So, it's my enormous pleasure to introduce Lucy for her presentation this afternoon. I apologise on behalf of the faculty. I'm the one person who is here because everybody else is in an assembly meeting. So, I'm sorry, Lucy, that we can't all be here. I think I got the good end of the deal. So, as you can see from Lucy's slide, she is a senior Harvard University PhD student who's here in Okinawa with us for the next 12 months. She's sharing her time between the Visiting Scientist Programme, which is part of what her presentation is today. But she's also spending time in our unit, the Human Developmental Neurobiology Unit and Kenji Doe's unit. So, she gets around. So, as I say, Lucy is a final year student at Harvard. Her adviser is Sam Gershwin. And her research applies information theory and reinforcement learning towards understanding how humans learn and make decisions under cognitive resource constraints. So, how good is your decision making when the resources that you have available to you are limited? She has been a somewhat restless researcher. In fact, I'm a little bit jealous of all the places that Lucy has had the opportunity to work at. She spent time at Baylor College of Medicine. She spent time at MIT at Janelia Research Campus and at UCL. Lucy's also a really passionate teacher, as well as a researcher, and I can attest to that because I imposed on her to give a lecture to my class, and it was better than any of the talks I gave to them, and she really engaged them. So, just be careful, because she'll have you up at the board doing all sorts of things during this presentation if I know her. She's obviously developed and taught many courses, including math tools for neuroscience and how music plays the brain. And she's served as a resident tutor at Harvard College, where she lived with an advised undergraduate students. And Lucy's going to be here for another few months. So, if you have an opportunity, either call in and see her at the theoretical scientist visiting program or come to our lab, we would love to see you.  So, I should probably stop talking because you didn't actually come here to listen to me today, and I'm going to hand over to Lucy. But she is going to leave some time at the end for questions, and please, please use the microphone so that the people on Zoom can hear your questions. So, Lucy, I'm going to stop talking and actually hand over to you, which is hope everybody here has come to listen to you today. Well, I haven't even said anything yet. Hi, everyone. So, thank you all first for coming today. I'm really excited to be here at Goyce. And my family is actually visiting me this week, so it's really special to have them all in the back row there. So, today, I'm going to tell you about how cognitive constraints shape our decisions and actions. Imagine you're a new graduate student, you're new to Japan, and you're grocery shopping at Saunei after work one day. You need to buy milk, so you go to the milk aisle, but there are so many options to choose from that are very unfamiliar. So, being in this new environment, you haven't figured out what's your favourite brand of milk yet.  Or you might need to rush back to lab after the grocery store to run some more experiments. And so, you decide that it's easier to just buy the same milk brand that you bought last week instead of trying out a new option or figuring out which one you like the most. So, as I've illustrated here with this example, our behaviour is undeniably shaped by constraints on our cognitive resources. This is because every decision comes with a cognitive cost, and we often make a trade-off between the value of our choices and the cognitive cost of making the choice. So, in other words, the more cognitive resources you spend on making a decision, the better your decision could be. So, the fundamental question I seek to tackle in my research is this. How do humans and other animals make decisions when their cognitive resources are limited? And I tackle this question using mathematical tools from reinforcement learning theory and information theory to build models of decision-making behaviour. So, as a quick primer to those who are unfamiliar, reinforcement learning is a branch of machine learning that describes how an agent learns from its environment. Now, information theory is a branch of mathematical statistics that quantifies how information is communicated from a source to a destination. And how novel that information is based off what is already known. Using these two formal frameworks, we can build mathematical models describing how people learn from their environment and what information they use to make decisions or select actions. Okay, so let's start cooking up a model together. So, in reinforcement learning models, you have a couple of ingredients. You first have an agent, in our case, a person or the brain, that takes actions in the environment and gets feedback in the form of a reward signal. So, here I'm going to sort of lay out the mathematical notation along with the concept and try to give you an intuition for these things. So, a policy is what the agent follows in order to know what action to take in every state. You can think of the policy as instructions that tell the agent what action to take and tell the agent what to do in every state. So, as a concrete example, you can think of states as different restaurants that you could go to and actions as different food orders that you could place at each restaurant. Now, each of these entries in the grid is the probability that you would order that particular food item at that restaurant. So, for example, at Sushiro, which is my favourite rotary sushi chain, you're probably most likely going to order sushi and not ramen or gyoza. Similarly, at Ichiran, which is a popular ramen chain in Japan, you're probably going to order ramen and not sushi or gyoza. And at an izakaya, which is just a very common Japanese eatery pub combination, you might be equally likely to order any of these three options. That's an example of what a policy would look like. Another ingredient in reinforcement learning models is value. So, value is the average reward earned by following a policy pie. And the goal of the agent in reinforcement learning is to find the optimal policy that maximizes value. So, what do I mean by this? I'll give you two contrasting examples. So, here's a policy which I would say is pretty bad. So, at Sushiro, you order ramen and nothing else. At Ichiran, you order gyoza and nothing else. At an izakaya, you order sushi. Maybe that's actually an okay choice. But, in general, you're not exactly maximizing the overall value of the food that you're eating at each of these restaurants. On the other hand, an optimal policy is one that maximizes value. So, here, you're ordering sushi at Sushiro, you're ordering ramen at Ichiran, and maybe this izakaya is actually known for its ramen and gyoza, so you could order them equally, for example. Now, this basic setup of reinforcement learning has been used to model human behavior in a lot of different learning tasks, but a lot of studies have also shown that people aren't exactly optimal at maximizing value. And the whole point of my talk and the whole thesis of the fields of resource rationality is that humans are not optimal at maximizing value, but instead they do the best that they can to learn and make decisions with limited cognitive capacity. Now, this is where information theory becomes useful for defining this problem, because another way to think about policies is as a limited information channel that transmits information about states of the environment to guide action selection. By combining reinforcement learning with information theory, we can now model decision-making with a resource-rational approach by introducing this following constraint. So, now you want to find a policy that maximizes value, but it's subject to a limit on capacity. And specifically, the policy complexity or mutual information between states and actions, if you're familiar with information theory, must stay under some kind of capacity limit C. And policy complexity, as we'll explore later, is the amount of information about the state used to select actions. So, in mathematics, this is what we call an optimization problem. And by solving this equation, we arrive at a solution which is a model that describes how people should behave if they want to maximize value with limited capacity. And this is that equation. So, before this equation scares you, I'm going to provide a translation that hopefully gives some intuition about what this means. So, what this equation is saying is that in state S, choose an action A by striking a balance between choosing the most rewarding action in the state and repeating an action that you've chosen most often in the past. In other words, even more simple, information from the environment or the state combined with your history of actions will determine the decision you make in the present. Now, if you're familiar with reinforcement learning, the components of the model map onto these familiar RL terms. So, the optimal policy combines state action values with the marginal action probability or sometimes called the default policy. And the balance is determined by this inverse temperature parameter beta, which also controls how much the agent explores or exploits. OK, so returning to this more concise definition first. So, what do I mean here by striking a balance between, right? How do you decide how much information from the environment to use versus how much to rely on your previous actions? OK, well, in fact, this beta parameter represents how much you're paying attention to the state to choose your actions. And in doing so, it's also the parameter that controls the trade-off between average reward and policy complexity. So, the larger the value of beta, the more complex your policy is, which you can also think of as the amount of cognitive resources you're willing to spend in order to increase your reward. This is kind of like the first milk example that I was giving. All right, if you're further mathematically curious, an interesting result is that the inverse of beta is actually the slope of this optimal reward complexity trade-off frontier. OK, to give one more concrete example before jumping into the data, here I've depicted a policy where the most rewarding action to take in this state is action two. But in your history of actions, you've most often taken action one. So, to compare and contrast, an individual with a high capacity, a capacity of one, and therefore a high value of beta, would pay more attention to the state and select an action that's best in that particular state. So, you can see that their decision-making policy ends up looking more like the state action values. On the other hand, an individual with a lower capacity of C equals 0.2 and also with a lower beta tends to select actions based off their action history, which, as you can see, action one is the action they've chosen the most. So, therefore that's reflected in their decision-making policy. So, one more time. Beta controls how much you're paying attention to the state, to the environment, and how much cognitive resources you're willing to spend in making your decisions. Now, to briefly summarize the main takeaways from the model we just built, any resource-limited agent, humans, animals, robots included, must learn and make decisions under a capacity limit. This leads to a trade-off between average reward or value and the policy complexity. As a result, policies will always be compressed to remain under the capacity limit. And this is why, in our work, we call this a model of policy compression. So, how do we know that our computational model of decision-making is actually an accurate description of human behaviour? Well, we designed simple decision-making tasks for people to do and see if their behaviour matches what the model predicts. So, in our experiments, people see a bunch of images, one after another, on a screen, and they have to learn which out of three keys on the keyboard to press in order to earn money. So, here's an example of a task progression. So, on every trial, the subject sees an image on the screen, and then they have to pick a key out of these