This is a very good example of description. This gives me a very off putting emotion and really convinces me that the mcrib is bad.

I can relate to this because I work at a barbecue catering company, and a Mcrib seems really gross because I have experienced what authentic barbecue tastes like.

This is very interesting to me, and grabs my attention. Even though I have never ate a mcrib before, it makes me feel as if Im missing out on a common thing people ate.

DOLLY RUN GIRL GET AWAY FROM THAT MAN!!

This passage relates to modern day america or basically anywhere today. This shows how women have to uphold this stranded in marriage to keep up with their beauty, take care of the house and kids.. and the prince seem he expects her downhill which I don't like..

I had to read the whole passage in order to come back to this, it seem like the Prince likes to live in " oblivion" . it comes off very selfish and arrogant. you can see he is putting his wife through emotionally distress after cheating on her, and instead of feeling bad it like he don't care! I think he is struggling with connecting with his emotions?

Okay so this make more sense to me now, Anna and her husband which I'm assuming just by reading this passage actions are very similar its like they both know they're unhappy in their marriage but yet seem to have this back and forth within themselves.

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he's making it seem like this is his wife's fault when he's the one that had an affair.

he was offended that the only way his wife might speak to him was to sell the wood?

this doesn't make sense to me

he was clueless on how to fix what he had done

how did he just not remember what happened? that doesn't even make sense. it's like he feels guilty but doesn't at the same time.

this affair was effecting more than just the couple. i feel like that usually happens when an affair happens

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each family may seem like they'r'e happy, but in reality each family is unhappy in their own way?

Although I think me and many people would choose this “home-cooked, locally-grown, heart-felt digital meals over factory-farmed fast food” every time, I don’t think many people really even know the difference. Some people may not care enough to learn where the content they consume comes from, while others may not have the media literacy skills needed to recognize the difference. This makes me think that having access to good information is only part of the problem; people also need to know how to identify it. Without these skills, it can be easy to consume low-quality or misleading content without even realizing it.

This paragraph makes me wonder what would happen if this trend continues. Will people eventually stop sharing their work online altogether? If there is no financial incentive from clicks and views, will fewer people be willing to share their research online, perhaps causing us to return to physical media such as books? This idea also reminds me of something called the “dead internet theory,” which suggests that people will eventually become so fed up with the amount of low-quality, AI-generated content online that they stop using the internet altogether. The theory suggests that, eventually, the internet could become mostly AI bots interacting with one another rather than real people.

As students, if we don’t properly cite our sources in our academic writing or work, we can get flagged for plagiarism, which can have serious consequences for our academic and professional careers. So, why is it fair for AI to take information from people’s work online without giving them proper credit and not face any consequences?

Nonetheless, someone out there took the time to write an article, answer a question, or share something useful online, and I think they deserve credit for that. It’s their work and their ideas, so AI shouldn’t be able to take that information, leave out the original source, and essentially take away from the people who created it. We can already see how this can hurt creators and publishers, with some websites seeing their traffic drop by over 50–90%, as mentioned in the article. Because of this, I think we should look at ways to change how AI works so people can still share their knowledge online while getting the recognition and support they deserve, kind of like how the open web was originally meant to work.

Its crazy to think how people are not realizing how BigAI companies are ignoring laws of the internet that have been taken precedent for decades now. The fact that they are getting away with running on their own rules/terms kind of like corrupt politicians who ignore the law/rules of their own country. There definitely needs to be a greater action taken by the public of such violations of the open web and I believe it has to start with educating and spreading awareness about this because many may not realize this.

Unfortunately, I believe that the barriers being put up are the beginning of a time when credible information will become even harder to find. Most students are not willing to pay to access information just to ensure that the sources they use for their papers are accurate. As more authors and scholars put their work behind paywalls, students may have to rely more heavily on information that is freely available, which is not always as reliable. I do not blame independent authors or scholars for using paywalls, however. I would not want my own work to be taken and used without receiving credit either.

This makes me think about how school boards and institutions may eventually have to create agreements with scholars and publishers so that students can access credible information for free through their school logins.

When it comes to newer content creators, you can see that they do not care as much about monetization as they do not have a large following, nor companies that are going to give them money due to the lack of following that they have. However, you start to see as people grow a following, they start to change the content that they are releasing online these various social media platforms, to be more "safe" as they want to receive an income. This causes content creators to conform to the social media platforms algorithm just so they can have their content sent out to more people, resulting in content online becoming less and less creative and unique, and more so the same as everyone else.

I agree that individuals should not be expected to carry the entire responsibility for addressing the problems created by big tech and AI. However, I believe that changing public attitudes and educating people about alternatives can give individuals more power to make meaningful choices. Even when people are already dependent on these technologies, taking smaller steps to reduce their harms can still encourage larger institutions to reconsider how they use them. For example, recently there has been a shift in the limits of how students can use AI. At first, there was a zero tolerance for AI usage in academic work, and that is now shifting towards the allowance of AI with proper citation. Ultimately, individual choices may seem small, but they can help contribute to broader cultural and institutional change.

This is a little bit of a divergence, but the topic of resources like research work and scientific analysis made me think about the fact that some science publishing companies charge people to read the articles. I think this is ridiculous. Furthermore, they charge the authors of the papers to publish in their journals. The fact that these studies were openly shared would help advance the research field much quicker and provide a more accessible and equitable approach to research only makes me appreciate the companies that do not privatize their resources behind a paywall.

I appreciate that the article starts off by addressing the biases that can come with the use of AI in statements like this one... Yes, AI use is very controversial in today’s society, but I think recognizing that AI can be used for people’s benefit as well as for their downfall is really important. Because honestly, its true. I know I can speak for all of us when I say we’ve probably all used AI for something at some point, whether it’s asking ChatGPT a random question, or using Google’s AI search feature without even really thinking about it seeing as AI has become a integral part of our everyday lives as we know it. So, before diving into an article focused around the attack of AI on our modern world today, I think this is a good reminder that AI isn’t just completely good or completely bad, and its important to recognize how we choose to use it matters too...

As a teacher I understand how extraordinary the open web is and how important it is for someone who loves to get new ideas and concepts to keep the students engaged. When people create content specifically for teachers/curriculum I think is it so important for anyone to be able to look at the content instead of putting it in a private group. I find a lot of different ideas to change up how I deliver content through the open web therefore I think it is sad for this to not be as popular anyone. Personally, I don't create much content on the web but I think it is sad that if I did anyone including AI bots could use it as their own and summarize everything I worked on without permission.

This monopolization is not only in the podcast industry, but also in the film industry, which often uses data and algorithms to determine and curate exactly what audiences want to watch. Many people may be fatigued by this which may explain why the success of "Obsession" in theatres was celebrated so much this year. I think this movie acted as a source of hope that genuine human creativity is still valued in the industry.

With AI having access to the internet and being able to piece together whatever information they find for the person inputting the prompt, is not giving the original publishers the credit that they deserve. Instead, as this example says, using the original content of others, and not giving the original author credit or even compensation for their information, only will further prevent/make people second guess what they are willing to upload onto the internet for free. It also only benefits the big corporations instead of the person putting their knowledge and research on the open web.

I found this part interesting because I never really thought about how much a website like “Stack Overflow” could impact someone’s life through their work or school. After doing some more research (since i am not tech-savvy in any way and dont quite understand code), I realized that these types of websites can play a huge role in giving people access to information and opportunities that can genuinely change their lives. Especially for those who may not have the same resources or opportunities as people in major tech companies. This realization also opened my eyes to how easy it is to take free information online for granted without thinking about how much it can help someone learn, find a career, or build a better future. Because of this, I think it is important to consider how AI could potentially hurt this if it starts replacing the open and shared information people rely on.

The use of AI to generate content is a topic that I find rather frustrating. Social media platforms are being flooded with mindless content that is almost exclusively AI generated. These creators are now making money to generate short videos that present information and in many case the creator really has no knowledge regarding the content. It just blows my mind that people are making money by spitting out endless short videos with no real background knowledge.

The article suggests that AI content is pulled from many resources without consent of the original content creator or individual. The information shared also becomes a blend of all resources, making it extremely difficult to understand the validity in information but also where the original content comes from. Information on Wikipedia has always faced the same issues as AI platforms, where the validity of information has been questionable. Does this mean that any platform to come out will face similar objections of providing the truth to viewers?

It is interesting to consider the lack of consent or compensation that occurs from AI content summaries. Often times there are no links provided to where this information has come from. I feel like this creates issues with citing work especially at a university or high school level, where individuals use information provided by AI, but to not know where to find academic articles where they can give credit to the researcher / authors. Furthermore, the AI content can come from a broad variety of sources, making it complicated to narrow it to specific research.

I have actually been thinking about this a lot as of late with the recent boom of horror movies being developed based off of Creepypasta's, Reddit stories, and original ARG concepts. While some of the original creators are working with the directors, i'm sure not all of them are. It makes me wonder about the future of scriptwriting and film, it seems like so many things are being based off of original stories found online and i'm curious if the creators have any stand in court to defend their creative property if proven they are the original mind behind it.

This paragraph reminded me of Elon Musk’s purchase of Twitter (now known as “X”). I remember in 2023, when he introduced the idea of charging a fee to use the app, there was a lot of outrage online. Although the idea was discussed, X did not end up requiring all users to pay to use the platform. This made me think about how much influence users can have when they come together and speak out about changes they disagree with. Although this may be an optimistic way of looking at the situation, I think it is important to recognize that the internet is not entirely out of our control.

I think this statement is so important as a teacher. As teachers we are always talking about what kind of culture you want to have in your classroom, and to build a culture everyone needs to be on the same page. As teachers is it so important to model this culture for the students and for that reason I think this point highlighted in the article is significant. To change the culture we need to educate people just like we educate the students to be on the same page of what we want for our classroom culture. If people aren't educated on the problems they won't know or want to do anything about it.

The idea of using “harm reduction approaches” stood out to me because I think it is a more realistic way of dealing with AI. Realistically, I don’t think we can expect everyone to completely stop using AI anymore, especially since so many people and institutions already rely on it and use it regularly for a variety of different things. So, instead of telling people to stop using AI, I think we should focus on finding a balance where people can still use it when it is helpful while being aware of its negative effects.

I do wish throughout this section of the article Dash gave us some real world examples of what these approaches could look like in schools, workplaces, or everyday life because I think that would make the idea easier to understand and actually put into practice...

The information in this link is super interesting. Consent in spaces on the internet is really important and AI companies are stealing everyone's public work. They aren't asking permission to use that visual artist's work. Or that musician's music. Or even that one blog post from 2015 that was created to solve some obscure computer problem you had. And this is obviously problematic as no one is getting credit and the creators and not getting the things that come from that.

I think this is so important to remember in a time like this! I believe to make change its not always about some big movement or action it can just be the small everyday things that individuals do to help the problem. Individual actions such as not using AI as often could feel like something small but if everyone did this it would be powerful and would make a statement. I think some people use these tools to take the easy way out when they don't want to take the time to complete their own work which is where the problem is.

I think this is very important to remember because most of us grew up in a time where we got to see the great side of the open web as well as the bad side that has been coming out more recently. I think as teachers it is so important to remind students about the good parts of the open web because many times they only hear about the bad things and how AI is changing things for the worse. We need to remember that these young students never got to see how the open web was before AI and because they only ever grew up with computers or phones its hard for them to not know any different.

I wonder if there will be any stricter rules, laws, or policies put in place in the future regarding the use of these tools by people and institutions. To be honest, I think it would've been a wise idea for proactive policies to have been in place before the release of generative AI tools to the public and I'm surprised that they just released it to the public so easily. I think that simply harm reduction is not sufficient; there needs to be preventative measures in place to avoid harm occurring in the first place. Bigger stakeholders need to care more about the potential harm of AI; the companies themselves need to be more concerned as well, in order for these approaches to work.

I believe one of the biggest and most obvious examples we have been able to witness of this on such a large scale has been the purchasing of Twitter (Now X). As someone that has been on the app both before and after Elon Musk's take over, I personally have noticed a drastic change and decline. What used to be a well mixed timeline is now fully riddled with Right-wing red pill slop and consistent rage bait because it gets the most clicks and these clicks are now making people money in the revenue share program if you are an X subscriber. What used to feel like an app full of real people and their thoughts is now taken over by bots trying to make a few bucks by spreading misinformation.

The example of Tim Berners-Lee mentioned here really stood out to me because he was one of the key people who helped create the World Wide Web as we know it today. He created something that has allowed so many companies and billionaire entrepreneurs to become extremely successful, yet the openness of the internet he helped create is now being threatened by those same kinds of companies as they continue to take more control over how information is shared and accessed. Thinking about this more in depth, it makes me wonder if the internet can continue to be a place where people freely create and share, or if money, AI, and large companies will slowly change what the open web was originally meant to be. Honestly, that idea is kind of terrifying...

As educators, we can take action and directly support sources by using them in our classes or lessons or cross-referencing them in our research with peer reviewed articles. For example, Wikipedia can be good if you need a little context on who someone is or facts about their age and location.

I think it is easy to forget how truly amazing the internet really is. Growing up, the internet was already very much in motion. Since it was always around, I tend to overlook the incredible nature of it and instead be hypercritical of it. Especially in the past few years, I find that the internet has become a breeding ground for negativity and insecurity. I think the constant exposure to other people’s idealized lives makes it difficult to recognize the positive aspects of being connected. It makes me wonder how different my relationship with the internet would be if I had grown up without it.

I would agree with this. I think as the decades progress, companies are somehow becoming even more money hungry. Things we used to be able to enjoy freely online are now riddled with ads and subscriptions, and things that ARE free usually have varying hidden costs such as risks to digital security and mining of data. It truly does feel like nothing is open or free anymore.

I have grown to love the word enshittifcation. I've personally found that it goes well beyond digital spaces and has become pervasive in nearly every aspect of life. It feels as though corporations consistently reach a size where their focus shifts entirely from consumers interest, to profit maximization.

Although I think it is extraordinary that just anyone can create content online, it is also scary. Too many people do not think critically about the content they see on the internet and do not question if the content is accurate or true. I remember growing up, teachers would warn us against using Wikipedia since the pages could easily be edited by anyone at the time, yet many took what the website had to say as fact.

I think this is important to think about how and why the open web matters especially in regards to what we will actually be able to remember of the digital world in the future. This made me think of how we preserve truth, fact and digital information over time. I know that there are people around the world that have been making digital archives of the internet which is super cool that we will have proof of its existence so we can know if something was changed or manipulated, and ultimately we can use this data to reference what is true as new research and discoveries are made.

Nowadays, many people who have no experience coding have greater access to building applications or scripts by asking AI to code it for them through "vibe-coding". I have no doubts that AI can code better than I can, but I wonder how much reliance (if any) companies and senior programmers place on using AI to help them program? Additionally, I worry about the impact AI has had on the job market for those with computer science degrees attempting to get entry-level jobs. I've heard from many of my friends in that field that it's been very difficult to get a job right now (maybe due to companies using AI instead of hiring human programmers?), even those that have been working for almost five years.

It is disappointing that scientists and researchers can no longer gain entry to what once was accessible to them - the only way research can advance is by building upon what is already known. When we lose access to past findings, it becomes almost impossible to conduct new research and build on existing knowledge.

Since AI summary bots are making it easier to spread false information, researchers might have to spend more time fact-checking what they find. As a society, this could lead to slower discoveries, and slower scientific progress overall.

