. (2001). Area under the curve as a measure of discounting. Journal of the experimental analysis of behavior, 76(2), 235–243. https://doi.org/10.1901/jeab.2001.76-235

Xu, T., Zhang, S., Zhou, F., & Feng, T. (2023). Stimulation of left dorsolateral prefrontal cortex enhances willingness for task completion by amplifying task outcome value. Journal of experimental psychology. General, 152(4), 1122–1133. https://doi.org/10.1037/xge0001312

Zhang, S., Verguts, T., Zhang, C., Feng, P., Chen, Q., & Feng, T. (2021). Outcome Value and Task Aversiveness Impact Task Procrastination through Separate Neural Pathways. Cerebral cortex (New York, N.Y. : 1991), 31(8), 3846–3855. https://doi.org/10.1093/cercor/bhab053

Zhang, S., Liu, P., & Feng, T. (2019). To do it now or later: The cognitive mechanisms and neural substrates underlying procrastination. Wiley interdisciplinary reviews. Cognitive science, 10(4), e1492. https://doi.org/10.1002/wcs.1492

Zhang, S., & Feng, T. (2020). Modeling procrastination: Asymmetric decisions to act between the present and the future. Journal of experimental psychology. General, 149(2), 311–322. https://doi.org/10.1037/xge0000643

(7) Coming back to the point about the statistical analyses not being described in enough detail: One important example of this is the inclusion of random slopes in their mixed-effects model which is unclear. This is highly relevant as omission of random slopes has been repeatedly shown that it can lead to extremely inflated Type 1 errors (e.g., inflating Type 1 errors by a factor of then, e.g., a significant p value of .05 might be obtained when the true p value is .5). Thus, if indeed random slopes have been omitted, then it is possible that significant effects are significant only due to inflated Type 1 error. Without more information about the models, this cannot be ruled out.

Thank you for sharing this very timely and crucial comment. After careful scrutiny, we identified this statistical flaw you pointed out - each participant was not yet modeled as random slopes but as random intercepts merely. As you kindly suggested, we have reanalyzed all the statistics by adding random slopes (i.e., (1 + day|SubjectID)). Results showed a statistically significant interaction effect for both procrastination willingness (β = -7.8, SE = 1.8, DF = 45.6, p < .001) and actual procrastination rates (β = -7.4, SE = 2.4, DF = 46.6, p = .004), indicating the effectiveness of multi-session neuromodulation in mitigating procrastination. In the post-hoc simple effect analyses, participants who engaged in active neuromodulation (NM) showed a significant increase in task-execution willingness (i.e., decreased procrastination willingness; NM-before: 35.65 ± 30.20, NM-after: 80.43 ± 19.92, t.ratio = 5.4, p < .0001, Tukey correction) and a decrease in actual procrastination rates (NM-before: 43.26 ± 39.09, NM-after: 0.00 ± 0.00, t.ratio = 5.1, p < .0001, Tukey correction), while no such effects were identified for participants in the sham control group (for willingness, SC-before: 37.57 ± 26.46, SC-after: 47.35 ± 30.49, t.ratio =0.3, p = .77, Tukey correction; for actual procrastination, SC-before: 46.47 ± 40.75, SC-after: 33.34 ± 37.82, t.ratio = 0.7, p = .48, Tukey correction). Taken together, we do appreciate your pointing out this definitely crucial statistical weakness, and have confirmed that our findings remain reliable after adjusting for Type 1 error by adding random slopes. Moreover, as you kindly suggested, we have incorporated these statistical details, particularly those concerning the GLMM, into the main text to facilitate your evaluation. Please see specific revisions below:

Methods Section (Page 8, Line 381-401)

“To clarify whether multiple-session HD-tDCS neuromodulation can reduce procrastination, the generalized mixed-effects linear model (GLMM) was constructed with full factorial design for subjective procrastination willingness (i.e., self-reported visual analog scores) and actual procrastination behavior (i.e., real-world task-completion rate before deadline). Here, sex, age and socioeconomic status (SES) were modeled as covariates of no interest. As the National Bureau of Statistics (China) issued (https://www.stats.gov.cn/sj/tjbz/gjtjbz/), on the basis of per capita annual household income, the SES was divided into seven hierarchical tiers from 1 (poor) to 7 (rich). To obviate subjective rating bias stemming from individual daily mood, we separately measured participants’ daily emotional fluctuation at 10:00 and 16:00 using a self-rating visual analog item (i.e., “How do feel for your mood today?”, 0 for “completely uncomfortable” and 100 for “definitely happy”). By doing so, the averaged score of those self-rating emotions at the two time points was modeled into the GLMM as covariate of no interests, yielding the final expression of “outcome ~ Group*Treatment_Day + Age + Gender + SES + Emotions + (1 + Treatment_Day | SubjectID)” in the statistical model”. This analysis was implemented using the “lme4” and “lmerTest” packages. Employing “emmeans” package, simple effects were also tested at baseline and post-last-intervention using Tukey-adjusted pairwise comparisons of estimated marginal means from the full GLMM, controlling for covariates and random-effects structure. To validate statistical robustness, instead of continuous outcomes for parametric tests, we also conducted a between-group comparison for the number of tasks that procrastination emerges by using the nonparametric x² test with φ correction or Fisher exact test....”

Results Section (Page 9, Line 428-449)

“To identify whether ms-tDCS targeting the left DLPFC can alleviate subjective procrastination willingness and actual procrastination behavior, a generalized linear mixed-effects model with Scatterthwaite algorithm was built, with task-execution willingness and actual procrastination rates (PR) as primary outcomes, respectively. For procrastination willingness, results showed a statistically significant interaction effect between multi-session neuromodulations and groups (β = -7.8, SE = 1.8, DF = 45.6, p < .001; Fig. 3A). In the post-hoc simple effect analysis, it demonstrated a significantly increased task-execution willingness (i.e., decreased procrastination willingness) after neuromodulation in the active neuromodulation group (NM-before: 35.65 ± 30.20, NM-after: 80.43 ± 19.92, t.ratio = 5.4, p < .0001, Tukey correction), but no such effects were identified in the sham control group (SC-before: 37.57 ± 26.46, SC-after: 47.35 ± 30.49, t.ratio =0.3, p = .77, Tukey correction) (Fig. 3B-C). A linear uptrend for task-execution willingness was further observed across multiple sessions in the active NM group, indicating gradually increasing neuromodulation effects (Fig. 3D; p < .01, Mann-Kendall test). For actual procrastination behavior, changes to actual procrastination rates across all the sessions have been detailed in the Fig. 3E. Similarly, a statistically significant interaction effect was identified here (β = -7.4, SE = 2.4, DF = 46.6, p = .004), and the simple effect analysis further revealed decreased actual procrastination rates after ms-tDCS in the active neuromodulation group (NM-before: 43.26 ± 39.09, NM-after: 0.00 ± 0.00, t.ratio = 5.1, p < .0001, Tukey correction), but no such prominent changes found in the sham control group (SC-before: 46.47 ± 40.75, SC-after: 33.34 ± 37.82, t.ratio = 0.7, p = .48, Tukey correction) (Fig. 3F-G). Also, a significant downtrend for procrastination rates across all the sessions was identified in the active NM group (Fig. 3H; p < .01, Mann-Kendall test).”