three keys to respond in under two seconds. And they'll receive feedback as to whether their choice was correct or incorrect. So, they don't know anything about which keys are correct for each image when they start. Then they see the next image, they make another response, they receive more feedback, at this time it's correct, and so on and so forth. And the goal of the participant is that they need to get as many trials correct as possible because, eventually, that converts into the amount of money they earn from participating in our experiment. Now, each image, which is the state in this task, maps on to an optimal action. So, that's the action that will most likely give a correct feedback in this task. It's very simple. Three different images and three different actions. Now, for each different state or image, there's one action that is optimal. And we can construct a reward matrix or a reward table that describes the probability of getting reward or correct feedback for each state-action combination. So, in this example, every time you see the blueberries and you hit the K key, you will always get green feedback. So, that's correct. Now, we can play with these reward matrices so that they're probabilistic. So, what that means is, for example, now here, if you press the K key for this image of blueberries, you're only going to get correct feedback 80% of the time, even though it's still the action with the highest probability of reward. So, sometimes you'll get rewarded by pressing J or L, but only 20% of the time. So, we do this because, one, it makes it harder for people to learn what the best action is for each state. And second, because we can see how people's behaviour changes in response to different reward distributions. Okay, so we have a series of tasks where we change the reward tables and we ask whether people's behaviour changes in a way that aligns with our policy compression model. So, in this first task, we have two conditions, Q1 and Q2, red and blue, and they share the same reward table. But in the blue condition, Q2, one of the states here appears more often than the other two states. So, practically this means that people are seeing one particular stimulus image like three times more than the other two images. And the effect that it should have if they learn this correctly is that in the blue condition, they're going to be using action A1 more often than in the red condition. So, that's what this little distribution here means. So, A1 here is being used more often than A2 or A3. Okay, so the model here predicts that there's going to be more policy compression when one state appears more frequently than others. The intuition here is that you can encode high-frequency states with more precision and the other states that occur less frequently with less precision. An example that relates to our running restaurant example is that if you visit a certain restaurant more frequently than others, you're more likely to spend more cognitive resources learning which dish is the best thing to order compared to a restaurant that you don't visit very often. Behaviourally speaking, the model predicts that when one state is more visited more frequently in the blue condition, people earn more reward with the same policy complexity and that choices are less variable. Now, this pattern is not predicted by a standard reinforcement learning model or another very successful model that combines reinforcement learning and working memory which is the RLWM model. And when the data came in, we indeed saw that people's behaviour matched the predictions of the policy compression model. Same task, we can also look at how people's choices are biased. So this task again manipulates the frequency of images that are appearing and like I said, as a result, people are picking action one more often in general. And so what we find is that or what we should expect is that people should tend to pick the optimal action for the state that appears the most, right? So A1 again. So again, the example is, if Gyoza is the best thing to order at the restaurant you visit the most often, maybe you're more likely to order Gyoza at other restaurants too. So to analyse this, we can compare how much people are picking action one compared to the other action that's not optimal but that they share the same reward value, right? So, you know, if people are picking with respect to the probabilities then they should pick A1 and the other action in equal number of times. But here we predict that people would pick action one more often than the other action. And that's exactly what we see and what's predicted by the policy compression model. So you see here that action one is being chosen more often than action three and action two in the other two states and that the standard RL model doesn't predict that choice bias. In the second task, we manipulate how frequently actions are chosen by designing the reward matrix such that several states share the same optimal action. Sorry, this laser is fading. But you can see that in Q2 all three states share action one being an action that is optimal. Now, you'll also notice that in state two and three there's actually two actions that are optimal, right? So choosing either action will always give you a reward. But as a result, overall, you're going to choose action one more often in general. So the prediction here is more policy compression when one action is chosen more frequently than others. And the intuition here is that you would encode states that share optimal actions with less precision because if you choose the same thing in every state then you don't need to think too much about what the state is, right? So the restaurant example here is if you have a favorite dish that is served at multiple restaurants, you don't need to think about which restaurant you're at in order to choose your food order. And again, this shows up in behavior. So when one optimal action is shared across states, people earn more reward for a less or the same policy complexity, their choices are overall less variable. And actually their choices are made slightly faster as well because if you don't need to think about it you can just make your choice. And again, so what's interesting here is like this pattern is actually predicted with the standard reinforcement learning model and the same pattern is predicted. So you might ask, well, how do you know your model is better than the other model? And again, you can look at the pattern of choice biases to answer this question because what happens is that in this particular condition, like I said, if you're just responding to the particular choice probabilities you should pick A1 and A2 equally in S2, for example. And A1 and A3 equally in S3. But the policy compression model predicts that you would actually pick A1 more often than A2 or A3 in the other two states because it's a simpler policy to follow. So again, if you always order gyoza you tend to order gyoza at other restaurants even if there's another dish that's just as good. So again, we can make that comparison and what we see here is indeed people are picking A1 more often than the other action that's just as good and it's not predicted by the standard reinforcement learning model. Finally, in a third task we wanted to know how time pressure changes the way that people behave. So in this condition, again, the two conditions share the same reward table but now in the second condition people have to make the response in one second rather than two seconds, right? So they don't have as much time. And the prediction here is that under time pressure policies will be further compressed. And the intuition here is that decoding the right action from the state takes time. So less time to do that means less precision. This is pretty intuitive. If you're in a hurry you don't have much time to think about what you wanna order. So here the paveral signatures are that under time pressure people have lower policy complexity usually lower reward and that their choices are made faster. And in our data actually we find that choices are a bit more stochastic because under time pressure people choose more randomly. That's not necessarily predicted by our model but again data are not always as clean as the model. Okay, so time pressure also intensifies choice bias. So when you're under pressure you're more likely to choose actions that you've chosen in the past. So you rely more on your action history. This is kind of like the milk example again where you just reach for the same milk you bought last week. Right, so if you're in a hurry you're more likely to go with your default order. And again this time what we can do is we can examine the tendency to pick an action that's not optimal. And we should see that in fact in state three people are picking action one more often under time pressure. So that's exactly what we see. So in state three people are picking action one more under time pressure which is the open bars. And this is just a difference between the two conditions. All right, so for time sake I will skip the result but we also find that choice bias increases with cognitive load and sort of here attribute to my other interest in computational psychiatry in another data set we find that patients with schizophrenia have more choice bias under cognitive load than controls do. Okay, so this is all great. We can show that behavior matches this model which is pretty cool. But why does this matter, okay? So I've thought about this really interesting application of this work which is that this allows us to design choice environments that help people make better decisions. So the logic here is that many people are going about their lives with high cognitive load. Like we're all busy, we have a lot of things to worry about and think about but if we understand how people's behavior is biased under cognitive resource constraints we can design choice environments to help people make better decisions. So this has already been done with sort of qualitative heuristics. So for example, one area that this is being done is in organ donation. So for example, there's this huge problem in America and worldwide too that people die every year because they are waiting for an organ transplant that they never get. And a lot of people are waiting for an organ transplant. So one solution here is to change the policy such that organ donation is actually opt out rather than opt in. So what that means is that opt in programs mean that you have to go and sign up to be an organ donor yourself, right? And this is like high cognitive load because it takes a lot of effort to go do the thing. But opt out programs mean that by default you're already enrolled as an organ donor. And you're automatically an organ donor unless you indicate that you don't want to be. So it's low cognitive load in terms of being already enrolled. Now this is really consistent with our computational model where people are biased towards the default status quo especially under high cognitive load or time pressure. You can also think of this as behavioral inertia. Like people want to keep doing what they've been doing even though what they've been doing is like nothing. So people want to continue doing nothing, right? And this is this very interesting result where countries with opt in organ donation policies have less organ donations than countries with opt out policies, right? So when your default is you're automatically enrolled to organ donation, you'll have more organ donors being registered. A second example, great example is saving for retirement. So there was a policy change in the last few decades in the US where retirement savings plans became automatic enrollment. So this is for example like 401k plans in the US which are provided by your employer to help you save for retirement. So what happens is when you get hired you're automatically enrolled to these retirement plans. So they found that these automatic enrollments increased participation. So for new hires 86% of new hires are participating in automatic enrollment which means only like 14% of people opted out of automatic enrollment. Whereas before automatic enrollment it took almost 20 years to reach that same level of participation in retirement savings. This automatic enrollment