This reminds me more broadly of the "benefits" that can result from war. I wonder how we can achieve them in other, less harmful ways.

I am enjoying the way the author is approaching this discussion. Although we want students to be fit, that is really not the end goal. It is something that people become fixated on though. It reminds me of how many adults want to be fit, so they set lofty goals (e.g. work out four times a week) that they cannot realistically meet. When it doesn't work, they quit until they have inspiration again. Another issue is that once people consider themselves "out of shape," there can be significant mental barriers to change. Many people do not know how to progress themselves toward a goal, as they were never taught how to. In my opinion, many people want to be fit. It is a common goal. The issue I see is that many people have no idea how to actually reach their goal or what exactly they are wanting from their goal (e.g. mobility, strength, confidence). If we as educators would be able to help students think about how fitness fits into their lives, I think those students will grow up with a better idea of how to continue with fitness.

1)TOPIC: In the poem Bury Me in a Free Land, Frances Ellen Watkins Harper talks about slavery and the contradiction of Americans valuing freedom while keeping people enslaved.

OPINION: Harper makes readers face the hypocrisy of Americans and realize how wrong it is to allow slavery.

SO WHAT? This matters because it makes us question our morals and reminds us that injustice must end.

Reminds me of the biases people hold - they're so focused on debating to prove they are right that they forget they are fighting with and against PEOPLE

This reminds me of the first book series I read. Cirque Du Freak by Darren Shane (The Vampire Assistant books). There were 13 books in the series, and I just couldn't put them down. Then twilight like everyone else and the harry potter books, The wheel of time series was amazing as well. I get so lost in them. Some I can't read at night because I won't sleep.

I highlighted this section to relate it to some of the times at work I have seen social workers fail their clients and not live up to the ethical standards placed by the NASW. While the NASW Code of Ethics is an important guide, putting it into practice isn’t as straightforward as it sometimes sounds. Our decisions are shaped not only by ethical theory and agency policies, but also by the real-life systems our clients are navigating every day. Many of the communities social workers work with, immigrants, people of color, those living in poverty and dealing with homelessness, are dealing with systems that were never designed with them in mind or rather it was staged as it was . Laws, policies, and even helping institutions can unintentionally reinforce the same inequalities they claim to address. As a Latina woman, I can’t ignore how racism, classism, and gender inequality show up in these systems, because I see how they directly impact the people sitting across from me. The code of ethics reminds social workers constantly to keep questioning the role social workers play within these power structures. Are we truly advocating for our clients, or are we sometimes acting as gatekeepers for systems that continue to marginalize them? Regardless there is much that needs to change.

This reminds me how sometimes the opposite can occur where a large business can actually influence politics with enough wealth an power.

This reminds me of the documentary covering a homicide that AI-generated a family photo of the victim smiling

This idea reminds me of the quote (from somewhere) "do not attribute to malice what you can attribute to ignorance," giving people the benefit of the doubt when they fall short of what you expect them to do

It's interesting how this fear has not subsided as we get to modern times, just been redirected. Even though the overwhelming majority of Americans trace their roots to immigration, there is constantly a sense of "us versus them", born from the fear of the unknown. This also reminds me of a book set around this time that I'm sure a lot of us have read in our English classes --- The Jungle by Upton Sinclair.

reminds me of googles cache history

This stands out because AI tools are so normal now for studying and drafting ideas, so being told to limit or avoid them feels different from most classes. It reminds me that the goal of this course is probably about practicing my own voice and critical thinking rather than just producing polished work. I see it as pushing us to struggle through the process ourselves, which might actually lead to deeper learning.

This is also really interesting because it reminds me of the rule "show, not tell." Even though you can describe something literally (It exploded), you can leave out details that are just as important in describing reality (It burst into flames).

This reminds me of what my mother used to tell me, be careful of the company you keep. And the famous saying "Birds of the same feather stick together."

This is the complete opposite of insanity, which states doing the same thing expecting different results. Its always best to re-try a failure in a different way, sometimes, many different ways. That reminds me of the Disney cartoon movie Meet The Robinsons. The young boy tried his experiment over and over in many different ways until he finally figured it out.

reminds me of the Turing Machine

Reflection: This historical point of view emphasizes the danger of disregard of ecological boundaries. In my career concern on sustainable development, it reminds me that policies or projects should not only look at short-term benefits of extracting resources or generating financial benefits without looking at the environmental effects on the long-term. It supports the role of considering ecological and social aspects when planning.

This sentence connects to me because it shows that loving something his father loved helped him feel closer to both the books and his father. It reminds me of my own relationship with my father, and how sharing an interest with him can create a deeper connection between us.

This stands out because it frames research as a set of tradeoffs, not "one best method". It reminds me that the "best" design isn't always the most controlled one, it's the one that answers the question while staying feasible and ethical.

I like this point because it reminds me that work today looks very different than it used to. Studying old workplace problems might not fully explain things like remote jobs or work–life balance issues people face now.

This reminds me of Romeo and Juliet, something most of us read in Freshman year of English.

Reminds me of the Heroes Journey and how lots of stories follow the basic plan of that.

this reminds me of our discussion in class on how it is slowly becoming less popular today and there is a cultural shift away from the books, but not that literature is not important today or has no value.

This reminds me of the book Fahrenheit 451 because they shamed books and therefore they became mindless.

Everything is worth archiving! I'm glad these women were able to conserve "common' plants disregarded by their male counterparts. We never know how impermanent seemingly permanent things are. It reminds me of chestnut trees! Once everywhere in Appalachia, they nearly all died due to chestnut blight.

(17.18) At first glance, this chunk of text reads somewhat like a boomer ranting about the integration of technology into everyday life (for a lack of a better comparison). The disdain for purple seems to stem from the idea of ritual propriety that Confucius heavily pushes for in the analects —he is against the replacement of more traditional practices that are grounded in some type of moral history for something that is shallow or convenient (this reminds me somewhat of modern-day fast fashion, in a way). Despite my initial take, I would not compare Confucius to a boomer because his concern is not unfounded or superficial. He seems to be concerned that people will lead themselves astray by draining their symbols and virtues of meaning (rituals, behavior, etc.).

This reminds me of the commercials that discuss lowering cortisol levels for weight loss. I have recently been reading about how lymphatic drainage, red light therapy and apple cider vinegar can potentially help to lower cortisol levels.

Stress plays a large role in cortisol levels. As mentioned by the author, cortisol regulates mood and sleep. Whenever I am stressed, I have difficulty falling asleep and I tend to be moody.

This reminds me of burnt out nurses. I am sure that everyone has encountered at least one. They cope with stress through poor health choices: smoking, junk food, lack of exercise, etc. This eventually leads to increased call ins partially due to a weakened immune system.

This is a great definition and reminds me of a patient I had just the other day in the emergency department. He came in for cold exposure and had pulseless BLE. Unfortunately, given his homelessness he had frostbite and ended up losing both of his legs. Obviously the temperature outside put stress on his body and he was no longer able to regulate or maintain homeostasis.

Several years ago, I directly experienced chronic insomnia due to stress and NOC shift work. I struggled to fall asleep and stay asleep over several weeks which left me fatigued, irritable, and unable to focus during the day. This scenario reminds me of how acute stress combined with sleep disorders disrupt normal physiologic function, as discussed on page 490 of the textbook. I will always remember that even mild or transient insomnia can impact cognitive performance and mood, which is clinically relevant for my future psych NP practice when assessing patients with sleep complaints or insomnia.

I like how this explains that storytelling is active and when someone tells a story out loud it feels more engaging than just reading silently. This reminds me of activities like class presentations, where speaking helps students understand content better and retain information for longer in my opinion.

Our own mind. Reminds me of the philosophical concept where I know I am a concise being but I can't know another person is because I don't know their mind and state of consciousness personally.

When I see the silver rule, it reminds me of the golden rule I grew up hearing, "Treat people how you want to be treated". I wonder if this rule was adapted by Confucius and his students' conversation about reciprocity.

This reading reminds me of a statement I heard during a diversity training from Baylor's Dean of Intercultural Engagement: "Diversity is a fact, unity is a choice, inclusion is an action, belonging is the outcome, and justice is the reason."

During that training one of the main emphasis was on the power of reconciliation through willingness to encounter difference. We wrote down our own identities (i.e. eldest daughter, musician, college student, etc.) and considered the impact those have had on our opportunities, treatment, and experiences. From that lecture and today's reading alike, I am encouraged to lean into difference and encounter discomfort in order to learn what shapes struggle around me that I may never see otherwise.

I think customs like this that include physical media are so cool. This particular example reminds me of the custom of the Na’vi, from the movie ‘Avatar’, in which they create ‘song cords’ with twine and beads representing major life experiences. Of course there are many other examples of art customs, like the Shabti Figures in Ancient Egypt or cylinder seals from Ancient Mesopotamia.

This reminds me that bots are not inherently beneficial. They are just automated programs that can be good or bad.

The invention of Email can be seen as one of the great step of how people communicate. The time to transport an information was significantly reduced within few minutes. This reminds me a fun fact that Her Majesty Queen Elizabeth II, the queen of Great Britain was one of the earliest adopters of email, probably the first ever email from a head of the state!

Reminds me of a class I took in the english department where we looked at American literature through the lens of extraction and prioritizing material wealth rather than experiential wealth.

this adage reminds me of our in class discussion about how fairytales foster creativity and expand curiosity within the youth

"making sense of nonsense" reminds me of how fairy tales gain extended meaning as people get older and brains develop further ex. adults noticing metaphors that they never saw as a child

This reminds me of The Picture of Dorian Gray.

Equality and equity aren’t the same, and I see that clearly in my job in healthcare. An equality approach would mean treating every patient exactly the same way—giving everyone the same instructions, the same amount of time, and the same type of support. But in reality, patients come in with very different needs. For example, I might have two patients with the same diagnosis, but one understands medical terms easily while the other struggles with health literacy or is overwhelmed by stress. If I give them both the exact same explanation in the same way, only one of them is truly being helped. The other might leave confused, anxious, and less able to follow their care plan.

For me, equity shows up when I adjust how I support each patient. I might slow down, use simpler language, check for understanding, or connect someone to extra resources. I’ve seen how much more effective this is than a one-size-fits-all approach. Just like giving everyone the same pair of glasses wouldn’t actually help everyone see, treating every patient the same doesn’t help them heal the same. My job constantly reminds me that fairness isn’t about sameness—it’s about meeting people where they are so they can actually move forward.

This being an example of pre-internet social media is so interesting. It reminds me of community based social media platforms such as reddit threads where messages are posted on one dedicated page. Would current graffiti tagging still be considered social media?

This reminds me of how following someone on social media is often quick and effortless, while unfollowing brings up a confirmation pop-up. Reading this made me reflect on how friction is designed into everyday digital interactions, sometimes to guide behavior. It also inspired me to think about how intentional friction could be added to personal devices to help reduce addictive usage.

Learning about Christine Mann Darden reminds me of "Hidden Figures", which highlighted three African American women: Katherine Johnson, Dorothy Vaughan, and Mary Jackson, essential mathematicians and engineers at NASA during the Space Race in the 1950s and 1960s, also overcoming racial and gender discrimination.

Reminds me of McLuhan's tetrad!

This reminds me of Lara Boyd's ted talk and how she Acknowledges healthy brain exercises. Activities together as classmates based off of a variety of interesting texts sounds like amazing weekly brain exercising!

this reminds me of an article I read for an earlier assignment about how a large portion of social media users are fake. It is bizarre the time that we makes this a profitable industry. It is safe to say we live in an attention based economy.

simile

yuck

reminds me a little bit about how black people weren't photographed in the early photography era because they didn't feel like figuring out how to more aptly design the process...the only reason it changed was because chocolate companies wanted pictures of their food in advertisements

This passage reminds me of a news article I read about universities improving accessibility for students with disabilities, like adding more adaptive technology and inclusive classroom designs. It also makes me think of how the Americans with Disabilities Act has changed opportunities over time. I can relate because I've seen friends benefit from these resources, which help them succeed just like any other student. It shows that support and awareness really makes a difference in education.

I like how the text says learning can be messy. It reminds me that growth isn't always comfortable, but it's worth it in the long run.

INFO 103 Winter 2026. I think this is absolutely right. It reminds me of the rapid development of AI in recent years. One point that people are extremely worried about is that if Ai really becomes so powerful that even humans cannot control it, and if it invays media platforms like a hacker and then changes the direction of public opinion, this would be a very terrifying thing. But at this point, how should we convict?