(8) Related to the previous point: The authors report, for example, on the first results page, line 420, an F-test as F(1, 269). This means the test has 269 residual degrees of freedom despite a sample size of about 50 participants. This likely suggests that relevant random slopes for this test were omitted, meaning that this statistical test likely suffers from inflated Type 1 error, and the reported p-value < .001 might be severely inflated. If that is the case, each observation was treated as independent instead of accounting for the nestedness of data within participants. The authors should check this carefully for this and all other statistical tests using mixed-effects models.

Thank you for underlining this very timely and helpful comment. As you correctly pointed out above, we did not include random slopes in the original GLMM, highly risking the inflation of the false-positive rate (i.e., Type-I error). By adding the random slopes, we reanalyzed all the statistics from the GLMM, and confirmed that all the findings are still reliable from those new GLMMs with random slopes. Again, thank you for this crucial statistical advice, and please see the above response for full details regarding what we have revised to address this comment you kindly raised.

(9) Many of the statistical procedures seem quite complex and hard to follow. If the results are indeed so robust as they are presented to be, would it make sense to use simpler analysis approaches (perhaps in addition to the complex ones) that are easier for the average reader to understand and comprehend?

We do thank you for this practical and helpful comment. In the original manuscript, we incorporated a joint model of longitudinal and survival data (JM-LSD), in conjunction with machine learning algorithms, to strengthen the robustness of our statistical findings. Nevertheless, we all agree with you on this point: there is no need to complicate the analyses by repeatedly probing the same research question to increase methodological robustness, at the expense of compromising readability and intelligibility for a broader audience. As you suggested, we have removed these complicated statistical methods, and merely maintained the primary ones - GLMM and X² cross-tab test, as well as a complementary one - Mann-Kendall linear trend test. Thus, we have almost rewritten the whole Results section. Please see the specific instances below:

Results Section (Page 9, Line 468-485)

“Ms-tDCS changes task aversiveness and task-outcome value

Both task aversiveness and task outcome value serve as key pathways determining whether one would procrastinate. To this end, we further utilized a generalized linear mixed-effects model to examine the effects of ms-tDCS on changes in task aversiveness and task outcome value. Task aversiveness changes across all the sessions are shown in the Fig. 4A and 4C. We demonstrated a statistically significant decrease in task aversiveness and an increase in task outcome value via ms-tDCS in the neuromodulation group (Task aversiveness: interaction effect, β = -0.12, SE = 0.04, DF = 46.7, p = .002; simple effect, NM-before _(AUC): 1.13 ± 0.53, NM-after _(AUC): 1.95 ± 0.85, t.ratio = 4.5, p < .001, Tukey correction; Outcome value: β = -6.8, SE = 1.74, DF = 46.2, p < .001; simple effect, NM-before: 35.86 ± 27.82, NM-after: 73.08 ± 23.33, t.ratio = 5.0, p < .001, Tukey correction; see Fig. 4B), but not in the sham control group (Task aversiveness: SC-before _(AUC): 1.07 ± 0.51, SC-after _(AUC): 1.28 ± 0.46, t.ratio = 1.3, p = .20, Tukey correction; Outcome value: SC-before: 34.00 ± 25.17, SC-after: 40.13 ± 28.94, t.ratio = 0.8, p = .41, Tukey correction; see Fig. 4D). In the neuromodulation (NM) group, task aversiveness steadily decreased with the cumulative number of stimulation sessions, while perceived task outcome value increased significantly (see Fig. 4E-F, p < .05, Mann-Kendall test). Thus, it provides causal evidence clarifying that neuromodulation to left DLPFC reduces task aversiveness and enhances task-outcome value meanwhile.”

Results Section (Page 10, Line 525-542)

“Long-term effects of ms-tDCS

We have also attempted to conduct a follow-up investigation to test the long-term retention of ms-tDCS in reducing actual procrastination. Almost all the participants had undergone follow-up except one in the neuromodulation group after last neuromodulation for 6 months (N_NM = 22, N_SC = 23). Thus, the GLMM was constructed, with the PR before first neuromodulation vs. PR after last neuromodulation for 6 months as covariates of interest. Results showed the statistically significant group*time interaction effects (β = 16.5, SE = 9.9, p = .049). Simple-effect model demonstrated a decrease in actual procrastination rates in the active neuromodulation group after last stimulation for 6 months compared to baseline (β = -22.05, SE = 10.0, p = .038, Tukey correction; NM-before: 40.68 ± 37.96, NM-after_6-months: 18.63 ± 29.80), and revealed null effects in the SC group (β = 1.26, SE = 9.78, p = .99, Tukey correction; SC-before: 46.47 ± 40.75, SC-after_6-months: 47.73 ± 39.18) (see Fig. 6).. Furthermore, using a nonparametric x² test to compare differences in the number of procrastinated tasks, we still found a statistically significant reduction in procrastination frequency in NM group after neuromodulation for 6 months compared to baseline (x² = 3.30, p = .035, NM-before: 68.19% (15/22), NM-after_6-months: 40.91% (9/22)), while no significant changes were observed in the SC group (x² = 0.11, p = .74, SC-before: 69.56% (16/23), SC-after_6-months: 73.91% (17/23)). Therefore, beyond to short-term effects, the benefits of ms-tDCS neuromodulation to reduce procrastination pose the long-term retention.”

(10) As was noted by an earlier reviewer, the paper reports nearly exclusively about the role of the left DLPFC, while there is also work that demonstrates the role of the right DLPFC in self-control. A more balanced presentation of the relevant scientific literature would be desirable.

We are grateful to you for noticing the unbalanced presentation of the literature on left DLPFC. As you kindly suggested, we have added literature to support the association between self-control and the right lateralization of the DLPFC. Please see below for what we have revised:

Introduction Section (Page 4, Line 137-143)

“...In addition to the left lateralization, there is solid evidence indicating significant associations between self-control and the right DLPFC indeed, particularly given that this region specifically functions in top-down regulation, future self-continuity representation and social decisions (Huang et al., 2025; Lin and Feng, 2024; Knoch & Fehr, 2007). Despite this case, Xu and colleagues demonstrated null effects of anodally stimulating the right DPFC to modulate either value evaluation or emotional regulation for changing procrastination willingness (Xu et al., 2023).”

(11) Active stimulation reduced procrastination, reduced task aversiveness, and increased the outcome value. If I am not mistaken, the authors claim based on these results that the brain stimulation effect operates via self-control, but - unless I missed it - the authors do not have any direct evidence (such as measures or specific task measures) that actually capture self-control. Thus, that self-control is involved seems speculation, but there is no empirical evidence for this; or am I mistaken about this? If that is indeed correct, I think it needs to be made explicit that it is an untested assumption (which might be very plausible, but it is still in the current study not empirically tested) that self-control plays any role in the reported results.