Another evocative writing choice from Dash. As the price of groceries climb ever higher and climate efforts call animal agriculture into question, innovations in the field of artificially cultivated food, namely meat, are coming to light.

Comparing artificial or processed food to AI slop is an excellent writing choice. In this analogy, human-produced content and information acts as the products from a family farm, whereas AI generated content becomes artificially produced and reclaimed product This debate is not solely about embracing technology or not. It's convenience vs integrity and corporations vs the individual.

This is a very chivalrous piece of motivation. I hope in the present and in the near future we are able gather to fight back against the AI takeover of the open web. But I do believe that a large portion of people act individually in their best interests with every choice. Most people would order an uber over calling a cab because it is cheaper, more easily accessible on your phone, and offers quicker methods of payment. In a similar way, when searching for content, a large portion of people will opt for the convenience of the quick, AI generated answer at the top of the search engine compared to spending time scouring web articles, or paying to view a webpage. This change has put users in a more difficult position of putting a greater cause before their own convenience, acting as a united front on the open web where they are accustomed to acting only for their own benefit.

Creative communities have always shaped the web, and that same spirit could drive the development of “good AI” built with ethics and care! Stronger policies and better education can help students feel empowered to choose tools that reflect their values instead of settling for whatever dominates the market.

Same change

Same change.

Needs to be changed to Gemini Notebook

How might access to public transportation also impact sleep latency? For instance, how might average sleep wake-up times differ between a city with reliable public transit (NYC, European cities) differ from rural areas or cities with unreliable transit?

Knowing people from history does not really matter. However, knowing what their contributions are is crucial to understanding history. This is because those contributions have caused historical imprints thus why we are talking about them and referring to them as historical figures in the first place. You take that away and they just seem like random people we could forget about.

concrete examples to impliment. Possible activity: -have the attendees choose one of the 6 tenets of digital pedagogy -based on an assignment they plan to give this semester, answer this prompt: with the principle of blank (the tenet they chose) in mind, please adjust one of your assignments to incorporate your chosen tenet. Which new tool from the CUNY Toolkit would you use for this assignment? Keep in mind the following themes we have covered today: 1. high engagement 2. fostering relationships student to student and student to professor 3. public facing/real world application

Interesting point

"in the high-interest-rate environment of 2022, every employee represented a financial trade-off, and firms had to weigh their cost against the guaranteed return of simply holding that cash in the bank."

This is crucial to know this: when you concatenate a list with n number of items and a list with 2n number of items then the result is there are a list full of 3n items, and when the list of n times is repeated 2 times it will yield a of list of 2n items.

😭😭😭

Notably Hunter has liked everyone of the IG reels chronicling this

These paragraph shows how the bats are in relation to the writers journey in Vietnam.

This makes me consider the fact that writing can be a way to deal with grief and and a way to truly understand what you are really feeling. This I can relate to because I enjoy journaling which helps me with my emotions. It shows how writing can help people deal with difficult emotions.

I like this piece because it shows how even though the father was sitting with his father they are still emotionally distant from each other. The part where it says they "leaned away from each other" really makes the reader realize how distant they really are from each other.

This detail shows the unexpectedness in the writing. It makes the reader start to wonder why someone would be at the door at 3am and shows how the mom was not expecting someone to be at the door at this time.

A river-goddess; this is the river that Achilles' mother dipped him into.

Cerberus

Death

Sleep

This same description was used for the Land of the Cimmerians. However, in the Odyssey, it is found at the edge of the earth, as opposed to below it in this case.

The underworld?

The transition between the Gold and Iron age?

Pour situer aussi ? Pour faire comprendre le contexte, ce sur quoi se base un propos, son historicité. Fonction des liens hypertextes et des références académiques : "si vous voulez creuser pourquoi je dis ça, allez voir ...". C'est d'ailleurs ce que l'on reprochait à Chat-GPT à sa sortie : "il ne cite pas, on ne sait pas d'où ça vient". Ce pourquoi ils ont rajouté du RAG aux LLM pour essayer de palier à ce défaut structurel de contexte… Je me dis que quand même, citer a un mérite épistémologique autre que la seule reconnaissance symbolique de quelqu'un/quelque chose de pouvoir et l'élection d'une "autorité". Enfin, si l'on distingue citer de créditer, bien que ça ne soit pas évident à dissocier ... tu me sembles surtout parler de créditer, ici.

Blur on hero image overlay overpowers the image a bit too much. To compensate for the white text, would it be possible to remove the overlay from the image and place the blurred black background behind text and search bar elements? See https://lib.utk.edu/ search bar.

In the tablet and mobile views, the bottom part of the search button outline isn't showing

good stuff

nanananananannananana

wow

I hope its not too difficult

uhigrew

intresting

Attention avec l'usage de "ce/cette/ces" (aussi deux fois dans le paragraphe précédent) : contribuer à une impression de flou quand il y en a trop. La généalogie de quoi? Dans le paragraphe précédente : cette continuité = la continuité entre quoi et quoi? Cette impression de rupture = l'impression de rupture entre quoi et quoi?

donner un exemple, parler en termes plus concrets (discours médiatiques qui disent quoi?)

Des données plutôt que des chiffres? Je pense que pour bien comprendre le lien avec les différents usages de l'IA en journalisme, parler de "chiffres" est un peu réducteur. La continuité pourrait être explicitée un peu plus encore.

Intégrer les références dans Stylo

à discuter : écritère épicène/inclusive

Hmm how important?

she

he

gegen = teacher, + la = honourifc, frequently collapsed into genla instead of gegenla

what is it zhu yi yo is honourific form of ser gyi yo

possessive pronoun

h. name

what

bo = tibet, bopa = tibetan

I am

no

polite

and

student

are you

yours

you

"I am" verb?

name

for

In this excerpt, we find a need for pathos on both sides for or against guns because emotions cause divisions. The rural community doesn't have officers to come and fight for them at a moments notice. They feel the need to be able to defend themselves and are right for thinking so. Many die in the outskirts because they can't fight back. Yet in the more urban communities the danger of a deranged individual outweighs the need for self protection. Officers can handle issues and the less guns people have, the less issues officers need to face. If these groups were to need to agree, it would take pathos and emotion for both protection and safety to make them understand each other's point of view. This is an example of how pathos can bring groups together.

This excerpt exemplifies the height of ethos by showing how a constant goal creates credibility. Ryan's approach to try and appeal to the thoughts of capitalism help to strengthen his creditability by connecting to the audience. He uses his actions and words to imply he will secure your freedom using his ethos to drive that point to his audience. His constant actions help people believe he will secure their freedom. Therefore people are willing to give him power. He gets all this control because he used ethos.

This part of the texts exemplifies how logos can come off as condescending. The text shows how the knowledge on the topic by the brother in the claim makes the use of logos more of an issue than a help to the claim. At best he will find the claim boring and at worst deeply condescending of his knowledge. A better approach would have been to use pathos to appeal than to show him things he already knows.

I believe it is true that facial expressions can bring a speech back to life. I say this because personally I tend to be bored when people present speeches and just keep a blank face for the whole speech which kills the speech. I think speeches should be interesting and alive by making appropriate facial expressions that match the tone. In conclusion facial expressions are needed in order to show how interested the speech giver is and how to keep it alive.

I believe it is true that having all eyes is good, but it just feels awkward for me. I say this because I do not like using eye contact and always try avoiding it because it makes me feel uncomfortable. I know that having all eyes means that the audience is interested or paying attention and is not necessarily a bad thing. In conclusion I still do not like having all eyes and rather have the opposite.

Same here is this an important question?

Is this an important question?

Weird phrasing, I think the point here is that you should choose a system that allows you to deal with increased complexity even if you don't have it yet, even if the complexity is still low.

As above, not sure if this is relevant

needs to contain the full setup is a huge it project and you need to take care of the system longterm with IT team etc.

if i was correct in my understanding anyway

cut

cut

Correct me if I'm wrong but I thought this competitor is super complex to set up, it's not a classical saas solution but instead a full ear pee that can compete with SAP and basically needs consultants and maybe an in-house team to handle it to set up. Is that incorrect? If that is true then that is the main downside.

these are all weak points. do they really hvae no weaknesses? i mean they charge mony for what they do, not a downside.

This is not a discussed point, pleas cut

Not sure if you discussed this at a different point, but I am not sure that it's worth going for the 34 as a number and discuss that there is a meaningful difference between the native order direct integrations versus the integrations via partner system. Actually partner system is the wrong word it's just a second contract. The connection is handled by a partner but it's still the same system. It's still directly integrated so I don't think it is very useful as a comparison. I get that this is what we would be winning but I think we can carry the comparison by being stronger on the P side interest having to hold package instead of being strong on one side or the other.

Obviously we were mentioning now is that we need to keep it consistent across the whole article so let's discuss and then we applied. I will not mention this point again in every section.

This is nowhere in the article and as far as I know also plenty one has a part of sale system but it's too much to mention every point so let's focus on the strength that you mentioned already in this section and not add more points here

cut. This is simply not following from from the argument

So this is supposed to be the place where the weaknesses are showing up so I wouldn't start it with a compliment. Also, the lack of channels is not their only weakness we must have other things I'm in you mentioned other things in the summary box.

Soften this part a little bit, mention that it's a complex system that can fulfil complex processes so it's a bit more effort and work to set it up

cut

cut

I think the whole section is very strong, the only problem I had is that the ear bee side of things is very very short. Maybe we can describe a little bit more what is actually means and what processes it covers. Maybe that's not possible with two sentences, but let's discuss.

Same as the last comment, this is incomplete and not enough to understand what you mean here. We can decide whether we just cut the whole sentence or half sentence or whether we extended to something meaningful.

This is incomplete. I'm not sure what you mean here.

cut

rewrite. problem is we compare it but we dont have enough space to go into detail about whats the difference. It's also not necessary, we just need to briefly explain or say that these points are additional aspect to relevant for direct to consumer brands without saying compared to not direct to consumer brands. There is no need for a comparison. I would start saying something like some additional points are especially irrelevant for director consumer brands because this and that..... and at the same time naming the points that you made already

cut

cut

i would put this intro section back on top as a starting point.

in fact we need to make this longer:

Fuer D2C Marken laueft in der regel erst alles über den eigenen Shop, dann kommt Amazon dazu, dann Otto oder Kaufland, und irgendwann fragt der erste Handelspartner nach Konditionen und einer Lieferantennummer. Jeder dieser Schritte bringt Prozesse mit, die vorher niemand gebraucht hat, und irgendwann steht dazwischen eine Tabelle, die niemand mehr pflegen will.

deshalb bewertet dieser vergleich funf.....

this will be a little longer than before, but that is alright actually...

Tester

Rights Lens: There are many people who can be subjected to these risks, which of course, affects their rights. Anyone who has to use AI for work, maybe for school, or just for anytime use are at risk. People should have their rights protected when it comes to AI usage, and like how I mentioned in my Care Ethics lens, it's up to the companies to not only make their AI safe overall, but to be transparent and communicate with its users so that they are also aware of the risks.

Care Ethics Lens: Companies making AI should be open with people. It's like taking care of someone by being honest and letting them know what's happening, especially since AI can be confusing and affect everyone. Being transparent helps build trust and shows the company cares about keeping people safe and informed.

Looking at AI alignment through an ethical lens shows us who's involved and what's a stake. The people making the AI, the people using it, and everyone else are all stakeholders. The goal is to create AI that's safe and helpful, which is a big benefit. But if AI isn't trained right, it could cause problems, which is harmful. We all have a right to expect AI to be safe, and those building AI have a duty to make it that way. It's about making sure AI reflects good values, like safety and fairness.

I think the rate of speaking is very important. I say this because if I speak too fast, the audience will not understand me. If is too slow the audience will get bored. When I was in speech class in high school, I would try speaking too fast, which would backfire on me because I would finish my speech too early before the timer.

Cosy's g....g...going to become Twilight's own daughter?

• All of Ponyville gasp in horror *

Ah, the time Twilight fell face first into the cake the cook had prepared as a little filly. How she longed for a daughter. She died as a stillborn because of lemonitis.

Careful, Cozy, don't fall!

And thus, the third incarnation of Mufasa knew what to do about seeing to the stars in the eyes of the kings.

Oooh! Cosy's having a dance party!

Or get eaten by Hellhorse Demons!

As a dog? No wonder she treats her and feeds her with greasy junk food like the main little dog character of Feast did before his owner fell in love.

I think she's referring to the MLP Annuals of the '90s and '00s. Don't worry, off the show Cosy is a nice girl.

Even in that womb you call an egg?! laughs scoffingly

• The Good, The Bad & The Ugly Theme plays*

Imagine if she was a nurse, forcing Cosy straight out of her mommy's own womb.

More like being trapped in a glowing egg in the middle of the gardens.

Before Twilight did, and Flurry Heart did....

Good for you, Cosy.

Are her tears made of green toxic acid?

More like Cosy's rebirth egg.

"Let's GET ON IT!" * Aunt Trixie giggles*

I believe that the impromptu delivery is the worst. I say this because I used to use this technique, and it would never help in speech. The impromptu delivery is not good because it takes no effort or practice which will give the speech giver a hard time presenting a speech. In conclusion, impromptu delivery just makes communication a lot harder than it needs to be and does not give any type of benefit.

I try my best to avoid using genAI as much as I can for my own personal reasons. But it can make finding sources much faster. All I have to do is send a prompt asking for information related to a certain topic, and it can help me find certain websites I normally would of overlooked at times.

starred highlight

May be out of date, but could provide an interesting perspective on a topic. For example, we could get an idea of how the general public viewed a certain thing before and after a notable event such as Covid. Before the pandemic, getting a vaccine was not considered as controversial of an issue as it is now.

What does "more substantial rights" mean? How much discretion does the department have here?

I believe I can relate to this topic of experiencing communication apprehension. I say this because I usually have a hard time presenting a speech and have always experienced communication apprehension. One example is when I had speech class in high school, I would feel communication apprehension and never be fully confident. In conclusion, I feel like communication apprehension is normal to me and is my weakness.

It is interesting how Eliot goes from bringing up these happy memories to saying that he “will show you fear in a handful of dust”. He instantly starts talking about Wagner’s Tristan and Isolde, which had a pretty sad ending, where two people who loved each had to die because of betrayal (spiritual emptiness of people and the world they lived in). As Lucas Ruiz mentioned on 21 of September 2025 this “sets a tone of grief and longing for the poem”. Then, another episode follows with the Hyacinths and the hyacinth girl. According to the myth, Hiacinthia was a festival honouring Hiacinthus and Apollo, where people mourned for Hiacinthus’ death on the first and third days and had fun on the second. It is intriguing that people had this amusement in the middle of this festival. It is interesting to see how people were easily able to go from one emotion to another in a span of three days. It is also important to note that they picked odd days for mourning and an even day for amusement, as in many countries it is usually the opposite (e.g. you give an odd number of flowers as a gift, and an even number of flowers you bring to a funeral/memorial etc.-- usually a symbol of death/memory and mourning) This myth also shows that Hyacinthus is young and beautiful, but his life ends suddenly, showing how fragile beauty and youth are under the pressure of time – this also connects back to the story about Sybil (especially considering that she was also involved with Apollo, but unlike Hyacinthus she got a much harsher treatment). However, hyacinths are a symbol of grief and pain, which shows that great affection can cause a lot of suffering. Apollo transformed his loved one into something very beautiful, but something that is a reminder of something bad → just like April (it uncovers all the secrets/memories that were buried under the winter snow). I also really loved Lucas Ruiz’s interpretation of the line “your arms full” of hyacinths, which basically means that the person that’s carrying so many hyacinths is actually holding onto grief, that they cannot let go of bad memories (death/loss). Why is it always Apollo that is involved in these resurrection and youth situations? He is the god of the sun, which might give him the power of life because nothing can live without light. This myth also connects to Huxley’s story very well, where the fortune-teller tells the girl “but you will not remain so [young] for long”. These texts show how beauty is closely connected to the passage of time and death. There is a very fine line between the two.

