467. Learning Reinforces Curiosity

Posted in engagement, higher education, learning, motivation
October 14, 2026

Many past guests have emphasized the role curiosity can play in stimulating student learning. In this episode, Yaniv Abir discuss his recent study examining the impact of satisfying initial curiosity on subsequent curiosity about related content.

Yaniv is a postdoctoral researcher at the Max Planck Centre for Computational Psychiatry and Ageing Research at University College London. He is one of the co-authors of a study entitled “Learning Reinforces Curiosity,” which was published earlier this year in the Proceedings of the National Academy of Sciences.

Show Notes

Transcript

John: Many past guests have emphasized the role curiosity can play in stimulating student learning. In this episode, we discuss a recent study examining the impact of satisfying initial curiosity on subsequent curiosity about related content.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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Rebecca: Our guest today is Yaniv Abir. Yaniv is a postdoctoral researcher at the Max Planck Centre for Computational Psychiatry and Ageing Research at University College London. He is one of the co-authors of a study entitled “Learning Reinforces Curiosity,” which was published earlier this year in the Proceedings of the National Academy of Sciences. Welcome, Yaniv.

Yaniv: Thank you, thank you. I’m very happy to be here.

John: Thanks for joining us. Today’s teas are:… Yaniv, are you drinking any tea with us today?

Yaniv: I am. I’m just having an English breakfast tea, actually, with milk.

John: Excellent. Which is the appropriate British way of doing it. [LAUGHTER]

Rebecca: I have a gin tea and rose today, John,

John: And I have a spring cherry green tea today.

Yaniv: That sounds nice.

John: It’s very good.

Rebecca: It always smells really good, but I don’t like the taste of it. [LAUGHTER] So we’ve invited you here today to discuss your study on the effect of learning on curiosity for related information. To set the foundation for the discussion, it might be helpful if you could explain the role that curiosity plays in learning.

Yaniv: The role that curiosity plays in learning, and the role that learning plays in curiosity, of course. So we think about curiosity as our motivation to seek information. So, sort of seeking information is a really important part in learning, and that’s what motivates us to do that. So we know that curiosity is very important for learning. If you’re motivated to do something, you’ll do more of it and you’ll do it better. We know, for example, that people remember better facts or skills that they learned when they were curious about those facts and skills versus when they weren’t, and we know that works across the lifespan. So if you let people memorize a few facts or anything, any new material, invariably you see that older adults perform slightly less well than younger adults. But one of the very few places where you don’t see that is when you are looking at information that people are curious about. Older adults would be just as good as younger adults. So there’s something interesting that curiosity is so powerful that it can save learning from one of the most robust and reliable effects that we know that affect learning. So older age memorizing becomes more difficult, except if you’re curious about what you’re memorizing. So curiosity is really, really powerful. We know it’s very important for childhood learning. People who build robots think that it’s very, very important for building robots that are good at handling the real world. So it’s a core part of learning, and obviously learning is a core part of curiosity because it’s what it’s about.

John: In the paper, you know that past studies have generally treated curiosity as being static and focused in those studies on the demographic and personal characteristics that affected curiosity. Why might that approach be somewhat problematic?

