What we’re going to read, and when.

In one of the most famous definitions of attention, from the beginnings of modern psychology, William James wrote: “Everyone knows what attention is.” On this first day, we’ll introduce ourselves, get familiar with class tech (bring your computer!), share what we already know about attention, and read the following pieces together.

Cook, Joanna. 2018. “Paying Attention to Attention.” Anthropology of This Century. http://aotcpress.com/articles/paying-attention-attention.

Popova, Maria. 2016. “Pioneering Psychologist William James on Attention, Multitasking, and the Mental Habit That Sets Great Minds Apart.” The Marginalian. https://www.themarginalian.org/2016/03/25/william-james-attention.

But some philosophers and cognitive scientists who study attention have argued that we don’t really know what it is, that it doesn’t really exist, or that it actually refers to many different things. Today, we’ll learn about these arguments that suggest that attention may be essentially fragmentary. [Note that these are rather technical papers, concerned mostly with how scientists use “attention”; you don’t have to understand everything in them, but make sure you get a sense of what exactly their main arguments are. These are probably the hardest readings of the semester, so I’m putting them early, before things get too busy.]

Anderson, Britt. 2023. “Stop Paying Attention to ‘Attention.’” WIREs Cognitive Science 14 (1): 1–6. https://doi.org/10.1002/wcs.1574.

Fernandez-Duque, Diego, and Mark L. Johnson. 2002. “Cause and Effect Theories of Attention: The Role of Conceptual Metaphors.” Review of General Psychology 6 (2): 153–65. https://doi.org/10.1037/1089-2680.6.2.153.

Taylor, Henry. 2023. “Attention as a Patchwork Concept.” European Journal for Philosophy of Science 13 (3): 36. https://doi.org/10.1007/s13194-023-00538-5.

Counterpoint: Wu, Wayne. 2023. “We Know What Attention Is!” Trends in Cognitive Sciences, December, 1–15. https://doi.org/10.1016/j.tics.2023.11.007.

Behaving ourselves, managing computers in the classroom.

There are a few ground rules about participating in this course:

Treat each other with respect and patience.

Avoid destructive critique of the readings.

Participate actively, even if you’re not confident in your thoughts.

Communicate openly and regularly, so I know how you’re doing.

As you’ll notice, I’m expecting you to use a computer in the classroom this semester. This is counter to a lot of current ideas about how to manage the attention of students, but I think it is worth the tradeoff to have materials ready at hand. (Also, you may guess that I have some opinions about how people talk about managing student attention.) You should only be using your computer for course-related work during class time. I may ask you to close the laptop if it seems like a distraction for you, and if we don’t need the computers for something, I’ll ask everyone to shut them.

In general, it’s nice to look at other people while they’re speaking and to not bury yourself in the computer. Let’s see if we can pull off constructive computer use in the seminar room this semester.

AI usage, accessibility, availability, late work, academic integrity.

The point of a class like this is to use your brain—to learn new ways of thinking that you can take with you when you graduate and to practice thinking so that you can get better at it. The way that the recent generation of AI tools has been marketed and designed typically works against that: having ChatGPT generate a summary of a text instead of reading it yourself, generating a paper using Gemini, or having Claude answer discussion questions are all analogous to bringing a forklift to the gym. This is not a necessary fact of large language models, but it is a problematic (for our purposes) feature of how they exist right now.

I work under the assumption that you’re here to learn and that the forklifts only come out when other pressures push you away from that goal. To continue over-extending this metaphor, I am like your personal trainer: I cannot lift the weights for you, but I’ve designed a program to help you build your capacities over the semester, and I will give you feedback and advice to hone your form as we go. I discourage the use of LLMs for any aspect of this course, but if you do use them, you should use them in ways that make things more challenging for you, not less. Figuring out what that means is your job. (Here’s some inspiration from the world of poetry.)

And, for my sake, do not submit computer-generated text as your own writing. (For related guidance, see the academic integrity policy below.) I’m not here to evaluate the output of machines, and you will be wasting both of our time. If you submit work that is obviously AI-generated (e.g., it includes hallucinated citations or quotes, or framing text like “as a large language model...” or “sure, I can write that for you...”), I will return it to you ungraded and you will receive a 0 on that assignment. If you submit work that seems like it is AI-generated (e.g., it is formulaic, does not seriously engage course materials, or reaches for obvious, perfunctory conclusions), then I will return it to you with the expectation that you will revise it to make it better (which is what I would do if you had generated such a submission with your own brain, too). AI-detection tools do not reliably work, and I do not use them.

Your success in this class is important to me. If there are any circumstances (personal, health-related, family-related, religious, etc.) that affect your ability to participate, please let me know as soon as possible so that we can work together to come up with reasonable accommodations for you. The sooner I know about any issues, the better. Any such discussion will remain confidential.