I would say direct examination, cross-examination, and redirect examination process ..... for clarity

eLife Assessment

Avoidance of UV and blue light by the nematode C. elegans is mediated by the unusual transmembrane protein LITE-1, a non-canonical photoreceptor. In this valuable work, the authors report the surprising finding that LITE-1 function is also required for avoidance of very high concentrations of the food-associated odorant diacetyl. Although no molecular evidence is provided, the paper reports convincing studies indicating that LITE-1 can serve as a chemoreceptor for very high concentrations of diacetyl, adding an unexpected layer of complexity to the function of this unusual protein.

Reviewer #1 (Public review):

Summary:

This paper describes an interesting phenotype of C. elegans lite-1 mutants. Previous work showed that lite-1 mutants lose a violet / blue light avoidance response. The authors show here that lite-1 mutants also show a defect in negative diacetyl chemotaxis. While wild-type worms avoid diacetyl at high concentrations, lite-1 mutants are instead *attracted* to it. The authors go on to perform Ca2+ imaging in sensory neurons and find that ADL and ASK neurons show altered Ca2+ responses to diacetyl in lite-1 mutants, suggesting LITE-1 is required for these responses. As unc-13 mutants with defective synaptic transmission show similar diacetyl Ca2+ responses as wild-type, this suggests these neurons respond cell autonomously to diacetyl. Indeed, expression of LITE-1 in ADL from a specific promoter shows phenotypic rescue. The authors then use a strain that expresses LITE-1 in the body wall muscles and show this expression is sufficient to engender them with sensitivity to diacetyl, as measured through altered swimming, hypercontractility, and egg laying. The authors interpret this result as LITE-1 may act as a diacetyl receptor. The authors test whether a structurally similar molecule, 2,3 pentanedione shows similar effects, and they find it does. Alpha-fold modeling and molecular docking analysis show where diacetyl might bind to the LITE-1 protein. They then test whether lite-1 mutants show chemotaxis defects to other molecules as seen with diacetyl.

Strengths:

Overall, the study follows up on an interesting and useful result. The experiments as presented are generally well-conceived and performed. The authors use a variety of behavior and imaging approaches to test how LITE-1 mediates diacetyl avoidance. The author revisions addressed the concerns I raised previously.

Weaknesses:

In response to the first submission, Reviewer 3 raised the possibility that light facilitates the production of diacetyl which then activates LITE-1. The authors helpfully revised the manuscript to incorporate this mechanisms. However, is it possible that diacetyl and 2,3-pentanedione are instead (or also) acting as photosensitizers, generating an(other) activator of LITE-1? Diacetyl has been previously shown to have chemical reactivity which is enhanced by light (citations below). I realize that the experiments have ruled out a role for acute light exposure in causing phenotypes in some of the experiments, but it is formally possible that prior light exposure may have caused diacetyl to generate peroxides or other photo-products that have the observed biological effect which is then lost in the lite-1 mutant. That is, what if the relevant molecule is already present in the diacetyl bottle / stock solution? At that point, further light exposure may not matter. This possibility was not really addressed in the manuscript.

-Huang CY, Li J, Liu W, Li CJ. Diacetyl as a "traceless" visible light photosensitizer in metal-free cross-dehydrogenative coupling reactions. Chem Sci. 2019 Apr 8;10(19):5018-5024. doi: 10.1039/c8sc05631e. PMID: 31183051; PMCID: PMC6530541.<br /> -Pengcheng Lian, Ruyi Li, Xiao Wan, Zixin Xiang, Hang Liu, Zhiyu Cao, Xiaobing Wan Acetylation of alcohols and amines under visible light irradiation: diacetyl as an acylation reagent and photosensitizer. Organic Chemistry Frontiers 2022, 9 (2), 311-319.<br /> -Rowell, Keiran N & Kable, Scott & Jordan, Meredith J. T. (2022). An assessment of the tropospherically accessible photo-initiated ground state chemistry of organic carbonyls. Atmospheric Chemistry and Physics. 22. 929-949. 10.5194/acp-22-929-2022.

Reviewer #2 (Public review):

Summary:

Koh and colleagues investigate the broader sensory role of LITE-1, a gustatory receptor previously linked to UV light detection in C. elegans. Their study explores whether LITE-1 also mediates avoidance of specific chemical stimuli-namely, high concentrations of diacetyl and 2,3-pentanedione. They show that LITE-1 is required in the ADL and ASK neurons for calcium responses to diacetyl, and that its expression in body-wall muscles is sufficient to trigger hypercontraction upon odorant exposure. Molecular docking suggests both odorants may directly bind to LITE-1 with micromolar affinity. These findings suggest LITE-1 may act as a multimodal receptor for both light and chemical stimuli.

Strengths:<br /> • Methodological Precision: The study is technically strong, with well-executed calcium imaging and quantitative behavioral assays that clearly show neural and muscular responses to chemical stimuli.<br /> • Novelty and Scope: The work presents a compelling case for LITE-1 functioning as a multimodal sensor, which is an intriguing expansion of its known role.<br /> • Potential Impact: If validated, the findings could significantly advance the understanding of sensory integration in C. elegans, and the tools developed may be broadly useful to the research community.<br /> • Relevance to the Field: The study adds to evidence that C. elegans uses non-canonical sensory pathways and may inspire further exploration of multimodal receptor functions in other systems.

Weaknesses:<br /> • Lack of Rescue Experiments: The absence of rescue experiments makes it difficult to definitively link the observed phenotypes to loss of lite-1.<br /> • Single Loss-of-Function Approach: The reliance on a single genetic mutant limits interpretability. Additional strategies such as RNAi (e.g., neuron-specific knockdown) would provide stronger evidence.<br /> • Unclear Neuronal Contribution: While calcium responses in ADL and ASK are reduced, it's unclear which neuron(s) are necessary for behavioral avoidance. Cell-specific rescue or knockdown experiments are needed.<br /> • Unvalidated Docking Data: The molecular docking predictions lack experimental validation. Site-directed mutagenesis would be needed to support claims of direct interaction.<br /> • Limited Odorant Specificity Testing: Docking analysis does not include non-binding odorants, making it difficult to assess binding specificity.<br /> • Incomplete Quantification: Some calcium imaging results (e.g., in AWA neurons of unc-13 mutants) lack statistical comparisons, which limits their interpretive value.

Comments on revisions:

I thank the authors for their thorough revision. The manuscript is substantially improved, and most of the concerns raised in my original review have been addressed.

The strongest improvement is the addition of new genetic evidence supporting a role for LITE-1 in high-concentration diacetyl avoidance. The use of multiple independent lite-1 alleles strengthens the conclusion that the phenotype is specifically due to loss of lite-1 function, and the ADL-specific rescue experiment is an important addition. While the rescue is not complete, it is convincing and supports the idea that LITE-1 activity in ADL contributes to the avoidance response.

The neuronal analysis is also stronger. The new statistical analysis across the sensory neurons addresses my previous concerns regarding quantification, and the revised interpretation of the calcium imaging data is more balanced. The revised title of this section is also more consistent with the data and appropriately focuses on ADL and ASK rather than ASH.

I also appreciate the additional controls addressing possible effects of ambient light. The light versus dark experiments make it unlikely that the observed behavioral phenotypes are secondary to unintended activation of LITE-1 by environmental illumination.

The expanded odorant analysis and inclusion of docking predictions for additional compounds are useful additions. Together with the ectopic expression experiments in body-wall muscles, these data strengthen the argument that LITE-1 can respond to diacetyl and related compounds.

My main remaining concern is the same one raised in the initial review: the docking results remain largely computational predictions and have not been tested experimentally through binding-site mutagenesis or other functional validation. As a result, the manuscript still does not demonstrate direct ligand binding to LITE-1. However, I think the authors have strengthened the indirect evidence considerably, and the conclusions are now generally written in an appropriately cautious manner. I would encourage the authors to continue framing the docking results as supportive of a direct interaction rather than definitive proof of one.

Overall, I believe the manuscript has been significantly strengthened by the revision. The remaining limitation is largely mechanistic and does not, in my opinion, undermine the central conclusions of the study.

Reviewer #3 (Public review):

In this work, Brown and colleagues report that the photosensor protein LITE-1 of the nematode C. elegans may also a chemosensor that can be activated by high concentrations of the compound diacetly. LITE-1 was described as a putative ion channel of the gustatory receptor family, which is mainly constituted by insect odorant receptors. These form tetrameric ion channels that can be activated by odorant. Specificity is achieved by forming heteromeric channels from three copies of the odorant receptor co-receptor (ORCO) and another subunit that resembles ORCO in the pore-forming C-terminus, but brings in a binding site for the respective odorant. LITE-1 has a very similar structure, according to Alphafold3 predictions, and also carries a binding pocket. In LITE-1, this was proposed to be occupied by a light-absorbing molecule that activates the channel when a photon is absorbed. Alternatively, compounds generated by absorption of high-energy photons may be formed in vivo and bound by the LITE-1 binding pocket. Koh et al. now demonstrate that another, non-light activated compound, diacetyl, at high concentrations, can activate cells expressing LITE-1. Such (chemosensory) cells are also responsible for the avoidance of high concentrations of diacetyl. For this aspect, the protein seems to act in neurons, which are not necessarily the same cells in which LITE-1 is evoking the photophobic response. LITE-1 activation in excitable cells, i.e muscles, causes strong body contraction and paralysis, and the authors show that this is also the case when diacetyl is present. This action is surprisingly rapid, i.e. within 10 seconds after adding diacetyl, raising the question of how the compound can enter the body so quickly. The authors further present molecular docking studies showing that diacetyl could occupy the binding pocket of LITE-1. Last, they show that another compound chemically resembling diacetyl, i.e. 2,3-pentanedione, can also induce avoidance in a LITE-1 dependent manner, though not as potently.

The data are intriguing and the demonstration of LITE-1 being a diacetyl chemosensor is interesting. Following the first submission and review, the authors addressed most of the questions that this reviewer had and improved the paper significantly. It will add to the further understanding of the still-mysterious multimodal sensory ion channel LITE-1.

The authors identified mutants lacking diacetyl responses. In their chemotaxis assay (Fig. 1A, B), they show that lite-1 mutants do not avoid high concentrations of diacetyl. However, the animals actually show attraction, as the chemotaxis index was positive. If the lite-1 animals were insensitive, they should be indifferent and the chemotaxis index should be close to zero. The authors now showed that other neurons contribute to the avoidance response that are not themselves bona fide chemosensory neurons, as the avoidance behavior remained in a tax-4 mutant, that is lacking most sensory neuron responses. The authors further showed that diacetyl responses of ADL can be rescued by expressing LITE-1 specifically in this neuron in a lite-1 mutant background, thus demonstrating that LITE-1 acts cell-autonomously in ADL to affect avoidance behavior.

The effect of diacetyl on muscle cells (Fig. 3C) is pretty rapid. As shown in the initial submission, already during 1 minute after application the animals are almost maximally contracted. The authors now provide a time course with data points every 10 seconds. This shows that contraction is maximal already after 10 seconds of exposure (provided the zero time point is the one where diacetyl is added). This is remarkable, as the compound would have to either pass the worm cuticle, or enter through the gut and diffuse through the body to reach the muscle cells. It would be of interest to compare this to time courses of other pharmacological agents that need to enter the worm's body for their action. As a comparison, often sodium azide is used to paralyze worms for imaging purposes. This molecule is even smaller than diacetyl, but azide action typically requires more time for maximal effects. Could there be active transport mechanisms involved? Maybe diacetyl can pass through some transporters for related molecules into / through intestinal cells quickly, to then reach the body fluid and muscle cells.

One alternative explanation could be that other mechanisms may be at play. E.g. diacetyl may be immediately sensed by ciliated chemosensory neurons that might release a signaling molecule that leads to activation of LITE-1 in muscles, or that sensitizes it somehow, responding to light used for filming animals. The authors addressed these concerns by repeating their assay in a lite-1 mutant background. The authors had tested unc-13 mutants to rule out indirect effects on the neurons recorded. Likewise, eliminating neuropeptide signaling via unc-31 mutants would have been informative, as neuropeptide signaling plays a role in LITE-1-mediated light avoidance behavior (PMID 40238937, 39489735).

Molecular docking studies are now described in more detail. The authors also provide a structural model of the diacetyl-docked LITE-1. In this model, only one of the four putative binding sites carries diacetyl. Maybe the authors could test how structure is affected if all four sites contain diacetyl? Mutations of LITE-1 that lack aminoacids shown to be contacted by diacetyl have been described (C300, R222). It would have been insightful to test if such mutants are still activated by diacetyl. This would also verify that there is not an unknown breakdown product of diacetyl that could affect LITE-1 function through oxidative pathways, as diacetyl has been implicated as a photosensitizer in the past.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This paper describes an interesting phenotype of C. elegans lite-1 mutants. Previous work showed that lite-1 mutants lose a violet/blue light avoidance response. The authors show here that lite-1 mutants also show a defect in negative diacetyl chemotaxis. While wild-type worms avoid diacetyl at high concentrations, lite-1 mutants are instead *attracted* to it. The authors go on to perform Ca2+ imaging in sensory neurons and find that ADL and ASK neurons show altered Ca2+ responses to diacetyl in lite-1 mutants, suggesting LITE-1 is required for these responses. As unc-13 mutants with defective synaptic transmission show similar diacetyl Ca2+ responses as wild-type, this suggests these neurons respond cell autonomously to diacetyl. However, whether lite-1 also acts cell-autonomously is not discussed. Indeed, because unc-13 and lite-1 mutants show different ADL and ASK Ca2+ responses, it seems the diacetyl response regulated by LITE-1 is likely acting outside of those cells. An interesting result that is not commented on is the switching of the valence of the ASK Ca2+ response in lite-1 mutants. ASK neurons still respond to diacetyl, but instead of a strong increase in Ca2+, diacetyl appears to drive it strongly lower. This may be consistent with the switch in valence in the diacetyl chemotaxis assay. It also argues against the idea that LITE-1 is a low-affinity diacetyl receptor that drives avoidance or the Ca2+ responses in ASK, since it is still present in lite-1 mutants. The authors then use a strain that expresses LITE-1 in the body wall muscles and show this expression is sufficient to engender them with sensitivity to diacetyl, as measured through altered swimming and hypercontractility. The authors interpret this result as LITE-1 may act as a diacetyl receptor. The authors test whether a structurally similar molecule, 2,3-pentanedione, shows similar effects, and they find it does. Alpha-fold modeling and molecular docking analysis show where diacetyl might bind to the LITE-1 protein. They then test whether lite-1 mutants show chemotaxis defects to other molecules, as seen with diacetyl. Generally, they find that the observed diacetyl responses are unique, although lite-1 mutants do lose their avoidance response to 2,3-pentanedione. However, unlike the acquisition of diacetyl attraction in lite-1 mutants, 2,3 pentanedione avoidance is *lost*; it is not switched to attraction. Overall, I felt the description of the results and their implications could have been more in-depth. Further, the evidence that LITE-1 is a chemoreceptor itself, rather than acting in some way to shape chemoreceptor responses (via light or otherwise), remains unclear, as conceded by the authors.