Yaniv: Right. So studying curiosity has been around for a long time, and it’s gone in and out of fashion in waves. In the last wave we’re part of and very much in debt to, cognitive scientists wanted to understand this phenomenon of curiosity. The first thing that we do as cognitive scientists is that if we find the human capacity we’re interested in and we want to understand how it works, first thing we do is break it into bits. We want to isolate it. We want it to reduce it to its absolute essence. Sort of the essence of curiosity? It’s someone being motivated to seek information, and there, people who studied curiosity added “for the sake of information,” information for its own sake. And why are they adding that? Because they’re coming from a tradition of studying learning, or what we call instrumental learning. So, learning where there’s a very clear goal to seek an immediate, tangible reward toward a good outcome, and that is sort of the tradition that they’re coming from. Sort of make sure that they’re not studying that because that is well known. That would be trivial, so they don’t want to study how people seek information when they’re learning for a test. For example, I recently moved to the UK. I have to retake my driving license here. I had to study for the theory of driving test. So I really have to memorize all these facts about the rules of the road in the UK, and I’m not curious about them. That’s an easy way to distinguish two different behaviors. But I have to do it, otherwise I won’t get my license. So there’s a clear goal that I can explain why this individual is seeking information, and that is not internally motivated. It’s not about themselves. It’s about something that they have to do that comes from the environment. So we definitely don’t want to study that. We want to study something pure, internally motivated curiosity, that thing that we all feel as academics, as people, as children. And in order to do that, they want to isolate the experience of curiosity from everything else. They will just look at a question that either makes a person curious or not, and we’ll measure just that, and we’ll isolate it from everything else that’s going on. And that was very, very important to sort of establish the ways that we’re measuring curiosity, the tasks that we’re using, the way we’re asking people. But what it did is it threw out the purpose of curiosity. Curiosity is for learning. It is to guide our learning. It is to make our learning better. And if we look at curiosity as an isolated instance, we’re throwing all that out because curiosity doesn’t live in isolation. We learn one fact, and that already leads us to be curious about the next question. We all have that experience of going down a rabbit hole in Wikipedia or whatnot. One thing leads to the next, and then we’re curious about this, and then we’re curious about that. Curiosity doesn’t live as one idea. I’m curious about a question. I got the answer. I’m satisfied. Curiosity quenched. That’s it.

Rebecca: Can you talk a little bit about how your approach in this study differs from this tradition?

Yaniv: So it’s not very different. The tweak that we made was very, very minor, and I’m actually quite proud, and I like that aspect of the study. It’s very simple. We didn’t invent anything too novel. We took a very well- established way of measuring curiosity, which wouldn’t surprise people very much. It’s showing people a question, a trivia question, and asking them whether they want to hear the answer. And the way we usually do that is we say you’re going to have to wait three seconds to hear the answer to this question. Are you willing to wait, or do you want to skip to the next question? And we know from a lot of previous studies that our participants are volunteers. Their willingness to wait to hear an answer tracks very well with their curiosity, and people are consistent. So they’re consistent within individuals. I agree with myself about what is interesting and what arouses my curiosity from one time point to the next. You’d be surprised. You’d think that curiosity is this very personal, idiosyncratic quality of a person, but people they agree by and large which trivia questions are interesting or not. So we took this very established paradigm, and the only thing that we did was we said let’s take trivia questions from a few different categories. So we have a little bit of variance in topics, and the one thing that we’re going to do is instead of just looking at question – Am I curious? – answer, I’m going to look at the previous answer and how I felt after I learned the previous answer, and what that does to my curiosity when I encounter the next question. And that next question may be related, so it could be from the same category, or it could be different, and then we asked, “Do I change my curiosity in any way after I read a very satisfying answer, or after I read a dissatisfying answer?” So that’s the only thing that we did. We said instead of trying to isolate each experience, which as experimental psychologists we call it a trials, instead of trying to isolate each trial, let’s look at how people learn from one experience to the next.

John: So it’s a more dynamic approach to investigating curiosity. Is that a good way of viewing this?

Yaniv: Yes, absolutely. And there’s already been a little bit of work on the dynamics of this. So there’s great work by David Lydon-Staley and Danny Bassett, and they looked at how people explore Wikipedia, and they looked at whether they tend to sort of explore pages in a single topic or whether they tend to jump around. And it turns out that different people tend to do different things, and they were looking at the dynamics of how you explore in this knowledge space led by your curiosity, but they took an approach of let’s describe the overall statistics, the type of jumps that you might see from one topic to the next. We did something simpler, but also more specific. We said, let’s see if we can predict by your response to one fact whether you’d be interested in the next fact. And then there’s also been really cool work from Alison Adcock’s lab looking at how curiosity unfolds within one experience. So if you have like a story going on or something visual that you’re seeing that unfolds, how curiosity unfolds over time and show how it fluctuates and it has a lot to do with your affective responses, which makes a lot of sense. So this is sort of a new approach that has been coming up in the literature in the past two, three years, five years by now.