This reminds me of when youtubers post videos of followers doing "day x until y" messages. I never considered the possibility that it was fake until now. If you combine this with the sleep feature and randomize the timeframe of the post, it could look very real. I also wonder if in the near future this could be done with AI to create automated videos.

When I think of this I think of slavery, and Malatesta's anarchic thought experiment of having your leg immobilised and telling you this is needed to walk, much like Arslan Senki's golden cage. Living in an isolated casquet means sensory and life reduction. If you've liven in and about rites, you will partake on those and these may look necessary to you. Most people today would not let go of on-demand shows or music, let alone their phones! These are engraved, inseparable of how they live, from their identities, an extension of their selves, a la McLuhan's cyborgs.

It also reminds me of absurdist takes like those of Camus whereby one lives to rebel in one way. To maximise your own identity in this already set-in-stone world, we need a maximal diversity of alternatives, else risk suicide out of meaninglessness.

I think this idea shows how social media can distort people's lives. When all we see is mostly good news, filters, and edited photos, it's easy to compare ourselves to something that was never real. Over time, this can affect our self-esteem and our expectations of what is "normal". This reminds me that we often forget that social media is more like a highlight reel than real life.

yes, reminds me of the 'getting to an explosive mix' work of the MIT guy I met in AMS. What backgrounds or skills are missing in a specific location, to make something fly.

this rings true.

Reminds me of compilations (beyond Queer Games Avantgarde) like Pride at Play, Indiepocalypse, or Feminine Play.

Reminds me of Ontario's Access to Justice game (https://ojen.ca/en/resource/access-to-justice-game/), except this one is more like A Normal Lost phone, or like Wednesdays (https://artefrance.itch.io/wednesdays).

Like myself Alper is his own most frequent reader of his blog. Mentions realising how much he did in a week earlier in his life. Same for me, things that now might be a big thing in a week, were Tuesday afternoon 15yrs ago. It's not just age I suspect, but also an overall attenuation that Covid brought?

Connect: This kind of racially charged behavior and attitude reminds me about something similar from my cultural media studies, specifically on the fixation of "the Other," where certain racial groups were objectified and their cultures were misconstrued. This potentially may have contributed to the influence of implicit biases in the viewers of that specific content.

This sentence is reminds me and other people have been around for only a short time compare to how long the earth has existed, and our writing history is just a small part of that long time. This help us stay humble and remember that many thing we think are important may not last forever

This is really helpful because it shows that professors don’t want complicated words just to sound smart. Using simple, clear language actually makes your ideas stronger. I sometimes overthink words to sound “fancy,” but this reminds me that clarity is more important than sounding scholarly.

Reminds me of Leviathan and Its Enemies: Mass Organization and Managerial Power in Twentieth-Century America by Samuel T. Francis | Goodreads

This seems like a loaded statement, let's break it down.

If the laundromats were all independently owned this math would not work. The capacity to leverage oneself to buyout companies is a privilege of a specific caste.

This reminds me of the story about how Pornhub (Mindgeek), being a tech company not a porn company, was a member of this specific caste. Pornhub, like youtube at the time, allowed anyone to upload whatever copyrighted material they wanted to share for free. This put the porn producing companies all out of business because no one bought their product since it was on Pornhub for free. As these porn producing companies went brankupt, Pornhub, with access to cheap debt was able to buy them all up.

This reminds me of Soma (Brave New World) - Wikipedia

REMINDS ME OF POLI SCI

ONLY PHYSICAL THOUGH AND REMINDS ME OF MY OWN INVISIBLE ONES

THIS IDEA REMINDS ME OF THE INVISIBLE GIRL READING

THIS REMINDS ME OF THE WITCH

Ecofeminism- this also reminds me of western v.s Indigenous Epistimoogy

This reminds me of people that speak languages, their narrative self speaks a specific language but they can learn a new one and switch too it. I wonder if we can do that for new sensory systems

The idea of Vocally told stories of lived experiences being passed down generation after generation is really fascinating. Reminds me of the Broken Telephone Game

I’ve learned how much students light up when I point out something they did well, even if it seems small. When I make an effort to notice the good choices such as when a student stays focused, helps a friend, or gives their best effort the entire class environment changes. It reminds me that praise builds trust, confidence, and a stronger classroom environment.

Movies that are made now have long credits that people do not sit for to read, however in older films, it would generally consist of a small preview of those who worked on the film. This reminds me of how Marvel would include a sneak peak of a film at the end of their movies, showcasing the credits. I think that that is good because it truly honors everyone’s hard work and you can see most names which is good.

This entire section is plagiarized from Eliason. Bizarre.

Reminds me of the theme in Wicked: For Good where "The Wizard" of says if Alphaba tells the people of Oz the truth they will not believe her because they will not want to

This reminds me of an aspect of eco-fascism where living things are priortized over human beings. This becomes racialized when Global South countries are judged for poor environmental practices. https://earthworks.org/blog/eco-fascism-a-tangible-present-danger/

This reminds me of the invasive species of the European Pine trees placed in Palestine during the Nakba. The Israeli forces planted pine trees while occupying Palestinian villages to replicate the European infrastructures, this is a biological warfare of colonialism (Josephson 2025). https://origins.osu.edu/read/environmental-nakba-israel-palestine-water

This reminds me of a conversation I had in my sociology class about hate crime and racial discrimination in the healthcare systems. How the demographics of nurses are BIPOC and women, and have faced tremendous racism and sexism at workplace during the COVID-19 pandemic.

I find it quite pathetic how easily people sexualize objects and living things and I cannot understand how that works, but I see the influence of scientists like Linnaeus encouraging this type of objectification in scientific studies. This reminds me of Paasnonen's concept of objectification, where people and things simply exist to be objectified, and that is due to the cultural dynamics and social constructions of a society.

The examples of Version A vs. Version B help me understand why key sentences matter. They don’t just introduce topics—they connect each paragraph to the argument.

This section reminds me how important paragraph organization is. I used to think content and style were separate, but the author shows they work together.

I resonate a lot with Cindy. I think trying to unlearn colonial ways can be difficult, but once you're aware of what they look like, it makes it a bit easier. This reminds me of when bell hooks stated, "The enemy within must be transformed before we can confront the enemy out." when referring to internalized sexism (hooks 2014, 12). Broadly speaking, when we talk about internalized sexism, it is all these internalized thoughts that we carry with us about gender norms that may be harmful to us. I think this is quite similar to colonial ways of thinking because, just like internalized sexism, colonial thinking is built into us because it played a role in every step of our lives when growing up in a Western society.

Often, we have a lot of behaviour that is deeply rooted in colonialism and patriarchy, yet we don't even realize it. So, it's important to understand this can happen, analyze what we need to change, and try to break free from these norms so that we can work towards decolonization.

Hartman's dilemma reminds me of Audre Lorde's famous quote published in This Bridge Called my Back about not using the master's tools to dismantle the master's house (Lorde, 2022). The author wants to help share marginalized stories but is afraid of committing further violence. In my opinion, this "further violence" is likely continuing to keep some voices/perspectives hidden or misinterpreting them to suit their own ideals (which Western epistemologies and archives have done).

Here, Hartman reminds me of Sara Ahmed's description of a "feminist killjoy," which is a feminist that is not complicit or silent to injustices even when others may prioritize agreeability (Ahmed, 2017). Like a killjoy, she refuses to allow readers to enjoy a sanitized version of history. By highlighting structural violence, she “kills the joy” of comfortable narratives and exposes what the archive hides.

Hartman raises a central ethical problem in feminist and Black studies: how to represent historical violence without reproducing it. This reminds me of discussions that are made in regards to horror movies or other media that depict sensitive subject matter, which is "what draws the line between accurate portrayal and the glamourization/aestheticization of abuse and suffering?" If the victims, like Emmett Till who was mentioned in the passage, cannot decide that how can historians decide what's appropriate?

This is an interesting point because it reminds me of the romanticization of social movements. This romanticization often views the process of liberatory struggles through an idealized and oversimplified lens, and overlooks internal conflicts and flaws. This means that progress is only measured through public-facing spectacles/displays of justice, rather than something that happens simultaneously behind closed doors and isn't easily televisual. In regards to this text, a romanticization of these struggles can often dismiss intellectual labour and progress that takes place in unconventional spaces such as within families, homes, and community spaces, or even within ourselves. The authors are trying to make the point that struggling for liberation and decolonialization isn't a one-size-fits-all-approach.

This is a central theme: the reframing of "queer" beyond its initial identity label. Most of my peers think that queer describes a particular sexual orientation or identity. But to be queer is defined through practices and actions that are inherently anti-oppressive.

This reminds me of a quote from bell hooks, where hook's describe queer as "the self that is at odds with everything around it and has to invent and create and find a place to speak and to thrive and to live." In this way, queerness is not a sexual identity but a method of moving through the world, a method that involves self-creation in opposition to hetero-normativity and patriarchy.

Reminds me of how nuclear weapons have been regulated and only certain countries are able to develop/possess them

This reminds me of our discussion about the movie Megan and how we would have to implement safeguards in AI in order to preserve the interests of the human race

This sentence shows that the writer has actually been to many classrooms and seen really good teachers in all kinds of schools, even in places that outsiders usually think are “bad” or “not enough.” It kind of breaks the easy idea that good education only happens in rich or famous schools, and that poor schools equal poor teaching. It reminds me of how some people think community college or less “prestige” schools must be low quality, but then you meet one prof who change the whole way you think about a subject. My question is, who really has the right to call a school “substandard” if they never sit inside the classroom and watch what happens there?

This “They Say / I Say” idea makes a lot of sense to me because in many past essays I either depended too much on sources or summarized them without connecting to my own point. Guptill’s explanation shows that good writing balances both: listening to what “they” say, then adding what I think. It reminds me that my voice matters, as long as I connect it directly to the sources.

This reminds me of how some people thought the invention of calculators would make people dumber and unable to do calculations themselves. However calculators just enabled people to learn harder math. I wonder what other inventions we judge too pessimistically at first.

There is material privilege from genocide. This reminds me of the statement "No one is illegal on stolen Indigenous lands", used to condemn the abuse that ICE is perpetrating in the US right now. Link to an article: https://www.culturalsurvival.org/news/no-one-illegal-stolen-indigenous-lands

This showcases gender regulation as a tool of genocide. It reminds me of the unproportioned violence rates against Indigenous queer people and Two-Spirit individuals. Link to the article: https://www.gbvlearningnetwork.ca/our-work/backgrounders/GBV%20Against%20Two%20Spirit%20Indigenous%20Peoples/index.html

Indeed! Reminds me of cherry picked Islamophobic assault compilations. Ragebait. Polarisation devices. This can also lead perhaps not too trauma, but to imposter syndrome, to loss of confidence, to de-motivation, and burnout. Surely, males experience these too, but less frequently, and notably, on their own. Not sharing and taking a stoic toxic masculinity stance then builds up to projection, hate, impotency, outward attacks not so much as inner pitying (it's others who are wrong, and I must defend myself). There is a certain dread and anguish I feel sorry males have to go through, much unhealthier than fems.

I like this point—it reminds me that digital literacies go beyond just knowing how to use tools. They include understanding the social, ethical, and practical context of technology use. #LiDA101

The author points out that “society” is often used without clear meaning, and I notice that I also tend to use the word in a broad and imprecise way. This section reminds me that academic writing requires specificity. When people blame “society,” they usually refer to certain institutions or groups, not every person. The author’s criticism pushes me to be more intentional about who or what I actually mean when I use the term.

Honestly, this chapter made me realize how invisible the role of English is in programming. I always took it for granted that terms like if, while, or return were “universal,” but they’re only universal because English basically colonized the coding world. It makes me think about how much harder programming must be for people who don’t speak English well. We talk a lot in tech about making things “accessible,” but the foundation of programming itself is already biased toward one language.

The part about Silicon Valley deciding what technologies the rest of the world uses also stood out to me. It feels weird that a whole country’s developers might rely on “outdated” languages just because the global tech trends are controlled by one region. It reminds me of how fashion or music trends spread — people follow whatever the cultural “center” is doing, even if it doesn’t fit their own needs.

One question I have is: would programming look totally different today if the early pioneers weren’t mostly English-speaking? Like, if the first mainstream languages came from Brazil or Japan, would we all be learning those instead? It makes me wonder how many innovations never spread just because they weren’t born in the “right” place.