Strengths:

Overall, the study follows up on an interesting and useful result. The experiments as presented are generally well-conceived and performed. The authors use a variety of behavioral and imaging approaches to test how LITE-1 mediates diacetyl avoidance.

Weaknesses:

The study is missing experiments needed to resolve whether LITE-1 is doing what they propose. The evidence that LITE-1 is a diacetyl receptor is lacking support since lite-1 mutants have their avoidance and calcium responses flipped, which would not be expected if it were acting solely as an avoidance receptor. Presumably, the authors are concluding that the attractive response that is left in the lite-1 mutant is mediated by ODR-10, but that experiment is not shown.

We interpret the shift from avoidance to attraction in lite-1 mutants as consistent with the loss of an aversive sensory component in the presence of an underlying attractive response to diacetyl. We initially hypothesised that this residual attraction was mediated predominantly by ODR-10. To test this, we now generated and analysed lite-1; odr-10 double mutants. The double mutants retained an attractive response to diacetyl, indicating that ODR-10 alone does not account for the attraction observed in the absence of LITE-1 and that additional receptors or sensory pathways are likely to contribute. This finding is consistent with previous studies where loss of ODR-10 did not lead to a complete loss of diacetyl responsiveness.

Similarly, the authors concede that "the use of lite-1 point mutants that affect specific LITE-1 function, such as light sensing, channel gating, or binding pocket, could further elucidate LITE-1 mechanisms." This reviewer agrees, and such experiments designed to localize diacetyl binding site(s) would be necessary to conclude definitively that LITE-1 is a diacetyl receptor. The body wall muscle assay used or some other heterologous experimental system could work for such a structure-function analysis. A concern is whether the extensive number of LITE-1 point mutants described in the literature affect cell surface expression vs. receptor function, which might complicate the interpretation of a result showing loss of diacetyl responses.

We agree that structure-function analysis using LITE-1 point mutants could help identify regions or residues that contribute to the diacetyl response and is an important future direction for research, which we have included in the discussion.

Reviewer #2 (Public review):

Summary:

Koh and colleagues investigate the broader sensory role of LITE-1, a gustatory receptor previously linked to UV light detection in C. elegans. Their study explores whether LITE-1 also mediates avoidance of specific chemical stimuli-namely, high concentrations of diacetyl and 2,3-pentanedione. They show that LITE-1 is required in the ADL and ASK neurons for calcium responses to diacetyl, and that its expression in body-wall muscles is sufficient to trigger hypercontraction upon odorant exposure. Molecular docking suggests both odorants may directly bind to LITE-1 with micromolar affinity. These findings suggest LITE-1 may act as a multimodal receptor for both light and chemical stimuli.

Strengths:

(1) Methodological Precision: The study is technically strong, with well-executed calcium imaging and quantitative behavioral assays that clearly show neural and muscular responses to chemical stimuli.

(2) Novelty and Scope: The work presents a compelling case for LITE-1 functioning as a multimodal sensor, which is an intriguing expansion of its known role.

(3) Potential Impact: If validated, the findings could significantly advance the understanding of sensory integration in C. elegans, and the tools developed may be broadly useful to the research community.

(4) Relevance to the Field: The study adds to evidence that C. elegans uses non-canonical sensory pathways and may inspire further exploration of multimodal receptor functions in other systems.

Weaknesses:

(1) Lack of Rescue Experiments: The absence of rescue experiments makes it difficult to definitively link the observed phenotypes to loss of lite-1.

We have now performed the rescue experiment expressing lite-1 in ADL, and showed that LITE-1 in ADL is sufficient for avoidance, although it is not a complete rescue to wild-type levels.

(2) Single Loss-of-Function Approach: The reliance on a single genetic mutant limits interpretability. Additional strategies such as RNAi (e.g., neuron-specific knockdown) would provide stronger evidence.

We observed the loss of avoidance in three independent lite-1 alleles. Combined with the new cell-specific rescue experiment, we think this provides sufficient support for the conclusion that the phenotype is due to loss of lite-1 function.

(3) Unclear Neuronal Contribution: While calcium responses in ADL and ASK are reduced, it's unclear which neuron(s) are necessary for behavioral avoidance. Cell-specific rescue or knockdown experiments are needed.

We have expressed lite-1 genomic DNA under the ADL-specific promoter srh-220, which restored the avoidance phenotype, although it is not a complete rescue of wild-type behaviour. Together with calcium imaging data, this suggests that proper avoidance likely requires input from both ADL and ASK neurons.

(4) Unvalidated Docking Data: The molecular docking predictions lack experimental validation. Site-directed mutagenesis would be needed to support claims of direct interaction.

We agree that the docking data does not in itself establish direct binding (we think the muscle expression and paralysis provides stronger evidence). Based on previously reported docking experiments, we wanted to check if diacetyl could occupy the same binding pocket. We have now also included docking data of the other odorants from the chemotaxis assays in the manuscript.

(5) Limited Odorant Specificity Testing: Docking analysis does not include non-binding odorants, making it difficult to assess binding specificity.

We agree and have now included docking data of the other odorants from the chemotaxis assays. 2-butanone, which is avoided by lite-1 mutants, was predicted to have a slightly higher binding affinity for LITE-1 than 2,3-pentanedione. This highlights the need to interpret the in silico docking data together with real experimental data, rather than using the computational predictions alone to infer functional receptor activation.

(6) Incomplete Quantification: Some calcium imaging results (e.g., in AWA neurons of unc-13 mutants) lack statistical comparisons, which limits their interpretive value.

We have generated the scatter plots of calcium imaging responses across the different sensory neurons, and the statistical significance was assessed using two-sided t-tests with FDR correction, which is now included in the manuscript.

Reviewer #3 (Public review):

In this work, Brown and colleagues report that the photosensor protein LITE-1 of the nematode C. elegans may also be a chemosensor that can be activated by high concentrations of the compound diacetyl. LITE-1 was described as a putative ion channel of the gustatory receptor family, which is mainly constituted by insect odorant receptors. These form tetrameric ion channels that can be activated by odorants. Specificity is achieved by forming heteromeric channels from three copies of the odorant receptor co-receptor (ORCO) and another subunit that resembles ORCO in the pore-forming C-terminus, but brings in a binding site for the respective odorant. LITE-1 has a very similar structure, according to Alphafold3 predictions, and also carries a binding pocket. In LITE-1, this was proposed to be occupied by a light-absorbing molecule that activates the channel when a photon is absorbed. Alternatively, compounds generated by absorption of high-energy photons may be formed in vivo and bound by the LITE-1 binding pocket. Koh et al. now demonstrate that another, non-light-activated compound, diacetyl, at high concentrations, can activate cells expressing LITE-1. Such (chemosensory) cells are also responsible for the avoidance of high concentrations of diacetyl. LITE-1 activation in excitable cells, i.e, muscles, causes strong body contraction and paralysis, and the authors show that this is also the case when diacetyl is presented. The authors further present molecular docking studies showing that diacetyl could occupy the binding pocket of LITE-1. Last, they show that another compound chemically resembling diacetyl, i.e., 2,3-pentanedione, can also induce avoidance in a LITE-1 dependent manner, though not as potently.

The data are intriguing, and the demonstration of LITE-1 being a diacetyl chemosensor is interesting. Yet, there are a few questions arising that the authors should address.

The authors identified mutants lacking diacetyl responses. In their chemotaxis assay (Figures 1A, B), they show that lite-1 mutants do not avoid high concentrations of diacetyl. However, the animals actually showed attraction, as the chemotaxis index was positive. If the lite-1 animals were insensitive, they should be indifferent, and the chemotaxis index should be close to zero. This means, other neurons contribute to the diacetyl response, and the result of these neurons being activated means/remains attraction? If so, the authors need to rule out any effects of these neurons on the effects they attribute to LITE-1 in the other assays.

We have tested tax-4 mutants in the chemotaxis assay and found that, contrary to the predicted chemotaxis index of zero, these animals retained strong avoidance of high concentrations of diacetyl. This indicates that tax-4 mutants are not chemosensory null for this stimulus and that TAX-4 independent sensory pathways contribute to high diacetyl avoidance. We agree that these experiments cannot completely rule out indirect neuronal effects. We have therefore revised the text to acknowledge this limitation. Nevertheless, the rapid paralysis and contraction observed when LITE-1 is expressed specifically in body-wall muscle in a lite-1 mutant background support the idea that LITE-1 is sufficient to confer a diacetyl-evoked response in these cells.

The effect of diacetyl on muscle cells (Figure 3C) is pretty rapid, i.e., already during 1 minute after application, the animals are almost maximally contracted. How fast is it really? Can the authors provide a time course with more time points during the first minute? This is a relevant question, as the compound would have to either pass the worm cuticle or enter through the gut and diffuse through the body to reach the muscle cells. Can one expect this to occur within (less than) a minute? In this context, the authors need to rule out that other mechanisms may be at play. E.g., diacetyl may be immediately sensed by ciliated chemosensory neurons that might release a signaling molecule that leads to activation of LITE-1 in muscles, or that sensitizes it somehow, responding to light used for filming animals. The authors should repeat this assay in a lite-1 mutant background.

We repeated the paralysis assays under red-filtered illumination to minimise potential effects of light, with animals maintained in darkness from hatching to adulthood. We also included lite-1 mutants to assess whether neuronal LITE-1 contributed to the paralysis response. In addition, the assay was repeated with more frequent time points, revealing that paralysis and body contraction occurred within 10 s and neuronal LITE-1 does not contribute to the effect.

Furthermore, the authors tested unc-13 mutants to rule out indirect effects on the neurons recorded. Likewise, they should eliminate neuropeptide signaling via unc-31 mutants (a recent paper cited by the authors showed involvement of neuropeptide signaling in LITE-1-mediated light avoidance behavior).

We agreed and have acknowledged and discuss in the manuscript that contributions from gap junction-mediated communication, neuropeptide signalling and other chemosensory pathways cannot be excluded.

Last, to demonstrate that effects are not indirect in response to chemosensory neurons, the authors should repeat the contraction or swimming assay in a tax-4 mutant, which largely lacks chemosensation. This also applies to the chemotaxis assay. Animals should exhibit a chemotaxis index to diacetyl of zero, then.

We have tested tax-4 mutants, and like wild-type animals, retained strong avoidance of high concentrations of diacetyl, indicating that TAX-4-independent sensory pathways contribute to this response. This indicates that tax-4 mutants are not chemosensory null for this stimulus and that TAX-4-independent sensory pathways contribute to high diacetyl avoidance. Therefore, repeating the contraction or swimming assay in a tax-4 background would not completely exclude indirect input from other chemosensory neurons. In addition, rapid paralysis and contraction were observed when LITE-1 is expressed specifically in body-wall muscle in a lite-1 mutant background, and together with the calcium imaging and rescue data, they support a role for ADL and ASK in mediating high diacetyl avoidance. The tax-4 chemotaxis data is now included in the manuscript.

Does diacetyl activate other neurons expressing LITE-1? A number of cells express LITE-1 at high levels, which the authors have not tested (they restricted their analyses to chemosensory neurons). This is important to address because it leaves the possibility that LITE-1 requires a specific partner only present in these chemosensory neurons to detect diacetyl. This partner would have to be present also in muscles, where diacetyl could activate ectopically expressed LITE-1. According to CeNGEN scRNAseq data, cells expressing LITE-1 can be identified. The ADL and ASH neurons actually come up only at the lowest threshold, so some of the other cells showing much higher levels of LITE-1 mRNAs, i.e., AVG, ALM, PLM, ASG, PHA, PHB, AVM, RIF, or some pharyngeal neurons, should be tested. ASG was among the cells the authors recorded from, but this neuron did not show a response.

We have acknowledged and discuss in the manuscript that other non-sensory neurons may contribute to the avoidance behavioural, and which should be the future direction for investigation.

The authors need to show that diacetyl responses of ADL and/or ASK can be rescued by expressing LITE-1 specifically in these neurons in a lite-1 mutant background.

We have expressed lite-1 genomic DNA under the ADL-specific promoter srh-220, which restored the avoidance phenotype, although it is not a complete rescue of wild-type behaviour. Together with calcium imaging data, this suggests that proper avoidance likely requires input from both ADL and ASK neurons.

Molecular docking studies are not described in detail. How was this done?

Molecular docking was performed in two stages. First, diacetyl was docked to the tetrameric LITE-1 model using DynamicBind without a predefined binding pocket. The generated complexes were ranked using the DynamicBind confidence score, and the highest ranked poses were used to identify the candidate binding site. The top DynamicBind pose was then used to define the box region for redocking with Gnina. Gnina poses were ranked using the CNN score. A more detailed molecular docking procedure has now been updated in the Methods section.

Diacetyl is a very small molecule. How well can docking algorithms assess this at all?

We agree that the small size of diacetyl limits the precision of docking scores because it forms relatively few protein contacts. However, its small size and limited conformational flexibility also simplify pose sampling. To increase robustness, we used two conceptually different docking approaches. First, DynamicBind was used without a predefined binding pocket to identify candidate binding regions while allowing ligand-associated protein conformational adjustments. Second, the resulting pocket was subjected to focused redocking and CNN-based pose ranking with Gnina. The results are interpreted as a structural hypothesis for the probable binding site and relative affinity ranking, rather than as definitive proof of binding or an accurate quantitative affinity measurement.

Did the authors preselect the binding pocket, or did the algorithm sample the entire molecular surface of the LITE-1 model and end up with the binding pocket?

The binding pocket was not predefined. Diacetyl was first docked to the tetrameric LITE-1 model using DynamicBind without specifying pocket residues or grid coordinates. DynamicBind therefore performed global, pocket-agnostic docking. The highest-ranked poses identified a candidate pocket, which was then used for focused redocking with Gnina.

The latter would be very convincing. The authors should provide control docking experiments with other molecules that caused avoidance in their hands (i.e. benzaldehyde, 2,4,5,trimethlythiazole, isoamyl alcohol, nonanone, octanone), but did not activate LITE-1. Also, they should try docking molecules related to diacetyl, and if there are some that do not dock under the same conditions, such molecules should be used in a behavioral experiment. Ideally, they should also not activate LITE-1. Examples could be, e.g., diacetyl monoxime or 2,4-pentanedione.

We have now included docking data of the other odorants from the chemotaxis assays. 2-butanone, which is avoided by lite-1 mutants, was predicted to have a slightly higher binding affinity for LITE-1 than 2,3-pentanedione. This highlights the need to interpret the in silico docking data together with real experimental data, rather than using the computational predictions alone to infer functional receptor activation.

Last, the authors should provide a PDB file with the docked diacetyl to allow readers to assess the binding for themselves. Since a large number of mutations of LITE-1 have been reported, it may be that amino acids shown to be essential for LITE-1 function are also required for diacetyl binding. If so, this could be backed up with an experiment.