We truly appreciate your pointing out this weakness with regard to conceptualization. Yes, you are correct in understanding this causal chain: we conceptually speculate that the HD-tDCS stimulation over the left DLPFC operates self-control to change procrastination, rather than empirically validating this component in the chain: brain stimulation→increased self-control→increased task outcome value→decreased procrastination. In this causal chain, we did not collect data to directly measure self-control at either baseline or post-neuromodulation times. Therefore, we all agree with your suggestion to explicitly claim this case in the main text. Following this advice, we have redrawn a portion of the Conclusion by clearly pointing out the hypothesis-generating role of self-control in mitigating procrastination, and have further claimed this case in the Limitation section:

Abstract Section (Page 2, Line 55-57)

“... This establishes a precise, value-driven neurocognitive pathway to account the conceptualized roles of self-control on procrastination, and offers a validated, theory-driven strategy for interventions.”

Results Section (Page 10, Line 489-492 and 520-522)

“Given the dual neurocognitive pathways identified above—reduced task aversiveness and increased task-outcome value—we proposed that these changes, conceptually driven by enhanced self-control via ms-tDCS over left DLPFC, account for how neuromodulation reduces procrastination. ...”

“In summary, these findings demonstrated a mechanistic pathway underlying procrastination: the self-control that was conceptualized to be governed by left DLPFC mitigate procrastination by plausibly increasing task-outcome value.”

Discussion Section (Page 13, Line 642-645)

“Moreover, this study did not collect data for assessing participants’ self-control at either baseline or post-neuromodulation, thereby limiting our ability to determine whether the effects on procrastination were uniquely attributable to neuromodulation-induced changes in self-control. ...”

(12) Figures 3F and 3H show that procrastination rates in the active modulation group go to 0 in all participants by sessions 6 and 7. This seems surprising and, to be honest, rather unlikely that there is absolutely no individual variation in this group anymore. In any case, this is quite extraordinary and should be explicitly discussed, if this is indeed correct: What might be the reasons that this is such an extreme pattern? Just a random fluctuation? Are the results robust if these extreme cells are ignored? The authors remove other cells in their design due to unusual patterns, so perhaps the same should be done here, at least as a robustness check.

Thank you for raising this highly important and helpful comment. Indeed, we fully understand that this result is somewhat extraordinary, a fact that was equally striking to us when unblinding the data. After carefully scrutinizing the data and statistics, we are thrilled to confirm that this pattern is true. In support of this observation, we were gratified to receive numerous thank-you letters from participants who engaged in active neuromodulation. They expressed gratitude to us, and reported that they have substantially ameliorated procrastination behavior in real-life activities after completing the trial. While this does not constitute formal scientific evidence, we are also glad to see the benefits of this neuromodulation for those procrastinators.

Two reasons could account for this pattern herein. One interpretation is to attribute this pattern to “scalar inflation”. In the present study, the procrastination rate was calculated as 1 minus the task-completion rate (e.g., 80%, 60%, 40%) by the deadline. At sessions # 6 and #7, all the participants completed their real-life tasks before the deadline, yielding a 0% (1 minus 100% completion rate) procrastination rate, without any between-individual variation. Thus, rather than there being no individual variation in procrastination, this scalar – the procrastination rate - is too insensitive to capture subtle differences per se. For instance, although participants #1 and #2 both showed a 0% procrastination rate - meaning that both completed their tasks before the deadline - Participant #1 might have completed it 3 hours before the deadline, whereas Participant #2 might have completed it only 10 minutes before. In this case, the “scalar inflation” emerges to let us perceive that both participants have equivalent procrastination rates, although participant #2 may have a higher procrastination level than #1. As conceptually defined in the field, procrastination is contextualized as “not completing a task before the deadline”. Thus, if this task is completed before the deadline, regardless of whether it was finished close to or far in advance of the deadline, this case is defined as “no procrastination”. In the present study, the primary outcome is whether a participant procrastinated on a real-life task before the deadline in real-world settings, irrespective of when she/he completed this task. Thus, this scalar - procrastination rate - fits our conceptualization of procrastination.

Another reason is the potential accumulative effects from sequential multi-session tDCS stimulation. As shown in Mann-Kendall trend tests, the procrastination rates show a significant linear downtrend in the active neuromodulation group across sessions, even after removing sessions #6 and #7. This indicates that the improvements of going against procrastination may be sequentially accumulative along with the increase in sessions, implying a potential “dose-dependent effect”. Despite a speculative interpretation, this “dose-dependent effect” in neuromodulation has been well-documented in previous studies, showing the robustly linear association between the number of sessions and effectiveness (c.f., Cole et al., 2020; Hutton et al., 2023; Sabé et al., 2024; Schulze et al., 2018). Therefore, although this extreme pattern is somewhat extraordinary compared to previous observations, it makes sense.

Yes, this is a definitely great idea to carry out a robustness check by removing sessions #6, #7, or both. We do believe that this analysis could support statistical robustness to go against potential biases from extreme cells. By doing so, we found that all the group*treatment_day interaction effects remained significant when removing either session #6 or session #7 (or even both, all p-values < .05), indicating high statistical robustness. Please see Supplementary table S3 and S4

Taken together, in spite of their being extraordinary, we confirm that those findings are statistically robust to extreme outliers. As you kindly suggested, we have added those findings of the robustness check into the revised Supplemental Materials section.

References

Cole, E. J., Stimpson, K. H., Bentzley, B. S., Gulser, M., Cherian, K., Tischler, C., Nejad, R., Pankow, H., Choi, E., Aaron, H., Espil, F. M., Pannu, J., Xiao, X., Duvio, D., Solvason, H. B., Hawkins, J., Guerra, A., Jo, B., Raj, K. S., Phillips, A. L., … Williams, N. R. (2020). Stanford Accelerated Intelligent Neuromodulation Therapy for Treatment-Resistant Depression. The American journal of psychiatry, 177(8), 716–726. https://doi.org/10.1176/appi.ajp.2019.19070720

Hutton, T. M., Aaronson, S. T., Carpenter, L. L., Pages, K., Krantz, D., Lucas, L., Chen, B., & Sackeim, H. A. (2023). Dosing transcranial magnetic stimulation in major depressive disorder: Relations between number of treatment sessions and effectiveness in a large patient registry. Brain stimulation, 16(5), 1510–1521. https://doi.org/10.1016/j.brs.2023.10.001

Sabé, M., Hyde, J., Cramer, C., Eberhard, A., Crippa, A., Brunoni, A. R., Aleman, A., Kaiser, S., Baldwin, D. S., Garner, M., Sentissi, O., Fiedorowicz, J. G., Brandt, V., Cortese, S., & Solmi, M. (2024). Transcranial Magnetic Stimulation and Transcranial Direct Current Stimulation Across Mental Disorders: A Systematic Review and Dose-Response Meta-Analysis. JAMA network open, 7(5), e2412616. https://doi.org/10.1001/jamanetworkopen.2024.12616

Schulze, L., Feffer, K., Lozano, C., Giacobbe, P., Daskalakis, Z. J., Blumberger, D. M., & Downar, J. (2018). Number of pulses or number of sessions? An open-label study of trajectories of improvement for once-vs. twice-daily dorsomedial prefrontal rTMS in major depression. Brain stimulation, 11(2), 327–336. https://doi.org/10.1016/j.brs.2017.11.002

(13) The supplemental materials, unfortunately, do not give more information, which would be needed to understand the analyses the authors actually conducted. I had hoped I would find the missing information there, but it's not there.