For disability-related accommodations, I encourage you to consult with the StAAR Center, which can arrange for formal accommodations across your classes. But you do not need to register with them or provide documentation to me in order to request accommodations in this course.

Following university expectations, this course is designed to meet in person; in some situations, I may move meetings online. Please check your email before we meet in case I’ve had to make a last-minute change.

I try to be a responsive emailer; you should expect a reply within 24–48 weekday hours of emailing me. (Please don’t use the Canvas inbox thing to message me—it’s hard enough to stay on top of one inbox.) If you don‘t get a reply in that window, you should feel free to follow up with me as your message may have gotten buried in my inbox. I generally don‘t answer emails over the weekend or after 5 pm.

I hold regular office hours to meet with students. Currently, these are held via Zoom, a few days per week. I use an online sign-up sheet to make sure that there‘s enough time for everyone who wants it and to spread you out through the hour. This time is reserved for you, and you should feel free to use it to talk to me about anything: the course, your academic program in general, or whatever you‘re interested in. You don‘t need to have a specific problem to sign up, and I enjoy talking with you outside the classroom, so feel free to make use of office hours as often as you like.

As I rule, I tend to be flexible about deadlines, guided by a few principles: I do not want to make your life hard for no reason; I value my own time as well as yours; and extensions are not always in your best interest. If you need an extension and ask for one before a deadline (and not just the night before), we can usually work something out that balances your needs with mine and does not inadvertently make your life harder later on. I will not automatically deduct points for late work (though in some cases, especially for small assignments, I will simply not accept late work). You can find information about any assignment-specific late policies on the pages of those assignments.

Philosophy, specifications, and how the points work.

I do not like assigning grades in courses like this, where the point is to collaboratively explore an area of interest and intellectually engage each other. Too much concern with grades tends to stifle creativity, as students who might go out on a limb and try something new instead play it safe so that they don’t risk dropping to a B+. The point of the assignments in this course is not to be great, but to practice, and practicing is easier and more effective without the burden of trying to squeeze out every last point from a rubric.

So I’ve adopted a system called specifications grading, or “specs grading,” which I’ve used across my classes for the past several years with some success. The idea is that every assignment is graded pass/fail (more or less, see details below), according to whether you’ve met the specifications laid out in the assignment description. If you don’t meet the specs, you usually have one chance to revise—generally within about a week from receiving your work back—informed by comments from me. (Details about specific revision timelines, including some constraints on revisions, are on the assignment pages.) All those pass/fail assignments are ultimately aggregated up into a letter grade, which will be your final grade in the course.

If you’re the kind of student who worries about the difference between an A and an A-, this style of grading should free you up to take more risks. If you’re the kind of student who just wants to know what needs to get done, then you can find that in the specifications and be confident that meeting the specs will get you full credit. (If you're taking this course pass/fail, then you should be able to easily calculate how many assignments you need to do to pass the cutoff.)

If you have any questions about the specifications for a given assignment (I try to be as clear as I can, but there are always possible points of confusion), let me know ASAP. Your classmates will thank you.

Work that meets the specs actually gets an A (95%) in the gradebook. If you do it all, you’re guaranteed an A in the class.

For work that exceeds expectations, you’ll get an A+ (100%) in the gradebook. This allows me reward exceptional work without punishing students who consistently meet the specs. You should not expect to get this regularly, and you should not need to, either.

Work that is not submitted or that does not meet the specs gets a 0; if you submit a revision by the assignment-specific revision deadline, you can bump that right back up to the standard 95%, with no penalty.

In the end, I’ll use a conventional percentage-to-letter conversion to determine the grade I submit to SIS: 0–59 F, 60–62 D-, 63–66 D, 67–69 D+, 70–72 C-, 73–76 C, 77–79 C+, 80–82 B-, 83–86 B, 87–89 B+, 90–92 A-, 93–100 A.

Because this system makes it relatively straightforward to get an A with regular, competent work, I do not automatically give the A+ based on percentages. I reserve the A+ for students who consistently go above and beyond in their engagement with the course assignments and discussions, or who show marked improvement over the semester.

Meaningfully contribute to class discussion.

This is a highly interactive, discussion-based class. You need to come to class to participate meaningfully, and therefore attendance is part of your grade. (I have experimented with much more lax attendance policies in this class, and I learned that some students stop showing up for one reason or another and find it basically impossible to get back into the flow, to their detriment and ours.)

Showing up is the first half of participation. You should also be prepared to contribute to our discussion (i.e., you’ve read the texts for the day and you have thoughts about them, questions to ask each other, and so on). If you are keeping up with your Commonplace Board, this should not be an issue.