We agree that structure-function analysis using LITE-1 point mutants could help identify regions or residues that contribute to the diacetyl response and which we have highlighted in the discussion as an important future direction for research. Additionally, we have now provided the PDB file containing a representative DynamicBind derived docking pose of diacetyl within the LITE-1 binding pocket.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) lite-1 mutant animals, as described, fail to avoid' high concentrations of diacetyl, but isn't it more accurate to say that the valence of the response is changed from avoidance to attraction? Do the authors believe that attraction is mediated by ODR-10? Can you build an odr-10; lite-1 double mutant and determine if they lose this attraction to high concentrations of diacetyl and/or 2,3 pentanedione?

Our initial interpretation was that, in the absence of LITE-1-mediated avoidance, attraction to high concentrations of diacetyl is driven by the low-concentration receptor ODR-10. Interestingly, however, the lite-1; odr-10 double mutants remained strongly attracted to high concentrations of diacetyl, suggesting that this phenotype is independent of ODR-10 and may instead be mediated by other, less specific odorant receptors. This is consistence with the odr-10 mutants not completely losing attraction to low concentration of diacetyl, suggesting the involvement of other potential/putative receptors (Sengupta et al., 1996; Taniguchi et al., 2015). The lite-1; odr-10 double mutant data is now added in the results section (lines: 82 to 90; Figure S1B).

(2) For ADL, yes, it seems like lite-1 mutants have a reduced diacetyl response, but the ASK response seems... different. While it goes up (slowly) in wild-type, cellular Ca2+ levels in ASK (and maybe ADL) are *reduced* by diacetyl in lite-1 mutants. Can the authors comment on this, and the behavioral responses change in valence?

ADL and ASK are involved in both attractive and aversive responses so it is possible that the attractive component of the diacetyl response suppresses their activity. In the absence of LITE-1 activation, the observed decrease in calcium responses may reflect the unopposed inhibitory input. The slower decay of the calcium signal in ASK neurons of unc-13 mutants further supports the presence of additional inhibitory signals influencing their activity. This explanation is now included in the results section (lines: 101 to 117).

(3) ADL and ASK calcium traces in unc-13 mutants look generally similar and lack the effects seen in lite-1 mutants. Does that mean the lite-1 effect is in cells other than ADL or ASK? Can the authors spend more time discussing these differences?

We acknowledge that LITE-1 is expressed in multiple cell types beyond chemosensory neurons, and that non-chemosensory neurons may also contribute to the observed phenotype. In the previous version, we had highlighted the interneuron AVG as a potential contributor, given its role in light-induced escape. In the revised discussion, we have now expanded this section to include additional possible contributors such as the LITE-1-expressing phasmid neuron PHA and the pharyngeal interneurons I2, both of which have been implicated in hydrogen peroxide sensing (lines: 177 to 181).

(4) Chemotaxis responses to diacetyl and 2,3-pentanedione in lite-1 mutants are rather different. Diacetyl switches from repulsive (CI < 0) to *attractive* (CI > 0), which is not what would be expected for mutations that eliminate a receptor. In contrast, the 2,3-butanedione responses are more what would be predicted: diacetyl goes from inhibitor to no effect (CI ~0). Again, if the authors feel that this is because of the ODR-10 function, can they discuss whether 2,3-butanedione is predicted to bind ODR-10 like diacetyl?

We think a switch to attraction is expected for the removal of a receptor for an aversive signal, in an attractive background signal. We did indeed think that this attraction was mediated by odr-10, but the double mutant results now show other receptors must be involved. This is also not wholly unexpected since previous work has identified other receptors of high-concentration diacetyl and the original odr-10 paper didn’t report a complete absence of diacetyl response, suggesting the presence of other receptors that mediate attraction to diacetyl (Sengupta et al., 1996; Taniguchi et al., 2014). The new data is now added in the results section (lines: 82 to 90; Figure S1B; Supplementary video 1 and 2).

(5) Were the behavior experiments performed in the dark? I realize the calcium imaging experiments and some of the video behavior recordings are not possible in complete darkness, but maybe the authors made efforts to exclude visible light effects (e.g., infrared illumination, etc.) in some assays that might help determine whether light plays *no* role in the effects observed. Alternatively, the authors could try repeating their chemotaxis experiments in the dark or at least communicate in the methods that this was not viewed as a concern (and why). As the authors propose and discuss LITE-1 modulating diacetyl responses via light sensation as a possibility, it is incumbent upon them to communicate the steps they took to overcome this concern for themselves.

We acknowledge this and have performed a chemotaxis experiment to compare assays performed under dark and ambient light conditions, and no significant differences were observed (results section: lines 75 to 80; Figure S1A, material and methods section: 241 to 246). Therefore, subsequent chemotaxis assays were carried out under ambient light while avoiding exposure to strong illumination.

Paralysis assays were repeated under red-filtered illumination to minimise light effects, with animals maintained in darkness from hatching to adulthood. Additionally, the assay was expanded to include lite-1 mutants, ruling out contributions from neuronal LITE-1 to paralysis. The new data is now incorporated into the result section (diacetyl; lines: 135 to 137, figures 4 and S3; 2,3-pentanedione; lines: 152 to 154, figures 4 and S6), materials and methods section have been updated to reflect these changes (lines: 250 to 253 and 257 to 260).

Reviewer #2 (Recommendations for the authors):

Minor Issues:

(1) Pmyo-3::LITE-1 worms shrink in the absence of odorants (Figures 3C, 4D); possible effects of ambient light should be discussed.

We acknowledge the possibility that worms expressing LITE-1 in body-wall muscle experience minor contractions under ambient light, though it is not sufficient to cause paralysis.

To minimise potential light-induced effects, the paralysis assays were repeated with red-filtered illumination to reduce light stimulation of LITE-1. Animals were maintained in darkness from hatching to adulthood, and in the updated assay, worm length remained relatively constant.

The new data (Figures 3C, 4E, S4C and S6C) and materials and methods section has been updated accordingly (lines: 250 to 253 and 257 to 260).

(2) The title is misleading, as ASH does not show altered activity in lite-1 mutants and should be removed from the claim.

ASH has been removed from the title (line: 97).

(3) Specific Kd values should be provided for the reported micromolar binding affinities.

The values from DynamicBind and Gnina are provided in Figure S5.

Recommendations:

(1) LITE-1, a member of the gustatory receptor family, was previously shown to mediate UV light responses in C. elegans. In this study, Koh and colleagues demonstrate that LITE-1 is also required for the nematode's avoidance of high concentrations of diacetyl - an odorant that is attractive at low levels but aversive at higher concentrations. Using calcium imaging, the authors show that LITE-1 is necessary in the sensory neurons ADL and ASK for calcium transients in response to high concentrations of diacetyl. Additionally, they find that expressing LITE-1 in body-wall muscles causes hypercontraction upon diacetyl exposure. Similar LITE-1-dependent responses were observed for 2,3-pentanedione, another structurally related odorant. Molecular docking analyses suggest that both diacetyl and 2,3-pentanedione directly bind to LITE-1 with micromolar affinity.

These findings are intriguing and have the potential to significantly advance our understanding of LITE-1 as a multimodal sensory receptor. However, several major issues need to be addressed to support the authors' conclusions:

(1) Rescue experiments are missing. The authors should rescue at least one lite-1 mutant to confirm that the observed avoidance defects are specifically due to loss of lite-1.

Because the avoidance defect was observed in three independent lite-1 alleles, we think background mutations are unlikely to be causal. We have now also performed a rescue experiment with lite-1 expressed in ADL neurons. In this strain, attraction is restored. The new data have been incorporated into the results section (lines: 119 to 121; Fig. 2D).

(2) Alternative loss-of-function approach. To strengthen the findings, the authors should use a different method to disrupt lite-1 function-such as RNAi by feeding or cell-specific RNAi driven by the lite-1 promoter (see PMID: 17459615).

We believe the multiple alleles (Fig. 1B) and new cell-specific rescue experiment (Fig. 2D) provide sufficient support for the conclusion that the phenotype is due to loss of lite-1 function.

(3) Clarify the role of ADL and ASK neurons. While calcium imaging data show reduced activity in these neurons in lite-1 mutants, it remains unclear whether lite-1 is required in ADL, ASK, or both for avoidance behavior. Cell-specific rescue or RNAi experiments, along with additional calcium imaging, are needed to determine the contribution of each neuron.

We agree that more in-depth work will be required to dissect the neuronal pathways involved in LITE-1-mediated diacetyl avoidance. We have avoided making specific comments on how exactly the observed imaging results relate to the behavioural phenotype. The new rescue experiment expressing lite-1 in ADL does at least show that LITE-1 in ADL is sufficient for avoidance, although it’s not a complete rescue to wild-type levels (lines: 119 to 121; Fig. 2D).

(4) Validation of molecular docking results. While molecular docking suggests direct binding of odorants to LITE-1, experimental validation is needed. Mutations that reduce predicted binding affinity (engineered in transgenes or via CRISPR) should be tested for functional impact on avoidance behavior.

We agree the docking does not in itself establish direct binding (we think the muscle expression and paralysis provides much stronger evidence). Based on previously reported docking experiments, we were simply curious whether diacetyl would be predicted to occupy the same binding pocket. We have now updated the discussion in the use of lite-1 mutants to test for impact on diacetyl avoidance (lines: 188 to 191).

(5) Include analysis of non-binding odorants. Docking results should also be presented for odorants that did not elicit LITE-1-dependent avoidance, to help establish specificity.

We have now included docking data of odorants from the chemotaxis assays, with the corresponding docking values shown in Supplementary Figure 5. 2-butanone, which is avoided by lite-1 mutants, was predicted to have a slightly higher binding affinity for LITE-1 than 2,3-pentanedione. This highlights the need to interpret the in silico docking data together with real experimental data, rather than using the computational predictions alone to infer functional receptor activation.

(6) Figure 2C concerns. In neurons such as AWA, calcium transients in unc-13 mutants appear reduced compared to wild-type. A statistical comparison for all the neurons should be included to assess significance.

Scatter plots of calcium imaging responses across the different sensory neurons were generated, and statistical significance was assessed using two-sided t-tests with FDR correction. Only ADL and ASK neurons showed significant differences between lite-1 mutants and wild-type animals. No significant differences were observed in any neuronal pairs between unc-13 mutants and wild-type, including AWA neurons, although the difference is close to significant (p = 0.07). The scatter plots were now included as Supplementary Figure 3.

Minor points:

(1) Figures 3C and 4D: Pmyo-3::LITE-1 worms appear to shrink even without diacetyl or 2,3-pentanedione. Could this be due to ambient light? The authors should discuss this possibility.

We acknowledge the possibility that worms expressing LITE-1 in body-wall muscle experience minor contractions under ambient light, though it is not sufficient to cause paralysis. We repeated the paralysis assays with red-filtered illumination to reduce light stimulation of LITE-1. Animals were maintained in darkness from hatching to adulthood, to minimise potential light-induced effects, and in the updated assay, worm length remained relatively constant.

The new data (Figures 3C, 4E, S4C and S6C) and materials and methods section has been updated accordingly (lines: 250 to 253 and 257 to 260).

(2) Title revision needed: The title "Chemosensory neurons ADL, ASK, and ASH are involved in avoidance of diacetyl" is misleading, as calcium transients in ASH appear unaffected in lite-1 mutants. The title should reflect the actual data.

ASH have been removed from the title (line: 97).

(3) Binding affinity clarification: The authors report micromolar binding affinity for LITE-1 but should provide specific dissociation constants for clarity and completeness.

The values from DynamicBind and Gnina are now provided in Supplementary Figure 5.

Reviewer #3 (Recommendations for the authors):

How did the authors measure body length if the animals were swimming in the diacetyl solution? Standard 6-well plates have an area of roughly 10 cm², meaning that if one adds 1 ml, the liquid level should be 1 mm. The animals would be able to move in 3D, so it is likely that animals swim up and down and do not move in one flat plane, i.e., head and tail would be out of focus, and only a projection image would be recorded that would lead to an underestimation of actual worm length.

We acknowledge that this issue may led to an underestimation of worm length in the previous assay. However, every effort was made to exclude worms that moved out of focus. The paralysis assay has since been modified to include spreading a thin layer of solution across the worms, which keeps them mostly in focus, particularly those that are paralysed. Worms that were partially out of focus were excluded from the analysis.

We have incorporated the new data into the results section, reflected in the updated Figures 3C, 4E, S4C, and S6C. Corresponding revisions have also been made in the materials and methods (lines: 250 to 253 and 257 to 260).

Could diacetyl be a compound that results from UV absorption in cells? This may be worth discussing. What could be the precursor molecule?

We are not aware of such a precursor, but we cannot rule it out. Even if UV absorption in cells leads to diacetyl production, it is likely that the resulting diacetyl levels are insufficient to activate LITE-1, as our data suggest that LITE-1 functions as a receptor for high concentrations of diacetyl. We have updated the discussion accordingly (lines: 169 to 173).

In lines 57-61, the references to Edwards 2008 and Ward 2008 do not seem to fit the statements made in this sentence.

The inclusion of Edwards et al., 2008 was an error, and it has now been removed. Ward et al., 2008 demonstrated that ASJ phototransduction requires cGMP and CNG channels, stating that “Our studies indicate that C. elegans photoreceptor cells also employ CNG channels and the second messenger cGMP for phototransduction.”

so did he not actually want to return? he seems like he's sick to his stomach that he did. like a gut feeling of guilt maybe?

is it saying that if they pity a woman that you would be lower your standards? like it isn't okay to feel pity for a woman? what if it was a man?

he realized that making love and just sleeping with a women are two different things. i do not understand the last part at all lol

some of this is hard to understand

it is weird that he celebrated a divorce like it was a marriage

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this is a very interesting quote and makes me think a lot about life

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eLife Assessment

This study provides a valuable contribution to our understanding of the neural basis of perceptual decision-making by jointly modeling behavioral outcomes and EEG signals in a contrast comparison task. The methods and analyses are solid, systematically comparing standard models assuming continuous evidence accumulation with models that track evidence without temporal integration (extrema detection). The authors show that behavior and neural signals are equally consistent with both alternatives, highlighting limitations in current modeling approaches and questioning the generality of evidence accumulation mechanisms.

Reviewer #1 (Public review):

Summary:

This paper examines whether humans use protracted temporal integration in a noise-free, deferred-response contrast discrimination task, using a covert evidence-duration manipulation combined with EEG (SSVEP, CPP, Mu/Beta). The key finding is that evidence for protracted sampling is behaviorally and neurally supported, but even joint CPP + behaviour fitting cannot fully discriminate a standard integration (DDM) model from a novel "extremum-flagging" non-integration model. The paper is transparent about this outcome.

Strengths:

This is a well-conducted and well-written study that makes a genuine contribution to the perceptual decision-making literature by introducing a clean experimental design for probing temporal integration without participants adapting their strategy and demonstrating for the first time that a non-integration model (extremum-flagging) can replicate CPP waveform dynamics that have long been considered hallmarks of evidence accumulation. The transparent treatment of equivocal modelling outcomes is commendable.

Weaknesses:

My main concerns relate to statistical power, the under-specification of the and the extremum-flagging mechanism. Addressing these would greatly strengthen the paper.

(1) The sample of 16 participants (15, after the exclusion of one participant) is described as "close to similar EEG studies" with no formal power analysis. Given that the paper's core claim rests on subtle quantitative differences between two model classes - differences that are, by the authors' own admission, not sufficient to declare a winner - even a modest increase in sample size might yield a more decisive outcome. At minimum, the authors should report a sensitivity analysis or post-hoc power calculation to indicate what effect sizes the current N could reliably detect, particularly for the rmANOVA comparisons and the neural constraint fitting.