also increases savings outcomes because people start saving earlier. And that's incredible because you can see here on the y-axis is the cumulative savings which is a percentage of the workers wages. And as you can see automatic enrollment is much higher than opting in policies. So the idea here is that computational modelling might allow us to do this even better to design choice environments that help people make better decisions. And there was some recent work from these two researchers suggesting that just kind of like the analogy to buildings and architecture. Like people saw an increase in the height of towers in the world once they switched from architectural thinking to engineering thinking. And so the idea here is that quantitative models of choice are potentially more useful than qualitative psychological principles which these policies that I told you about are currently based on. So here's what we learned so far. Humans adapt decision making strategies under cognitive resource constraints. And as a result choices are biased by the frequency of encountered states. So you're more likely to devote extra cognitive resources to states that you visit often. Choices are also biased towards frequently chosen actions. So people tend to choose actions that they've chosen most often in the past. Three, choice bias increases with cognitive load and under time pressure. You're more likely to go with familiar choice when you're extra limited in cognitive resources. And understanding these biases can help us design choice environments that help people make better decisions. All right, so as a neuroscientist a natural follow up question to ask is where in the brain can we find evidence for cost sensitive action selection? And this is important because somehow even with its limited capacity the brain is still able to generate flexible and adaptive behavior in many different environments. So to tackle this question we teamed up with Bensai Owekski's lab at Harvard and they study motor learning in rats. And specifically they study this circuit between the motor cortex and the striatum namely the dorsal lateral striatum, DLS and the dorsal medial striatum, DMS which some of you might also study. And with the data that they were seeing from their lab we really were able to map on our model and our model predictions to what they were seeing in this animal motor learning behavior. So I'll tell you sort of like the overarching hypotheses first and then show you the data in support of that hypothesis even though it was more of a cyclical journey of looking at data coming up with hypotheses you guys all know. So our hypotheses are that first motor cortex modulates attention to external state. So in our model that's like the beta. Second dorsal medial striatum learns about rewarding state action pairs. So it basically learns the values in this state action value table. And three dorsal lateral striatum acts as a default policy that stores frequently used actions or action patterns which you can think of as like habits. So again our approach here is in collaboration with their lab we're going to look at a motor sequence learning task and what we're going to do is lesion these different areas of the brain before and after learning to see how they affect the animals behavior and also we can do the same thing in our model to see how it affects the models behavior. Okay. So a really talented former graduate student in Benson's lab came up with this piano playing task for rats. So in this task there are several different kinds of sequence learning tasks. So in one of them it's they're visually cued sequences. And so here what happens is that the animal gets cued for each action that it's going to take. So here's a video of an animal doing this task. Oops. Here we go. It's a bit laggy. Uh-oh. Maybe it's the resolution is not very high here. It's not playing on the computer either or see it's kind of moving but. Okay. I'll just describe what's happening. So the animal sees a light light up and then it presses the lever associated with the light and another light lights up and it presses that lever and then a third light lights up, presses that lever and then finally it gets rewarded after three distinct lever presses, cued lever presses. Okay. In contrast another task that deals with automatic sequences an animal is overtrained.  And that's the only sequence it knows. So in the next video which may show up the animal basically there's no cues involved. It just goes to the levers and just starts pressing the sequence itself. Right. Okay. So all right. So this is what we learned from that study. So here if what Kevin did was he basically lesioned each of these different areas in the brain. Okay. And then he basically and these are in animals that have already learned these two different tasks. And so he looked at whether animals were still able to do each different task after the lesion. So here what happened is that animals with a motor cortex lesion were still able to do the automatic sequence but they were not able to do the visually cued sequence. So this is a fraction success is how many trials they got correct. This is before pre lesion and post after the lesion. Now when he'd lesioned DMS dorsal medial striadum surprisingly he found that the animals could still perform automatic sequences and they could still also perform the visually cued sequences. Now you might think that's interesting because I thought you were deleting this part of the equation. But like I said, the hypothesis is that DMS learns about rewarding state action pairs. So if you've already learned what you needed to learn and then you cut out that part of the brain then there's no learning involved anymore. So animals would still be able to do the two tasks. Okay finally when he lesioned DLS dorsal lateral striadum which is in charge for frequently used actions as you might expect they weren't able to do the automatic sequences anymore which required the animal to do a habit that they've already learned. But animals were actually able to do the visually cued sequences which is very interesting. Okay so we have a host of results here. I got the spinning wheel of death. Oh there we go, okay. All right, so that was a whole line of work. His whole PhD of seven years. But you know then a new PhD student came in and with another postdoc in the lab, Chesha and Kea decided to zoom in on the function of dorsal medial striadum. And so here what they basically wanted to compare was what is the role of DMS in learning because they sort of had this vague similar hypothesis. And so they had two different tasks in their setup and one, the animal basically only had to do hit one lever to get reward. So these were all cued tasks. So they saw a visual cue light up and they would have to press the corresponding lever to that visual cue and then they would get reward. Now all animals first learned this task. And then afterwards they learned the three lever tasks. So that's the same task as I described before where animals are seeing visual cues one after another and then pressing the corresponding lever one after another. It's a shame because these videos are pretty cool but maybe you can see them later. Okay, so what they found is that DMS is critical for learning sensory guided representations. So in blue I have the performance of control animals so not lesioned animals. And in red is the performance of animals that have DMS lesions. And you can tell that even though they end up both learning the task up to 85% accuracy the DMS lesion animals take way, way longer in order to learn it. And one sort of hallmark signature of this is that they ignore visual cues early in learning which is a signature of low policy complexity. Now in the three lever tasks so once they've learned the one lever task now they do the three lever task. And again what you can see is that controls are able to kind of learn this task maybe 50 to 60% but DMS lesion animals are all over the place and their average accuracy is very low. And as you can see the red traces are much shorter than the blue traces and that's because animals actually just give up at some point. So after eventually learning the one lever task these naive DMS lesion animals cannot learn the three lever task. So again remember these are animals that are lesion before they've learned anything. And specifically they're unable to break this action chunk of lever to waterport. So what happens is that animals are still sort of doing the one lever task behaviour in the three lever task. So they see a light and they'll press the lever and then they'll go to the waterport even though they're supposed to press the next lever and then the third one before they can get reward. And this suggests that a habit has really formed. They're unable to break this habit of pressing the lever and going to the waterport. And again I wish you could see these videos but it's very distinct behaviour here where the DMS lesion animals really cannot stop doing this behaviour that they've learned in the previous task. We used our model to confirm that learning is impacted in the DMS lesion. And the way that we did this was that we first fit the learning parameters in the model on the one lever data. So these parameters are responsible for learning the different state action values in the model. And when we fit the model to the data we see that the learning rate is much higher for the controls than for the DMS lesions which is consistent with our hypothesis. And as you can see if you take the model and then you simulate behaviour you have to model do the same task as the animals. We can recapitulate the result here. You can also take the same fitted model parameters on the first task and then simulate it and run the model on the second task, the three lever task. And you can actually get pretty good comparable results to the three lever task even though the model has never seen the three lever task before. Okay, so that's encouraging. So now we can add to our host of results that we have observed. And we can also then use our model to model the lesions in our own equations. So again, for motor cortex lesions in our model would correspond to setting beta to zero. Lesions DMS again would correspond to setting these learning rates to zero. And then for DLS lesions would correspond to completely cutting off this entire module of the model. So, you know, modelling is very useful because it allows us to explore a large hypothesis space that we haven't explored before. So to give you a sense of that you have three different lesions sites and then you have like three different points that you could actually lesion the animal. So you could lesion the animal before it's ever learned anything. You could lesion the animal after it's learned the second three lever task and you can lesion the animal between the two tasks, right? And now you can also look at a behaviour before and after the lesions in each of the three different tasks. So modelling is very useful in exploring like a wide variety of hypotheses that you might not have thought about before. So you can do this for all three lesion sites. And so this is the result I just showed you. So here if you lesion DMS before any learning has occurred animals can kind of do the one lever task. These are just lesion animals and then the three lever task, there's no bar here because it's just zero. So the animals can't do the three lever task at all. And then going back to the first result I showed you. So I'm only going to show you the results from the cued task in Kevin's data. So here all of his animals are experts. So they only learn the three lever task. And you can see here that again, the model recapitulates this result, although more extremely. So before the lesion, it can perform the task very well. And then after lesion, basically zero, which is consistent with his data. And then again for DMS, similarly, there should be no significant change between before and after lesion for the three lever task. And then similarly with DLS, dorsolateral striadome, there should be no significant difference before and after the lesion. You can see some idiosyncrasies in how the model deals with things, but generally speaking, it's qualitatively consistent. And so now you can simulate a host of different hypotheses that you might expect for different points of lesions and different tasks. And this is really interesting because it allows us to see things that we might not have thought about before. So for example, an interesting prediction here is that is that expert animals with a DMS lesion should be able to perform the three lever task, which is what Kevin showed, but they shouldn't be able to go back and learn the one lever task after they've already been lesioned. So that's not something that you would think about intuitively. And so that's something that we could test in the future. Another interesting thing to look at is that DLS lesion animals should always be able to perform any queued task no matter when their lesion happens. OK, so the big picture here, and I'll sum up, is that the brain balances reward and complexity by penalising complexity during learning.  