Sorry to offer uninformative supplemental materials (SM) in the original submission. As you suggested, we have added a substantial number of details to clarify how we conducted data analyses in the main text, and also tightened the whole SM section to improve readability and comprehensibility. We do hope that this revised manuscript could offer clear and adequate information in understanding methods and statistics for broader readers.

In sum, the reported/cited/discussed literature gives the impression of being incomplete/selectively reported; the analyses are not reported sufficiently transparently/fully to evaluate whether they are appropriate and thus whether the results are trustworthy or not. At least some of the patterns in the results seem highly unlikely (0 procrastination in the verum group in the last 2 observation periods), and the sample size seems very small for a between-subjects design.

Thank you for this very helpful summary. As you kindly suggested above, we have overhauled this manuscript to address those points that you listed here, particularly where we added relevant literature to balance our claims, added a huge amount of details to sufficiently/transparently report statistics, and conducted a robustness check to confirm the statistical robustness of our findings to those plausible extreme patterns (sessions #6 and #7), as well as justified how we determined this sample size fulfilling medium statistical power in a priori. Please see above for full details regarding how we addressed those comments, point-by-point.

Reviewer #2 (Public Review):

Chen and colleagues conducted a cross-sectional longitudinal study, administering high-definition transcranial direct stimulation targeting the left DLPFC to examine the effect of HD-tDCS on real-world procrastination behavior. They find that seven sessions of active neuromodulation to the left DLPFC elicited greater modulation of procrastination measures (e.g., task-execution willingness, procrastination rates, task aversiveness, outcome value) relative to sham. They report that tDCS effects on task-execution willingness and procrastination are mediated by task outcome value and claim that this neuromodulatory intervention reduces procrastination rates quantified by their task. Although the study addresses an interesting question regarding the role of DLPFC on procrastination, concerns about the validity of the procrastination moderate enthusiasm for the study and limit the interpretability of the mechanism underlying the reported findings.

Strengths:

(1) This is a well-designed protocol with rigorous administration of high-definition transcranial direct current stimulation across multiple sessions. The approach is solid and aims to address an important question regarding the putative role of DLPFC in modulating chronic procrastination behavior.

(2) The quantification of task aversiveness through AUC metrics is a clever approach to account for the temporal dynamics of task aversiveness, which is notoriously difficult to quantify.

Thank you for taking your invaluable time to review our manuscript, warmly applauding the strength in research design and the conceptualization of scaling task aversiveness, as well as kindly sharing such helpful and insightful evaluations. As you correctly pointed out, we are aware of the absence of detailed, clear and understandable reporting of measures (e.g., real-world procrastination), statistics and methods, in the original manuscript. Following all your suggestions, we have thoroughly revised this manuscript to address those comments that you kindly made, point-by-point. Please see the full response underneath.

Weaknesses:

(1) The lack of specificity surrounding the "real-world measures" of procrastination is problematic and undermines the strength of the evidence surrounding the DLPFC effects on procrastination behavior. It would be helpful to detail what "real-world tasks" individuals reported, which would inform the efficacy of the intervention on procrastination performance across the diversity of tasks. It is also unclear when and how tasks were reported using the ESM procedure. Providing greater detail of these measures overall would enhance the paper's impact.

We genuinely appreciate your raising this very crucial comment. We are sorry for omitting a tremendous number of methodological details to comply with the editorial requirement on the manuscript’s length, which hampered the comprehension of how we measure “real-life tasks” and “real-world procrastination”.

As shown in the schematic diagram for experimental procedure (Fig. 1), the experimental protocol alternated between Neuromodulation Days (Days 2, 4, 6, 8, 10, 12, 14) and Task Days (Days 1, 3, 5, 7, 9, 11, 13, 15). On each Neuromodulation Day, participants received either active or sham HD-tDCS, and—critically—before stimulation—were instructed to specify a real-life task they were required to complete the following day, with a deadline between 18:00 and 24:00. This ensured ≥24 hours between neuromodulation and task execution, isolating offline after-effects. For instance, on Day #2 (Neuromodulation Day), before carrying out stimulation, participants were asked to report a real-life task that has a deadline within 18:00 - 24:00 for tomorrow’s “task day” (Day #3) (please see the schematic diagram in Author response image 2).

Author response image 2.

There are some real-life tasks that they reported in our experiment as examples: “Complete and submit a homework assignment”, “Complete a standardized English proficiency test”, “Complete an online course module required for applying a Class C driver’s license”, “Prepare slides for a seminar presentation”, “Practice guitar”, “Practice Chinese calligraphy”, and “Do the laundry”. Reported tasks spanned academic (e.g., submitting an assignment), occupational (e.g., preparing a presentation), administrative (e.g., applying for a license), self-improvement (e.g., practicing guitar for ≥30 min), domestic (e.g., laundry), and health-related domains (e.g., running ≥ 2,000m for exercise), indicating a plausible task diversity.

On each “task day”, participants engaged in an intensive Experience Sampling Method (iESM) protocol via a custom-built mobile app. Using this app, participants were required to report a subjective task-execution willingness score (i.e., a one-item 100-point visual analog scale, “How willing are you to do this task?”, 0 for “I will definitely procrastinate this task” and 100 for “I will take action to complete this task immediately”; procrastination willingness = 100 – the task-execution willingness score), the subjective task aversiveness (i.e., a one-item 100-point visual analog scale), the subjective task outcome value (i.e., a one-item 100-point visual analog scale), and the objective procrastination rate, respectively.

Rather than self-reported scores from those one-item visual analog scales, we asked participants to report real “task completion rate” for the objective quantification of the “real-world procrastination behavior”. Specifically, at the deadline, each participant was asked to report whether she/he had completed this task. If she/he reported not having yet completed the task (i.e. procrastination behavior emerged), she/he was further required to report the percentage of the task completed (1% - 99%), which was defined as the task completion rate. By doing so, we could calculate the real-world procrastination rate for the real-life task as the “1 – the task completion rate”. For instance, if a participant did not complete her/his real-life task before the deadline (i.e. she/he procrastinated this task) and reported completing 75% of this task at the deadline, her/his real-world procrastination rate was computed as the 25% (1 - 75%) (Please see the schematic diagram in Author response image 3).

Moreover, rather than merely a self-reported task completion rate, each participant was also asked to upload proof (e.g., screenshots of submitted assignments, photos of printed documents, system timestamps) to the ESM digital system for validation.