(2) The Extremum-flagging model is the paper's most novel contribution, yet its physiological basis is underspecified. The model posits that each decision-terminating bound-crossing triggers a stereotyped, half-sine-shaped centroparietal signal, but no neural circuit or computational mechanism is proposed for how the brain could detect the first bound-crossing event in a non-accumulating evidence stream or generate a temporally precise, fixed-amplitude signal in response. Possible connections to P3b theories of context updating and response facilitation are acknowledged, but these are vague functional descriptions rather than mechanistic accounts. I think the discussion should engage more directly with potential neural substrates that could generate this flagging signal, and whether these are consistent with the known generators of the CPP/P3b. Without this, the extremum-flagging model risks being viewed as a mathematical convenience rather than a biologically plausible alternative.

(3) The Integration model at the preferred neural weighting estimates a high-to-low contrast drift rate ratio of 8.7, whereas the empirical Mu/Beta lateralization slopes suggest a ratio of approximately 3.5. The authors attribute this discrepancy to the nonlinear contrast response function of early visual cortex and the salience of the high-contrast evidence onset, but these explanations are speculative. These outcomes are arguably the most quantitatively damaging result for the integration model, so they deserves more than a brief discussion. I would recommend that the authors (a) estimate what range of contrast response nonlinearities would be required to close this gap, (b) test whether an alternative drift rate parameterization (e.g., scaling drift rates directly by SSVEP amplitude rather than contrast) reduces the discrepancy, or (c) be more explicit about treating this as a point against the Integration account.

(4) The sensitivity analysis over neural constraint weightings (w = 0.1 to 1000) is thoughtful, but the paper ultimately acknowledges that the preferred weighting is w=10, chosen because it achieves "a good fit to CPP dynamics without substantively sacrificing behavioral fit" - a qualitative criterion. No principled statistical framework is used to select the optimal weighting or to compare models at a given weighting. A Bayesian model comparison could provide a more formal framework for combining behavioral and neural fit components, and would allow a clearer statement about the relative posterior probability of each model.

Comments on revisions:

In reply to my comments, the authors have added a post-hoc power analysis that provides adequate justification for the sample size, a more nuanced discussion of neural mechanisms that could support an extremum flagging model, and several supplementary analyses that show the generality of findings across parameter levels. These are welcome additions that strengthen confidence in key findings while acknowledging nuances involved in quantitative analyses and model fitting.

Reviewer #2 (Public review):

The manuscript by Hajimohammadi, Mohr, O'Connell and Kelly is intended to demonstrate that participants integrate evidence over time to make a decision, even in a noise-free, static decision context. This is validated by the observation that 1) participant accuracy improves with increased exposure to the stimulus; and 2) there is a correlation between participant accuracy and a neural index of evidence accumulation, as measured by centro-parietal positivity (CPP).

Strengths:

(1) Joint modelling of accuracy and CPP dynamics is a significant achievement, as behaviour alone often cannot distinguish between competing theories of decision-making. In the case of protracted sampling in particular, the absence of reaction times (RT) due to the delayed nature of the response makes this method highly appealing.

(2) The experimental manipulations and the method used to extract the different neural indices are well chosen, enabling the mapping of putative cognitive processes such as evidence accumulation and motor preparation onto the recorded EEG with clarity.

(3) The in-depth discussion of the results clearly articulates those reported by the authors and in previous works.

Weaknesses:

(1) Regarding the first point I raised in the first version of the manuscript, I'm satisfied with the author's response.

(2) For the second point, however, I'm still unconvinced, noting that my understanding of the fitting routine is relatively limited. To me, the comparison of the behavioral and the joint-modelling is relatively weak in terms of evidence. Despite the revision, it is still unclear whether the behavioral models are well conditioned given the very weak constraint exerted by (aggregated, see below) binary decision data only. To my previous comment, the authors reply, "We can instead address identifiability somewhat indirectly through parameter estimate consistency across the 10 fits we conducted with different instantiations of noise". I'm worried that the different instantiations of noise (limited to 10), generated for the parameter search, have any impact at all. A better quantification of the uncertainty in parameter estimates, as well as the AIC, is needed, for example through a cross-validation scheme, or bootstrapping/jack-knifing at the level of the participants. Without this, I remain unconvinced that the behavioral models are suited for the comparison with the joint-neural models:

(3) Relatedly, regarding the response of the authors to my minor comment 4, after the revision of the manuscript, it is clearer that the behavioral models were fitted only on 6 data points (accuracies averaged over participants for each condition) for models with up to 3 parameters. I understand that the authors average behavior to make a comparison with the joint neural model, whose neural signals are noisy at the participant level. However, if the neural model, or the chosen linking function, needs this aggregation to outperform the behavioral model, that questions a bit whether the joint model is really useful in practice.

While I see these two last points as serious for the comparison between behavioural and joint-neural models, this does not change the main addition of the paper: that is, the joint model and the evidence for protracted sampling.

Reviewer #3 (Public review):

Summary:

The authors aim to compare proposal models of perceptual decision making using a joint modeling approach, where they fit models to both behavioral outcomes as well as CPP. Most notably, they compare a standard evidence accumulation model with models that track the evidence without integrating it over time (extrema detection). The authors report that the joint CPP-behavioral data do not discriminate between two of their proposals.

Strengths:

This is an interesting finding that reinforces the idea that what we believe to see based on aggregation over trials may not be what happens on every single trial. The models are creative and the simulations are convincing, relating the models to multiple neural markers of decision formation. These include the CPP but also mu/beta power spectra.

Initial weaknesses:

Contrary to the original draft, the current version now clarifies the goals of the study as well as the role of internal vs external noise.

The authors clarify their stance on the optimality of extrema detection in the Public Review, where they make some good points. Specifically, they argue that models that have studied optimal decision making in the past have considered a quite narrow definition of optimality, for example, ignoring energetic costs.

The authors clarified the fixed value of the scaling parameter (which apparently was already mentioned in the initial draft, which I had missed). My comment about the discrepancy between the bounded integration model and the EZ diffusion model can thus be ignored. However, I do think that the similarity of the bounded integration model and the EZ diffusion model could be acknowledged in the manuscript somewhere.

I still think the choice of modeling the grand average accuracy and EEG signal may distort the results. As Figure 1 reveals, there is in fact substantial variation in the change in accuracy across conditions. I can imagine that one model may be preferred over another based on the aggregate data, while the other model is preferred for some participants. On the group level, this may lead to different conclusions, at least quantitatively (e.g., in terms of G2), but potentially even qualitatively. This is especially true for the neurally-informed models because of the covariation between behavioral and neural data.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This paper examines whether humans use protracted temporal integration in a noise-free, deferred-response contrast discrimination task, using a covert evidence-duration manipulation combined with EEG (SSVEP, CPP, Mu/Beta). The key finding is that evidence for protracted sampling is behaviorally and neurally supported, but even joint CPP + behaviour fitting cannot fully discriminate a standard integration (DDM) model from a novel "extremum-flagging" non-integration model. The paper is transparent about this outcome.

Strengths:

This is a well-conducted and well-written study that makes a genuine contribution to the perceptual decision-making literature by introducing a clean experimental design for probing temporal integration without participants adapting their strategy and demonstrating for the first time that a non-integration model (extremum-flagging) can replicate CPP waveform dynamics that have long been considered hallmarks of evidence accumulation. The transparent treatment of equivocal modelling outcomes is commendable.

Weaknesses:

My main concerns relate to statistical power, the under-specification of the and the extremum-flagging mechanism. Addressing these would greatly strengthen the paper.

(1) The sample of 16 participants (15, after the exclusion of one participant) is described as "close to similar EEG studies" with no formal power analysis. Given that the paper's core claim rests on subtle quantitative differences between two model classes - differences that are, by the authors' own admission, not sufficient to declare a winner - even a modest increase in sample size might yield a more decisive outcome. At a minimum, the authors should report a sensitivity analysis or post-hoc power calculation to indicate what effect sizes the current N could reliably detect, particularly for the rmANOVA comparisons and the neural constraint fitting.

We appreciate the reviewer’s concern regarding sample size and statistical sensitivity. To address statistical robustness throughout the paper, we have now reported effect sizes for our statistical tests (e.g. η2 for rmANOVA; including Tables S1 and S2), and we provide error-shading around the ERP waveforms to indicate the reliability of the key patterns our models are aimed at capturing (i.e. the dramatically higher and earlier CPP peak for high-contrast, and very little systematic differences across the four low-contrast durations - see revised Figure 3). We also conducted an indicative post-hoc power analysis using the G*Power software based on the behavioural data. Using the observed partial η2 = 0.44 for the test of duration effect on accuracy among only the low-contrast conditions, and the final sample of 15 participants, this amounts to a statistical power of 0.998.

On the model comparison, while we agree that larger sample sizes are generally beneficial for population-level inferences, we respectfully maintain that our current sample size is sufficient to support the core claim that qualitative dynamics of neural signatures of decision formation, usually assumed to reflect temporal integration, can be successfully reproduced using non-integration models in the delayed-response task conditions we examine here. Any marginal changes in quantitative fit resulting from having a higher N contribute to grand averages are unlikely to substantively alter this conclusion of the model comparison. The statistical reliability of the data to which our models are fitted is also bolstered by the number of trials (about 256 per condition per participant). It is common in behavioural modelling studies for data to be collected from a much smaller sample (e.g., fewer than 10 subjects) but with a high trial yield - a relevant precedent for us being Stine et al., (2020), who provided a compelling demonstration of similar model fits for Extrema and Integration models using only 6 subjects. In sum, the key qualitative data patterns of accuracy improvements with duration and broader, lower and duration-invariant low-contrast CPPs are statistically robust and provide a strong basis to reveal the fundamental principle that the Extremum-flagging and Integration models are both able to produce these key qualitative dynamics.

(2) The Extremum-flagging model is the paper's most novel contribution, yet its physiological basis is underspecified. The model posits that each decision-terminating bound-crossing triggers a stereotyped, half-sine-shaped centroparietal signal, but no neural circuit or computational mechanism is proposed for how the brain could detect the first bound-crossing event in a non-accumulating evidence stream or generate a temporally precise, fixed-amplitude signal in response. Possible connections to P3b theories of context updating and response facilitation are acknowledged, but these are vague functional descriptions rather than mechanistic accounts. I think the discussion should engage more directly with potential neural substrates that could generate this flagging signal, and whether these are consistent with the known generators of the CPP/P3b. Without this, the extremum-flagging model risks being viewed as a mathematical convenience rather than a biologically plausible alternative.

We thank the reviewer for this constructive comment. While the focus of this paper was indeed on simple mathematical descriptions in the spirit of classical cognitive modelling, we agree that expanding on the potential neural substrates of the Extremum-flagging model strengthens its utility as an alternative framework. We have revised the Discussion to engage with potential biological mechanisms, particularly those previously proposed to underlie the P300/P3b, such as Nieuwenhuis’ (2005) proposal that it reflects a phasic arousal response mediated by the LC/NE system that serves to activate task-relevant areas following completion of a decision. We agree that aside from this, accounts of ERP component functions over the years have often been vague and non-mechanistic, but the idea that they reflect discrete neural activations marking an internal cognitive event in a stereotyped way persists, and remains a basic assumption of several new and influential ERP signal analysis toolboxes (e.g. Ehinger 2019; Weindel 2024). If the flagging signal’s fixed amplitude seems physiologically implausible, all-or-nothing neural activation events are not generally unheard of in neurophysiology, and, again, we are taking an approach favouring parsimony in the spirit of cognitive modelling, and we found that we did not need to assume any variation in the amplitude of the flagging signal in order to capture the key decision signal dynamics alongside behavioural accuracies in this particular case.

We also discuss the study of Latimer et al. (2015), who demonstrated that discrete, step-function state transitions that on single trials may mark extrema detection events, can produce ramp-like signals when trial-averaged. While the biological plausibility of such step-function dynamics remains a subject of debate, it serves as another example of how continuous evidence integration is not the only way to reproduce the ramping neural signals traditionally observed in grand-average neural signals.

(3) The Integration model at the preferred neural weighting estimates a high-to-low contrast drift rate ratio of 8.7, whereas the empirical Mu/Beta lateralization slopes suggest a ratio of approximately 3.5. The authors attribute this discrepancy to the nonlinear contrast response function of early visual cortex and the salience of the high-contrast evidence onset, but these explanations are speculative. These outcomes are arguably the most quantitatively damaging result for the integration model, so they deserve more than a brief discussion. I would recommend that the authors (a) estimate what range of contrast response nonlinearities would be required to close this gap, (b) test whether an alternative drift rate parameterization (e.g., scaling drift rates directly by SSVEP amplitude rather than contrast) reduces the discrepancy, or (c) be more explicit about treating this as a point against the Integration account.

We agree that the quantitative discrepancy we demonstrated between the empirically observed buildup rate ratio in motor preparation signals (3.5) and the greater drift-rate ratio (8.7) required by the Integration model to fit the CPP waveforms is an important one that should be emphasised and discussed with greater depth and clarity. As we said, nonlinear contrast response functions and a boosting effect of the salient high-contrast step-change are two plausible ways that a drift rate might scale disproportionately more steeply with contrast, but in principle, assuming straightforward transmission of evidence accumulation to the motor level, Mu/Beta lateralization slopes should then reflect this steeper drift rate scaling, or at least approach it even when allowing for some temporal blurring. We have thus put more emphasis on the discrepancy by confirming that if we constrain the drift rates to be directly proportional to contrast, the Integration model is indeed significantly hampered in its ability to produce the much steeper CPP buildup for higher-contrast trials, much more so than the Extremum-flagging model (Figure 4 - Supplement 7). We have also applied a temporal blurring equivalent to the short-time Fourier Transform to the simulated motor preparation waveforms (convolving with a boxcar of the same duration as the Fourier window) in Figure 4N-P so that the real and simulated traces are on an equal footing in this respect. We have also revised the Discussion to elaborate on how this quantitative discrepancy represents a point against the Integration account, and possible ways it might be reconciled with an Integration account. One reason, for example, why the relative steepness of the Centroparietal ERP in the high-contrast condition so far exceeds that of Mu/Beta might be that additional processes are evoked by the very salient step-change, which may make a positive-polarity contribution to the centroparietal ERP waveform and hence cause overestimation of how early and steeply the underlying, high-contrast CPP decision signal rises. We looked into this by carefully examining time courses and topographies through the initial period of buildup, with no additional smoothing low-pass filter applied, now presented in Figure 3 - Supplementary Figure 1. While the smoothed waveforms that we show in the main paper and to which we fit models could be seen to have a brief inflection during the main buildup for the high-contrast condition, removing the smoothing shows that this arises not from random noise but from a distinct bimodal morphology, with a distinct early peak and lull during the buildup, which temporally coincides with a very strong bilateral occipital N2 (associated with a low-level evidence-onset detection or ‘target selection’ process - Loughnane et al 2016), in a way that suggests that the positive tail-end of the dipolar neural generators of the N2 may contribute to the initial part of the positive centro-parietal buildup. It is difficult to estimate the extent to which the neurally-constrained model estimate of high-contrast drift rate is inflated by this initial overlapping potential, because we can’t precisely know the ground truth of the N2 tail’s contribution, but this analysis provides a potential explanation that can be explored in future (e.g. through softening the strong-evidence onset with a ramp or use of auditory evidence). We thank the reviewer for raising this as we feel that this extra discussion positively adds to the theme of the paper to highlight methodological challenges with neurally-constrained modelling. In the process, we have updated the methods section to present in full detail the centroparietal electrode selection and waveform smoothing that was applied to provide the models with a relatively uninterrupted buildup signal to capture, which is important for readers to appraise the potential impact of this overlapping potential.