Rebecca: Can you talk a little bit about what question your study was hoping to address with this dynamic approach?

Yaniv: Yes. So we realized that a very simple question can actually resolve a conundrum that’s been plaguing the curiosity, or exploration, or information-seeking literature. There’s been a lot of names for it for the past more than 50 years. So the very simple question was: “What happens when you encounter a satisfying answer?” You learn the answer, and you satisfied your curiosity. What happens next? So one account that sort of comes from the word satisfaction says the question aroused something in you. That thing was something that needed to be resolved, and the answer resolved that, maybe this slightly aversive thing that you felt, and now everything is over. Your curiosity is satisfied, and you’ll actually be less curious. And another version of this actually says, and I think a lot of people would find that intuitively appealing, is: “If you learn something interesting, satisfying that only opens and grows and reinforces your curiosity to learn more in the same topic,” and these two predictions come from different theories about curiosity. So the older one looks at curiosity from the lens of optimal arousal theory. It’s this idea that people don’t like to be bored, basically. So even if all your needs are met, you need sort of a minimum optimal level of stuff happening, of interesting stuff in your life to be happy and satisfied. And you’re always aspiring to maintain that very minimal level of arousal. So you don’t want stuff to be boring. You don’t want to be sitting in an empty room with nothing happening, but you don’t want to be overstimulated, so you’re titrating yourself all the time in this homeostatic process, where if I’m bored, I seek out stimulation, but if I’m overstimulated, I seek less of it. So that’s the idea. If I learn something interesting, then now it’s time to tamp it down. Let’s relax. If I’m bored, if I didn’t learn anything interesting recently, then I seek out. And the other approach frames curiosity as part of reinforcement learning. So this is really a wide-ranging framework that people may have heard of a lot in connection to machine learning because it’s a very efficient way of training machines to be intelligent, and we see that relevance in AI and LLMs and all of that. But it’s a wide-ranging framework of thinking about how organisms learn in the environment. And the basic idea is that if something good happened, I want to repeat the action that led to it. So, if I learn something interesting, I want more of the same. I want to explore that topic. I want to get more information from that information source, and the’re different ways of thinking about curiosity. One kind of sets it apart from instrumental learning, from doing stuff for other goals, while the other one says it’s just another way of instrumental learning. It’s another way of achieving our goals. For our goals, we need money and food and shelter. We also need a lot of information, and we realize we could adjudicate between these two accounts by looking at this very simple question: After you satisfy your curiosity, what happens? Does curiosity grow or is it diminished?

John: We don’t want to get right to the results of your study because we want to keep people in suspense, let their curiosity build a little bit. So, could you tell us a little bit about the experiment that you designed? You talked a little bit about how you measured curiosity, but what was the actual experiment you used to test between those two hypotheses?

Yaniv: So, we invited volunteers online, and what they did was play this game that we call the waiting task. So, in every round of this game, we present a trivia question, such as which country in the world has the most libraries, and we tell them if you want to learn the answer to this question, you’re going to have to wait an X amount of seconds, could be 3, 6, 9, or 12 seconds, and if they choose to wait, they wait and they see the answer, which is probably Russia. But if they’re not interested, they can just skip on to the next question. And crucially, we tell them in advance: look, you’re going to be here for 20 minutes, no matter what. So how you choose is not going to affect how long this is going to take or anything else. Please base your choices on your actual interest. Another thing is that every time they see an answer, we ask them, “Was it worth the wait?” and they rate whether it was worth the wait. So they tell us about their satisfaction with the answer that they read, and after they complete this game, we show them another bunch of questions that come from these same set of trivia questions, and we ask them to rate their curiosity in learning the answer to each question. Here, we don’t show them the answers; we just want their ratings. So overall, what we have is their expectations about how good of an answer they’re going to get if they pursue a question. That’s their curiosity rating. We have their satisfaction after having read the answer, and we have their choices of whether to pursue this bit of information or not. And then the way we bring them together answers our main question of what happens after you read a satisfying answer versus a dissatisfying answer.