I am not shy about calling on people who have not been contributing or pushing you to refer back to passages from the readings in detail. Conversely, if you find yourself talking a lot more than everyone else, you should try to hold back a bit to make space for your fellow students. (No shame; your professors were often this person when we were in seminars.)

Here are some suggestions for ways to participate:

Ask questions about things you found confusing in the readings.

Respond to your fellow students’ prompts for discussion.

Share specific parts of the readings that you found interesting, for whatever reason.

Bring examples of course-related stuff to class to discuss (physical objects, websites, videos, whatever). This is super helpful for grounding our discussion with specific objects!

Each week you’re in class and participate substantively in discussion (see list above), you'll get a point. Each week you’re absent, or present but do not participate substantively, you won’t. I hope you’ll fly beyond bare minimums in this class, but the bare minimum here is sharing something you saved to your commonplace board. You can even just read directly from your board. This policy includes our first class meeting during the first week of school.

13 meetings x 1 point = 13 points. Since participation is 20% of your grade, that means that each day is about 1.5% of your final grade (or enough to move you from a B to a B- or B+).

Stuff happens, and you can’t always make it to class. Here’s what to do if you need to miss a day. Some things to keep in mind:

Maintain a personal log of course material.

The first time I taught this class, back in 2017, I had students keep a physical journal to record notes, plan their final projects, and collect thought-provoking materials. (See the marvelous Syllabus, by Lynda Barry.) Inspired by a version of this assignment made by Ryan Cordell, I’m bringing it back, in altered form. (Some of this page is adapted from his assignment to maintain a course “commonplace book”—a traditional way of gathering meaningful quotations, memories, and other textual or flat materials. Here’s a nice blog post about analog commonplace books, which you may find inspirational.)

This time, we’re going digital, using Kinopio—a weird and opinionated little mind-mapping/conspiracy wall-making platform. I’m a long-time fan of the site and its small-scale ethos, and I’ve used it successfully in a few prior iterations of this course. If you’ve used collaborative canvas-editing platforms like Miro or Jamboard, you’ll be familiar with the general idea, though Kinopio is a bit distinctive. Basically, you can make cards with text or images and connect them. Here’s an example board I made to demonstrate some of the site’s features. I've collected more boards for inspiration in a list at the bottom of this page.

You don’t need to sign in to use Kinopio, but it will be easier to manage our collaborative boards in a group (and to see who’s doing what) if you make a free account, so please do that, ideally using your Tufts email address. Free accounts are only limited by the number of cards they can make on their own boards. Because I have a paid account, I’ll be making boards for you; you can place unlimited cards on these for class purposes. By default, I’m making these boards private to the class; if you want to share your board more widely, I can change the permissions.

Here’s what you should be doing on your boards (at minimum—feel free to go beyond this and to use your board, or to create more, to capture anything you like that’s relevant to class).

Find and learn about an attention measurement system.

In this first phase, you’ll choose an attention measurement system to study over the course of the semester and learn about how it works.

First, you’ll collect a set of 3–5 candidate systems. Search around online, think about your own encounters with attention measurement, talk to me and your classmates, riff on the examples we’re talking about in class. Put your candidate topics on your Kinopio project board, with some notes about them. On the day they’re due, we’ll talk about them in small groups in class, which may help you refine your ideas. (Or, if one of your classmates has a cool idea they’re not going to use, you might pick up a new topic!)

Here are some criteria to help you choose good topics (“good” here means that you’ll have a lot to write about and your project won’t be unnecessarily hard to do):

An “attention measurement system” should be something concrete, technological, and specific. Sometimes people think of practices like “making eye contact” as a kind of attention measurement—that might be a way that people assess attentiveness interpersonally, but it’s not a system, and it would be hard to write about and study in detail.

If you have an idea like that, you might ask, “Is there a technology that tries to measure this?” And you might then start thinking about, for instance, eye tracking.

Eye tracking would be a good start, and you could start to learn about how eye-tracking works, but it is still not specific enough. You can get more specific by looking at a particular use of eye-tracking—maybe in VR headsets, say.

Even then, you can get more specific. Are there different techniques VR headset designers use to track eye movement? How do those work? How about Eye Tracking on the Meta Quest Pro?

Now we’re getting specific enough. With a concrete example like that, you’ll have more things to talk about: Are there online user communities? How do those people feel about the feature? How does this particular device fit within the larger field and history of eye tracking? (See, eye tracking in general would be fine to talk about and research, but you will have more specific ways to talk about it once you’ve got a specific object in mind.)

It’s okay if not every item on your candidate list makes it down to that level of detail. They also don’t all have to be totally distinct in the techniques they use. But they should be different enough that you have some range to choose from.