And here the sort of anatomical picture is that dorsal medial striatum here learns the state-dependent policy. Dorsal lateral striatum stores the automatic default policy, which these two get combined to form the full policy. And that cortical areas like motor cortex, and I didn't even talk about prefrontal cortex, but there's evidence and it projects to dorsal medial striatum, that these two cortical areas modulate attention to external state. And then finally, the last part of it is that learning happens through dopamine, which is related to average reward and maybe controls this attention to external state through cortical projections. All right, so to end, the final takeaways here are that cognitive constraints shape our decisions and actions. And any resource-limited agent, humans, rats or robots, must compress their policies. And that, as a result, choices are biased by the distribution of states, actions and rewards in the decision environment, as well as external factors such as cognitive load and time constraints. And finally, lesion studies reveal that distinct brain areas work together to balance the automatic execution of learned behaviors with the more cognitively demanding ability to flexibly respond to relevant sensory information. All right, so with that, I just want to thank my lab, past and present, all the people I've gotten to work with and then all my collaborators in the Oveski lab, which they're in the Department of Organismic and Evolutionary Biology at Harvard. So thank you. Well, Lucy, I want to thank you for an amazing talk. I'm exhausted, but I'm also really engaged. So there's a little bit of balance going on there. I think we have time for some questions. So I'd like to open it up. Yeah, so they start with something even more simple, which is simply just like basic is just going to the water port and realizing that's where they get water. And then next step is associating the lever with the water port. So knowing that if they press the lever, they get water. And then finally, the next stage is like queue, lever, water port. Oh, well, one cool thing about the Oveski lab is that they've built these automatic animal behavior boxes where they can just like let the animal do its thing in there like 24 seven, basically. But even then, it still takes months to learn these queue tasks. Yeah. Okay, other questions. You have to turn it on, maybe. Hello. So you gave some examples of using this information from these models to help people make better choices. Do you have examples when these things were used to make people do necessarily better choices? Like worse choices. Let's say, or better for somebody else. Classic example is social media. Like the whole point of social media, right, is to keep you on social media for longer. And, you know, I mean, I don't know what kind of research goes on at social media companies, but I suspect a lot of it is like, how do you increase engagement? And what are the psychological principles behind addiction, basically? Yeah. So, you know, use the power wisely. I'm going to give you an example of any parents in the room. One of the things we've all learned is if you want your child to choose quickly, give them a choice of two things, not an open choice. You know, so at its most basic level, that's limiting your options. Yeah, yeah, exactly, yeah. I have a question on that comment, actually. Lucy, thank you very much for that talk. It was amazing. I would like to know if you could map the concept of analysis-paralysis when you have too many options and you don't want to take, or maybe you just need to be handed down two best options. Yeah, yeah, yeah. What do you think from that modelling perspective? Yeah, so I think a cognitive load is essentially what that is, right? So, because if you, it's hard to evaluate each particular option, which you can think of as the different states, right? So, if you have too many options to evaluate, then, you know, you're kind of overwhelmed by the cognitive costs you have to pay to make the decision. So, and you might as well just go with whatever someone tells you, for example. Yeah, something that doesn't require you to think as much. If you'd really like to take a look at that, Gaston, come into our lab one day and watch us let children choose a prize from the prize box. Not only is it exhausting for them, it's exhausting for the appearance. Questions? Yeah, over here. Do you have a microphone near? Hello? Oh, OK. You talked, thank you for the talk. You talked a lot about cognitive load and I was wondering if you model or if you plan to model the stop of, how can I phrase this, just the animal or the model stopping the actual making process as a whole, instead of just choosing an action quickly or choosing whatever, just making the choice to not choose anything at all and continue without reward. Yeah, so like you're kind of talking about the part where the animals had just given up. Yeah, yeah, yeah. So in other studies, not this one, I've modeled inaction as an action. So they can choose to not do anything, for example. So that's definitely something that I could incorporate into the model to make it even more similar to how the animals behave. Yeah, and for example, you could think about making inaction even less costly. So it's less costly to do nothing, obviously, than to do something. Yeah. I was curious if that was the reason why you called beta inverse temperature. Yeah, so in reinforcement learning, that's a very common parameter that people use. And temperature is a parameter that basically describes how stochastic your choices are. So sometimes people write it as like that Q over T but instead using beta as a parameter is sometimes more intuitive because it's multiplicative rather than divisive. So that's what they call inverse temperature. OK, any other questions? It's just turned four o'clock, so if anybody needs to be anywhere, yeah, we've got one up here. Yeah, that I asked the questions from Zoom. There's two, actually. So the first one is from HiHow. What explains the high rate of organisation in the US despite being up in just culture? I didn't know if that way was very high. Really, the organ donor. I mean, I guess if I think about it myself, like I'm an organ donor in the US, they do make it kind of easy to check the box. Like for example, they pair it with another action.  You're like, yeah, sure. So making choices easy, again, like Gail said, like very few number of options might also increase participation, yeah. OK, thank you. And the second one is from Fabienne. I was wondering about increased elasticity in participants experiencing time pressure. Wouldn't it be caused by the decision-making process being cut short and whatever the state is at that point ending up being the choice? Say the last part again, whatever the...  Yeah, people have different strategies for what they do under time pressure. Like if you think about a keyboard task, if you're limited in time, sometimes people are just kind of like pressing keys randomly with three fingers, you know. Other times people are just jamming one key over and over. So it kind of just depends on what people's like time-constrained strategy is, yeah. Thank you. Yeah, we can hit mine. Yeah, hi. Thanks a lot. I was wondering about the inverse temperature parameter because presumably the compression should make it less computationally expensive, but at the same time it's just a multiplication. So it doesn't really make the computation per se any easier, does it? Yeah, so that's related to... So that's related to the information theory part. So in reinforcement learning, we think about beta as high beta is less stochastic, but in information theory, beta also represents, in this setup, beta also represents how much information you're paying attention, how much attention you're giving to the state when you're choosing your action. So the value of the complexity is an information theoretic value and not like a... Like there's different kinds of costs, right? An information theoretic cost is more of a memory cost rather than like a statistical cost. Yeah, it's a bit, I'd have to sort of show you sort of the breakdown of different types of complexity, but that's a good question, yeah. Maybe one or two final questions and then we'll let Lucy relax. Nick. Hello, cheers for a cool talk. I have a broader, maybe slightly philosophical question, I guess. So it seems like from this perspective, generally and from your model, there's a kind of normative dimension where like doing well or making good decisions is somehow maximising reward or optimising. And I've noticed in the broader culture recently, there's a bit of a pushback against this kind of like single metric where we're trying to optimise everything. And we've kind of, I don't know, optimised ourselves into a corner where we're just doing maximising this kind of one thing. And I wonder about like the implications of something like these types of models where they kind of celebrate that tendency, right? And I mean, my own feeling is there's, we do a lot more satisfying than we do optimising. We're kind of doing a lot more, this is good enough, this will do. Yes. And when we have like really constrained experimental conditions, yeah, we get the kind of feedback that we get when we do these types of things. But in real life, it seems like it is a lot more stochastic or it looks to be a lot more stochastic, at least. Yes, 100%. And that's the argument that I have been having with my adviser. And let me tell you that in the full paper actually, we show that there's two different ways to look at this problem, which have equivalent mathematical formulation but two different objectives. So like you said, instead of optimising to some constraint you could have satisfying where you're like, this level of reward is good enough for me. Or like, maybe I don't even care about reward, there's another dimension that I care more about, right? Although a lot of people try to define reward now in much broader ways, like reward might be like play or like curiosity, right? Like that kind of stuff. But at least here, there's another formulation of the model where instead of saying like you have a capacity constraint, you have a satisfying level, right? So instead of saying like vertical line C, this is my capacity constraint, it's actually horizontal line. This is just the amount of reward that I'm fine with. Because sometimes you're like, I don't need to do well on this test, for example. Or like I don't need to put that much effort into this because I don't really care about the outcome. And so therefore you also adjust your behaviour based on that. Even though you have the ample of resources to do it if you wanted. It's a really good thought. Final question for Lucy. Okay, well I think the fact that everybody stayed past four o'clock is an indication that they found being here reinforcing. So I'd like us to now reinforce Lucy in this standard manner.


Our ministry to the cities is a bit artificial. You have no idea what the street child goes through. When he has to wake up in the morning, no breakfast, and he has to start the first person he meets, he has to beg. Some of those children have told you are bright. If you take them to school, they are students, very bright. But what they are lonely of is not necessarily a place to sleep. It's where's my mother, where's my father.