Rebecca: So, are you saying we are left hanging at the end of this game and not knowing the answers to some of the questions we’re curious about?

Yaniv: Yes, yes, that’s unfortunately [LAUGHTER] a part of the deal. Yeah, you’re left without an answer to some of these questions, and that’s of course important because at that part of the experiment we want to make sure that we don’t have these learning effects of one question interferes with the next question. So there’s a lot to say about this purist reductionist way of measuring curiosity, but also we want to see how curiosity evolves and adapts from one bit of information to the next. That’s why we leave our participants without answers to some of the questions, unfortunately. But I’m sure that they can find the answers on the internet if they’re really interested.

Rebecca: How large was your sample for this experiment?

Yaniv: So we actually run this experiment twice, so to speak. The first time we recruited 300 participants, which is an OK sample, and would show us anything when we’re thinking about sample sizes. If you can show something reliably in small samples, that means that it’s a large meaningful effect. But to make sure that we can show it reliably, we also replicated it in a much larger data set that we had, that we previously collected actually during COVID of over 2000 participants. So it’s quite big, and happily we find the same patterns in both.

John: So you gave one example about libraries as a type of question, could you give us a few other examples of questions that were raised during this trivia game?

Yaniv: Yes, definitely, and this is going to tell us a little bit how we’re going to learn from people’s choices and reactions to the questions, because some of the questions in our dataset people found quite interesting. So, for example, what is the most stolen food in the world? A lot of people thought that that’s quite an interesting question. On a scale of one to five, they rate it at above four, so quite good. And the answer to that is cheese. And when you look at whether people were satisfied with the answer, they were quite satisfied. So people are happy with the answer. So it’s an interesting question, interesting answer. And we’re going to call this a high-engagement stimulus or question-and-answer pair. And then of course there were boring questions and answers in our stimulus set. So one question like that was, “In which city was Bob Crane killed?” Actually, to this day, I don’t know who Bob Crane is, [LAUGHTER] and most of our participants didn’t either and didn’t care, and they give it a very sort of low-curiosity rating. And the answer is Scottsdale, Arizona, and people don’t really care about the answer either. So this is a question and a low satisfaction answer. So we call this a low-engagement question and answer. But then, importantly, there’s a question like “Which book was written in response to an editor’s challenge to write a book using fewer than 50 different words,” and people are sort of mid-level curious about this question. But then they read that the answer is Dr. Seuss’s Green Eggs and Ham, and they’re very satisfied with the answer. They know the book; it’s a funny book, it’s an interesting book, so they’re pleasantly surprised by the answer. And this is important. This pleasant surprise, in reinforcement learning terms, we call this a positive prediction error. So I had a prediction, how interesting the answer is going to be, and that was my mid-level curiosity. But then there was an error. My satisfaction was actually higher than I expected. So this is a positive surprise, and this is really important because if the reinforcement learning hypothesis is correct, this is the learning signal. Something is interesting here that you didn’t think about before. And then we had questions like, “Cured salmon is the main ingredient of which food?” And again, people are sort of mid-level curious about this question. The answer we gave was gravlax, which people are really disappointed about. It’s obvious it’s not interesting. I mean, gravlax is cured salmon, so I didn’t learn much. And people give the answer a very low satisfaction rating. So again, we have a prediction error here. Our prediction didn’t match the outcome, but now it’s a negative prediction error. I find it surprisingly dissatisfying, and that is also important. That is a learning signal. Maybe in this task, questions of this type are going to be disappointing, and I should avoid them. Maybe this type of knowledge is disappointing in general, and I should avoid it. So that’s the idea. And then you already have sort of hypotheses about what should happen next. So if the reinforcement-learning hypothesis is correct after learning an answer that gives you a positive prediction error or high-engagement answer in general, you should want more of the same kind of knowledge, and vice versa. If it’s disappointing or just not interesting you should want less, whereas the optimal arousal theory says, “If it was good, I want less of everything. If it was bad, if it was dissatisfying, I want more information.” Doesn’t matter which. So very different predictions.