Ideally, everyone will be working on something different from everyone else, but we may have some collisions. If that happens, I’ll try to help you find complementary approaches so that you’re not working on exactly the same thing.

Within a week after you share your candidate list in class, you should pick one and email it to me. If it needs to be more specific, I’ll offer some suggestions about how to get there. Be sure to look ahead to the future phases to ensure that you’ve got something you think will work for the whole semester. No points for this stage, but you have to do it before you can move on. (And we’re going to start in on the research during class time on the due date. You’re welcome to send your choice to me earlier.)

Learn about the attentional subjects managed by your system.

Different people are expected to pay attention in different ways in different situations. We can call these various kinds of people “attentional subjects”: Benedictine monks, tweens, college students, call center workers, Spotify users, and Uber drivers all live with expectations about how they’re supposed to pay attention. Over the course of the semester, we’ll encounter a lot of these.

In this phase of the research project, you’ll identify an attentional subject associated with your attention measurement system and explore ideas about how they ought to pay attention.

Drawing on the previous phase, you first need to figure out whose attention is being measured by the measurement system. What kind of person is this? No need to overthink this part (that’s for later). Are they truck drivers? High school students? Surgeons? People have lots of qualities—for now, focus on the ones most clearly involved in the relationship with your system. This will likely be something like a job, but not necessarily! We’ll share these in class.

Once you’ve identified your general type of attentional subject, you’ll need to figure out where you can learn more about them. You may have started to make progress toward this during phase 1, but now, you should look for sources of information and appraise them. For example: You might find forums where users of a VR headset talk and complain; you might find a little subfield of academic literature about people who take online surveys. Alternately, you may not find easily available digital sources. (If you’re interested, say, in brainwave measuring headbands used on Chinese schoolchildren, you probably won’t find those kids talking about their situations somewhere you can read it.) Possible sources will generally be either about the subjects in question or by them. If you can, try to balance these out with each other—it’s interesting to know how people subjected to attention measurement think about their situations, but also how people in their position are imagined by others, since we’re looking at ideas about how they should pay attention. But, you may be constrained by what exists out there.

Produce a list of 3–5 major sources (by “major source,” I mean something at the scale of a subreddit, or a blog, or a subfield of academic literature—i.e., not just individual research papers or blog posts, if you find such things). For each source, write a few sentences about whose point of view it typically represents (maybe more than one?) or other limitations, and what you think it would be useful for (e.g., does it capture some workers’ points of view? does it inform how the people who implement the systems think about their usefulness?).

Drawing on your list of sources, you should gather information about the attentional subjects who are subjected to your attentional measurement system. Keep in mind that we are both interested in them as actual people (with perspectives of their own, internal variety, and other things going on in their lives than just the part we’re talking about here) and as a kind of ideal type, who have ways they’re supposed to behave, stereotypes about tendencies and motivations, and so on. You should be trying to capture both sides of this.

Come up with a way to fool your system.

Your attention simulation technique may derive from actually existing forms of resistance that you learned about in the previous phase, or it may be something of your own creation, derived from your understanding of how your attention measurement system works. Depending on how much actually existing attention simulation you find, this phase may look more like a report on techniques used in the field or more like design documents for your own technique—or something in between!

Inspired by our weeks on attention measurement and subterfuge, you should try to come up with a technique that you think would successfully fool your attention measurement system. This would be something like hanging an ankle weight on a car steering wheel or using a mouse jiggler to maintain an active status on Teams.

You’ll need to present about this on the last day of class (see step 2), but it doesn’t have to be complete until you submit the final report (step 3). Because this is the last phase, the project board and summative report components are due at the same time; you’ll probably want to work on the board first, though.

[More detailed specifications TBA: Expect something like a Kinopio board, sketches of a physical object or flowcharts explaining a digital one, some speculation about possible countermeasures and counter-countermeasures.]

Gather your research from the semester into a final report.

To culminate your research work over the course of the semester, you should compile your findings into a summative report. You have some flexibility in how this is formatted and delivered. You might imagine, for instance, that you’re writing a competitive analysis for a company that’s interested in entering this space; or for investors who want to understand how it works; or for a regulatory body of some sort. (Don’t spend too much time role-playing here—I just want you to think about this as a kind of bureaucratic report, rather than an essay, per se.) Depending on what caught your interest, the emphasis of your report may vary.

This is less of an argument than a very detailed description of many aspects of your system, informed by the work you’ve done over the whole semester.

[More detailed specifications TBA: Expect a final paper-sized object that should repeat stuff from earlier phases and refer explicitly to texts from the class as well as your own outside research; it should not require much, if any, new research.]