(4) The sensitivity analysis over neural constraint weightings (w = 0.1 to 1000) is thoughtful, but the paper ultimately acknowledges that the preferred weighting is w=10, chosen because it achieves "a good fit to CPP dynamics without substantively sacrificing behavioral fit" - a qualitative criterion. No principled statistical framework is used to select the optimal weighting or to compare models at a given weighting. A Bayesian model comparison could provide a more formal framework for combining behavioral and neural fit components, and would allow a clearer statement about the relative posterior probability of each model.

We agree with the reviewer that theoretically, the Bayesian framework provides a principled way to combine behavioural and neural evidence by weighting each source according to its statistical reliability. However, a Bayesian formulation typically quantifies reliability through across-trial variance, which applies quite differently for accuracy and EEG data. While the precision of EEG measurements can be estimated empirically (e.g., from noise characteristics), we currently lack a formal measure of uncertainty for the linking function itself, that is, the theoretical mapping between neural signatures and latent decision processes. This represents an unresolved methodological issue rather than a straightforward parameter estimation problem. Second, although hierarchical Bayesian approaches are well established for standard diffusion models, the mechanisms examined here for extremum flagging do not currently have tractable closed-form formulations suitable for Bayesian integration. Developing a dedicated hierarchical Bayesian framework for these non-standard mechanisms would require substantial methodological work and is beyond the scope of this research.

Thus, rather than imposing a single assumed reliability relationship between neural and behavioural data, we chose to perform a systematic sweep across weighting values. We view this approach as a transparent sensitivity analysis that accommodates different scientific priors regarding the relative contribution of neural versus behavioural constraints. By presenting the full range of w (including in supplemental tables and figures), readers can directly evaluate how model behaviour changes when emphasis is shifted between behavioural data and neural data, transparently revealing how the behavioural and neural signal fits can trade against one another.

Reviewer #2 (Public review):

Summary:

The manuscript by Hajimohammadi, Mohr, O'Connell and Kelly is intended to demonstrate that participants integrate evidence over time to make a decision, even in a noise-free, static decision context. This is validated by the observation that (1) participant accuracy improves with increased exposure to the stimulus; and (2) there is a correlation between participant accuracy and a neural index of evidence accumulation, as measured by centro-parietal positivity (CPP).

Strengths:

(1) Joint modelling of accuracy and CPP dynamics is a significant achievement, as behaviour alone often cannot distinguish between competing theories of decision-making. In the case of protracted sampling in particular, the absence of reaction times (RT) due to the delayed nature of the response makes this method highly appealing.

(2) The experimental manipulations and the method used to extract the different neural indices are well chosen, enabling the mapping of putative cognitive processes such as evidence accumulation and motor preparation onto the recorded EEG with clarity.

(3) The in-depth discussion of the results clearly articulates those reported by the authors and in previous works.

Weaknesses:

(1) One main issue to support the interpretation of the authors toward the need for protracted sampling is the timing of the evidence. By design, participants believe that the signal is present for 1.6 seconds (reinforced by the fact that easy trials were displayed for 1.6 seconds). However, the difference in stimuli is turned off either 1.4, 1.2, 0.8 or 0 seconds before the cue to respond. While this makes sense in the context of the authors' question, it also raises the possibility that participants will focus on the last samples before answering. Even if participants apply equal weighting, this still favours them delaying evidence accumulation until they are sufficiently certain that the evidence should be present (e.g. participants might start accumulating after the stimulus has disappeared in the 0.2 condition). I do not see an easy way to test these alternative explanations outside of running a study in which the evidence is always offset before the go cue.

This is a reasonable question about the design - if participants were under the impression that they had a whole 1.6 sec of stimulation, couldn’t they afford to wait until later into the stimulus to start sampling? However, the task was designed to be so difficult that participants would be deterred from ignoring any initial evidence, and the fixed and explicitly instructed lead-in period as well as the interleaved easy trials, would have continually reinforced their ability to time their sampling onset quite precisely. Indeed, key aspects of the data confirm they did not appreciably delay sampling. First, accuracy in even the shortest (0.2 s) condition was reliably above chance (t(15) = 2.60, p = 0.0201) and improved steadily across evidence durations (Figure 1B). This places an upper bound on the accumulation onset: participants cannot have delayed accumulation until after the evidence disappeared and still achieve above-chance performance; if they only used the ‘last samples,’ at the end of the stimulus, they would have performed at chance level for all durations except 1.6 sec. Second, we fit a model that allowed for such a delayed sampling onset, captured in the parameter ‘sampT,’ which, across the range of neural weightings (Tables S3, S5-8), consistently landed within a few tens of msec of evidence onset (often slightly before rather than delayed), and improved the overall model fit very little relative to the addition of starting point variabilities or collapsing bound. The Methods section now addresses these aspects of task design.

(2) Regarding the behavioural models, are these identifiable based on accuracy data alone? This should be addressed using a parameter recovery study, in which a set of parameters is used to generate data, and the same fitting routine used for the real data is used to estimate the parameters. This would enable us to determine what can be inferred from the model comparison presented. This is not a serious problem for the manuscript, as it specifically aims to go beyond behaviour. It is, however, worth noting that such a parameter recovery addition could be used to demonstrate the need for a joint modelling framework to answer the question of protracted sampling on delayed response times (RT).

As the reviewer notes, we did have the specific aim of going beyond behaviour, and the need to do so is demonstrated in the inability to adjudicate between the alternative models based on behaviour alone. We took this as sufficient justification without a formal parameter recovery test to assess the degree to which behaviour-only models could accurately estimate parameter values. Still, we agree that it is valuable to address parameter identifiability in some way. Since a full parameter recovery covering the full possible parameter space for each of the many models would be too great in volume to add to this paper, we can instead address identifiability somewhat indirectly through parameter estimate consistency across the 10 fits we conducted with different instantiations of noise; we now provide the standard deviations alongside the mean parameter values for the D1, D2 and B parameters of each of the behaviour-only models in Table 1 - Table Supplement 1, which indicates that the parameter estimates were reliable across 10 different instantiations. 

Minor comments:

(1) I would advise authors to fix the D1 parameter and use it as a scaling parameter across all models. Currently, as I understand it, the models are scale-free, meaning the same fit is achieved by multiplying all parameters by two, for example. This makes the fit more complex (bounds on parameter values are required) and means that the models are less comparable in terms of their estimates. Perhaps I'm missing something, but I would have thought that fixing D1 (the common parameter across all models) would solve these issues.

The models are not scale-free because they are constrained relative to a fixed sampling noise parameter value of s = 0.1; All tables in the main text have now been updated to make this more immediately clear. Aside from this being standard in diffusion modelling (Ratcliff & Smith, 2004), this enabled us to replicate the observation by Stine et al., (2020) that since the non-integration models depend on the magnitude of individual evidence samples rather than an integration of many, the drift rate values must be set much higher to achieve the same choice accuracy as the integration models (Table 1).

(2) Why is the snapshot model so bad despite being a good model in Stine et al 2020? Can the authors speculate in the discussion?

We thank the reviewer for querying this. We had originally thought that the poor performance of the snapshot model made sense because the continued presentation of zero contrast difference for short-evidence trials renders it a bad strategy. Because our main purpose was to briefly substantiate the principle that accuracies alone are an insufficient basis for model comparison and move on to the main goal of jointly modelling accuracies and CPP dynamics, we did not take the same level of care to ensure we attained the very best fit of the behaviour-only models, as we did for the neurally-constrained models. In the neurally-constrained modeling, we took care to check for every parameter whether the range of allowed values (Table S4) was narrow enough to avoid the optimisation algorithm getting lost in untenable parts of parameter space, yet wide enough to include the optimum point, and wherever we saw parameter values landing at or near the edge of the allowed range we expanded that range and re-ran the model fit. Applying these same checks to the behaviour-only fitting, we found that the SnapShot model needed a wider range on drift rate and when we applied this, the fit was much more competitive, in line with Stine et al., (2020), though it remained the worst-fitting model among all two-drift-rate behaviour-only models (see updated Table 1). We similarly conducted these checks across all behaviour-only models and re-ran them. The extrema detection model with last-sample default when no bound is hit also improved its fit, though again it did not fit better than the version with guess default. Thus, the point we were making with this section, that behaviour alone can be captured competitively by a range of integration and non-integration models, is bolstered by the updated model fits. Since the last-sample default was competitive in the behaviour-only fits, we also ran a version of the Extremum-flagging model jointly fit to accuracies and CPP dynamics with a last-sample rather than random guess default when a bound was not reached, and show in new Figure 4 - Figure Supplement 8 that the conclusions are the same. Again, thank you for prompting us to look back at those fits.

(3) The meaning of the flag width is unclear. Figure 4 provides the reader with an intuitive understanding of the model that the authors have in mind. However, the tables in the appendices report values between 0.2 and 0.9. I understand that these values represent the width of the half-sine in seconds. This suggests that the actual estimated values for these flag events are much broader than those displayed in Figure 4. While this is probably fine for most models, it can be problematic for the extremum-flagging model, as it means that the rise to the peak takes between 0.1 and 0.45 seconds. While strictly speaking, this is still a 'flag' model, such a slow rise to the peak, given the usual expectation of evidence accumulation, would place this model closer to a smooth integration model than to a boundary-crossing flagging mechanism.

We thank the reviewer for raising this about the flag width parameter. In so doing, they enabled us to catch that our schematic depiction of the model in Figure 4 was misleading, and have now revised it to make clear that the flag signal is a post-decision one triggered by the bound crossing, and we have updated explanations accordingly (in ‘Neurally-constrained models’ and Discussion). The reviewer is correct that the reported values in the supplemental materials (approximately 0.2–0.9 s) correspond to the width of the half-sine kernel used to model the post-decision flag event. However, the flag signal is stereotyped, evidence-independent, and is triggered once the decision threshold has already been crossed, so it does not share the key characteristics of evidence integration, regardless of how wide the model estimates it. In the extremum-flagging model, the boundary crossing remains a discrete event. The width parameter instead captures the temporal extent of the neural process that follows this commitment event. Such a post-decision neural process unfolding over several hundred milliseconds is in line with some classic theories of the centroparietal P300/P3b component, and we now expand our discussion point on this to address proposed neural substrates (e.g. Nieuwenhuis et al’s (2005) implication of a phasic noradrenaline system response).

(4) In the modelling section, it is not clear overall (i.e. for G² and R²) how the participant dimension is taken into account. Are these individually fitted models, and if so, how are the secondary statistics generated from the individual estimates? Or were these fitted over all participants?

All models were fitted to the grand-average neural and behavioural data across participants, rather than to individual participant data. We chose this approach as the CPP signal at the individual level is highly noisy, which can introduce substantial instability and noise into the model fitting procedure. We have revised the Modelling section to explicitly state that the reported G² and R² values are derived from models fitted to the grand-average data, and in the revised discussion acknowledged this as a limitation of the current modelling framework.

(5) On page 7, in the last sentence of the first paragraph of the section titled 'Decision-Related Neural Signals', the authors state that 'this stable contrast-difference encoding suggests that a constant (i.e. non-adapting) drift rate is a reasonable simplifying model assumption'. However, I am not sure how this is true given that SSVEP quantifies encoding, yet the drift rate can vary due to non-sensory aspects (e.g. attention).

The reviewer makes a good point - even if sensory encoding is stable, non-sensory factors like attention could cause dynamic changes in the effective drift rate independently of the sensory representation itself. However, our point in that section, which we have revised to put more clearly, was to test for one particular well-known time-varying effect that could impact drift rate, namely sensory adaptation, a well-established phenomenon behaviorally and at the level of sensory neuronal responses, where prolonged stimulation produces reductions over time in sensory neural activity. If strong adaptation were present in the sensory evidence representation indexed by the SSVEP, we would expect corresponding temporal changes in the signal. The absence of such changes lends support to the simplifying assumption (as in most accumulation models) that the drift rate is approximately stationary over time, even if we cannot be sure there isn’t a time-varying effect downstream.

(6) The mu/beta lateralisation does indeed favor the integration model more, but in terms of boundary estimation and starting-point analyses, both models are pretty far apart. Providing an interpretation of this observation, e.g. regarding alternative linking functions for mu/beta, would add to the manuscript.

In response to this comment, we revised the manuscript in the Discussion to say that in the current analyses, we implicitly assume an approximately linear mapping between Mu/Beta amplitude and decision units. However, the true relationship may instead reflect another monotonic transformation (e.g., involving power rather than amplitude, logarithmic scaling such as dB units, or a nonlinear saturating function). This uncertainty could affect the apparent correspondence between the neural signal and the model-derived estimates of boundary position or urgency dynamics. While our analyses support a close relationship between Mu/Beta lateralisation and the evolving decision process, the precise quantitative mapping remains uncertain. One possibility is that urgency itself evolves nonlinearly (e.g., decelerating over time), even if the measured neural trajectory appears approximately linear under the current transformation assumptions.

Reviewer #3 (Public review):

Summary:

The authors aim to compare proposal models of perceptual decision making using a joint modeling approach, where they fit models to both behavioral outcomes as well as CPP. Most notably, they compare a standard evidence accumulation model with models that track the evidence without integrating it over time (extrema detection). The authors report that the joint CPP-behavioral data do not discriminate between two of their proposals.

Strengths:

This is an interesting finding that reinforces the idea that what we believe to see based on aggregation over trials may not be what happens on every single trial. The models are creative, and the simulations are convincing, relating the models to multiple neural markers of decision formation. These include the CPP but also mu/beta power spectra.

Weaknesses:

The paper makes some strong points, and the work seems generally well-executed. The weaknesses that I identified are twofold:

(1) Embedding in the literature/exposition of the main argument.

The focus in the introduction is on the noise-free nature of the stimulus and the prolonged presentation time. However, after reading the paper, I felt these were mostly experimental design choices that enable comparison of the different models using the CPP. Perhaps my misreading of the goals of the paper stems from two other observations:

(a) The fact that the stimulus is noise-free does not entail that perception is noise-free. Thus, the argument that using a noise-free stimulus precludes the necessity of temporal integration seems not completely valid. Of course, one could argue that noise is limited in this case, but that makes a noise-free stimulus more of a design choice.

(b) The focus on prolonged stimulus presentation, but at the same time the contrast with expanded judgement, did not make sense to me. Perhaps, as a non-native speaker, I am misreading the subtle difference between "protracted sampling" and "longer sampling", but again, the longer duration seems mostly a design choice.