John: So I gather that your sample did not include very many people in their 60s, 70s, and 80s from the U.S., because they probably would have been more curious about Bob Crane because he was a lead of a popular TV show back in the 60s and 70s called Hogan’s Heroes, which was based on a prison camp in Germany. That was very popular in the U.S., but probably before most of your participants were familiar with it. [LAUGHTER]

Yaniv: Yes, absolutely. I think that’s the right inference to make. Then we have some older participants, but always more difficult to get them online. So yeah, most of our participants are around the age of 30.

Rebecca: Well, I know I have a burning question that we’ve been holding out on, which is, what did you find? [LAUGHTER]

Yaniv: You wouldn’t be surprised by the way I described it so far that what we found was evidence in favor of the reinforcing hypothesis. So if you learn something that is very satisfying, especially if it was surprisingly satisfying, you’re more likely to want more of that kind of information going forward. So if it was the questions about animals, and you were pleasantly surprised with the answer, you’re going to be more likely to want to wait for answers about animals going forward, and vice versa. If it was disappointing, you’ll be less likely to wait for an animal-related question on the next trial. And it was very robust and reliable, and we find it across different types of questions and across different data sets. So, really strong evidence in favor of thinking about curiosity as something that we learn from experience to experience. So, we learn what to be curious about and how much curiosity is warranted by the way the world is, which is a very important insight. We know some people are more curious than others. We know that in some situations we’re more curious than other situations. Thinking about it from the frame of we learned to be that way, rather than this is an innate trait, is really really helpful when you think about how can we make people more curious, more engaged with whatever content it is that we want them to be engaged with.

John: So when people get a positive result in terms of the curiosity, does that increase curiosity in general, or is it just within that category, that domain of knowledge?

Yaniv: Absolutely, just within that category is what we see. So if you learn something interesting about food or about geography, it is going to increase your subsequent curiosity just within that category. Sometimes we see that it might make you less interested in other categories. Sometimes not. I suspect it sort of depends on the structure of the problem. Do you feel that this is sort of a competition between different categories, or whether that aspect is not there, and we did this once, where in an experiment, as I described, where there were four distinct categories: animal, foods, geography, the arts, and we saw this dynamic of you learn only within a category. But then, when we replicated it in our larger previous experiment, then we didn’t have distinct categories. The questions were just from everywhere in the internet. There were trivia questions and interesting tips that you can find on the internet, so sort of a very large variety. And what we did there was rather than look at distinct categories, which was very easy, we measured how similar each question and answer were to the ones that came next, and again we saw the same pattern. When they were similar in terms of the content, then you saw that learning positively satisfying increased your curiosity, positively surprisingly dissatisfying decreased your curiosity. But when they were very very different from one another, you didn’t see that, it didn’t affect each other.

Rebecca: So that certainly leads me to more curiosity about how this might be applied in a classroom setting and what the implications are for teaching and learning. What thoughts do you have about what instructors might consider as they take this information in and think about their own classroom contexts?