Good afternoon. I'm Ted Osius. I'm with CSAS and with the State Department. And it's a great honor to be here today to be able to introduce Putra Sampurna, a true friend to the United States, and a champion of US-Indonesia relations. Putra, for those of you who know him, you'll know that Putra Sampurna is the real deal. He takes action to implement his ideals. I visited two of the Sampurna academies. And these are amazing places. They're places where Indonesians with no means receive really top quality education. A lot of the students whom I met at these academies had never before slept in a bed before they came to these boarding schools. Their families, on average, earn less than $3 a day. They are highly motivated students with no sense of entitlement. Now, there are students who did not know English 3 and 1 half years ago who are earning 4.0 GPAs at US universities. This is a system that works. You'll meet a couple of these students a little later on. The Sampurna Foundation works on more than education, although I think education is very much the core of what this Sampurna Foundation does. But it also works on women's empowerment, on entrepreneurship, and disaster relief. We're focusing on education today, specifically because education is the number one goal of our comprehensive partnership with Indonesia. And the Putra Sampurna Foundation, to be honest, has sent more students to the United States than all our government programs. I urge you to read the white paper that's on the side table there. And I also urge you to pick up a copy of this book, also on the side table over there. It's by Walter McMahon. And I think Mr. Sampurna will touch on it. I want to point out that we are on the record today. We will be posted this session we posted on the web later on. We are live tweeting this event. And the hashtag is CSIS Live. And our handle is at Southeast Asia DC, S-E-A-S-I-A-D-C. And again, the materials that will be presented today will be on our website after this event. So it is a great pleasure to introduce my friend Putra Sampurna. Thank you. Thank you, Ted. Good afternoon, ladies and gentlemen, and thank you, Ted and CSI, for having me here. The goal of this roundtable discussion is to advance new ideas to energize the US-Indonesia partnership. From a social perspective, I believe that we need not look any further than for the United States to demonstrate a definitive commitment to invest in Indonesia's education. Such action will convey the strongest message that the United States does care. And that you can act boldly with impactful and meaningful actions beyond the usual bold commitments to security initiatives and traditional development strategies. President Obama, whilst in Israel, said, peace will have to be made among peoples, not just governments. I agree. And accepting foreign students into our respective institutions of higher learning provides a vital link to creating the people-to-people ties that grow partnerships with countries around the world. There are many long-term studies that consistently show that increasing the numbers of bona fide bachelor degrees in developing countries has a faster, more emphatic, more measurable, and sustainable effect across a society than the traditional government interventions like direct welfare or even infrastructure projects. It is noteworthy to consider that the United States is the only country in the world that has the capacity to absorb so many into their universities. And yes, higher education diplomas are one of America's last world leadership commodities. Autos and steel are gone, but the 200,000 Chinese coming to the United States for a unique education quality product is a big endorsement. Some of you here, like the Chinese, must have read Dr. Walter McMahon's book, this book that you have copy of. Higher education greater good is the most authority to source to solidly prove through decades of longitudinal studies that more university degrees in developing nations result in less inequality, poverty, hunger, and malnutrition. And it accelerates social development providing for healthier families, fewer human rights violations, less risk of instability, lower crime, and a healthier environment. If this book was in the form of a newly discovered vaccine it would cure poverty. On the other hand, the record of military interventions building sustainable political or social stability or goodwill is somewhat less conclusive, to say the least. I have run some numbers on US aid and security programs. And you can see these calculations in a white paper that you have before you. Therein I offered a few provocative thoughts that will likely be considered outrageous propositions by the entrenched interest in security and aid divisions. I asked myself what could be achieved by repurposing 5% of US aid budget and 2.5% of the Department of Defense budget and diverting that fraction of their respective budgets to providing higher education for deserving students from developing countries in the United States. The result would have produced around 30,000 American-educated MBAs per year from USAID funding and another 250,000 American-educated MBAs per year from DOD funding. President Obama has also proclaimed the Asia rebalance with billions and billions of dollars to be spent to move personal assets from the Middle East to Asia. So I asked myself another question. What if the United States were to send one impoverished but gifted Asian student to university in the US for every soldier or sailor deployed in the region? Matching students to soldiers would undoubtedly be a monumental public relations coup for the United States, creating a tremendous goodwill and producing a lot of good citizens in the process. Imagine the possibilities provided by thousands of American-educated local scientists, scholars, public policy professionals, and business executives going back and contributing to the development of their own countries. Also consider what contributes in the empathy of so many American-educated individuals towards their US counterparts would bring towards efforts to achieve peaceful coexistence. It's about time the paradigm shifted in the US international strategies. Instead of gun to boat diplomacy, how about diplomacy? How about shifting from the world's policemen into the world's educator? If I'm not mistaken, the ratio between the annual costs of supporting university students to soldiers deployed overseas in non-combat areas is one to three. Social transformation through education is something that we at the Foundation are fully committed to with the development of future leaders as the main focus of our initiatives. One of our goals is to raise some of the brightest and poorest out of poverty and end to full participation in Indonesia's growth economy. Our challenge is to find innovative ways to drastically reduce the cost of providing an international benchmark education. The Sampoorn Academies. Our academies are rigorous Spartan boarding senior high schools three years for academically exceptional students carefully selected for the potential of becoming future leaders. For these students, we imbue character, honesty, hard work, and social commitment. The entire curriculum is taught in English and students graduate with the equivalent of an international baccalaureate accreditation. This means that upon graduation, these kids would qualify to enter any English-speaking university in the world. The Sampoorn University is our own recently licensed university. The curriculum is also taught in English and it provides for an internationally benchmark four-year degree program. In a unique collaboration with the Lone Star College System, Texas, our university now offers an American-Indonesian mobility study program that allows its freshmen and sophomores, like the Lone Star peers, to earn an all-American associate degree after the first two years at the university. This affords them to transfer their earned credits seamless into over 100 US universities. The umbrella organization for our institutions of learning is the student's own cooperative. So throughout the learning journey of our students, financial assistance packages are organized and provided by the cooperative, a student-owned but professionally managed cooperative that also provides career development and mentoring services. Additionally, the cooperative provides a network of support systems for our poor and socially isolated students that is similar to the kind of social and professional networking available to most middle class US students through the social clubs, fraternity memberships, university affinity groups, professional and trade associations, and the traditional alumni associations. The poor, even those with advanced degrees, don't typically have these connections. So we are building one for them. Before I go on, I must tell you that our foundation has been entrusted with a loan guarantee from the USAID department, Development Credit Authority, for $5 million, and that money is doing some of the work of sending students, like our two students here, to the US. We consider the trust of the US government to be an emblem of our fiduciary liability. In addition to our initiatives of leadership development, our university school of education has also developed a three-year outreach program which provides for improving the quality of state-owned Indonesian high schools through the professional development of their teachers, their school leadership and management programs, and their school government's programs. The university delivers these services through a conference of whole school development approach and the required funding per school running in the range of $350,000 to $500,000 over a three-year period. Our clients are basically large corporates and NGOs who want to make a meaningful impact in their areas of operations or nationally. A case in point is our high school in Bali, which we took under our wing. Before it entered the SDO program, it was academically ranked 41st in the nation with around half of their students failing the national exams. Just three years later it was ranked number nine in the nation with 100% exam passing rate. Since then it has continued to improve on its own and is today ranked sixth in the nation. But more telling than all of this is the fact that the student body now includes kids from well-to-do families. Over the last six years we have successfully serviced 40 large corporations with 52 programs in 118 schools and nine madrasas affecting some 57,000 teachers. Also over the last few years the SDO programs has modularized its product offering and is now able to service much smaller contracts on a specific needs basis. Based on the SDO successes the foundation in October of last year took the lead role in establishing the national movement for education for the purpose of assisting state schools in improving their teaching learning capacities. This movement represents a public-private partnership among the Ministry of Education, the Regencies, our foundation providing the software and NGOs providing hardware resources and the CSR programs of companies with local operations providing some or all of the required funding. National and local government schools, corporate sponsors, a win-win-win situation with the foundation as being the catalyst. I'm happy to report that since its launching last October the movement has gained considerable traction and is now looking at a demand for service across 12 provinces, 141 Regencies and over 700 schools operating within those Regencies. And this is putting a tremendous strain on our resources. At this point I'm happy to announce that we have with us today Chancellor Richard Carpenter of the Lone Star College System of Texas. He's a partner and a friend and a courageous leader in higher education. Lone Star College is the largest institution of higher education in the Houston area and the fastest growing community college system in the nation with 78,000 students currently enrolled in academic courses. Lone Star College is also the winner of the National 2013 Simon Award for Comprehensive Internalization by any university or college. But before I officially introduce Dr. Carpenter, let me tell you why I'm so excited about our combined aims program. First and foremost, it is an American-Indonesian initiative established by two individuals from