The chapter emphasizes plot as the most important part of drama, and I like how it connects this to fiction. What stands out is the idea that plot structure isn’t fixed playwrights can shape events however they want as long as it creates an emotional effect. This reminds me that drama depends on intention more than a rigid formula.

this reminds me of codeswitching

this reminds me that we shouldn't just accept things at face value. we have to actually pay attention to how things are being shown and what message that creates. Hall's basically saying that the media doesn't just reflect reality, it shapes it by adding meaning, changing meaning, or even creating it from scratch. so when we see people or events in the media, we're not seeing the real thing, we're seeing someone else's version of it.

I particularly like this xkcd comic titled "The Pace of Modern Life". It vividly conveys the feeling that our pace of life is getting faster and faster, as if there is never enough time and information comes too fast. After reading it, I couldn't help laughing and felt somewhat empathetic. I realized that my speed of using social media and handling emails is completely unable to keep up with the pace of the world. This comic reminds me that sometimes slowing down and giving oneself some breathing space is more important than constantly chasing after things.

Chokepoints, vigilancy guard towers, straits. Kinda reminds me of The Witness architectural analysis (https://www.gdcvault.com/play/1020552/The-Art-of-The).

Whenever I see the page about Steve Jobs, I always think of him not just as a technological innovator, but more like a person who constantly challenges the norm. His story reminds me that success often comes from the courage to pursue what one truly loves, rather than just following the routine. Reading about his experiences makes one feel that entrepreneurship, creation, and even life itself can be full of passion and creativity.

This reminds me of our own personal moʻolelo. I had a conversation with one of my hoa from Keaukaha about Kamapuaʻa and how she had a different recollection of the moʻolelo from her other friend who was in the next town over, but the ending/moral of the story was still the same. It's interesting to see this play out in different cultures as well.

The section on “culture wars” shows how the word “war” doesn’t just describe conflict — it produces it. Jeffords connects the term to debates over education, art, and politics, which reminds me of how the “celebrity” essay explored power structures and social influence. What inspires me here is the method: Jeffords uses history, politics, and examples from public controversies to show how a keyword reflects deeper tensions in American culture. For my research project, I can follow this model by showing how my own keyword shapes debates, identities, or values today.

Jeffords shows how the meaning of “war” expands in the 20th century to describe social problems — the “war on poverty,” “war on drugs,” and “war on terror.” This reminds me of the “celebrity” essay because both authors explore how language shapes public attitudes. By calling these issues “wars,” politicians create urgency, fear, and conflict even when the situation is not military at all. This metaphorical framing is something I want to use in my own research: analyzing not just what a word means, but what it does in society.

Jeffords’s opening reminds me of last week’s “celebrity” essay because both authors start by showing how a single word appears constantly in everyday life. Just like “celebrity” was more complicated than it first seemed, “war” also carries multiple meanings beyond literal combat. I like how Jeffords uses examples from sports, politics, and media to show how the term shapes how Americans think about conflict. This approach gives me ideas for my own keyword project — especially the strategy of starting with common uses before digging into deeper cultural meanings.

The mirrored words here make it almost certain this is a fabrication. This reminds me of passages from the bible where phrases are repeatedly identically.

This sentence reminds me of when I was a child and was scolded by adults. If I only heard, "How could you be so undisciplined!" that kind of hurt would linger in my heart for a long time. But if someone added a sentence after being angry, "I still love you very much, but you can't do this thing," the feeling would be completely different. It makes the child know that mistakes can be corrected, but they are not unlovable just because they made a mistake. Such comfort is actually teaching the child a safe self-perception: I can make mistakes, but I can also become better.

This loosely related to the topic at hand, but this in the section just above that I made a note on actually reminds me of the TV show. Everyone hates Chris and that we see how segregate schools once combined. They still fundamentally hold racism and their teachings in their educational methods and also punishment wise. I know the show was based around Chris and how everyone is against him but punishment wise we see in several different episodes of Chris is harshly punished compared to that of his white counterparts simply because of his blackness

The second icon, yes, it reminds me of Minecraft hunger bars, which were not represented by energy, but rather by chicken wings.

Reminds me of the infatigable environment of Rain World, Death Stranding, and Frostpunk. Death looms.

Reminds me of Sandbox games: Minecraft Hardcore and Project Zomboid make it patently clear.

time spent together//reminds me of olives//knowing the land

The idea of things being 'experienced simultaneously but separately' is so interesting. It reminds me of discussions I have heard or seen that debate whether or not social media has made us more connected or more isolated/lonely.

This reminds me of a sense of a censorship of who gets heard.

Author Response

Reviewer #2 (Public Review):

"The cellular architecture of memory modules in Drosophila supports stochastic input integration" is a classical biophysical compartmental modelling study. It takes advantage of some simple current injection protocols in a massively complex mushroom body neuron called MBON-a3 and compartmental models that simulate the electrophysiological behaviour given a detailed description of the anatomical extent of its neurites.

This work is interesting in a number of ways:

• The input structure information comes from EM data (Kenyon cells) although this is not discussed much in the paper - The paper predicts a potentially novel normalization of the throughput of KC inputs at the level of the proximal dendrite and soma - It claims a new computational principle in dendrites, this didn’t become very clear to me Problems I see:

• The current injections did not last long enough to reach steady state (e.g. Figure 1FG), and the model current injection traces have two time constants but the data only one (Figure 2DF). This does not make me very confident in the results and conclusions.

These are two important but separate questions that we would like to address in turn.

As for the first, in our new recordings using cytoplasmic GFP to identify MBON-alpha3, we performed both a 200 ms current injection and performed prolonged recordings of 400 ms to reach steady state (for all 4 new cells 1’-4’). For comparison with the original dataset we mainly present the raw traces for 200 ms recordings in Figure 1 Supplement 2. In addition, we now provide a direct comparison of these recordings (200 ms versus 400 ms) and did not observe significant differences in tau between these data (Figure 1 Supplement 2 K). This comparison illustrates that the 200 ms current injection reaches a maximum voltage deflection that is close to the steady state level of the prolonged protocol. Importantly, the critical parameter (tau) did not change between these datasets.

Regarding the second question, the two different time constants, we thank the reviewer for pointing this out. Indeed, while the simulated voltage follows an approximately exponential decay which is, by design, essentially identical to the measured value (τ≈ 16ms, from Table 1; ee Figure 1 Supplement 2 for details), the voltage decays and rises much faster immediately following the onset and offset of the current injections. We believe that this is due to the morphology of this neuron. Current injection, and voltage recordings, are at the soma which is connected to the remainder of the neuron by a long and thin neurite. This ’remainder’ is, of course, in linear size, volume and surface (membrane) area much larger than the soma, see Fig 2A. As a result, a current injection will first quickly charge up the membrane of the soma, resulting in the initial fast voltage changes seen in Fig 2D,F, before the membrane in the remainder of the cell is charged, with the cell’s time constant τ.

We confirmed this intuition by running various simplified simulations in Neuron which indeed show a much more rapid change at step changes in injected current than over the long-term. Indeed, we found that the pattern even appears in the simplest possible two-compartment version of the neuron’s equivalent circuit which we solved in an all-purpose numerical simulator of electrical circuitry (https://www.falstad.com/circuit). The circuit is shown in Figure 1. We chose rather generic values for the circuit components, with the constraints that the cell capacitance, chosen as 15pF, and membrane resistance, chosen as 1GΩ, are in the range of the observed data (as is, consequently, its time constant which is 15ms with these choices); see Table 1 of the manuscript. We chose the capacitance of the soma as 1.5pF, making the time constant of the soma (1.5ms) an order of magnitude shorter than that of the cell.

Figure 1: Simplified circuit of a small soma (left parallel RC circuit) and the much larger remainder of a cell (right parallel RC circuit) connected by a neurite (right 100MΩ resistor). A current source (far left) injects constant current into the soma through the left 100MΩ resistor.

Figure 2 shows the somatic voltage in this circuit (i.e., at the upper terminal of the 1.5pF capacitor) while a -10pA current is injected for about 4.5ms, after which the current is set back to zero. The combination of initial rapid change, followed by a gradual change with a time constant of ≈ 15ms is visible at both onset and offset of the current injection. Figure 3 show the voltage traces plotted for a duration of approximately one time constant, and Fig 4 shows the detailed shape right after current onset.

Figure 2: Somatic voltage in the circuit in Fig. 1 with current injection for about 4.5ms, followed by zero current injection for another ≈ 3.5ms.

Figure 3: Somatic voltage in the circuit, as in Fig. 2 but with current injected for approx. 15msvvvvv

While we did not try to quantitatively assess the deviation from a single-exponential shape of the voltage in Fig. 2E, a more rapid increase at the onset and offset of the current injection is clearly visible in this Figure. This deviation from a single exponential is smaller than what we see in the simulation (both in Fig 2D of the manuscript, and in the results of the simplified circuit here in the rebuttal). We believe that the effect is smaller in Fig. E because it shows the average over many traces. It is much more visible in the ’raw’ (not averaged) traces. Two randomly selected traces from the first of the recorded neurons are shown in Figure 2 Supplement 2 C. While the non-averaged traces are plagued by artifacts and noise, the rapid voltage changes are visible essentially at all onsets and offsets of the current injection.

Figure 4: Somatic voltage in the circuit, as in Fig. 2 but showing only for the time right after current onset, about 2.3ms.

We have added a short discussion of this at the end of Section 2.3 to briefly point out this observation and its explanation. We there also refer to the simplified circuit simulation and comparison with raw voltage traces which is now shown in the new Figure 2 Supplement 2.

• The time constant in Table 1 is much shorter than in Figure 1FG?

No, these values are in agreement. To facilitate the comparison we now include a graphical measurement of tau from our traces in Figure 1 Supplement 2 J.

• Related to this, the capacitance values are very low maybe this can be explained by the model’s wrong assumption of tau?

Indeed, the measured time constants are somewhat lower than what might be expected. We believe that this is because after a step change of the injected current, an initial rapid voltage change occurs in the soma, where the recordings are taken. The measured time constant is a combination of the ’actual’ time constant of the cell and the ’somatic’ (very short) time constant of the soma. Please see our explanations above.

Importantly, the value for tau from Table 1 is not used explicitly in the model as the parameters used in our simulation are determined by optimal fits of the simulated voltage curves to experimentally obtained data.

• That latter in turn could be because of either space clamp issues in this hugely complex cell or bad model predictions due to incomplete reconstructions, bad match between morphology and electrophysiology (both are from different datasets?), or unknown ion channels that produce non-linear behaviour during the current injections.

Please see our detailed discussion above. Furthermore, we now provide additional recordings using cytoplasmic GFP as a marker for the identification of MBON-alpha3 and confirm our findings. We agree that space-clamp issues could interfere with our recordings in such a complex cell. However, our approach using electrophysiological data should still be superior to any other approach (picking text book values). As we injected negative currents for our analysis at least voltage-gated ion channels should not influence our recordings.

• The PRAXIS method in NEURON seems too ad hoc. Passive properties of a neuron should probably rather be explored in parameter scans.

We are a bit at a loss of what is meant by the PRAXIS method being "too ad hoc." The PRAXIS method is essentially a conjugate gradient optimization algorithm (since no explicit derivatives are available, it makes the assumption that the objective function is quadratic). This seems to us a systematic way of doing a parameter scan, and the procedure has been used in other related models, e.g. the cited Gouwens & Wilson (2009) study.

Questions I have:

• Computational aspects were previously addressed by e.g. Larry Abbott and Gilles Laurent (sparse coding), how do the findings here distinguish themselves from this work

In contrast to the work by Abbott and Laurent that addressed the principal relevance and suitability of sparse and random coding for the encoding of sensory information in decision making, here we address the cellular and computational mechanisms that an individual node (KC>MBON) play within the circuitry. As we use functional and morphological relevant data this study builds upon the prior work but significantly extends the general models to a specific case. We think this is essential for the further exploration of the topic.

• What is valence information?

Valence information is the information whether a stimulus is good (positive valence, e.g. sugar in appetitive memory paradigms, or negative valence in aversive olfactory conditioning - the electric shock). Valence information is provided by the dopaminergic system. Dopaminergic neurons are in direct contact with the KC>MBON circuitry and modify its synaptic connectivity when olfactory information is paired with a positive or negative stimulus.