Author response image 3.

To determine the sampling time points for this mobile app in the ESM, we capitalized on both the conceptual temporal decision model and the statistical Myerson algorithm. Specifically, the Temporal Decision Model (TDM) was originally proposed by our team (Xu et al., 2023; Zhang et al., 2019, 2020, 2021), which theoretically conceptualizes procrastination as the failure of the trade-off between task outcome value (i.e., motivation to take actions now for pursuing task reward) and task aversiveness (i.e., motivations for avoiding taking action now for avoiding negative experiences). Once task aversiveness overrides the pursuits of task outcome values, procrastination emerges. One overarching hypothesis in this theoretical model is that the task aversiveness is hyperbolically discounted when approaching the deadline: it would be discounted sharply when far from the deadline but discounted slowly when nearing the deadline (Zhang et al., 2019). To maximize statistical power to fit dynamic motivational curves, we employed a log-spaced temporal sampling scheme (Myerson et al., 2001) (please see the schematic diagram in https://uen.pressbooks.pub/behavioraleconomics/chapter/the-reality-of-homo-sapiens, where each point indicates a sampling time):

By this fitting algorithm (Myerson et al., 2001), five time points were selected to fulfill the statistical prerequisites for hyperbolic model fitting, with increasing sampling density toward the deadline (e.g., for a task due at 20:00: sampled at 10:00, 16:00, 18:00, 19:30, 20:00). Once the task-specific five sampling time points were determined per participant, this mobile app sent a digital message to ask her/him to immediately report the task aversiveness and the task outcome value then. As the primary outcomes, the procrastination rate (i.e., 1 – the task completion rate) and the procrastination willingness were sampled at the deadline point.

Furthermore, yes, we fully concur with you on this great idea, that is, transparency about task diversity strengthens the generalizability of our findings. In response, we have tabulated these real-life tasks that were reported in this experiment in the independent Appendix 1, with automatic translations from Chinese to English via Qwen GPT. Please see below for what we have added to the main text:

Methods Section (Page 6-7, Line 238-308)

“Nested cross-sectional longitudinal design

This study used a nested cross-sectional longitudinal design to investigate whether the multiple-session anodal HD-tDCS targeting the left DLPFC could reduce actual procrastination behavior and to probe how this effect manifests. To assess procrastination in daily life, we implemented a 15-day protocol alternating between Neuromodulation Days (Days 2, 4, 6, 8, 10, 12, 14) and Task Days (Days 1, 3, 5, 7, 9, 11, 13, 15). On the Neuromodulation days, the 20-min anodal HD-tDCS neuromodulation targeting the left DLPFC was performed for HD-tDCS active group at intervals of 2 days, while the sham-control group received sham HD-tDCS training. This HD-tDCS training was repeated for a total of seven sessions, and lasted 15 days (see Fig. 1a). Crucially, to capture procrastination in ecologically valid contexts, prior to receiving either active or sham HD-tDCS (administered between 09:00–18:00), participants were instructed to specify a real-life task they were personally obligated to complete the following day, with a self-defined deadline strictly constrained to 18:00–24:00 to ensure ≥24 hours between stimulation offset and task deadline, thereby isolating offline after-effects. This task should meet the following three criteria: (a) it should be already assigned in the real-world settings; (b) deadline should be constrained to 18:00-24:00 (see above); (c) it should be more likely to induce procrastinate. By doing so, more than 300 real-life tasks were collected, spanning academic (e.g., “submit a statistics homework assignment”), occupational (e.g., “draft and email a project proposal”), administrative (e.g., “complete online application for Class C driver’s license”), self-improvement (e.g., “practice guitar for ≥30 minutes”), domestic (e.g., “do laundry ”), and health-related (e.g., “running 2,000m for exercise”). Full task list has been tabulated in the Appendix 1. As primary outcomes, all the participants were required to reported task-execution willingness (TEW) (Zhang & Feng, 2020; Zhang, Liu, et al., 2019), for a real-life task 24 hours post-neuromodulation. Thus, procrastination willingness was quantified as 100-TEW score (see underneath for details). Furthermore, we asked participants to report the actual task completion rate (CR) of the task at the deadline (e.g. participant A finished 90% homework at deadline and reported this situation to us at deadline). In this vein, the actual procrastination rate (PR) was quantified as 1-CR.

On the Task day, we developed a mobile app to implement experience sampling method (ESM) for tracking one’s real-time evaluation of task aversiveness and task outcome value (see Fig. 1). The task aversiveness describes how disagreeable one perceives performing a given real-life task to be, whereas outcome value refers to the subjective benefits of the task outcome brought about by completing the task before the deadline (Zhang & Feng, 2020). As theoretically conceptualized by the temporal decision model (TDM) of procrastination, the perceived task aversiveness is hyperbolically discounted when approaching deadline, showing sharply discounting when faring away from deadline but slowly discounting once nearing deadline (Zhang & Feng, 2020; Zhang et al., 2021). Thus, considering this nonlinear dynamics inherent in this hyperbolic discounting, the five recording moments of ESM were selected per task a prior by using a log-spaced temporal sampling scheme (Myerson et al., 2001), with increasing sampling density toward the deadline, such as moments of 10:00 (earliest), 16:00, 18:00, 19:30, 20:00 (deadline). The five sampling points could meet statistical prerequisite in the hyperbolic model fitting (requiring ≥ 4 points; Green & Myerson, 2004). To do so, recording moments of tasks were individually tailored for each task per participant in this ESM procedure. To obviate the confounds of daily emotions in task aversiveness evaluation, we used the averaged scores of PANAS at 10:00 (noon) and 16:00 (afternoon) as anchoring points to quantify one’s daily emotions by using this ESM app. Before each session of HD-tDCS training, each participant was required to report a real-life task whose deadline is tomorrow. To obtain the long-term effect of HD-tDCS (i.e., the interval between HD-tDCS and task completion is at least 24 hours), the task deadline that participants reported was required to be between 18:00 - 24:00. Once a sampling time reached, this app would send a digital message to require participants to fill online form for data collection.

Quantification of covariates of interests

Outcome variables of this study were twofold: one is task-execution willingness and another is procrastination rate (PR). Task-execution willingness is used to evaluate one’s subjective inclination to avoid procrastination (Zhang & Feng, 2020). In this vein, we used a 100-point scale to require participants to report their task-execution willingness (0 for “I will definitely procrastinate this task” and 100 for “I will take action to complete this task immediately”). This metric was recorded 24 hours after neuromodulation to examine its long-term effects. PR is used to quantify the extent to which one task has been procrastinated, and was calculated as 1 - CR (task completion rate). Critically, at the precise deadline, the app prompted participants to (a) indicate task completion status (yes/no), and if incomplete, (b) report the percentage completed (1–99%), defined as the Task CR, while simultaneously uploading objective evidence (e.g., screenshots of submitted files, photos of physical outputs, system-generated logs, or app-exported records). If the task was actually completed before the deadline, the CR would be 100% and the PR would be calculated as 0% (1-CR). PR was recorded at the actual task deadline for each participant. We were also interested in re-investigating their actual procrastination by using PR 6 months after the last neuromodulation to test the long-term retention of this neuromodulation effect.”