the opposite sides of the Pacific Ocean who forged the partnership primarily on the belief that it is in the long-term interest of both their countries that it happens. It provides a wonderful setting for President Obama to refer back to the spirit of the Comprehensive Partnership Agreement signed by both our presidents when last he visited Indonesia. Around the world, there are many branch campuses of major league universities. These are elite and expensive. Our aims program is the first that opens up a pathway to achieving a U.S. higher education by average Indonesians. At around $10,000 border tuition per year at our university, we can drastically reduce the in-state or out-of-state cost of obtaining a four-year bachelor's degree from a ranked U.S. university. I'm also very confident that deserving students who finished their first two years of the aims program with a GPA greater than, say, 3.25 would easily be able to find corporate sponsors or academic scholarships for them to continue onto their bachelor or master degrees on a bonded basis in the United States. Our university with its aims program in place will provide a venue for reducing the over-cost of a diploma diplomacy should such a policy in whatever form be adopted. Finally, one of the goals of the Comprehensive Partnership Agreement is to double the number of American students studying in Indonesia from 200 to 400 within the next five years. Imagine that it takes just $2 million per year in grants or student loans to immediately accomplish these goals and at no loss of earned credits as these students return to their respective state universities to finish off their education. So now without further ado, let me introduce to you Dr. Carpenter and invite him to say a few words. Good afternoon, everyone. As you can see, Pak Putra is a remarkable man. He's quite a visionary. I've had the privilege of working with him for several months now and one of the things that I admire most is that everyone in the room knows they're talkers and they're doers. This man matches his words with resources and action and that's the reason the partnership that we developed was made possible because we had someone who did more than talk about it. We had someone committed to doing it. The AIMS program that you've heard referred to again, I remind you that's American, Indonesian, Mobility Study Program is all about access. All about access to higher education and economic prosperity for the many, providing affordable opportunity and rapidly expanding the middle class and Indonesia. What we're talking about here is providing the mechanism for developing the future leaders for Indonesian globalization. What we are creating and will launch in September is no less than a global entry, global exit model for higher education, very unique, with worldwide applicability. Now I have a very personal reason to be passionate about what we're doing. I too, like Ted mentioned, have been to the San Pono Academies. I have met impoverished students from rural Indonesia whose futures were bleak. I have seen these students progress to enroll at the Lone Star College System. And I have watched, hear this, every one of them earned 4.0 grade point averages, every one of them. Now I relate personally to these students. You see I grew up in rural Louisiana and as a foster kid I had three sets of parents. One of them finished high school, none of them went to college and the trajectory of my own life was forever altered by access to a community college, affordable access to higher education. Who could have looked at me as a kid and seen a CEO of a college system enrolling 78,000 students and employing almost 8,000 faculty and staff. Now look at the opportunities that we are extending to rural Indonesians and imagine. Just imagine the impact that we will have on the future of that country. Look into their eyes today and you can see the future of a nation. As already has been mentioned, Lone Star is the fastest growing community college in the US. We attract students from 108 countries. We are an open door institution, proudly admitting students of all backgrounds to reach their dream, just as Pak Petra provides to his Indonesian students. And listen to this. Every year, we transfer students to institutions like Harvard, Columbia, Duke, USC and almost every land-grant university in the United States. Now, the San Bernard academies, we've got nine of their students this year at Lone Star and as I mentioned, they're all making 4.0 grade point averages but there's more to it. The definition of a partnership indeed is about mutual benefit, isn't it? As we provide affordable access to these students, they in turn raise the bar in our American classrooms. They are motivated. Faculty love them. Rarely a day goes by that I'm not getting an email from one of our faculty saying, please send us more of those, we love them. Now let's explore together how we can progress in talking about our comprehensive partnership with Indonesia to actualizing it. As Pak Petra said earlier, let's demonstrate the audacity to link action to our words. Thank you. Thank you, Dr. Carpenter. We're joined today by Timmy and Anga, future leaders of Indonesia. Both are from rural East Java. Anga is Muslim, Timmy is Christian. I urge you to meet with him after our Q and A session. The academies really are serious about teaching the future leaders of Indonesia. Like all of Dr. Carpenter's nine Indonesian students, both Timmy and Anga are earning 4.0 GPAs at Texas Tech. And I just wanted to point out that Anga didn't speak English three and a half years ago. It's an extraordinary proof of concept. So it's great to have you both here today. Thank you for coming. We're gonna open it up to Q and A in just a moment. I would ask that when you ask your question, if you identify yourself and use the microphone, if it's anywhere nearby, so that our folks who watch on the internet can also hear. I'm gonna take the prerogative as the convener to ask the first question, if I may. We've just heard about, Mr. Semperon, we've just heard about three successful models. And I would point out that they're interlocking models, Ames, American Indonesian Mobility Study, Schools Development Outreach, and the Development Credit Authority. Any of these three models could be scaled up. Which would you prioritize? Which would be your top priority for scaling up at this stage? To me, the top priority is still the leadership development, okay? I mean, that's what's gonna make, that's what's going to change the country. So to me, the Ames program would be the most beneficial to the country. And bilateral relations between the two countries, I think. Thank you. And we're now open for questions from anybody. Again, if you would identify yourselves, please, Adam. Yeah, Adam, the Ames program is in Indonesia. So it's bringing American know-how, okay? And bringing the first two years, the freshman and sophomore years in college, to Indonesia. So overall, it becomes a lot cheaper. I did some calculations. And for an American student themselves, going spending the first two years in Indonesia, and then going back to their home state like Texas A&M or whatever, it will reduce their cost of a bachelor's degree by 30%. Okay, and you get to learn culture too at the same time. So yeah, you're absolutely right. For us at the foundation to send our enrollment in the academy, unfortunately, it's only 200 per year. But for me to send 200 students a year to US colleges from day one is not possible. Okay, so you're talking about, if you're talking about six years at 200, you're talking about a student body of 1,500 people, kids at 35,000 a year is not, for me it's not personally possible. Ambassador? That actually takes to a question. How much, what is your general view on the question of distance learning and the use of the internet to not, is a substitute for teacher-student interaction but is a supplement to it? Have you been able to take advantage of that to date or do you anticipate it being useful in the future? Dr. Carpenter has been, and I have spent a lot of time talking and debating this issue.  I mean even for the kids. Okay, they really have to discipline themselves in getting through it. And we believe, correct me if I'm wrong, Dr. Carpenter, but you need to have a hybrid. Okay, where you need to have at least a face-to-face with the students at least three, four times a year. Okay, or somewhere where they can go and sit there and ask questions on what they're learning on the internet. So it's kind of a hybrid system. And in our university program in Indonesia, we will be introducing some of that. And where we as an organization, Indonesian organization can put across a archipelago, university offices if you will, where kids can actually go if they have a problem with what they learn on the internet. But without that, yeah, we hear a lot about online, online learning and all that, distance learning. But I really have my reservations as to whether it can be done totally offline without some kind of interaction with me. And I think in Houston itself, okay, I think Dr. Carpenter, the Lone Star College system insists that at least there's twice a semester that they have them on hybrid. So it is a hybrid system. We have about, at Lone Star in Houston, we have about 30,000 online students from all over the world. Remember though, what we're doing in Indonesia is we're reaching a very broad spectrum of the population. And what we found at Lone Star in our own work is that there are those for whom pure online education works. There are those for whom that works. There are those for whom it won't work. And what we wanna do is we wanna make sure that as we launch this program that we're doing a little bit more hand-holding. One of the unique things about it, as everyone knows about a community college, is that we really are nurturing environments for students. So it's not just a student who could go anywhere, but it's a student who couldn't go anywhere else that we're interested in serving. And typically that student at best, fares better in a hybrid setting and more mature students fare better, will typically do better than those students in an online setting. We do have a very robust online offering, and we try to address the needs of the student as they vary. It sounds as though you're talking about online in the form of kind of pure online where you take a course and then hybrid sounds like it means a few times a year you come into the classroom. What about a hybrid where the online is actually taught by a teacher in the classroom, but it raises the quality of, I mean, I would think, for example, for an Indonesian teacher teaching international finance, having a good online resource available would raise the quality of what he or she could teach. Is that of interest or not? All of these approaches are of interest. For instance, we're also discussing now how do you sit down and provide schools in the remote areas like plantations or forests, okay, where you have like the little house in the prairie, you have one in one school, you've got kids of five years old all the way to 16. And yeah, in those cases, we're working with Wilmar, and we're developing the courses where we send teachers from that area that Wilmar or the company pays for, and then send them back and like what you say, teach off the courses that we send, where they don't have internet connection, we will send them the DVDs, okay, and I'm sure there are a lot of specialty courses, okay, that can be been from the US if you will, okay, but I think right now to be practical, I think we need to sit there and get going, if you will, and that's gonna be face-to-face type of education. Let me further follow up on your question, I could, one of the things that we are also doing is everyone probably has read about flipped classes and we're seeing mixed results with that depending on the nature of the class, actually depending on the nature of the discipline in some cases, where the student studies online, then comes to the classroom with a faculty member to apply and discuss what they've studied online so that the actual, so almost the classroom, typical classroom environment occurs online, but then they come together as a group to either apply that knowledge or to discuss or to have exercises around that, that we haven't, we're just launching our program