• It seems that Martin Nawrot’s work would be relevant to this work

We are aware of the work by the Nawrot group that provided important insights into the processing of information within the olfactory mushroom body circuitry. We now highlight some of his work. His recent work will certainly be relevant for our future studies when we try to extend our work from an individual cell to networks.

• Compactification and democratization could be related to other work like Otopalik et al 2017 eLife but also passive normalization. The equal efficiency in line 427 reminds me of dendritic/synaptic democracy and dendritic constancy

Many thanks for pointing this out. This is in line with the comments from reviewer 1 and we now highlight these papers in the relevant paragraph in the discussion (line 442ff).

• The morphology does not obviously seem compact, how unusual would it be that such a complex dendrite is so compact?

We should have been more careful in our terminology, making clear that when we write ’compact’ we always mean ’electrotonically compact," in the sense that the physical dimensions of the neuron are small compared to its characteristic electrotonic length (usually called λ). The degree of a dendritic structure being electrotonically compact is determined by the interaction of morphology, size and conductances (across the membrane and along the neurites). We don’t believe that one of these factors alone (e.g. morphology) is sufficient to characterize the electrical properties of a dendritic tree. We have now clarified this in the relevant section.

• What were the advantages of using the EM circuit?

The purpose of our study is to provide a "realistic" model of a KC>MBON node within the memory circuitry. We started our simulations with random synaptic locations but wondered whether such a stochastic model is correct, or whether taking into account the detailed locations and numbers of synaptic connections of individual KCs would make a difference to the computation. Therefore we repeated the simulations using the EM data. We now address the point between random vs realistic synaptic connectivity in Figure 4F. We do not observe a significant difference but this may become more relevant in future studies if we compute the interplay between MBONs activated by overlapping sets of KCs. We simply think that utilizing the EM data gets us one step closer to realistic models.

• Isn’t Fig 4E rather trivial if the cell is compact?

We believe this figure is a visually striking illustration that shows how electrotonically compact the cell is. Such a finding may be trivial in retrospect, once the data is visualized, but we believe it provides a very intuitive description of the cell behavior.

Overall, I am worried that the passive modelling study of the MBON-a3 does not provide enough evidence to explain the electrophysiological behaviour of the cell and to make accurate predictions of the cell’s responses to a variety of stochastic KC inputs.

In our view our model adequately describes the behavior of the MBON with the most minimal (passive) model. Our approach tries to make the least assumptions about the electrophysiological properties of the cell. We think that based on the current knowledge our approach is the best possible approach as thus far no active components within the dendritic or axonal compartments of Drosophila MBONs have been described. As such, our model describes the current status which explains the behavior of the cell very well. We aim to refine this model in the future if experimental evidence requires such adaptations.

Reviewer #3 (Public Review):

This manuscript presents an analysis of the cellular integration properties of a specific mushroom body output neuron, MBON-α3, using a combination of patch clamp recordings and data from electron microscopy. The study demonstrates that the neuron is electrotonically compact permitting linear integration of synaptic input from Kenyon cells that represent odor identity.

Strengths of the manuscript:

The study integrates morphological data about MBON-α3 along with parameters derived from electrophysiological measurements to build a detailed model. 2) The modeling provides support for existing models of how olfactory memory is related to integration at the MBON.

Weaknesses of the manuscript:

The study does not provide experimental validation of the results of the computational model.

The goal of our study is to use computational approaches to provide insights into the computation of the MBON as part of the olfactory memory circuitry. Our data is in agreement with the current model of the circuitry. Our study therefore forms the basis for future experimental studies; those would however go beyond the scope of the current work.

The conclusion of the modeling analysis is that the neuron integrates synaptic inputs almost completely linearly. All the subsequent analyses are straightforward consequences of this result.

We do, indeed, find that synaptic integration in this neuron is almost completely linear. We demonstrate that this result holds in a variety of different ways. All analyses in the study serve this purpose. These results are in line with the findings by Hige and Turner (2013) who demonstrated that also synaptic integration at PN>KC synapses is highly linear. As such our data points to a feature conservation to the next node of this circuit.

The manuscript does not provide much explanation or intuition as to why this linear conclusion holds.

We respectfully disagree. We demonstrate that this linear integration is a combination of the size of the cell and the combination of its biophysical parameters, mainly the conductances across and along the neurites. As to why it holds, our main argument is that results based on the linear model agree with all known (to us) empirical results, and this is the simplest model.

In general, there is a clear takeaway here, which is that the dendritic tree of MBON-α3 in the lobes is highly electrotonically compact. The authors did not provide much explanation as to why this is, and the paper would benefit from a clearer conclusion. Furthermore, I found the results of Figures 4 and 5 rather straightforward given this previous observation. I am sceptical about whether the tiny variations in, e.g. Figs. 3I and 5F-H, are meaningful biologically.

Please see the comment above as to the ’why’ we believe the neuron is electrotonically compact: a model with this assumption agrees well with empirically found results.

We agree that the small variations in Fig 5F-H are likely not biologically meaningful. We state this now more clearly in the figure legends and in the text. This result is important to show, however. It is precisely because these variations are small, compared to the differences between voltage differences between different numbers of activated KCs (Fig 5D) or different levels of activated synapses (Fig 5E) that we can conclude that a 25% change in either synaptic strength or number can represent clearly distinguishable internal states, and that both changes have the same effect. It is important to show these data, to allow the reader to compare the differences that DO matter (Fig 5D,E) and those that DON’T (Fig 5F-H).

The same applies to Fig 3I. The reviewer is entirely correct: the differences in the somatic voltage shown in Figure 3I are minuscule, less than a micro-Volt, and it is very unlikely that these difference have any biological meaning. The point of this figure is exactly to show this!. It is to demonstrate quantitatively the transformation of the large differences between voltages in the dendritic tree and the nearly complete uniform voltage at the soma. We feel that this shows very clearly the extreme "democratization" of the synaptic input!

This reminds me of Wikipedia, but it also makes me wonder: if anyone can anonymously edit a page, what happens when someone adds misleading or inappropriate content? And what mechanisms exist to detect and correct these issues？

I find the Kickstarter source interesting because it reflects how crowdfunding has changed the way people launch creative projects. Instead of relying on traditional investors, creators can pitch directly to the public, which makes the process feel more democratic. But at the same time, looking at this platform also reminds me that many projects never deliver what they promise, and backers take on risks without real protection. This makes me think about how trust works online and how platforms should balance creativity with accountability.

This point is well taken, and it is easy to make a mistake. Treating repeated measurements as if they were separate artificially inflates the sample size and may result in misleading p-values. It reminds me of the importance of study design: often, statistical mistakes reflect structural problems in how evidence was collected and analyzed.

text- the words are in a regular text but in all black to get their message across but not in a harsh forcing way, it talks about property and hunger. color- the red and blue kind of remind me of the usa flag, the red reminds me of urgency,and hunger, while the blue reminds me of coldness and isolation. meaning- the target for this poster is for someone who is passionate about their nation

This self-reflection is always the first step towards any type of change, and it also reminds me of the positionality readings we did earlier on in the term. As teachers (or tutors!), we are bringing our own identities to the classroom, and it would be a shame to not understand our strengths and also our limitations before interacting with others.

This reminds me of the statistic Dorinda Carter Andrews shared in her TED Talk, that there is an "inherent cultural mismatch" between students of color and the majority of their teachers being white. So when teachers preach "I don't see color" and try to approach every student in the same way without understanding the inequalities some students are facing, then they're failing as educators.

This sentence reminds me of the meaning of a religion.There are nice religions that give people hope and tide people together for spiritual comfort, but there are some other religions, or maybe cult, tend to control people and force them to do things that hurt themselves. When religions trying to disapprove people from homosexuality, is it because of the religion or is it because of the group of people not want homosexuality to appear and view them as 'heretic'? I think the idea of religion represents the mindset for 'what is good' of a group of people.

This closing statement is emotional and powerful. It captures the author’s plea for humanity, insisting that Lydia’s identity is far richer than her diagnosis. I think this quote reinforces the central message of the article: that true inclusion requires seeing every child as a full person with strengths, dreams, and dignity—rather than as a category to manage. It also reminds me how important it is for educators to acknowledge students as individuals, not labels.

This reminds me of the common idea of college students to make a lot of money in the ten years after college and then do what they're passionate about. I agree with this point on a larger scale, that tossing away years as a number is foolish.

Sarah T. Roberts’ Behind the Screen really opened my eyes to how hidden and emotionally damaging content moderation work can be. The book reveals how the people who clean up the internet—filtering through disturbing images, videos, and hate speech—are often underpaid, outsourced, and given little emotional support. What struck me the most was how invisible this labor is, even though it’s essential for keeping social media platforms usable. Reading about the trauma moderators face makes me think differently about platforms like Facebook or TikTok, which profit from user-generated content but rely on poorly supported workers to make it “safe.” It makes me question whether platforms should be legally required to provide better pay, mental health care, and transparency about their moderation processes.

This Reddit post raises an important ethical question about unpaid labor on large online platforms. It’s surprising that Reddit, a company worth billions, depends so heavily on volunteer moderators who receive no compensation. I think this shows a contradiction between the platform’s profit and the unpaid effort that keeps it running. It reminds me of how Wikipedia also relies on unpaid editors, but in Reddit’s case, the moderators face more pressure and even harassment. Personally, I believe moderators should receive at least some form of payment or recognition for their work. Without them, Reddit would not function — so their contribution deserves more respect and support.

I like how MLK Jr. breaks this down into steps. It kind of reminds me of how a teacher would explain a process. This quote shows how peaceful protests were actually built on a smart plan, and not just emotion. I wonder if he realized back then actually realized how organized and thoughtful he actually was.

I think this is a really important point when it comes to thinking about designing in an inclusive way. This reminds me of some of the stuff I learned about when I took INFO 102: Gender and Technology, when we focused on the many instances of people with marginalized identities creating tech and not being given credit for it. Learning about "alternative histories of technology" can help us understand today how important it is to have different voices involved in a design process (and credited for it).

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Wang et al. studied an old, still unresolved problem: Why are reaching movements often biased? Using data from a set of new experiments and from earlier studies, they identified how the bias in reach direction varies with movement direction, and how this depends on factors such as the hand used, the presence of visual feedback, the size and location of the workspace, the visibility of the start position and implicit sensorimotor adaptation. They then examined whether a visual bias, a proprioceptive bias, a bias in the transformation from visual to proprioceptive coordinates and/or biomechanical factors could explain the observed patterns of biases. The authors conclude that biases are best explained by a combination of transformation and visual biases.

A strength of this study is that it used a wide range of experimental conditions with also a high resolution of movement directions and large numbers of participants, which produced a much more complete picture of the factors determining movement biases than previous studies did. The study used an original, powerful, and elegant method to distinguish between the various possible origins of motor bias, based on the number of peaks in the motor bias plotted as a function of movement direction. The biomechanical explanation of motor biases could not be tested in this way, but this explanation was excluded in a different way using data on implicit sensorimotor adaptation. This was also an elegant method as it allowed the authors to test biomechanical explanations without the need to commit to a certain biomechanical cost function.

We thank the reviewer for their enthusiastic comments.

(1) The main weakness of the study is that it rests on the assumption that the number of peaks in the bias function is indicative of the origin of the bias. Specifically, it is assumed that a proprioceptive bias leads to a single peak, a transformation bias to two peaks, and a visual bias to four peaks, but these assumptions are not well substantiated. Especially the assumption that a transformation bias leads to two peaks is questionable. It is motivated by the fact that biases found when participants matched the position of their unseen hand with a visual target are consistent with this pattern. However, it is unclear why that task would measure only the effect of transformation biases, and not also the effects of visual and proprioceptive biases in the sensed target and hand locations. Moreover, it is not explained why a transformation bias would lead to this specific bias pattern in the first place.

We would like to clarify two things.

Frist, the measurements of the transformation bias are not entirely independent of proprioceptive and visual biases. Specifically, we define transformation bias as the misalignment between the internal representation of a visual target and the corresponding hand position. By this definition, the transformation error entails both visual and proprioceptive biases (see Author response image 1). Transformation biases have been empirically quantified in numerous studies using matching tasks, where participants either aligned their unseen hand to a visual target (Wang et al., 2021) or aligned a visual target to their unseen hand (Wilson et al., 2010). Indeed, those tasks are always considered as measuring proprioceptive biases assuming visual bias is small given the minimal visual uncertainty.

Author response image 1.