Yaniv: Yeah, I think that’s a very interesting question. So first of all, to sort of qualify, this is not an applied research, so we didn’t test it in the classroom with real material. But rather, we’re trying to flesh out the theory of how curiosity works and how it changes dynamically. And I think that’s really important. But then we also have to do the applied research about adapting this theory now into practice. But I think one of the reasons I started my journey into the research of curiosity was that I used to work as an outdoor educator, and the most basic question we found ourselves thinking about all the time was how to get the audience we were working with, so kids from various backgrounds, how to get them engaged, how to get them to be curious about the things we cared about, which was the outdoors and plants and animals and ecology and being a better citizen for the planet and that sort of thing. And that question was recurring, and we had various mentors and training, and a lot of applied knowledge that passes from one educator to the next about how to get people curious. And this sense that, I mean, I’m sure that teachers and educators know that when students are curious, everything works better, and everyone has their own tricks. It’s a craft to get their audience curious. And there’s this idea that yeah, if you can approach people through something that you know would get them interested, something that speaks to their personal interest already, and you can connect whatever you’re trying to teach to that, that is going to be beneficial. And I think by and large our findings support that. But they also say it has to be connected, and the connection has to be real. The person, the student, has to conceive these as really related or part of the same information-seeking action. These are of one kind because if that doesn’t happen, either the learning won’t occur, or even worse, you’ll have a compensatory effect. So I’m really, really interested in whatever content you started the lesson with, and then the transition into the actual content that we have to learn was so stark that now this comparison is actually going to backfire, I’m less curious about it. I’m more preoccupied with whatever you showed me before. But I think obviously these are speculations based on the basic science that we find. How it works in the classroom. I’m sure is more complicated than that, and I’m actually very interested in delving into how educators are thinking about these topics, because I think there’s a lot of wisdom in the craft that we can pursue as interesting questions when we’re thinking about the next stage of understanding this very human phenomena.

Rebecca: I think you even demonstrated with the kinds of questions that you asked in your study that like some are good hooks and some aren’t, but they can still probably even be in the same domain of information, [LAUGHTER] thinking about how to start something and having a good hook question seems really important and relevant to even how your study works.

Yaniv: Absolutely, and we’ve been staring at these lists of questions forever, trying to formalize what makes a good hook and what makes a satisfying answer versus a dissatisfying answer, and it’s not at all easy to formalize. And what I’m very very happy about is actually one of our reviewers on the paper said, “Look, there’s now all these AI tools, large language models. I’m sure they’d be able to explain what is a satisfying answer and what is a dissatisfying answer. They’d be able to predict it, they have all of human knowledge, all of human language. That’s the solution.” And we did that. We did that with four or five models, even new models at the time. We’re very poor at predicting people’s satisfaction, and I think because of two things. So first of all, they’re models of the whole of human knowledge, so to speak, almost, but not a specific person’s human knowledge, and that was very very important because when we substituted your own personal ratings and judgments for the group average, we couldn’t see your learning anymore. So whatever was your idiosyncratic component in there was very very important. And then the other thing is that it’s still not simple. Not even these fancy models can solve it so simply. So there’s still something interesting and very human and very hard to objectify and to reduce in that curiosity phenomenon. And even though we’re explaining it with a framework of reinforcement learning that we use to explain things like how people study for tests or how rats learn to press levers, there’s still that very interesting and funny and uniquely human essence to it, which remains.

John: So, if we’re successful in provoking curiosity in students in our classes, it may encourage them to delve more deeply into the discipline potentially, but it may not make them more successful in other areas. In fact, it might potentially decrease their curiosity in other unrelated areas, or at least not increase it there. Would that be a fair prediction to take from this study, or is that going a little too far?