References

Myerson, J., Green, L., & Warusawitharana, M. (2001). Area under the curve as a measure of discounting. Journal of the experimental analysis of behavior, 76(2), 235–243. https://doi.org/10.1901/jeab.2001.76-235

Xu, T., Zhang, S., Zhou, F., & Feng, T. (2023). Stimulation of left dorsolateral prefrontal cortex enhances willingness for task completion by amplifying task outcome value. Journal of experimental psychology. General, 152(4), 1122–1133. https://doi.org/10.1037/xge0001312

Zhang, S., Verguts, T., Zhang, C., Feng, P., Chen, Q., & Feng, T. (2021). Outcome Value and Task Aversiveness Impact Task Procrastination through Separate Neural Pathways. Cerebral cortex (New York, N.Y. : 1991), 31(8), 3846–3855. https://doi.org/10.1093/cercor/bhab053

Zhang, S., Liu, P., & Feng, T. (2019). To do it now or later: The cognitive mechanisms and neural substrates underlying procrastination. Wiley interdisciplinary reviews. Cognitive science, 10(4), e1492. https://doi.org/10.1002/wcs.1492

Zhang, S., & Feng, T. (2020). Modeling procrastination: Asymmetric decisions to act between the present and the future. Journal of experimental psychology. General, 149(2), 311–322. https://doi.org/10.1037/xge0000643

(2) Additionally, it is unclear whether the reported effects could be due to differential reporting of tasks (e.g., it could be that participants learned across sessions to report more achievable or less aversive task goals, rather than stimulation of DLPFC reducing procrastination per se). It would be helpful to demonstrate whether these self-reported tasks are consistent across sessions and similar in difficulty within each participant, which would strengthen the claims regarding the intervention.

Thank you for raising this very crucial comment. We indeed agree with you on this point that the reported effects may vary with task difficulties and task-execution proficiency, which potentially confound the effects of stimulation on mitigating procrastination. As you correctly comment, given no data collection on difficulties or other relevant characteristics of tasks, we cannot completely rule out this confounder in interpreting our findings on the one hand. As a result, we have explicitly claimed this limitation in the Discussion section.

On the other hand, despite no quantitative evidence, this risk of confounding main effects with disparities in task characteristics was controlled experimentally. As we reported above, all the reported tasks were mandated to meet three criteria: (a) they were already assigned in the real-world settings; (b) the deadline was constrained to 18:00-24:00; (3) they were likely to lead to procrastinate. To do so, each participant was clearly instructed to report a real-life task that was more likely to be procrastinated in real-world settings, and was not allowed to report easy, achievable and cost-less tasks. Supporting this case, those reported tasks were found spanning academic (e.g., submitting an assignment), occupational (e.g., preparing a presentation), administrative (e.g., applying for a license), self-improvement (e.g., practicing guitar for ≥30 min), domestic (e.g., laundry), and health-related domains (e.g., running ≥ 2,000m for exercise), indicating a plausible task diversity and difficulty. This was resonated by observing the high within-subject task homogeneity. For instance, for Participant #5, she/he reported the tasks that were almost all around academic activities across all the sessions. Therefore, as the task list reported (please see Appendix 1), these self-reported tasks were plausibly consistent across sessions and similar in difficulty within each participant.

In addition, as we tested, almost all the participants reported they were receiving treatment, with 91.30% (21/23) for the active neuromodulation group (NM) and with 86.95% (20/23) for the sham control group (SC) (x² = 0.224, p = .636), indicating the effectiveness of the double-blinding methods. If participants learned across sessions to report more achievable or less aversive task goals, their procrastination willingness and procrastination rates for their reported tasks would all increasingly decrease, irrespective of whether they were in the active neuromodulation-effect group or the sham group. However, no such effects - procrastination willingness and procrastination rates for their reported tasks increasingly decreasing across sessions - existed in the sham control group (Mann-Kendall test, for procrastination willingness, tau = 0.60, p = .13; for procrastination rate, tau = 0.61, p = .13), indicating no statistically significant learning effect or strategic effect on task performance. Again, thank you for this very crucial comment, and we do hope these clarifications could address it.

Limitations Section (Page 12, Line 637-640)

“In addition, despite instructing to report valid real-life tasks with high probabilities to procrastinate, we had not yet measured the task difficulty and consistency across sessions for each participant. Consequently, interpreting the effects of neuromodulation to mitigate procrastination as “unique contributions” should warrant cautions. ...”

(3) It would be helpful to show evidence that the procrastination measures are valid and consistent, and detail how each of these measures was quantified and differed across sessions and by intervention. For instance, while the AUC metric is an innovative way to quantify the temporal dynamics of task-aversiveness, it was unclear how the timepoints were collected relative to the task deadline. It would be helpful to include greater detail on how these self-reported tasks and deadlines were determined and collected, which would clarify how these procrastination measures were quantified and varied across time.

We do appreciate your highlighting the importance of clarifying how to measure procrastination, substantially helping readers to interpret these findings. As reported above, the primary outcomes of this experiment included subjective procrastination willingness and objective actual procrastination rate. For the subjective procrastination willingness, using the purpose-built mobile app, participants were required to report subjective task-execution willingness score (i.e., one-item 100-point visual analog scale, “How willing are you to do this task?”, 0 for “I will definitely procrastinate this task” and 100 for “I will take action to complete this task immediately”). Thus, the procrastination willingness was computed as “100 – the task-execution willingness score”. For the objective procrastination rate, rather than self-reported scores from those one-item visual analog scales, we asked participants to report the real “task completion rate from 1% to 99%” for the objective quantification of the “real-world procrastination behavior”. Full details can be found in Response #1.

For determining sampling time points for the quantification of AUC, we capitalized on both the conceptual Temporal Decision Model and the statistical Myerson algorithm. Specifically, the Temporal Decision Model (TDM) was originally proposed by our team (Xu et al., 2023; Zhang et al., 2019, 2020, 2021), which theoretically conceptualizes procrastination as the failure of the trade-off between task outcome value (i.e., motivation to take actions now for pursuing task reward) and task aversiveness (i.e., motivations for avoiding taking action now for avoiding negative experiences). Once task aversiveness overrides the pursuits of task outcome values, the procrastination emerges. One overarching hypothesis in this theoretical model is that the task aversiveness is hyperbolically discounted when approaching the deadline: it would be discounted sharply when being far from the deadline but discounted slowly when nearing the deadline (Zhang et al., 2019). To maximize statistical power to fit dynamic motivational curves, we employed a log-spaced temporal sampling scheme (Myerson et al., 2001). By this fitting algorithm (Myerson et al., 2001), five time points were selected to fulfill the statistical prerequisites for hyperbolic model fitting, with increasing sampling density toward the deadline (e.g., for a task due at 20:00: sampled at 10:00, 16:00, 18:00, 19:30, 20:00).