Second, the critical difference between models is in how these biases influence motor planning rather than how those biases are measured. In the Proprioceptive bias model, a movement is planned in visual space. The system perceives the starting hand position in proprioceptive space and transforms this into visual space (Vindras & Viviani, 1998; Vindras et al., 2005). As such, bias only affects the perceived starting position; there is no influence on the perceived target location (no visual bias).

In contrast, the Transformation bias model proposes that while both the starting and target positions are perceived in visual space, movement is planned in proprioceptive space. Consequently, both positions must be transformed from visual space to proprioceptive coordinates before movement planning (i.e., where is my sensed hand and where do I want it to be). Under this framework, biases can emerge from both the start and target positions. This is how the transformation model leads to different predictions compared to the perceptual models, even if the bias is based on the same measurements.

We now highlight the differences between the Transformation bias model and the Proprioceptive bias model explicitly in the Results section (Lines 192-200):

“Note that the Proprioceptive Bias model and the Transformation Bias model tap into the same visuo-proprioceptive error map. The key difference between the two models arises in how this error influences motor planning. For the Proprioceptive Bias model, planning is assumed to occur in visual space. As such, the perceived position of the hand (based on proprioception) is transformed into the visual space. This will introduce a bias in the representation of the start position. In contrast, the Transformation Bias model assumes that the visually-based representations of the start and target positions need to be transformed into proprioceptive space for motor planning. As such, both positions are biased in the transformation process. In addition to differing in terms of their representation of the target, the error introduced at the start position is in opposite directions due to the direction of the transformation (see fig 1g-h).”

In terms of the motor bias function across the workspace, the peaks are quantitatively derived from the model simulations. The number of peaks depends on how we formalize each model. Importantly, this is a stable feature of each model, regardless of how the model is parameterized. Thus, the number of peaks provides a useful criterion to evaluate different models.

Figure 1 g-h illustrates the intuition of how the models generate distinct peak patterns. We edited the figure caption and reference this figure when we introduce the bias function for each model.

(2) Also, the assumption that a visual bias leads to four peaks is not well substantiated as one of the papers on which the assumption was based (Yousif et al., 2023) found a similar pattern in a purely proprioceptive task.

What we referred to in the original submission as “visual bias” is not an eye-centric bias, nor is it restricted to the visual system. Rather, it may reflect a domain-general distortion in the representation of position within polar space. We called it a visual bias as it was associated with the perceived location of the visual target in the current task. To avoid confusion, we have opted to move to a more general term and now refer to this as “target bias.”

We clarify the nature of this bias when introducing the model in the Results section (Lines 164-169):

“Since the task permits free viewing without enforced fixation, we assume that participants shift their gaze to the visual target; as such, an eye-centric bias is unlikely. Nonetheless, prior studies have shown a general spatial distortion that biases perceived target locations toward the diagonal axes(Huttenlocher et al., 2004; Kosovicheva & Whitney, 2017). Interestingly, this bias appears to be domain-general, emerging not only for visual targets but also for proprioceptive ones(Yousif et al., 2023). We incorporated this diagonal-axis spatial distortion into a Target Bias model. This model predicts a four-peaked motor bias pattern (Fig 1f).”

We also added a paragraph in the Discussion to further elaborate on this model (Lines 502-511):

“What might be the source of the visual bias in the perceived location of the target? In the perception literature, a prominent theory has focused on the role of visual working memory account based on the observation that in delayed response tasks, participants exhibit a bias towards the diagonals when recalling the location of visual stimuli(Huttenlocher et al., 2004; Sheehan & Serences, 2023). Underscoring that the effect is not motoric, this bias is manifest regardless of whether the response is made by an eye movement, pointing movement, or keypress(Kosovicheva & Whitney, 2017). However, this bias is unlikely to be dependent on a visual input as similar diagonal bias is observed when the target is specified proprioceptively via the passive displacement of an unseen hand(Yousif et al., 2023). Moreover, as shown in the present study, a diagonal bias is observed even when the target is continuously visible. Thus, we hypothesize that the bias to perceive the target towards the diagonals reflects a more general distortion in spatial representation rather than being a product of visual working memory.”

(3) Another weakness is that the study looked at biases in movement direction only, not at biases in movement extent. The models also predict biases in movement extent, so it is a missed opportunity to take these into account to distinguish between the models.

We thank the reviewer for this suggestion. We have now conducted a new experiment to assess angular and extent biases simultaneously (Figure 4a; Exp. 4; N = 30). Using our KINARM system, participants were instructed to make center-out movements that would terminate (rather than shoot past) at the visual target. No visual feedback was provided throughout the experiment.

The Transformation Bias model predicts a two-peaked error function in both the angular and extent dimensions (Figure 4c). Strikingly, when we fit the data from the new experiment to both dimensions simultaneously, this model captures the results qualitatively and quantitatively (Figure 4e). In terms of model comparison, it outperformed alternative models (Figure 4g) particularly when augmented with a visual bias component. Together, these results provide strong evidence that a mismatch between visual and proprioceptive space is a key source of motor bias.

This experiment is now reported within the revised manuscript (Lines 280-301).

Overall, the authors have done a good job mapping out reaching biases in a wide range of conditions, revealing new patterns in one of the most basic tasks, but unambiguously determining the origin of these biases remains difficult, and the evidence for the proposed origins is incomplete. Nevertheless, the study will likely have a substantial impact on the field, as the approach taken is easily applicable to other experimental conditions. As such, the study can spark future research on the origin of reaching biases.

We thank the reviewer for these summary comments. We believe that the new experiments and analyses do a better job of identifying the origins of motor biases.

Reviewer #2 (Public Review):

Summary:

This work examines an important question in the planning and control of reaching movements - where do biases in our reaching movements arise and what might this tell us about the planning process? They compare several different computational models to explain the results from a range of experiments including those within the literature. Overall, they highlight that motor biases are primarily caused by errors in the transformation between eye and hand reference frames. One strength of the paper is the large number of participants studied across many experiments. However, one weakness is that most of the experiments follow a very similar planar reaching design - with slicing movements through targets rather than stopping within a target. Moreover, there are concerns with the models and the model fitting. This work provides valuable insight into the biases that govern reaching movements, but the current support is incomplete.

Strengths:

The work uses a large number of participants both with studies in the laboratory which can be controlled well and a huge number of participants via online studies. In addition, they use a large number of reaching directions allowing careful comparison across models. Together these allow a clear comparison between models which is much stronger than would usually be performed.

We thank the reviewer for their encouraging comments.

Weaknesses:

Although the topic of the paper is very interesting and potentially important, there are several key issues that currently limit the support for the conclusions. In particular I highlight:

(1) Almost all studies within the paper use the same basic design: slicing movements through a target with the hand moving on a flat planar surface. First, this means that the authors cannot compare the second component of a bias - the error in the direction of a reach which is often much larger than the error in reaching direction.

Reviewer 1 made a similar point, noting that we had missed an opportunity to provide a more thorough assessment of reaching biases. As described above, we conducted a new experiment in which participants made pointing movements, instructed to terminate the movements at the target. These data allow us to analyze errors in both angular and extent dimensions. The transformation bias model successfully predicts angular and extent biases, outperformed the other models at both group and individual levels. We have now included this result as Exp 4 in the manuscript. Please see response to Reviewer 1 Comment 3 for details.

Second, there are several studies that have examined biases in three-dimensional reaching movements showing important differences to two-dimensional reaching movements (e.g. Soechting and Flanders 1989). It is unclear how well the authors' computational models could explain the biases that are present in these much more common-reaching movements.

This is an interesting issue to consider. We expect the mechanisms identified in our 2D work will generalize to 3D.

Soechting and Flanders (1989) quantified 3D biases by measuring errors across multiple 2D planes at varying heights (see Author response image 2 for an example from their paper). When projecting their 3-D bias data to a horizontal 2D space, the direction of the bias across the 2D plane looks relatively consistent across different heights even though the absolute value of the bias varies (Author response image 2). For example, the matched hand position is generally to the leftwards and downward of the target. Therefore, the models we have developed and tested in a specific 2D plane are likely to generalize to other 2D plane of different heights.

Author response image 2.

However, we think the biases reported by Soechting and Flanders likely reflect transformation biases rather than motor biases. First, the movements in their study were performed very slowly (3–5 seconds), more similar to our proprioceptive matching tasks and much slower than natural reaching movements (<500ms). Given the slow speed, we suspect that motor planning in Soechting and Flanders was likely done in a stepwise, incremental manner (closed loop to some degree). Second, the bias pattern reported in Soechting and Flanders —when projected into 2D space— closely mirrors the leftward transformation errors observed in previous visuo-proprioceptive matching task (e.g., Wang et al., 2021).

In terms of the current manuscript, we think that our new experiment (Exp 4, where we measure angular and radial error) provides strong evidence that the transformation bias model generalizes to more naturalistic pointing movements. As such, we expect these principles will generalize were we to examine movements in three dimensions, an extension we plan to test in future work.

(2) The model fitting section is under-explained and under-detailed currently. This makes it difficult to accurately assess the current model fitting and its strength to support the conclusions. If my understanding of the methods is correct, then I have several concerns. For example, the manuscript states that the transformation bias model is based on studies mapping out the errors that might arise across the whole workspace in 2D. In contrast, the visual bias model appears to be based on a study that presented targets within a circle (but not tested across the whole workspace). If the visual bias had been measured across the workspace (similar to the transformation bias model), would the model and therefore the conclusions be different?

We have substantially expanded the Methods section to clarify the modeling procedures (detailed below in section “Recommendations for the Authors”). We also provide annotated code to enable others to easily simulate the models.

Here we address three points relevant to the reviewer’s concern about whether the models were tested on equal footing, and in particular, concern that the transformation bias model was more informed by prior literature than the visual bias model.

First, our center-out reaching task used target locations that have been employed in both visual and proprioceptive bias studies, offering reasonable comprehensive coverage of the workspace. For example, for a target to the left of the body’s midline, visual biases tend to be directed diagonally (Kosovicheva & Whitney, 2017), while transformation biases are typically leftward and downward (Wang et al, 2021). In this sense, the models were similarly constrained by prior findings.

Second, while the qualitative shape of each model was guided by prior empirical findings, no previous data were directly used to quantitatively constrain the models. As such, we believe the models were evaluated on equal footing. No model had more information or, best we can tell, an inherent advantage over the others.

Third, reassuringly, the fitted transformation bias closely matches empirically observed bias maps reported in prior studies (Fig 2h). The strong correspondence provides convergent validity and supports the putative causality between transformation biases to motor biases.

(3) There should be other visual bias models theoretically possible that might fit the experimental data better than this one possible model. Such possibilities also exist for the other models.

Our initial hypothesis, grounded in prior literature, was that motor biases arise from a combination of proprioceptive and visual biases. This led us to thoroughly explore a range of visual models. We now describe these alternatives below, noting that in the paper, we chose to focus on models that seemed the most viable candidates. (Please also see our response to Reviewer 3, Point 2, on another possible source of visual bias, the oblique effect.)

Quite a few models have described visual biases in perceiving motion direction or object orientation (e.g., Wei & Stocker, 2015; Patten, Mannion & Clifford, 2017). Orientation perception would be biased towards the Cartesian axis, generating a four-peak function. However, these models failed to account for the motor biases observed in our experiments. This is not surprising given that these models were not designed to capture biases related to a static location.

We also considered a class of eye-centric models where biases for peripheral locations are measured under fixation. A prominent finding here is that the bias is along the radial axis in which participants overshoot targets when they fixate on the start position during the movement (Beurze et al., 2006; Van Pelt & Medendorp, 2008). Again, this is not consistent with the observed motor biases. For example, participants undershoot rightward targets when we measured the distance bias in Exp 4. Importantly, since most our tasks involved free viewing in natural settings with no fixation requirements, we considered it unlikely that biases arising from peripheral viewing play a major role.

We note, though, that in our new experiment (Exp 4), participants observed the visual stimuli from a fixed angle in the KinArm setup (see Figure 4a). This setup has been shown to induce depth-related visual biases (Figure 4b, e.g., Volcic et al., 2013; Hibbard & Bradshaw, 2003). For this reason, we implemented a model incorporating this depth bias as part of our analyses of these data. While this model performed significantly worse than the transformation bias model alone, a mixed model that combined the depth bias and transformation bias provided the best overall fit. We now include this result in the main text (Lines 286-294).