here, but we envision that over time these are the kinds of things that we'll be introducing into that program as well. But I think the strength of the Lone Star System, you know, when I first met with Dr. Carpenter and you talk about 78,000 academic enrollment and you talk about another 30,000 online and my head goes click, click, click, click, click, click, I can use all of this, okay, without trying to reinvent the wheel. And that's where I'm really very excited about the partnership we have because all of these things that you're talking about, you know, Paul is doable and over the long-term period, but they got to know how and we can build the infrastructure throughout the archipelago. Okay, and that's where, that's what's gonna take to make it work. Investor Merrill. David Merrill, President of US Indonesia Society, Yusindo, and most of you know me and you all know that Yusindo supports strongly through itself and through the Joint Council on Education Partnership, the goal and of how to increase the number of Indonesians going to the United States and the other way around. And so we're looking for and applaud innovative models such as the one that's being presented today. It's not easy to figure out how to do this. And we've all found that the goals in the Comprehensive Partnership Document of Doubling, though it was achieved 10 years ago or in 1987, it's harder to achieve than anybody really thought. So any model that works is one to be encouraged and applauded and I do that to this one. We've publicized that. Now, particularly within that, the two years in an associate degree, followed by two years for a full BA is one that we think has great promise. And for one thing, it cuts the cost so drastically as Mr. Simperna pointed out. And the other, at least in some models, at least those that involve community colleges in the United States, some of the admission requirements, some of the testing requirements are not required as much or at all. So it's an easier pathways, both cost-wise and admissions-wise. This particular one is, I'm not sure if it's unique. I think it is probably unique and I wanna understand it better. So there's two years in an associate degree in Indonesia and then that is credited in the United States. Now, what I'm not clear on is this aimed more at, or maybe it's both, American students going there, which has been mentioned, or Indonesian students preparing there and taking their two years? What's the balance? What's the strategy? I don't differentiate between Indonesian students or American students studying at our university. I mean, that's the beauty of this thing. I see. So after two years, you're gonna get two associate degrees for all practical purposes. One is from our university and the other one's from Lone Star College. Okay, you can use whichever, you can use the Lone Star Associate Degree, which I call the All-American Associate Degree, and use that to transfer all your credits that you've earned over the two years through Texas A&M, Yale, Harvard, or any of the 100 universities, over 100 universities in the US that accept Lone Star's credits. Well, you haven't been able to send me any American students. I just challenged you before this meeting. Yeah, you know. Yeah, I mean, but look at the possibilities. At $10,000 a year for the first two years, that's 20,000 bucks, okay? At 35,000, out-of-state rates of per year, there's another 70,000, that's 90,000, as opposed to 140,000. So it's a, yeah, even for American students. So you should be sending me 200 or more. Okay, but I mean, that's the whole purpose of exchange programs, okay? So you've got American students studying, you know, joking aside. But in this case, what excites me is that you don't have to learn about Kamalan or Indonesian puppetry. You know, you can learn whatever you have to in mathematicals and technical stuff. Cultural exchange is just as important. So you spend two years over there, you learn Indonesian culture, you learn how you got Indonesian friends.  And that's what excites, that's what makes it exciting. So it's not only cost-saving for us, okay?  So unless you sit there and replicate this program, you know, across Barbara Harvey. Barbara Harvey, a retired Foreign Service Officer. I wanted to ask where you get the faculty for your academies and what is the relationship between the faculty of the state universities in Indonesia? You know, in our university, we have a school of education. And so our teachers are trained ourselves and higher paid than other teachers. And in comparison, in comparing teachers from state universities, far cry or big difference, okay? And basically in the pay and the quality of the teachers. And like I say, if you look at our SDO program, okay, why were we able to improve the quality of schools so by so much? It's basically we retrain all the teachers that they have. Okay, and we actually go to the schools and sit there and say, you know, these teachers aren't gonna make the grade so far. And if they don't get fired, we just leave the school. We just abandon the program because it's not worth our time. You know, it doesn't matter what you pay me. Like I can't do the job. If we cannot get the principal to buy it, nothing's gonna happen. Okay, so you're talking about state schools where the regencies have to pay. And yeah, if you pay it, fine. But we're not just out to make the money. We have to sit there and show that we can improve the school. So it's a change in the curriculum or change in how it's taught. Training the teachers in new methods of teaching. And basically teaching them how to govern the school better, et cetera, et cetera, et cetera. Including PTAs or getting parents involved. And that to me is a big, big, big, big plus. A second part of your question though, I think. I heard from the university in Indonesia, those faculty are recruited from around the world. Truly world-class faculty. And so what we're doing as a partner, remember we're providing the first two years essentially a four-year degree program there. We are using a combination, dominated by faculty who are at the university, but also augmented by full-time faculty that we will rotate in and out from Lone Star, Texas. We are, because we're in Texas, Southern Association accreditation is very dear to us. And it's the value of our program in Indonesia, that accreditation. So we are required under that accreditation to have lead faculty on site there. So it's not a remote where we're just somewhere over here saying that we're doing this. This is a very active partnership where our faculty are intermingling from day one. And the beauty for us is that it gives us an opportunity. We've got a long waiting list of faculty who are saying, me, me, yeah, I wanna go, I wanna go. So they'll go in and they'll teach a semester or a year and rotate out and others will come back in. And so they'll work in partnership with the university faculty there. Thank you. My name is Dr. Prakash and I am the founder of Bridging Nations on the sixth floor here. And I am in the process of setting up a university between here and India. And it is going to be quite a different model. And I want to ask you, in my research, ran into a couple of different very important discoveries. How much of employers are involved in curriculum development? Because what I find in most of our higher education, I came to this country only for higher education, you know. And hardly there is any attention paid or to a very small extent. The curriculum has any influence on the corporations. Because ultimately I think the customer of the institution is employer and not the student. Student we think is like a product. So we really have to make sure they are well qualified. And I'm experimenting with blended learning. And like two days, two hours a week is asynchronous. And only one hour a week is asynchronous for a three credit course. So I would like to ask Indonesia, how much influence does the employers have in curriculum development? And what is the employability of the graduates when they come out of school? And what are the actual numbers? Like in India, those are very poor numbers. Like some statistics have only 20% are employable. So there is a big mismatch between the universities and the corporate world or let's say the employees. I would like to get some idea of how you do it there. I'm not an accommodation, but if you look at our kids, I don't have the same problem. You know, I'm not government. So I don't have to educate everybody. I can educate the best. So the output of my university, I believe, will always be employable because they are the best. So we take in the top 5% academically from middle schools, and then we sit there and put them through a very rigorous culling process, if you will, for only those kids that show leadership potential. So if I only take in 200 or if I hope to take in 1,000 a year, I mean, that is a very small number of graduates. Now we're also working with, if you look at our paper, we're also working at the cooperative. It also has clients, if you will, or partners, like Exxon and all these large corporations, Citibank and all. And we try to sit there and provide them with internships within these corporations.  So I'm not trying to shirk the question, but for us it's a different set of problems. I don't have to solve the country's education, that's for my minister of education to solve. Okay, I can only do my part to sit there and create a higher level of leaders of good moral fiber that can. Let me give you something. You know, when Ambassador Marshall went to one of my academies, okay, and then he had leaves, I had a press conference. And chicly one of the lady journalists at the end of the, and but what are you doing all of this? And I said, well, have you seen my kids? He said, yes. I said, what do you think of them? I said, they're top notch, right? He said, yes. I said, well, imagine this. I got 250 kids out there. I say, our parliament is made up of 500 individuals. Now imagine if our parliament was made up of 250 of my kids. Nothing to say. It's obvious. Again, I'm not trying to skirt the question, but you're talking about it from a national perspective. Exactly, exactly. Well, overall, we're in dire straits and that's why it's not solvable here. But I'm saying, we in Indonesia are short 700,000 university seats. So we cannot even take graduates from high school. Okay, now state universities, the tuition other than the other fees here and fees there. Okay, it's 2,500 to 3,500 a year. To me, it's garbage in, garbage out. What kind of education can you provide for 3,000 bucks a year? So we have serious problems.  Your job as a graduate out of an Indonesian university is 300 bucks a month. Okay, I pay my teachers more than that. Okay, I believe teachers should be paid at MBA rates. Okay, because that to me is the value of a teacher. Okay, but like I say, I don't have the nation's burden or problem on my shoulders. I can just do what I can do to take care of that small percentage and that's the program that we're after. Hey, bring it on. So to touch a little bit on the doctor's point that I was just talking, I know you don't wanna give statistics of how your graduate's good job, but is there a certain curriculum that you're driving them for? Or are you doing self-select?  Our ratio is imbalanced. Too many girls. 55% girls, wrong. We gotta change that. No, joking aside. When we first came out with scholarships before we ran our schools, we noticed that the graduates out of the local universities that we gave scholarships to could not get jobs. Okay, so we did a whole assessment of why that was. Okay, and if you look at our programs today, they're very tailored to what we care more about where are you going to be after you graduate? Okay, so right now it's like in engineering, okay, business. Okay, we don't do gamelan or drama classes. And the next step for us, and we do IT, media and IT. And the next step, those are the main three disciplines that we are at. The next one will be engineering and the one after that will be plantation agriculture. Okay, so all of these are the big demand jobs. Yes, sir. Pakputra, I actually just answered my question. I was wondering if you were trying to stream kids into certain specialties. But that's one question and I think you at least partially answered that. The other question I was gonna ask you is where Indonesia as everybody knows is a very far-flung place and some very remote places. How do you go about finding the top 5% that you mentioned?  