We thank the reviewer for this impression, which has helped us revise the introduction to more clearly motivate the paradigm as an interesting case for close examination. The primary driver of our choice of stimulus and task parameters was not to enable model comparison using the CPP; it was to examine a decision scenario that exists in everyday life but that has not been examined in terms of underlying decision mechanisms because it offers only sparse behavioural data - the scenario in which plainly visible objects (without noise or stochasticity, as in daylight conditions) need to be examined for a subtle feature difference to guide a later action. The reviewer echoes our point in the Intro, that despite the absence of physical noise in the stimulus, perceptual processing itself is not noise-free. Therefore, temporal integration is certainly not precluded, but its benefit is minimised and less obvious to the decision maker. Given the examples we raise where integration was found to not be employed to its optimal extent (e.g. bound setting foregoing accuracy improvements with duration), and the various theoretical accounts citing energy costs associated with integration and the fleeting nature of many natural environments where prolonged deliberation about a static stimulus is not the norm (e.g. Uchida et al 2006), it is quite hard to guess a priori whether humans will engage in protracted sampling and integration in this case, in practice, even if it is optimal under basic assumptions. As we make clear in our revised Intro, this theoretical interest in the uncertain case of long, noise-free stimuli where perfect, unbounded integration may be optimal but seems doubtful given extant empirical findings, is coupled with a methodological interest in the extent to which neural signatures of decision formation can ‘come to the rescue’ and provide grounds for reliable adjudication between competing mathematical models, when behavioural data fall short.

More could be said about the optimality of the extrema detection methods. In particular, decades of work (centuries?) have shown that evidence integration is an optimal decision-making procedure: For example, the Sequential Probability Ratio Test is Bayes-optimal wrt mean RT (Wald, 1946); evidence accumulation together with collapsing threshold serves to maximize rewards in repeated choices (e.g., Bogacz et al., PsychRev, 2006; Boehm et al. APP, 2020). Given all this work, why would the brain have evolved to adopt a different mechanism? I realize that the paper is not about optimal decision making, but some discussion of this point seems warranted.

We had a similar impression initially when reading Stine et al., (2020) where extrema-detection was pitted against integration - is extrema detection so suboptimal that it is too implausible to even consider? Ditterich (2006) argued that signal-to-noise ratio would have to be implausibly high for extrema-detection to produce the behaviour observed on typical decision tasks. However, the fact is, we do not know the effective signal-to-noise ratio, nor can we precisely quantify the costs associated with prolonged evidence accumulation, such as attentional or energetic costs (Drugowitsch et. al., 2012). Even if the extrema detection strategy appears implausibly suboptimal, it is an important principle to demonstrate how not only behavioural but also neural decision signal dynamics can be so nicely consistent with integration yet technically can be quantitatively captured with non-integration mechanisms.

(2) Modeling choices.

The authors introduce a parameter, sampT, that represents uncertainty in the sampling onset time. It was not clear to me whether this parameter represented an offset of all trials, or a distribution (probably the latter). I wonder how exactly this parameter was integrated into the models, and in particular, if and how it interacts with the starting-point parameters. My intuition is that on a single-trial, IF early sampling occurs, you can model that with either a negative sampT and z at 0, or with sampT at 0 but a shift in z. This would suggest trade-offs between these parameters, making them hard to estimate independently. Since the paper does not depend on the identification of parameter estimates, this may not be a huge problem, but nevertheless it is good to explore the consequences.

We thank the reviewer for raising an important question regarding the relationship between sampT and starting-point variability (sz). Mechanistically, early accumulation onset can indeed generate effects that resemble starting-point variability: if accumulation begins during a period containing only zero-mean noise, then by the time informative evidence appears, the decision variable will already have diffused away randomly from zero. In this sense, negative sampT can induce variability in the state of the accumulator at evidence onset. However, the two mechanisms are not mathematically equivalent. The sz parameter assumes a uniform distribution over starting points, whereas the variability induced by early accumulation onset would instead reflect the distribution resulting from integrating zero-mean Gaussian noise over variable durations. Aside from this distinction between distribution shapes, the reviewer is correct that these parameters could partially trade off with one another when sampT takes negative values. In our model, however, sampT was allowed to take either positive or negative values. Positive values delay the onset of evidence integration relative to the evidence, thereby ignoring the first samples, very different from the effect of starting point variability. Nevertheless, to the extent that they can partially trade off each other to some degree, the consequent problem this might cause to accurately estimating both parameters is part of the reason we do not fit a model that includes both simultaneously.

The way the Bounded Integration model (BIntg) is formulated seems very close to the EZ-diffusion model (Wagenmakers et al., PBR, 2007). This model states that the proportion of correct responses Pc = 1/(1+exp(-B*D/s^2), with B and D the bound and drift rate parameters, respectively. However, filling in the numbers for the high contrast condition from Table 2, and assuming that s=2 (because the model description states that dt=2, with s undefined), I get a Pc of 80% for the 1.6H condition. This seems substantially less than what Figure 2 suggests.

As we had stated in the Methods section, the model used “a standard deviation of 0.1 arbitrary units” for the evidence. We now make it more explicitly clear that this corresponds to setting within-trial noise s = 0.1 as the scaling parameter (the first paragraph of ‘Model Fits to behaviour only’ and the first paragraph of ‘Integration models’ in Methods). Replacing (s = 0.1) in the suggested calculation yields a predicted accuracy close to 1 for the high-contrast condition, consistent with both the behavioural data and the model predictions shown in Figure 2. We have now clarified this parameter explicitly in the revised Methods and updated Table 1 and Table 2 to avoid confusion.

On some occasions, it is unclear to me what modeling choices are being made:

(a) It seems as if the models are fit on accuracy data alone (before introducing the neural data). This seems suboptimal given that the authors do report differences in RT.

Because of the delayed-report feature of the task, RTs do not directly reflect decision termination time, which is the basis of the use of RT in cognitive modelling normally. Here, whether the decision process has concluded during the stimulus or not, indeterminate response-cue detection and motor execution processes intervene between the stimulus and RT, which would necessitate complicating the models with additional mechanisms, of which there are several possibilities as reflected in the response to the reviewer’s final comment about the RT effects below. This could potentially obscure the core mechanisms of decision formation during the stimulus itself, which was the focus of the study.

(b) Are the models fit on all data combined, or on the data of individual participants? Fitting individual participant data is preferred, as combined or aggregated data may be distorted by individual differences.

Because of the noise and variability of EEG data at the single-participant level, we model data averaged across participants, which we have ensured is clear in the revised paper. We provide individual accuracy trends in Figure 1, to verify that the accuracy improvements with increasing evidence duration seen on average are representative of the vast majority of individual subjects. We also added a comment on the limitation this incurs regarding individual difference analysis in the revised discussion.

(c) The authors seem to suggest that the diffusion coefficient s is estimated (in the section "Integration models"). Most likely, however, this is set to a fixed value. Obviously, it matters for the model comparison using AIC whether this parameter was freely estimated or not.

As noted in our response to an earlier Comment, the diffusion coefficient was fixed at s=0.1, and to make this explicit, we have entered it for all models in the revised Table 1 and Table 2.

Not really a weakness, but I wondered about the effect of stimulus duration on RT. In particular, what hypothesis (or post hoc explanation) do the authors have for these RT effects? I could think of at least three hypotheses that are consistent with the behavioral data:

(a) H1: The shorter the evidence duration, the more likely participants are to require a double-check before response execution, reflecting their uncertainty about their decision.

(b) H2: There is a collapsing threshold that initiates at stimulus offset, leading to quicker responses on trials where there is more evidence.

(c) H3: motor preparation is correlated with the evidence signal, which leads to faster responses on trials with more evidence.

We thank the reviewer for these hypotheses. We agree that the RT effects admit multiple possible interpretations, and while we are cautious not to overinterpret them mechanistically in the paper given our focus on the decision process during the stimulus preceding these response-cue-triggered responses, we do take them to signify that the decision process has not always fully completed and been transformed to a finalised action plan by the time of response cue (start of Results section). To consider these interesting possibilities further:

We agree that the longer RTs for shorter duration, more uncertain trials could reflect a “double-checking” process (H1), but it could alternatively reflect the fact that if a bound has already been reached during the stimulus, this commitment can be translated to fully-selected action plan that only needs triggering, whereas if a bound has not been reached by stimulus offset, which would occur more often for shorter evidence-duration trials, more of the motor action-selection process would yet need to be completed to initiate the action, causing the slight delay in RT. In other words, on longer-duration trials, the accumulated evidence is more likely to have already reached the bound before the response cue, allowing participants to both commit to a choice and prepare the associated motor response in advance. Therefore, RTs would be shorter as the remaining processes after the cue primarily involve cue detection and motor execution.

This interpretation is broadly compatible with the reviewer’s H3 account, in the sense that motor preparation may track the evolving decision variable/evidence state. It is also possible that collapsing bounds are set on a post-stimulus, cue-evoked process (H2), which is not mutually exclusive with the above possibilities. It would be hard to determine whether such a process is primarily a response cue-detection decision process that is modulated by uncertainty state at stimulus offset, or a cue-triggered “double-check” process that perhaps operates on the iconic memory of the evidence, and our paradigm does not allow these alternatives to be cleanly dissociated.

Recommendations for the authors:

Reviewing Editor Comments:

As you can see, the reviewers are positive about the work and highlight several strengths, while at the same time offering recommendations for improvement. Once these points are satisfactorily addressed, this may also lead to a revision of the eLife assessment below.

Reviewer #1 (Recommendations for the authors):

As outlined in my public review, my most important recommendations relate to sample size and possible neural bases of the extremum-flagging model.

Minor Comments:

(1) p. 4 rmANOVA statistic is listed as 12.35.31.

This has been corrected, with thanks for spotting it.

(2) The stable d-SSVEP amplitude during the evidence period is used to justify a constant (non-adapting) drift rate assumption. This is a reasonable inference, but the SSVEP reflects early sensory encoding rather than the decision variable per se. Neural adaptation or gain changes at later processing stages could still produce a non-constant effective drift rate even with a stable sensory representation. This inference should be qualified.

This is true. We have clarified that these checks for one important potential source of a time-varying drift rate, namely adaptation at the level of early sensory representation, but admit that other effects may happen downstream.

(3) The Methods describe a leaky accumulation extension; the Results note leak ≈ 0.0002 at w=10, which is effectively zero. This is a positive result (evidence against leaky integration) that should be stated more explicitly in the Results or Discussion rather than appearing only in supplementary tables.

We thank the reviewer for highlighting this. We have pointed to this result now in the first paragraph of the Discussion.

Reviewer #2 (Recommendations for the authors):

(1) The panels in Figure 4 H, I, J are not discussed in the Neurally-constrained models Section, while I believe they are probably more informative than Figure 4E alone.

The manuscript has been revised to explain Figure 4H,I, J fully under ‘Neurally-constrained models’.

(2) In the method section, we don't know how many trials were rejected based on the chosen threshold.

The Method section is updated with the rejected trials after preprocessing. It now reads, “After preprocessing, 12017 trials remained across all conditions, with an average rejection rate of 15% (± 12.9%) across participants.”

(3) Figure 1: For b and c, data are mean {plus minus} s.e.m. after between-participant variance was factored out. -> reference or detailed method.

We have now explained in the caption that this is done by subtracting the overall mean of each individual from their data and adding back the grand mean, retaining the between-condition differences - that is, we remove the component of variance that repeated-measures tests ignore.

(4) Figure 2: Shouldn't the snapshot model only feature one sample, as in Stine et al. 2020? This figure and others would also benefit from a better resolution.

The reviewer is correct regarding the schematic of the snapshot model presented in Stine et al., (2020). We used small, light orange dots to represent the evidence samples presumably being encoded and a larger orange dot to indicate the single randomly chosen sample used as evidence for the decision. In our revised figure, we have increased the visual distinction between the evidence dots and the selected dot, and pointed this out in the caption. We have also improved the figure resolutions.

(5) Typo:

- Semi-saturation Table S4.

- pi missing in the text of the G^2 equation.

Thank you for catching these.

Reviewer #3 (Recommendations for the authors):

Small, random points:

(1) How was it ensured that participants indeed did not detect the change in contrast throughout the 1.6s interval? In previous work (Winkel et al., PBR, 2014) we did something similar in a random-dot motion task, but observed that participants always observed the change, if we did not slowly change the coherence of the stimulus (unfortunately, Winkel et al., 2014 is not explicit about the exact parameters of the change, but Figure 2 suggests that the change in coherence lasted 50ms, independent of stimulus strength).

It is true that abrupt changes in stimulus strength are more salient and detectable than a ramped change. The experimenters tested the stimulus during the task design subjectively, to satisfy themselves that they could not tell when the contrast stepped back to baseline, but this was not verified systematically with psychometrics, nor can we be sure that a more sensitive observer couldn’t sometimes detect the change. However, based on the task design, stimulus properties, and both behavioural and neural data, we are confident that participants are very unlikely to have been sufficiently confident in detecting the step-back in contrast to ceasing their contrast-comparison decision process at that point:

(1) Participants were naive to the underlying manipulation. They were informed that trials would naturally vary in difficulty. It was normal for them to perceive some trials as harder than others without suspecting a mid-trial structural change.

(2) The contrast difference in the hard condition was very subtle, and though it is possible that the step-down in contrast could be detected with above-chance accuracy if instructed to do so, given there was no instruction on whether and when the step-down would happen, it is very unlikely they could be detected with sufficient confidence to be certain there is no remaining evidence in the stimulus. Furthermore, the rapid, flickering nature of the stimulus would have helped to mask the transition point, in comparison with a sudden change in a continuously-playing random-dot motion stimulus (as in Winkel et al., 2014).

(3) Our behavioural post-cue RT data imply the participants did not cease decision formation at evidence offset. If they had, then they would have been afforded the most time to prepare their chosen action in advance of the response cue in the case of the earlier evidence offset (i.e. shorter durations), yet these were the conditions with the longest, not the shortest post-cue RTs.

(4) The low-contrast CPP traces remained elevated for the full 1600 ms interval, unperturbed by the evidence offsets. If participants were explicitly detecting a sudden change in contrast, we would expect to see a transient evoked response locked to that change, marking that detection. Instead, the sustained elevation of the CPP is characteristic of a continuation of the decision process, uninterrupted, through the subtle offsets.

(2) Could you include the regression coefficients of the statistical modeling of the behavioral data?

The regression coefficient is now added to the second paragraph of the Results section.

(3) I felt Figure 5B was a bit confusing: Are the dashed lines here the ipsilateral sides or the non-linear bounds? This was confusing because "data" only has a solid line in the legend.

We agree that the subtle nonlinearity, which appears visually close to the linear model, may have caused this confusion. To improve clarity, we have added arrows to Figure 5B to explicitly indicate which legend refers to which panel, and distinguish the dashed nonlinear bounds from the other traces.

(4) "amplitude variations [...] to be used as an independent evaluation of model fit": Could you refer to where these model predictions are presented? I think this is in Figure 4 - Sup 5?

We thank the reviewer for raising this. The model-predicted waveforms showing amplitude variations across durations for all neural weightings are presented in Figure 4 - Supplementary Figures 2 and 5. We have clarified this in the revised manuscript.

References

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I am actually very surprised to hear this, what made them choose to trade one of their own community members and what did they possibly get in return? Were Europeans willing to trade their own as well?

During this time, would this include diseases that were brought over by the Columbian Exchange?

Powhatan Confederacy - An alliance of roughly 30 Algonquian-speaking tribes in the Chesapeake region, led by Chief Powhatan (Wahunsenacawh) that dominated the area English colonists encountered when they founded Jamestown in 1607. Relations cycled between trade, tension, and open warfare (the Anglo-Powhatan Wars) as English land hunger grew.

This relates to the theme: Migration and Settlement patterns. What determined how Native people migrated and what they chose to change about their way of life or political structure?

Makes understanding each system easier.