We also note that the “visual bias” we referred to in the original submission is not restricted to the visual system. A similar bias pattern has been observed when the target is presented visually or proprioceptively (Kosovicheva & Whitney, 2017; Yousif, Forrence, & McDougle, 2023). As such, it may reflect a domaingeneral distortion in the representation of position within polar space. Accordingly, in the revision, we now refer to this in a more general way, using the term “target bias.” We justify this nomenclature when introducing the model in the Results section (Lines 164-169). Please also see Reviewer 1 comment 2.

We recognize that future work may uncover a better visual model or provide a more fine-grained account of visual biases (or biases from other sources). With our open-source simulation code, such biases can be readily incorporated—either to test them against existing models or to combine them with our current framework to assess their contribution to motor biases. Given our explorations, we expect our core finding will hold: Namely, that a combination of transformation and target biases offers the most parsimonious account, with the bias associated with the transformation process explaining the majority of the observed motor bias in visually guided movements.

Given the comments from the reviewer, we expanded the discussion session to address the issue of alternative models of visual bias (lines 522-529):

“Other forms of visual bias may influence movement. Depth perception biases could contribute to biases in movement extent(Beurze et al., 2006; Van Pelt & Medendorp, 2008). Visual biases towards the principal axes have been reported when participants are asked to report the direction of moving targets or the orientation of an object(Patten et al., 2017; Wei & Stocker, 2015). However, the predicted patterns of reach biases do not match the observed biases in the current experiments. We also considered a class of eye-centric models in which participants overestimate the radial distance to a target while maintaining central fixation(Beurze et al., 2006; Van Pelt & Medendorp, 2008). At odds with this hypothesis, participants undershot rightward targets when we measured the radial bias in Exp 4. The absence of these other distortions of visual space may be accounted for by the fact that we allowed free viewing during the task.”

(4) Although the authors do mention that the evidence against biomechanical contributions to the bias is fairly weak in the current manuscript, this needs to be further supported. Importantly both proprioceptive models of the bias are purely kinematic and appear to ignore the dynamics completely. One imagines that there is a perceived vector error in Cartesian space whereas the other imagines an error in joint coordinates. These simply result in identical movements which are offset either with a vector or an angle. However, we know that the motor plan is converted into muscle activation patterns which are sent to the muscles, that is, the motor plan is converted into an approximation of joint torques. Joint torques sent to the muscles from a different starting location would not produce an offset in the trajectory as detailed in Figure S1, instead, the movements would curve in complex patterns away from the original plan due to the non-linearity of the musculoskeletal system. In theory, this could also bias some of the other predictions as well. The authors should consider how the biomechanical plant would influence the measured biases.

We thank the reviewer for encouraging us on this topic and to formalize a biomechanical model. In response, we have implemented a state-of-the-art biomechanical framework, MotorNet

(https://elifesciences.org/articles/88591), which simulates a six-muscle, two-skeleton planar arm model using recurrent neural networks (RNNs) to generate control policies (See Figure 6a). This model captures key predictions about movement curvature arising from biomechanical constraints. We view it as a strong candidate for illustrating how motor bias patterns could be shaped by the mechanical properties of the upper limb.

Interestingly, the biomechanical model did not qualitatively or quantitatively reproduce the pattern of motor biases observed in our data. Specifically, we trained 50 independent agents (RNNs) to perform random point-to-point reaching movements across the workspace used in our task. We used a loss function that minimized the distance between the fingertip and the target over the entire trajectory. When tested on a center-out reaching task, the model produced a four-peaked motor bias pattern (Figure 6b), in contrast to the two-peaked function observed empirically. These results suggest that upper limb biomechanical constraints are unlikely to be a primary driver of motor biases in reaching. This holds true even though the reported bias is read out at 60% of the reaching distance, where biomechanical influences on the curvature of movement are maximal. We have added this analysis to the results (lines 367-373).

It may seem counterintuitive that biomechanics plays a limited role in motor planning. This could be due to several factors. First, First, task demands (such as the need to grasp objects) may lead the biomechanical system to be inherently organized to minimize endpoint errors (Hu et al., 2012; Trumbower et al., 2009). Second, through development and experience, the nervous system may have adapted to these biomechanical influences—detecting and compensating for them over time (Chiel et al., 2009).

That said, biomechanical constraints may make a larger contribution in other contexts; for example, when movements involve more extreme angles or span larger distances, or in individuals with certain musculoskeletal impairments (e.g., osteoarthritis) where physical limitations are more likely to come into play. We address this issue in the revised discussion.

“Nonetheless, the current study does not rule out the possibility that biomechanical factors may influence motor biases in other contexts. Biomechanical constraints may have had limited influence in our experiments due to the relatively modest movement amplitudes used and minimal interaction torques involved. Moreover, while we have focused on biases that manifest at the movement endpoint, biomechanical constraints might introduce biases that are manifest in the movement trajectories.(Alexander, 1997; Nishii & Taniai, 2009) Future studies are needed to examine the influence of context on reaching biases.”

Reviewer #3 (Public review):

The authors make use of a large dataset of reaches from several studies run in their lab to try to identify the source of direction-dependent radial reaching errors. While this has been investigated by numerous labs in the past, this is the first study where the sample is large enough to reliably characterize isometries associated with these radial reaches to identify possible sources of errors.

(1) The sample size is impressive, but the authors should Include confidence intervals and ideally, the distribution of responses across individuals along with average performance across targets. It is unclear whether the observed “averaged function” is consistently found across individuals, or if it is mainly driven by a subset of participants exhibiting large deviations for diagonal movements. Providing individual-level data or response distributions would be valuable for assessing the ubiquity of the observed bias patterns and ruling out the possibility that different subgroups are driving the peaks and troughs. It is possible that the Transformation or some other model (see below) could explain the bias function for a substantial portion of participants, while other participants may have different patterns of biases that can be attributable to alternative sources of error.

We thank the reviewer for encouraging a closer examination of the individual-level data. We did include standard error when we reported the motor bias function. Given that the error distribution is relatively Gaussian, we opted to not show confidence intervals since they would not provide additional information.

To examine individual differences, we now report a best-fit model frequency analysis. For Exp 1, we fit each model at the individual level and counted the number of participants that are best predicted by each model. Among the four single source models (Figure 3a), the vast majority of participants are best explained by the transformation bias model (48/56). When incorporating mixture models, the combined transformation + target bias model emerged as the best fit for almost all participants across experiments (50/56). The same pattern holds for Exp 3b, the frequency analysis is more distributed, likely due to the added noise that comes with online studies.

We report this new analysis in the Results. (see Fig 3. Fig S2). Note that we opted to show some representative individual fits, selecting individuals whose data were best predicted by different models (Fig S2). Given that the number of peaks characterizes each model (independent of the specific parameter values), the two-peaked function exhibited for most participants indicates that the Transformation bias model holds at the individual level and not just at the group level.

(2) The different datasets across different experimental settings/target sets consistently show that people show fewer deviations when making cardinal-directed movements compared to movements made along the diagonal when the start position is visible. This reminds me of a phenomenon referred to as the oblique effect: people show greater accuracy for vertical and horizontal stimuli compared to diagonal ones. While the oblique effect has been shown in visual and haptic perceptual tasks (both in the horizontal and vertical planes), there is some evidence that it applies to movement direction. These systematic reach deviations in the current study thus may reflect this epiphenomenon that applies across modalities. That is, estimating the direction of a visual target from a visual start position may be less accurate, and may be more biased toward the horizontal axis, than for targets that are strictly above, below, left, or right of the visual start position. Other movement biases may stem from poorer estimation of diagonal directions and thus reflect more of a perceptual error than a motor one. This would explain why the bias function appears in both the in-lab and on-line studies although the visual targets are very different locations (different planes, different distances) since the oblique effects arise independent of plane, distance, or size of the stimuli. When the start position is not visible like in the Vindras study, it is possible that this oblique effect is less pronounced; masked by other sources of error that dominate when looking at 2D reach endpoint made from two separate start positions, rather than only directional errors from a single start position. Or perhaps the participants in the Vindras study are too variable and too few (only 10) to detect this rather small direction-dependent bias.

The potential link between the oblique effect and the observed motor bias is an intriguing idea, one that we had not considered. However, after giving this some thought, we see several arguments against the idea that the oblique effect accounts for the pattern of motor biases.

First, by the oblique effect, perceptual variability is greater along the diagonal axes compared to the cardinal axes. These differences in perceptual variability have been used to explain biases in visual perception through a Bayesian model under the assumption that the visual system has an expectation that stimuli are more likely to be oriented along the cardinal axes (Wei & Stocker, 2015). Importantly, the model predicts low biases at targets with peak perceptual variability. As such, even though those studies observed that participants showed large variability for stimuli at diagonal orientations, the bias for these stimuli was close to zero. Given we observed a large bias for targets at locations along the diagonal axes, we do not think this visual effect can explain the motor bias function.

Second, the reviewer suggested that the observed motor bias might be largely explained by visual biases (or what we now refer to as target biases). If this hypothesis is correct, we would anticipate observing a similar bias pattern in tasks that use a similar layout for visual stimuli but do not involve movement. However, this prediction is not supported. For example, Kosovicheva & Whitney (2017) used a position reproduction/judgment task with keypress responses (no reaching). The stimuli were presented in a similar workspace as in our task. Their results showed four-peaked bias function while our results showed a two-peaked function.

In summary, we don’t think oblique biases make a significant contribution to our results.

A bias in estimating visual direction or visual movement vector Is a more realistic and relevant source of error than the proposed visual bias model. The Visual Bias model is based on data from a study by Huttenlocher et al where participants “point” to indicate the remembered location of a small target presented on a large circle. The resulting patterns of errors could therefore be due to localizing a remembered visual target, or due to relative or allocentric cues from the clear contour of the display within which the target was presented, or even movements used to indicate the target. This may explain the observed 4-peak bias function or zig-zag pattern of “averaged” errors, although this pattern may not even exist at the individual level, especially given the small sample size. The visual bias source argument does not seem well-supported, as the data used to derive this pattern likely reflects a combination of other sources of errors or factors that may not be applicable to the current study, where the target is continuously visible and relatively large. Also, any visual bias should be explained by a coordinates centre on the eye and should vary as a function of the location of visual targets relative to the eyes. Where the visual targets are located relative to the eyes (or at least the head) is not reported.

Thank you for this question. A few key points to note:

The visual bias model has also been discussed in studies using a similar setup to our study. Kosovicheva & Whitney (2017) observed a four-peaked function in experiments in which participants report a remembered target position on a circle by either making saccades or using key presses to adjust the position of a dot. However, we agree that this bias may be attenuated in our experiment given that the target is continuously visible. Indeed, the model fitting results suggest the peak of this bias is smaller in our task (~3°) compared to previous work (~10°, Kosovicheva & Whitney, 2017; Yousif, Forrence, & McDougle, 2023).

We also agree with the reviewer that this “visual bias” is not an eye-centric bias, nor is it restricted to the visual system. A similar bias pattern is observed even if the target is presented proprioceptively (Yousif, Forrence, & McDougle, 2023). As such, this bias may reflect a domain-general distortion in the representation of position within polar space. Accordingly, in the revision, we now refer to this in a more general way, using the term “target bias”, rather than visual bias. We justify this nomenclature when introducing the model in the Results section (Lines 164-169). Please also see Reviewer 1 comment 2 for details.

Motivated by Reviewer 2, we also examined multiple alternative visual bias models (please refer to our response to Reviewer 2, Point 3.

The Proprioceptive Bias Model is supposed to reflect errors in the perceived start position. However, in the current study, there is only a single, visible start position, which is not the best design for trying to study the contribution. In fact, my paradigms also use a single, visual start position to minimize the contribution of proprioceptive biases, or at least remove one source of systematic biases. The Vindras study aimed to quantify the effect of start position by using two sets of radial targets from two different, unseen start positions on either side of the body midline. When fitting the 2D reach errors at both the group and individual levels (which showed substantial variability across individuals), the start position predicted most of the 2D errors at the individual level – and substantially more than the target direction. While the authors re-plotted the data to only illustrate angular deviations, they only showed averaged data without confidence intervals across participants. Given the huge variability across their 10 individuals and between the two target sets, it would be more appropriate to plot the performance separately for two target sets and show confidential intervals (or individual data). Likewise, even the VT model predictions should differ across the two targets set since the visual-proprioceptive matching errors from the Wang et al study that the model is based on, are larger for targets on the left side of the body.