Let me give you an example. Last year, okay, 8,000 applicants out of the top 5%. Out of the 8,000, 3,000 would have made our qualifications and leadership because we sit there and interview them one-on-one, et cetera. We put them in groups and you see where the alphas come from, et cetera. Okay, so 3,000 out of 8,000. So 8,000 were in the top 5%. 3,000 we could have accepted. We only have funding for 250, okay? So what a waste of intellectual capacity. Next is Angela and then Mike. Angela Dickey, Foreign Service Officer assigned to the U.S. Institute of Peace. If I'm not mistaken, Lone Star also accepts quite a few Vietnamese students. My experience with working in Vietnam showed that the program you're describing works very well for the Vietnamese students. I haven't lived in Indonesia, I still hope to, but in Vietnam, Vietnam has successfully become, I think, like the number eighth or ninth national sender of students to U.S. universities and a lot of those students are going to community colleges. But it's working less well when the colleges try to set up a program in Vietnam because there's no academic freedom in Vietnam and you can only teach certain subjects there like computer engineering and so forth, no humanities, no liberal arts. So my question would be, and I gather since Indonesia is now a democracy, there's no such issue for you and your accreditation is safe and you're fully satisfied on that count. Well, first we are operating in Vietnam. Have been now for about three years. The challenges are considerably greater for the reasons that you articulated and there are limits to what we can do there. We could never create a program like this there and when I say we're operating there, not as a degree granting institution like we will be doing Indonesia, but as a course specific, ESOL, very limited and that's more on the Vietnamese side than ours. We were quite willing to do more but it sounds like you know the challenges are there. We have found nothing like that in Indonesia. What we found in Indonesia is open arms, a can do attitude, lots more flexibility, lots more freedom. That's why we're actually moving to have a full blown accredited campus presence there. We could not do that in Vietnam. Mike Anderson. Next and then next. Thank you. Could you talk a bit about the regulatory environment in which you have to operate as a private sector university, issues like authority to grant degrees, work permits, taxes, repatriation of profits and also what reaction are you getting from the ministry which historically has been fairly tightly controlling over higher education in Indonesia? You gotta go back. That's changed. Before medical and education was the purview of non-profit organizations in Indonesia. And yeah, at the beginning there were challenges and why can we not do this? Well because the rule says that you got, and so going through the process it was a challenge but we got all the licensing that we need and the minister is very happy. We're the only new university license granted in the last three, four years. Because the ministry is looking at it and saying we got too many universities. Run by whatever, whatever. And so we want to consolidate them into fewer universities, et cetera, et cetera, et cetera.  Okay, so look at who our partners are, look at the programs that we're trying to do, et cetera, et cetera, et cetera and we got the full backing of the ministry on that. Okay, so we just got our license and we're off and running. Joe Rothstein from EIN News. Your materials argue for a diversion of money from DOD and aid to education and I'm wondering what you're doing to try to accomplish that and if I sense a bit of frustration here in your materials, how's it going? Sorry. It's a redirection, reallocation of, okay. If you sit there and look at all the programs, it's not really a frustration, but I don't see the US getting enough credit, PR credit. There's no PR win in anything that you do. Neither the World Bank, by the way. So it's why are we spending those tax dollars on doing things, okay? And I'm saying, you know, the State Department and I'm not American and the Department of Defense should just take one motto, okay, in America cares, okay?  I mean, that's where you're gonna win the hearts and minds. Okay, but I, you know, I question, to me, USAID is under the whims and fancies of Congress and its constituents. So you've got programs all over the place without having any focus on getting anything done right. The DOD, in my opinion, is very focused in what they do. They only have one thing that they want to accomplish and that's to win, okay? So my proposition here is why don't we do a study and I'm more than willing to sit there and subscribe to this funding of doing that study of, you know, what is the value of the United States bringing in one kid from an impoverished region whether it's from Yemen or Jordan or Iran even, okay? Of teaching them and educating them in the States. I mean, I was educated in the States and I might have philosophical differences with you but at least I understand where you're coming from. You understand where I'm coming from. We speak the same damn language and we can come to some compromise on a lot of things. So if you sit there and say, you know, the other thing from an economic standpoint, you know, after Sukarno fell, okay, the only country that we Indonesians could go to without a visa was Germany. And by the way, we could get a tertiary education in Germany at the same cost as a German citizen, which means zero, you just pay your living expenses. Okay, so now I come back and I'm running a company and I have all my engineers coming from Germany, okay? And every time there's a requisition for equipment or anything, it's always German equipment, okay? And that's where I'm sitting there and saying, you know, there is value of bringing and, you know, I really would like to know the result of a study, say what's the value of one foreign student in America as compared to one soldier you're gonna put in Germany or Okinawa, okay? But the cost is three to one.  Okay, now, which one is going to produce better results? It's a long run. I just think you mentioned one last question. Just a quick comment on this. First of all, I don't know what has happened to AID over the decades, but when I was in Indonesia in the 80s, virtually every chancellor of every university was a product of something called the Kentucky program, which wasn't just University of Kentucky, those big 10 schools, funded by AID. And it was a huge asset for the United States to have these people there. But by the 1980s, AID had decided, I don't think they've changed their minds, that higher education is an elite good and therefore we don't fund it from development funds, which is crazy in my view for all the reasons you've just said. The second comment I would make is if you wanna do something seriously along the lines of what you're talking about, I would at least explore the possibility of getting a line item in the defense budget for this purpose, because what you run into is anytime you talk about taking money from one department and putting it to another, not only run into more resistance in that department, but more importantly, you run into unbelievable resistance from the jurisdictional committees on the Hill. We tried, when I was at the Pentagon, to take some money for peacekeeping support from DOD and transfer it to state, and it was like Hill getting it done. It's easier to, it's probably, I mean the defense budget has environmental protect, a whole bunch of things that you would be surprised to find in there, this might be a good addition. All I'm doing is coming out with a proposition. No, I realize that. Okay, so what is it? One soldier to one foreign student train? Is it equal or not? If it is equal, then I'm telling you, it's, it's, you know. Thank you for this program. It's really been enlightening. Excuse my voice. It's temporarily departed. I'm Ronna Freiberg. I'm with LSI Associates, which is a consulting firm here in Washington. I'm former USIA and State Department. My question focuses on English language learning. You mentioned that all the courses are conducted at the academy in English. You also mentioned that Timmy and Anga both learned English in three and a half years. First question is, do most of your students come to the program with limited English ability? Zero. Okay, second question, how do you do it? I am, but with such a success rate. We put them and they have to speak on the campus to each other in English and we, you know, so it's immersed learning.  But basically, the reason that we have to teach in English is because, like I said earlier, this is a resource that this country has that nobody recognizes.  Okay, what's China gonna do? Send 200,000 students to Australia? That'll be 1% of the Australian population. You can't do it. This is the only country where, you know, and the Chinese have figured this out and they're sending you 200,000 of their students. You know, so what is it that they understand about higher education that we don't or that we're missing? Okay, I went to the East-West Center in Hawaii and they were saying, oh yeah, I'm sorry, we got delayed because we just had to finish off with the Chinese delegation. I said, oh yeah, and what do they want? They said, well, they told us that they want to send 10,000 teachers, and our job is to find places for those 10,000 teachers, but they will pay for it.  So that's what I'm saying, as an export commodity, okay, you are the only country that has this capacity. And so, you know, when we were looking at it and said, okay, where do I send my kids? It's only the U.S. that we can send them to. Okay, then we sit there and say, okay, I got 250 Timmy's coming in every year. I came for it, okay? Because I promised these kids, I say, you make it to the academy, I personally guarantee that they will get a tertiary education. Now, you do the math. Okay, I can't send them all to the U.S., okay? And that's when we came out and sat there and talked and said, okay, fine. Okay, now I've reduced the cost by half. Now, I just got to figure out how to reduce the cost even more. And that's when it comes to student loans, and that's why I'm very appreciative for the DCA facility that we have from the USAID, okay? But more can be done, okay? So what are the, you know, if you sit there and talk about student loans or the DCA, okay? So what guarantees do you need?  Okay, so that's where we are. I mean, if to me foreign policy has to be reformed, okay, you're spending so much money all across everything. And what impact do you get? Okay, I was in Ache. I'm not trying to de-write anybody from USAID or World Bank even, okay? I went to Ache and I saw, I went to visit some schools there. And I saw them, kindergarten, painting. Oh yeah, we have this program now for kindergarten to paint, you know, on a piece of paper. And it's for today. And it's sponsored by USAID. I didn't see one representative of USAID there. And how much money are you spending on that one day? Give them crayons? But what are you achieving? Okay, so to me it's a lot of waste. You know, I was joking with Ted on the plane on the way here. I said to him, it seems like all these institutions are getting old. Fulbright is 60 years old, I don't know, you know. And it's like us when we get old, the mind wants to do something, the body just came to it. So we had to start working on putting some bionics into our systems. With that, let me thank Mr. Semperna. Thank you, Dr. Carpenter, and ask you to join me in thanking me. Thank you very much. And thanks to Timmy and Anga. So please do have a chance to chat with Timmy and Anga while they're here. Thank you.


Do you know the Opus 88 Calorodemonstrator fountain pen? If you don't, I think you should know it. That's this week's staff pick. This is Sarah Beth with Pinchelay. Your favorite online retailer. I picked the Opus 88 Calorodemonstrator for several reasons. I love that they offer it in a flex nib now. I love that it's an eyedropper. You can get so much ink in there. And I love their vivid colors. Today I inked it with a new ink from Van Diemen's, the feline collection. Follow for more staff picks.