Once the task-specific five sampling time points were determined per participant, this mobile app sent a digital message to ask her/him to immediately report the task aversiveness and the task outcome value then. After capturing the task aversiveness from those five time points, the task aversiveness discounting was calculated as 1- (A(t) / A(earliest)), where t(earliest) was the earliest sampling point (e.g., 10:00), serving as the reference for immediate execution. Subsequently, using the GraphPad Prisma software (v9, 525), we estimated the AUC from those five data points based on the Myerson algorithm (Myerson et al., 2001), which was computed via the trapezoidal integration between task aversiveness discounting and time. By this modelling method, a higher AUC reflects stronger temporal discounting of task aversiveness, which means that participants experience a faster decline in subjective aversiveness as execution is delayed, yielding lower effective aversiveness and reduced avoidance behavior. That is to say, if a participant showcases a greater discounting of task aversiveness as reflected by a higher AUC, she/he experiences a more pronounced reduction in subjective aversiveness upon postponement, plausibly yielding less procrastination.

Taken together, following your suggestion, we have added a substantial number of details to clarify how to measure procrastination, when to sample the data and how to estimate the AUC into the revised manuscript. Please see them in Response #1.

(4) There are strong claims about the multi-session neuromodulation alleviating chronic procrastination, which should be moderated, given the concerns regarding how procrastination was quantified. It would also be helpful to clarify whether DLPFC stimulation modulates subjective measures of procrastination, or alternatively, whether these effects could be driven by improved working memory or attention to the reported tasks. In general, more work is needed to clarify whether the targeted mechanisms are specific to procrastination and/or to rule out alternative explanations.

Yes, we fully agree with you on this consideration: we should tone down the conclusions currently claimed in the main text, given the inherent shortcomings mentioned above. As you helpfully suggested, we have moderated our overall claims regarding the effects of multi-session neuromodulation in alleviating chronic procrastination. Please see specific instances below:

Abstract Section (Page 2, Line 55-57)

“... This establishes a precise, value-driven neurocognitive pathway to account the conceptualized roles of self-control on procrastination, and potentially offers a validated, theory-driven strategy for interventions.”

Conclusion Section (Page 13, Line 657-664)

“In conclusion, this study potentially provides an effective way to reduce both procrastination willingness and actual procrastination behavior by using neuromodulation on the left DLPFC. Furthermore, such effects have been observed for 2-day-interval long-term after-effects, and were also found for 6-month long-term retention in part. More importantly, this study identified that the ms-tDCS neuromodulation could decrease task aversiveness and increase task outcome value while, and further demonstrated that the increased task outcome value could predict decreased procrastination, a relationship conceptually driven by enhancing self-control. In this vein, the current study enriches our understanding of neurocognitive mechanism of procrastination by showing the prominent role of increased task outcome value in reducing procrastination. Also, it may provide an effective method for intervening in human procrastination.”

Moreover, yes, as we clarified above, in addition to the objective measure of procrastination behavior, we also leveraged a one-item visual analog scale (i.e. one-item 100-point visual analog scale, “How willing are you to do this task?”, 0 for “I will definitely procrastinate this task” and 100 for “I will take action to complete this task immediately”) to measure subjective procrastination willingness. Results demonstrated that the subjective procrastination willingness significantly decreased across neuromodulation sessions in the active group, but not in the sham control group, consistent with the observed reduction in the objective procrastination measure. In addition, we all perceive it as helpful and crucial to note that we cannot draw the conclusion that the effects of neuromodulation on mitigating procrastination are contributed by increasing task outcome value uniquely. Given no measures or evidence of other factors, such as working memory and attention, we cannot rule out other neurocognitive pathways. To address this point, we have removed or rephrased such statements throughout the whole revised manuscript, and explicitly constrained to interpret this neurocognitive mechanism (i.e., increased task outcome value) within the theory-driven framework of the temporal decision model.

Reviewer #3 (Public review):

This manuscript explores whether high-definition transcranial direct current stimulation (HD-tDCS) of the left DLPFC can reduce real-world procrastination, as predicted by the Temporal Decision Model (TDM). The research question is interesting, and the topic - neuromodulation of self-regulatory behavior - is timely.

Many thanks for kindly dedicating time to review our manuscript, and for the helpful comments detailed below. Thank you for appreciating the novelty of this study.

However, the study also suffers from a limited sample size, and sometimes it was difficult to follow the statistics.

Thank you for pointing out these crucial concerns. As you correctly raised, the sample size is somewhat small in any case, but we confirm that this sample size is adequate to obtain medium statistical power.

For estimating the sample size, we determined the a priori effect size based on the existing work we published (Xu et al., 2023, J Exp Psychol Gen;152(4):1122-1133). In this pilot study, we identified a significant interaction effect between single-session tDCS stimulation (active vs sham) and time (pre-test vs post-test) (t = 2.38, p = .02, n = 27; 95% CI [0.14, 1.49]) for changing procrastination willingness in laboratory settings, indicating a medium effect size. Therefore, this pilot study provides supportive evidence to determine this effect size a priori.

Using the GPower software with an estimation of a medium effect size, we determined that a total sample size of N_total = 34 could reach adequate statistical power. Please see outputs of the GPower in Author response image 1.

As for the statistics, we genuinely acknowledge that the vague methodological descriptions and complex algorithms indeed complicated the understanding of the methods and statistics. To address this, echoing the comment raised by Reviewer #1, we have removed the complicated statistics and methods, and further clarified how we used the generalized linear mixed-effect model (GLMM) for statistical analysis. Please see the specific revisions below:

Methods Section (Page 8, Line 378-403)

“Statistics

All the statistics were implemented by R (https://www.rstudio.com/) and R-dependent packages.

To clarify whether multiple-session HD-tDCS neuromodulation can reduce procrastination, the generalized mixed-effects linear model (GLMM) was constructed with full factorial design for subjective procrastination willingness (i.e., self-reported visual analog scores) and actual procrastination behavior (i.e., real-world task-completion rate before deadline). Here, sex, age and socioeconomic status (SES) were modeled as covariates of no interest. As the National Bureau of Statistics (China) issued (https://www.stats.gov.cn/sj/tjbz/gjtjbz/), on the basis of per capita annual household income, the SES was divided into seven hierarchical tiers from 1 (poor) to 7 (rich). To obviate subjective rating bias stemming from individual daily mood, we separately measured participants’ daily emotional fluctuation at 10:00 and 16:00 using a self-rating visual analog item (i.e., “How do feel for your mood today?”, 0 for “completely uncomfortable” and 100 for “definitely happy”). By doing so, the averaged score of those self-rating emotions at the two time points was modeled into the GLMM as covariate of no interests, yielding the final expression of “outcome ~ Group*Treatment_Day + Age + Gender + SES + Emotions + (1 + Treatment_Day | SubjectID)” in the statistical model”. This analysis was implemented using the “lme4” and “lmerTest” packages. Employing “emmeans” package, simple effects were also tested at baseline and post-last-intervention using Tukey-adjusted pairwise comparisons of estimated marginal means from the full GLMM, controlling for covariates and random-effects structure. To validate statistical robustness, instead of continuous outcomes for parametric tests, we also conducted a between-group comparison for the number of tasks that procrastination emerges by using the nonparametric x² test with φ correction or Fisher exact test. Regarding the 6-month follow-up investigation, this GLMM was also built to examine the long-term retention of neuromodulation on reducing actual procrastination.”