To be clear, in the Transformation bias model, the vector bias at the start position is also an important source of error. The critical difference between the proprioceptive and transformation models is how bias influences motor planning. In the Proprioceptive bias model, movement is planned in visual space. The system perceives the starting hand position in proprioceptive space and transforms this into visual space (Vindras & Viviani, 1998; Vindras et al., 2005). As such, the bias is only relevant in terms of the perceived start position; it does not influence the perceived target location. In contrast, the transformation bias model proposes that while both the starting and target positions are perceived in visual space, movements are planned in proprioceptive space. Consequently, when the start and target positions are visible, both positions must be transformed from visual space to proprioceptive coordinates before movement planning. Thus, bias will influence both the start and target positions. We also note that to set the transformation bias for the start/target position, we referred to studies in which bias is usually referred to as proprioception error measurement. As such, changing the start position has a similar impact on the Transformation and the Proprioceptive Bias models in principle, and would not provide a stronger test to separate them.

We now highlight the differences between the models in the Results section, making clear that the bias at the start position influences both the Proprioceptive bias and Transformation bias models (Lines 192200).

“Note that the Proprioceptive Bias model and the Transformation Bias model tap into the same visuo-proprioceptive error map. The key difference between the two models arises in how this error influences motor planning. For the Proprioceptive Bias model, planning is assumed to occur in visual space. As such, the perceived position of the hand (based on proprioception) is transformed into visual space. This will introduce a bias in the representation of the start position. In contrast, the Transformation Bias model assumes that the visually-based representations of the start and target positions need to be transformed into proprioceptive space for motor planning. As such, both positions are biased in the transformation process. In addition to differing in terms of their representation of the target, the error introduced at the start position is in opposite directions due to the direction of the transformation (see fig 1g-h).”

In terms of fitting individual data, we have conducted a new experiment, reported as Exp 4 in the revised manuscript (details in our response to Reviewer 1, comment 3). The experiment has a larger sample size (n=30) and importantly, examined error for both movement angle and movement distance. We chose to examine the individual differences in 2-D biases using this sample rather than Vindras’ data as our experiment has greater spatial resolution and more participants. At both the group and individual level, the Transformation bias model is the best single source model, and the Transformation + Target Bias model is the best combined model. These results strongly support the idea that the transformation bias is the main source of the motor bias.

As for the different initial positions in Vindras et al (2005), the two target sets have very similar patterns of motor biases. As such, we opted to average them to decrease noise. Notably, the transformation model also predicts that altering the start location should have limited impact on motor bias patterns: What matters for the model is the relative difference between the transformation biases at the start and target positions rather than the absolute bias.

Author response image 3.

I am also having trouble fully understanding the V-T model and its associated equations, and whether visual-proprioception matching data is a suitable proxy for estimating the visuomotor transformation. I would be interested to first see the individual distributions of errors and a response to my concerns about the Proprioceptive Bias and Visual Bias models.

We apologize for the lack of clarity on this model. To generate the T+V (Now Transformation + Target bias, or TR+TG) model, we assume the system misperceives the target position (Target bias, see Fig S5a) and then transforms the start and misperceived target positions into proprioceptive space (Fig S5b). The system then generates a motor plan in proprioceptive space; this plan will result in the observed motor bias (Fig. S5c). We now include this figure as Fig S5 and hope that it makes the model features salient.

Regarding whether the visuo-proprioceptive matching task is a valid proxy for transformation bias, we refer the reviewer to the comments made by Public Reviewer 1, comment 1. We define the transformation bias as the discrepancy between corresponding positions in visual and proprioceptive space. This can be measured using matching tasks in which participants either aligned their unseen hand to a visual target (Wang et al., 2021) or aligned a visual target to their unseen hand (Wilson et al., 2010).

Nonetheless, when fitting the model to the motor bias data, we did not directly impose the visual-proprioceptive matching data. Instead, we used the shape of the transformation biases as a constraint, while allowing the exact magnitude and direction to be free parameters (e.g., a leftward and downward bias scaled by distance from the right shoulder). Reassuringly, the fitted transformation biases closely matched the magnitudes reported in prior studies (Fig. 2h, 1e), providing strong quantitative support for the hypothesized causal link between transformation and motor biases.

Recommendations for the authors:

Overall, the reviewers agreed this is an interesting study with an original and strong approach. Nonetheless, there were three main weaknesses identified. First, is the focus on bias in reach direction and not reach extent. Second, the models were fit to average data and not individual data. Lastly, and most importantly, the model development and assumptions are not well substantiated. Addressing these points would help improve the eLife assessment.

Reviewer #1 (Recommendations for the authors):

It is mentioned that the main difference between Experiments 1 and 3 is that in Experiment 3, the workspace was smaller and closer to the shoulder. Was the location of the laptop relative to the participant in Experiment 3 known by the authors? If so, variations in this location across participants can be used to test whether the Transformation bias was indeed larger for participants who had the laptop further from the shoulder.

Another difference between Experiments 1 and 3 is that in Experiment 1, the display was oriented horizontally, whereas it was vertical in Experiment 3. To what extent can that have led to the different results in these experiments?

This is an interesting point that we had not considered. Unfortunately, for the online work we do not record the participants’ posture.

Regarding the influence of display orientation (horizontal vs. vertical), Author response image 4 presents three relevant data points: (1) Vandevoorde and Orban de Xivry (2019), who measured motor biases in-person across nine target positions using a tablet and vertical screen; (2) Our Experiment 1b, conducted online with a vertical setup; (3) Our in-person Experiment 3b, using a horizontal monitor. For consistency, we focus on the baseline conditions with feedback, the only condition reported in Vandevoorde. Motor biases from the two in-person studies were similar despite differing monitor orientations: Both exhibited two-peaked functions with comparable peak locations. We note that the bias attenuation in Vandevoorde may be due to their inclusion of reward-based error signals in addition to cursor feedback. In contrast, compared to the in-person studies, the online study showed reduced bias magnitude with what appears to be a four peaked function. While more data are needed, these results suggest that the difference in the workspace (more restricted in our online study) may be more relevant than monitor orientation.

Author response image 4.

For the joint-based proprioceptive model, the equations used are for an arm moving in a horizontal plane at shoulder height, but the figures suggest the upper arm was more vertical than horizontal. How does that affect the predictions for this model?

Please also see our response to your public comment 1. When the upper limb (or the lower limb) is not horizontal, it will influence the projection of the upper limb to the 2-D space. Effectively in the joint-based proprioceptive model, this influences the ratio between L1 and L2 (see  Author response image 5b below). However, adding a parameter to vary L1/L2 ratio would not change the set of the motor bias function that can be produced by the model. Importantly, it will still generate a one-peak function. We simulated 50 motor bias function across the possible parameter space. As shown by  Author response image 5c-d, the peak and the magnitude of the motor bias functions are very similar with and without the L1/L2 term. We characterize the bias function with the peak position and the peak-to-valley distance. Based on those two factors, the distribution of the motor bias function is very similar ( Author response image 5e-f). Moreover, the L1/L2 ratio parameter is not recoverable by model fitting ( Author response image 5c), suggesting that it is redundant with other parameters. As such we only include the basic version of the joint-based proprioceptive model in our model comparisons.

Author response image 5.

It was unclear how the models were fit and how the BIC was computed. It is mentioned that the models were fit to average data across participants, but the BIC values were based on all trials for all participants, which does not seem consistent. And the models are deterministic, so how can a log-likelihood be determined? Since there were inter-individual differences, fitting to average data is not desirable. Take for instance the hypothetical case that some participants have a single peak at 90 deg, and others have a single peak at 270 deg. Averaging their data will then lead to a pattern with two peaks, which would be consistent with an entirely different model.

We thank the reviewer for raising these issues.

Given the reviewers’ comments, we now report fits at both the group and individual level (see response to reviewer 3 public comment 1). The group-level fitting is for illustration purposes. Model comparison is now based on the individual-level analyses which show that the results are best explained by the transformation model when comparing single source models and best explained by the T+V (now TG+TR) model when consider all models. These new results strongly support the transformation model.

Log-likelihoods were computed assuming normally distributed motor noise around the motor biases predicted by each model.

We updated the Methods section as follows (lines 841-853):

“We used the fminsearchbnd function in MATLAB to minimize the sum of loglikelihood (LL) across all trials for each participant. LL were computed assuming normally distributed noise around each participant’s motor biases:

[11] LL = normpdf(x, b, c)

where x is the empirical reaching angle, b is the predicted motor bias by the model, c is motor noise, calculated as the standard deviation of (x − b). For model comparison, we calculated the BIC as follow:

[12] BIC = -2LL+k∗ln(n)

where k is the number of parameters of the models. Smaller BIC values correspond to better fits. We report the sum of ΔBIC by subtracting the BIC value of the TR+TG model from all other models.

For illustrative purposes, we fit each model at the group level, pooling data across all participants to predict the group-averaged bias function.”

What was the delay of the visual feedback in Experiment 1?

The visual delay in our setup was ~30 ms, with the procedure used to estimate this described in detail in Wang et al (2024, Curr. Bio.). We note that in calculating motor biases, we primarily relied on the data from the no-feedback block.

Minor corrections

In several places it is mentioned that movements were performed with proximal and distal effectors, but it's unclear where that refers to because all movements were performed with a hand (distal effector).

By 'proximal and distal effectors,' we were referring to the fact that in the online setup, “reaching movements” are primarily made by finger and/or wrist movements across a trackpad, whereas in the inperson setup, the participants had to use their whole arm to reach about the workspace. To avoid confusion, we now refer to these simply as 'finger' versus 'hand' movements.

In many figures, Bias is misspelled as Bais.

Fixed.

In Figure 3, what is meant by deltaBIC (*1000) etc? Literally, it would mean that the bars show 1,000 times the deltaBIC value, suggesting tiny deltaBIC values, but that's probably not what's meant.

×1000' in the original figure indicates the unit scaling, with ΔBIC values ranging from approximately 1000 to 4000. However, given that we now fit the models at the individual level, we have replaced this figure with a new one (Figure 3e) showing the distribution of individual BIC values.

Reviewer #2 (Recommendations for the authors):

I have concerns that the authors only examine slicing movements through the target and not movements that stop in the target. Biases create two major errors - errors in direction and errors in magnitude and here the authors have only looked at one of these. Previous work has shown that both can be used to understand the planning processes underlying movement. I assume that all models should also make predictions about the magnitude biases which would also help support or rule out specific models.

Please see our response to Reviewer 1 public review 3.

As discussed above, three-dimensional reaching movements also have biases and are not studied in the current manuscript. In such studies, biomechanical factors may play a much larger role.

Please see our response to your public review.

It may be that I am unclear on what exactly is done, as the methods and model fitting barely explain the details, but on my reading on the methods I have several major concerns.

First, it feels that the visual bias model is not as well mapped across space if it only results from one study which is then extrapolated across the workspace. In contrast, the transformation model is actually measured throughout the space to develop the model. I have some concerns about whether this is a fair comparison. There are potentially many other visual bias models that might fit the current experimental results better than the chosen visual bias model.

Please refers to our response to your public review.

It is completely unclear to me why a joint-based proprioceptive model would predict curved planned movements and not straight movements (Figure S1). Changes in the shoulder and elbow joint angles could still be controlled to produce a straight movement. On the other hand, as mentioned above, the actual movement is likely much more complex if the physical starting position is offset from the perceived hand.

Natural movements are often curved, reflecting a drive to minimize energy expenditure or biomechanical constraints (e.g., joint and muscle configuration). This is especially the case when the task emphasizes endpoint precision (Codol et al., 2024) like ours. Trajectory curvature was also observed in a recent simulation study in which a neural network was trained to control a biomechanical model (2-limb, 6muscles) with the cost function specified to minimize trajectory error (reach to a target with as straight a movement as possible). Even under these constraints, the movements showed some curvature. To examined whether the endpoint reaching bias somehow reflects the curvature (or bias during reaching), we included the prediction of this new biomechanical model in the paper to show it does not explain the motor bias we observed.

To be clear, while we implemented several models (Joint-based proprioceptive model and the new biomechanical model) to examine whether motor biases can be explained by movement curvature, our goal in this paper was to identify the source of the endpoint bias. Our modeling results reveal a previously underappreciated source of motor bias—a transformation error that arises between visual and proprioceptive space—plays a dominant role in shaping motor bias patterns across a wide range of experiments, including naturalistic reaching contexts where vision and hand are aligned at the start position. While the movement curvature might be influenced by selectively manipulating factors that introduce a mismatch between the visual starting position and the actual hand position (such as Sober and Sabes, 2003), we think it will be an avenue for future work to investigate this question.