Yaniv: I think it’s going a little too far, yes, because another thing that we’re doing as we’re getting them to be curious about whatever it is that we’re teaching is that we’re also teaching them how to seek information, and we do that explicitly. But that’s not the part that we measured in truth about in the study. What we thought about is how people learn the craft of seeking information through trial and error, through the satisfying and dissatisfying bits of information that they encounter after seeking information. So that’s really important, and I think that’s one of the really core skills that we acquire, especially in higher education. I think it applies also to kindergarten: how to seek information, how to identify what you’re interested in and pursue that and help that grow. That’s a very important skill, and crucially, it takes so much learning and information to become a good information seeker and learner. So I think that’s important. I think that backfiring dynamic is more at the level of the single lesson than the material at hand. If you’re dealing with an audience that’s low in motivation and you’re trying to motivate them, then that dynamic of introducing something from far out that doesn’t really connect in their eyes is maybe not going to help you. But I think in general, introducing interesting information from maybe fields that they didn’t realize were interesting is really great for teaching them how to seek information and also teaching people that seeking information is worthwhile. So one of my strongest experiences from undergraduate studies… so I studied psychology in a department where 90% of people wanted to be clinical psychologists. So, starting first-year undergrad, they really didn’t want to hear about statistics. And of course, when you study psychology, statistics is most of what you do. And then you had these 10% of people who thought we’re going to be clinical psychologist, find the statistic class really, really interesting. Some of my friends left the course and switched to studying statistics instead. But something that you didn’t expect is going to be interesting because you didn’t know much about, and because maybe the cultural way it is viewed is negative, find that that is actually interesting. That the structure, that there’s something that you didn’t know about before, that’s a great experience and one of the best things that university education can give you.

Rebecca::One of the things that you’re pointing to is the social context that curiosity takes in a classroom setting that was absent from the study. If you’re answering questions in isolation [LAUGHTER] on a computer, that’s different than in a classroom setting, and maybe some of the expectations or judgments around what is acceptable to be curious about could also be at play in a context where there’s other humans.

Yaniv: Right, completely absent. So we’re still reductionist at heart. We want to study curiosity. We’re not studying social learning, but of course, this all works in tandem on one another, and we know that learning from others, seeing others’ responses to outcomes, is such a strong reinforcer in and of itself. So of course, when you have a whole classroom, that’s going to be really, really important. But it also suggests that you have a really, really strong tool harnessing that social aspect, the fact that students are together. If you can harness it towards making them curious and other human beings is something that we’re very curious about. You can see that manifests as gossip, but it also manifests as caring and any sense of the way about our fellow people around us that share a reality with us. So that’s, of course, something potentially that can be very, very powerful in harnessing and getting people curious, getting people involved. And one of the co-authors on this study,, my fellow mentors, Caroline Marvin, actually taught me a course in mandatory practicum in teaching when I was a graduate student in Columbia. And a lot of these ideas and what we know and don’t know about best practices in teaching, where there’s evidence and where this evidence comes from. So a lot of the best evidence about practices of teaching, as far as I know, I’m not an expert, actually comes from like physicists and chemists and hard STEM people who have really thought about how they teach in class and then done the experiments, rather than from social scientists, humanities, psychologists, where you’d think it’s where it’s at. And of course, there’s a lot of work in those fields as well. But it was striking that so much comes from there that got us thinking about these questions and looking at these questions and looking at the links. But of course, our work is at the basic science level, and we’re in no means experts in this literature on this field of how the applied craft and evidence and science of teaching a class.

John: There’s been a lot of focus in terms of professional development for teachers in emphasizing intrinsic motivation and de-emphasizing extrinsic motivation, and that’s a topic you talk about a bit in the paper. What do your results suggest about the relative importance of intrinsic and extrinsic motivation?