The preregistration and ecological design (ESM) are commendable, but I was not able the find the preregistration, as reported in the paper.

We are sorry to encounter a serious technical barrier that has rendered our preregistration invisible and inaccessible. The OSF has disabled my OSF account, as it claimed to detect “suspicious user’s activities” in my account. This has prevented access to all materials deposited in this OSF account, including this preregistration. We have contacted the OSF team, but received no valid technical solution to recover this preregistered report (please see the screenshot below). We reckon that this may be due to my affiliation change to the Third Military Medical University of People’s Liberation Army (PLA).

To address this unexpected circumstance and to ensure transparency, we have explicitly reported this case in the main text, and added the “Reconstructed Preregistration Statement” to the Supplemental Materials (SM). Also, as it has been out of best practices in preregistration, in addition to transparently reporting this case, we have removed this statement regarding preregistration elsewhere throughout the revised manuscript.

Overall, the paper requires substantial clarification and tightening.

We are grateful for your evaluation, and we fully agree with you. In response, we have added a tremendous number of details to clarify how to measure procrastination, how to conduct the statistical analyses, and how to collect real-life tasks, as well as other experimental materials. Please see the revisions in the Methods section of the revised manuscript. Again, thank you for those helpful suggestions.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) In the Supplemental Materials, page 4, lines 163 to 167 seem to be from a different manuscript (as the section talks about neural markers, significant clusters, and brain networks).

We are sorry for erroneously embedding this irrelevant section here. We have removed it, and have double-checked the document to avoid such mistakes.

(2) I'm no expert here, but some of the trace and density plots in the SOM look problematic (e.g., Figure S5 top panel). But it's not made clear to which model/analysis these plots belong, so they are not very helpful without that information.

Thank you for bringing these potentially problematic plots to our attention. Following your great suggestion, these results have been removed from the SM to amplify readability and comprehensibility.

(3) Table S1 reports side effects "from the neurostimulation" (this is also the language used in the main manuscript), but having the flu is rather unlikely to be a side effect from the stimulation, isn't it? Thus, this language is highly confusing, and when reading the main text, it's not clear that these are just life events that are most likely unrelated to the stimulation, but have the potential to affect the measured variables (i.e., ultimately, they seem a source of noise).

We apologize for this confusing wording. Here, the “side effects” are defined as confounding effects deriving from unexpected life events that uncontrollably disrupt task execution and task performance, such as “having the flu”, or “an unexpected mandatory CCP (Communist Party of China) meeting assignment”. To obviate misunderstanding, we have rephrased “side effects” as “unexpected life events disrupting task execution” in both the main text and the SM section both.

(4) The use of the English language could be improved.

Thank you for your very practical suggestion. As you kindly suggested, we have invited a proofreading editor to edit and polish the English of the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) It would be helpful to include greater detail about the ESM procedure and details of the self-reported tasks. This would help rule out potential confounds of difficulty or learning (e.g., participants may have learned to identify more achievable and less difficult tasks across the sessions, which would mean they are learning to perform the task better rather than to procrastinate less). Further elaboration on the quantification of procrastination measures would help clarify the mechanism underlying this behavior, which is important for clarifying how these effects arise and what aspect of procrastination behavior is being targeted by the tDCS intervention (and rule of alternative explanations).

We wholeheartedly appreciate your sharing this very crucial recommendation. As we mentioned above, we fully followed your helpful suggestions, particularly by adding massive details to fully report how to collect real-life tasks (with consistent and plausible difficulty across sessions), how to determine sampling time points, and how to quantify metrics (e.g., subjective procrastination willingness score, objective procrastination rate, AUC of task aversiveness, and task outcome value) to the revised manuscript. We do believe that these revisions and clarifications are imperative and necessary. By including these details, we do believe that the readability and clarity have been substantially improved in the current form. Please see the specific revisions and clarifications above.

(2) It would be helpful to proofread for grammatical and spelling typos (e.g., DLPFC is spelled incorrectly in line 140, Satterwaite is spelled incorrectly in Line 415).

Thank you for your kind suggestion. Both spelling typos have been corrected, and we have double-checked the revised manuscript to ensure no such typos remain. As you kindly suggested, we have invited a proofreading editor to edit and polish the English of the revised manuscript.

(3) Please clarify in Figure 4 that a higher AUC is associated with lower task aversiveness (which is stated in the methods but not clearly in the figure).

Many thanks to you for your helpful suggestion. As you kindly suggested, we have clarified this case in the figure legend.

Reviewer #3 (Recommendations for the authors):

I want to see the preregistration.

Thank you for your helpful recommendation. As we replied above, a serious technical issue on OSF occurred, making our preregistration invisible and inaccessible. OSF has disabled my account, claiming to detect “suspicious user’s activities” in my account. As a result, there is no access to all materials that were already deposited in this OSF account, including this preregistration. We have reconstructed this preregistration based on archived documents, and reported it in the SM. As we reported above, although this partially addresses the problem, it no longer fulfills the best practices of preregistration. Consequently, in addition to transparently reporting this case, we have removed all the preregistration statements throughout the revised manuscript.

acronym for " Graph Representation Of property ValuEs".

so property values form a graph

in TrailMarks InNotation

Rheme Names

composed of Verb Trail sequence separated by '-'

Subject Term anything from the last - to ~ o end of TrailMark Name Terms Sequence Target Subject/Object Trails and Qualifier ~

A "grove construction process" uses a notation processor to recognize instances of "classes" and their "properties" as defined in a property set, and represents the recognized instances as "nodes" in a graph structure known as a "grove".

This does not seem like a full citation.

What about the Paris context? Can you find any connection to her occupying the worlds of Paris and New York?

citation needed -- add a parenthetical to indicate which Keyser title and a page reference, even when you are paraphrasing.

can you give an example of how they presented her? Without firsthand evidence, your claim relies solely on what's called a warrant of authority, i.e. because Keyser said, it is so.

no need for comma after author's name in MLA style

Where and how? provide evidence from the poem to support your claims.

Can you provide evidence to show where and how poverty, shame, and survival manifest in the poem?

put poem titles in quotation marks, book titles in Italics

missing citation for Howard, too.

I'm curious to read an example of this conversation.

citation?

Excellent quotation & analysis. Where is the citation for this quotation?

fun, interactive prompt

Excellent, clear, concise exposition and thesis.

I wonder how long it took for their houses to be fixed up again. What did the owners do and where did they stay?

He got bit by a dog got rabies and this caused him to b mean to Janie

la limitation à ces deux critères est un choix ?