The model fitting section is barely described. It is unclear how the data is fit or almost any other aspects of the process. How do the authors ensure that they have found the minimum? How many times was the process repeated for each model fit? How were starting parameters randomized? The main output of the model fitting is BIC comparisons across all subjects. However, there are many other ways to compare the models which should be considered in parallel. For example, how well do the models fit individual subjects using BIC comparisons? Or how often are specific models chosen for individual participants? While across all subjects one model may fit best, it might be that individual subjects show much more variability in which model fits their data. Many details are missing from the methods section. Further support beyond the mean BIC should be provided.

We fit each model 150 times and for each iteration, the initial value of each parameter was randomly selected from a uniform distribution. The range for each parameter was hand tuned for each model, with an eye on making sure the values covered a reasonable range. Please see our response to your first minor comment below for the range of all parameters and how we decide the iteration number for each model.

Given the reviewers’ comments in the individual difference, we now fit the models at individual level and report a frequency analysis, describing the best fitting model for each participant. In brief, the data for a vast majority of the participants was best explained by the transformation model when comparing single source models and by the T+V (TR+TG) model when consider all models. Please see response to reviewer 3 public comment 1 for the updated result.

We updated the method session, and it reads as follows (lines 841-853):

_“_We used the fminsearchbnd function in MATLAB to minimize the sum of loglikelihood (LL) across all trials for each participant. LL were computed assuming normally distributed noise around each participant’s motor biases:

[11]       𝐿𝐿 = 𝑛𝑜𝑟𝑚𝑝𝑑𝑓(𝑥, 𝑏, 𝑐)

where x is the empirical reaching angle, b is the predicted motor bias by the model, c is motor noise, calculated as the standard deviation of x-b.

For model comparison, we calculated the BIC as follows:

[12] BIC = -2LL+k∗ln(n)

where k is the number of parameters of the models. Smaller BIC values correspond to better fits. We report the sum of ΔBIC by subtracting the BIC value of the TR+TG model from all other models.”

Line 305-307. The authors state that biomechanical issues would not predict qualitative changes in the motor bias function in response to visual manipulation of the start position. However, I question this statement. If the start position is offset visually then any integration of the proprioceptive and visual information to determine the start position would contain a difference from the real hand position. A calculation of the required joint torques from such a position sent through the mechanics of the limb would produce biases. These would occur purely because of the combination of the visual bias and the inherent biomechanical dynamics of the limb.

We thank the reviewer for this comment. We have removed the statement regarding inferences about the biomechanical model based on visual manipulations of the start position. Additionally, we have incorporated a recently proposed biomechanical model into our model comparisons to expand our exploration of sources of bias. Please refer to our response to your public review for details.

Measurements are made while the participants hold a stylus in their hand. How can the authors be certain that the biases are due to the movement and not due to small changes in the hand posture holding the stylus during movements in the workspace. It would be better if the stylus was fixed in the hand without being held.

Below, we have included an image of the device used in Exp 1 for reference. The digital pen was fixed in a vertical orientation. At the start of the experiment, the experimenter ensured that the participant had the proper grip alignment and held the pen at the red-marked region. With these constraints, we see minimal change in posture during the task.

Author response image 6.

Minor Comments

Best fit model parameters are not presented. Estimates of the accuracy of these measures would also be useful.

In the original submission, we included a Table S1 that presented the best-fit parameters for the TR+TG (Previously T+V) model. Table S1 now shows the parameters for the other models (Exp 1b and 3b, only). We note the parameter values from these non-optimal models are hard to interpret given that core predictions are inconsistent with the data (e.g., number of peaks).

We assume that by "accuracy of these measures," the reviewers are referring to the reliability of the model fits. To assess this, we conducted a parameter recovery analysis in which we simulated a range of model parameters for each model and then attempted to recover them through fitting. Each model was simulated 50 times, with the parameters randomly sampled from distributions used to define the initial fitting parameters. Here, we only present the results for the combined models (TR+TG, PropV+V, and PropJ+V), as the nested models would be even easier to fit.

As shown in Fig. S4, all parameters were recovered with high accuracy, indicating strong reliability in parameter estimation. Additionally, we examined the log-likelihood as a function of fitting iterations (Fig. S4d). Based on this curve, we determined that 150 iterations were sufficient given that the log-likelihood values were asymptotic at this point. Moreover, in most cases, the model fitting can recover the simulated model, with minimal confusion across the three models (Fig. S4e).

What are the (*1000) and (*100) in the Change in BIC y-labels? I assume they indicate that the values should be multiplied by these numbers. If these indicate that the BIC is in the hundreds or thousands it would be better the label the axes clearly, as the interpretation is very different (e.g. a BIC difference of 3 is not significant).

×1000' in the original figure indicates the unit scaling, with ΔBIC values ranging from approximately 1000 to 4000. However, given that we now fit the models at the individual level, we have replaced this figure with a new one showing the distribution of individual BIC values.

Lines 249, 312, and 315, and maybe elsewhere - the degree symbol does not display properly.

Corrected.

Line 326. The authors mention that participants are unaware of their change in hand angle in response to clamped feedback. However, there may be a difference between sensing for perception and sensing for action. If the participants are unaware in terms of reporting but aware in terms of acting would this cause problems with the interpretation?

This is an interesting distinction, one that has been widely discussed in the literature. However, it is not clear how to address this in the present context. We have looked at awareness in different ways in prior work with clamped feedback. In general, even when the hand direction might have deviated by >20d, participants report their perceived hand position after the movement as near the target (Tsay et al, 2020). We also have used post-experiment questionnaires to probe whether they thought their movement direction had changed over the course of the experiment (volitionally or otherwise). Again, participants generally insist they moved straight to the target throughout the experiment. So it seems that they unaware of any change in action or perception.

Reaction time data provide additional support that participants are unaware of any change in behavior. The RT function remains flat after the introduction of the clamp, unlike the increases typically observed when participants engage in explicit strategy use (Tsay et al, 2024).

Figure 1h: The caption suggests this is from the Wang 2021 paper. However, in the text 180-182 it suggests this might be the map from the current results. Can the authors clarify?

Fig 1e is the data from Wang et al, 2021. We formalized an abstract map based on the spatial constrains observed in Fig 1e, and simulated the error at the start and target position based on this abstraction (Fig 1h). We have revised the text to now read (Lines 182-190):

“Motor biases may thus arise from a transformation error between these coordinate systems. Studies in which participants match a visual stimulus to their unseen hand or vice-versa provide one way to estimate this error(Jones et al., 2009; Rincon-Gonzalez et al., 2011; van Beers et al., 1998; Wang et al., 12/2020). Two key features stand out in these data: First, the direction of the visuo-proprioceptive mismatch is similar across the workspace: For right-handers using their dominant limb, the hand is positioned leftward and downward from each target. Second, the magnitude increases with distance from the body (Fig 1d). Using these two empirical constraints, we simulated a visual-proprioceptive error map (Fig. 1h) by applying a leftward and downward error vector whose magnitude scaled with the distance from each location to a reference point.”

Reviewer #3 (Recommendations for the authors):

The central idea behind the research seems quite promising, and I applaud the efforts put forth. However, I'm not fully convinced that the current model formulations are plausible explanations. While the dataset is impressively large, it does not appear to be optimally designed to address the complex questions the authors aim to tackle. Moreover, the datasets used to formulate the 3 different model predictions are SMALL and exhibit substantial variability across individuals, and based on average (and thus "smoothed") data.

We hope to have addressed these concerns with the two major changes to revised manuscript: 1) The new experiment in which we examine biases in both angle and extent and 2) the inclusion in the analyses of fits based on individual data sets.

This reminds us of the conversation we'd had about tropes like the "bitter cripple"

This reminds me of Dante manuscripts because from what I have been learning so far, the pages can be really decorative and visually interesting, but that does not automatically make them easy to analyze. Good looking does not always mean simple to understand.

Good context. This is reminds me of the poem by Drayton.

This article points out that Facebook's research about Instagram and teen mental health wasn't as clear as people thought. I like that it reminds us how complicated this issue really is. It's easy to blame social media for everything, but the truth is more mixed. For some teens it might make things worse, but for others it can be a place to find support. It made me think we should focus more on how people use these apps instead of just saying they're good or bad.

This reminds me of how class debates feel more engaging and thought-provoking than formal research papers.

This sentence is really interesting because it reminds me of how many people's first instinct when restaurant food comes is to take a picture of it. Even while having fun and being excited for food, the first instinct is to take a picture to remember it/show it to people. It's almost like having an extra observer in your brain judging everything.

AI affects everyone from patients to healthcare workers to society. When new evolutions are introduced too quickly, they can harm the social fabric. It reminds me of how the internet changed society after COVID hit. It became our main way to work and learn, but it also took away a lot of real human connection. Instead of hanging out or talking face-to-face, we started relying on screens and text messages for almost everything.

I think this is interesting because it reminds me of copypastas that can be found on the internet. Sometimes, there will be a TikTok in my feed that is of the same nature, urging people to repost and use the audio for good luck. I did not know chain letters were a thing and it's really interesting to see how they are carried over in the digital age.

This reminds me so much of the chain texts people sent in middle school. I remember receiving these texts and actually being scared that bad things would happen. I think it's interesting that this format has stayed the same and that it exploits people's superstitions through a carrot and stick method.

This reminds me that, before the printing press, the version of book might be slightly different due to the error of coping. This might cause the misunderstanding for the past people. Also the way of approaching a book was pretty hard, and this limit the spread of knowledge in ancient time.

Reading this section about pre-internet virality really made me reflect on how deeply rooted our desire to share and connect is. The example of chain letters especially stood out to me — even without social media, people still felt compelled to pass messages along, sometimes out of fear, sometimes out of hope. It’s interesting that what motivated them was often emotional rather than logical. This reminds me of how similar patterns appear today on social media: people still share posts promising “good luck” or “positive energy,” and even I’ve occasionally reshared something because it felt comforting or meaningful at the moment. It makes me realize that virality isn’t just about algorithms or technology; it’s about human emotions — our longing to be part of something bigger, our belief that our small actions can ripple outward.

This reminds me of something quite silly but I think it's worth mentioning. While this term was later adapted to refer to what we today call a meme, it was still in use a this definition before and did circle through media, which made the media retroactively very comedic through the redefining of the word meme. My favorite example of this is the 2013 game Metal Gear Rising: Revengeance, which has a plot points revolving around how the only thing that truly matters to a persons self and decisions is memes and the ideas that their culture pass on to them. But with our modern definition, all the thoughtful speeches throughout the game become unintentionally very funny.

I find the description of internet memes as "cultural genes" quite interesting. It reminds me that the evolution of online information mirrors biological evolution; the most "adapted" ideas survive because they spread faster or attract more attention. However, unlike biological evolution, internet memes don't require authenticity to survive. Therefore, social media algorithms, like "natural selection," prioritize promoting content with the highest engagement.

This is content I often see on social media, it could be content related to books, shows or movies ("I read ___ so you don't have to"). However this kind of content related to food reminds me of tiktok trends in 2020 where people were trying weird food or watching people try weird foods out of pure boredom. This could be a kind of trolling, people trying bad or weird food combinations to get views, engagement or create discourses online.

This type of content reminds me a lot of what we have learned about trolling. This type of non-sensical reaction-evoking content, often called rage bait, is made to prompt viewers to interact with the content and ultimately make the creator more money by getting them more views. I think it is interesting how emotionally invested people get in non-consequential, silly content that doesn't truly affect them.

The article explains how the quote “A lie is halfway around the world before the truth has its boots on” has changed over time, I thought it was interesting how many people wrongly credit it to Mark Twain or Churchill, which shows how we like to attach big names to make a saying sound more powerful. It really reminds me of how fast misinformation spreads on social media today，people share things so quickly that the truth never has time to catch up. Even though the quote is old, it still feels completely true in our world now.

This line really stood out to me because it captures both empowerment and unity. Sojourner Truth reminds her audience that women’s strength has always been transformative, Eve “turned the world upside down,” and now women collectively have the power to restore justice and balance. I find this message timeless because it speaks not only to women’s resilience but also to the importance of solidarity in achieving equality. Truth’s words challenge the idea that women are passive or fragile; instead, she reframes them as powerful agents of change who can reshape society when they work together.