Yaniv: Our results actually suggest that it may not be as useful a dichotomy as we think it is when we look at how people talk about extrinsic and intrinsic motivation. Extrinsic motivation is really easy to identify. If I am telling you you have to pass this test and otherwise you’re failing the course, or I’m going to give you pizza if you read three books, that’s really really easy to identify, and it’s very difficult to know if someone’s intrinsically motivated because that’s inside them. And if you are an outsider observing them, then that’s an unknown for you. But making lists of what is intrinsically motivated versus what is extrinsically motivated doesn’t really teach us a lot about the mechanism. And that’s where a lot of this literature has been at. Let’s like make a long list of intrinsic motivations, and let’s try to encourage those. There’s a very interesting take on this that I like quite a lot from Arie Kruglansky at the University of Maryland, where they analyze this from the frame of what is the goal and what is the means to arrive at that goal. So when we are thinking about extrinsic motivation, they’re separated. So there’s an end and there’s a mean, and they’re separate. So the end is getting praise for my parents, or getting a degree, or getting a good grade, and the means is studying for the exam. But when we’re thinking about stuff that should be intrinsically motivated, really, it’s the means and the ends have become fused. So seeking information in a certain field has already become fused with an end and is an end in and of itself. And the other place that we see that is in habits. So when a habit forms, it’s like the goal and the means to achieve that have become fused. And I think that’s really interesting. Reading or seeking information is now a habit. I’ll be curious even if I don’t have enough evidence to warrant my curiosity, and that is super super useful. And why is it super useful? Because we’re really bad at estimating what would interest us. So it’s kind of easy in trivia questions, but when you think about stuff like what degree to pursue, I had absolutely no idea what psychology was about before I undertook my major in psychology. Really, I didn’t know. I had a complete misunderstanding of the field, and I’ve seen that many times in my education. Something that initially I thought no way I’d be interested in that, or something that I thought I’m actually very interested in that, with a little bit of experience that changes your mind a lot, and being open to that, being open to that little bit of experience, can teach you a lot. Because without that habit of curiosity, you’re going to shut off these potential experiences based on actually very little knowledge, even though you might not be aware of it. Thinking about it as habit is really really important. And thinking about it, how do we form habits? And everything that we know about forming strong habits that applies to smoking could actually apply to being curious.

Rebecca: There’s so much to think about and be curious about. [LAUGHTER] But we always wrap up by asking: “What’s next?”

Yaniv: First thing that is next is really to acknowledge my fabulous collaborators and mentors on this project. So Daphna Shohamy, who was my PhD advisor, and Caroline Marvin, who’s the Dean of General Studies at Columbia, and Chris Baldassano and Jane Mok, who was a research assistant and really helped with collecting all this data. And this was a really fun team to work with. Thinking about what’s next is this is one tidbit of information about how curiosity works. And even in this conversation, we thought about all these factors came out. How does it work in the social domain? What are the boundaries? So what really is an experience here? So do I get these interesting dynamics within a lesson. Do I get it within a degree, within a whole year of experience, a whole three years of experience? Do people ever get satiated and have had enough information seeking because they got a lot of interesting information? We kind of have a feeling that it should exist at some scale. That at some point, we will become overstimulated. That enough is enough. So, really understanding the boundaries of these phenomena, I think, is interesting. And then, of course, thinking about whether any of this could be translated into things that we could actually test in a classroom and see whether different practices change outcomes in terms of curiosity. I think measuring curiosity when we’re doing all these applied research is really an interesting avenue to complement how we’re thinking. Not think only about exam results as an outcome, but also these intermediate cognitive states and is important of them, I think. Curiosity can really inform us about the mechanisms of what works and what doesn’t work, and I think for me personally, I’m really interested in how this interfaces with our well-being. I think being curious, being interested in the world, is you saying the world is good enough, interesting enough, it has enough structure and predictability for me to invest in it. And I think that’s really, really important for our well-being when we’re feeling well and performing well and flourishing. I think that’s an important part of it. But also when we’re not doing that great, when we’re depressed or when we have difficulties, reigniting interest and curiosity in the environment is a really, really important part of being motivated, and I think that’s something that could be researched more in all of these fields.

Rebecca: Well, it sounds like you’re going to be really busy. [LAUGHTER]

Yaniv: Hopefully.

Rebecca: Thanks for joining us. This is a great conversation.

Yaniv: Thank you. Thank you very much for having me. I really enjoyed our talk.

John: Well, thank you, and you’ve left all of us with much to be curious about.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

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