374. Uncovering the Secrets of the Brain: A Tour of the Human Behavior Lab

Welcome to episode 374 of the Brainy Business, Understanding the Psychology of why People Buy. Today's episode is an introduction to the Human Behavior Lab at Texas A and M University. Ready? Let's get started.

You are listening to the Brainy Business podcast, where we dig into the psychology of why people buy and help you incorporate behavioral economics into your business, making it more brain friendly. Now, here's your host. Host Melina Palmer.

Hello. Hello, everyone. My name is Melina Palmer and I want to welcome you to the Brainy Business Podcast. Today's episode is the very first interview I ever did on the show and one of the few I've done in person, which was on my first visit to the Human Behavior Lab at Texas A and M University, far before I started working there. This was the first time I met Jeff Pool and Dr. Marco Palma in person and a fun opportunity to look back on that first interview and conversation after I'd taken a tour of the lab and Dr. Palma told us all about the fantastic work and research they were doing there. This episode first aired way back in January of 2019. It really is amazing to think of how much has happened after this first visit, including, you know, starting to work together, doing some research, building out a full certificate in Applied Behavioral economics, writing three books and lots of speaking and just so many things, things that have happened. And so it's a lot of fun to listen back to this one today. So if you're wondering why I'm refreshing this one today, it's because my interview this coming Friday is with Eve Boudreau, who was one of the first students in that program at Texas A and M, the Certificate in Applied Behavioral Economics. And as we mentioned in that episode, I think he and Jeff were such early listeners and advocates of the podcast, it felt like a great opportunity to refresh this one when Eve is here Friday, we're talking about the intersection of AI and behavioral science and the work he does at Google Cloud. Pretty interesting stuff. So if you aren't already a subscriber of the Brainy Business podcast, now is a great time to do so. To ensure you never miss an episode including that one, don't forget there are links for everything, including past episodes, books and articles, all waiting for you in the show Notes for this episode, which are found within the app you're listening to and at the brainy business.com 374. All right, let's learn all about the Human Behavior Lab at Texas A and M University. Dr. Marco Palma, welcome to the Brainy Business Podcast.

Oh, thank you very much. I feel very honored to be here. I know that you have done a lot of work, and being the first ones in the show to us means a lot. So thank you very much for the opportunity.

Absolutely. And thank you for letting me come down to tour the lab. It's been so exciting for everybody that's listening. I have been spending the entire morning getting to walk around and test out the eyeglasses and the different sensors on the computer simulations and have lots of great conversations. So it's been a lot of fun to test. Oh, and your simulated little shopping area and everything. Can you tell us a little bit about what you've been working on here at the lab?

Absolutely. So, in the lab, we have these different type of sensors that allow us to really understand what are the behavioral underpinnings of human behavior, what drives our behavior, what drives our emotions. And in many cases, you've talked about this in several of your podcasts, but we were heavily influenced by the emotions or the context in which we make decisions. In some cases, we don't even realize what's motivating our decisions. And so these devices allow us to really understand all of the drivers that act behind as to why we make these decisions. And so some of the sensors included here look at the attention that we pay onto something, and it's interesting to see what we look at in terms of what drives our attention. But our attention can be driven because we like something or maybe because we dislike something, and so we want to be able to figure it out what's really driving that. So we have facial expression readers that allow us to really correlate the movement in the muscles of the face with emotions. So we not only know what you're looking at, but we also know how you are reacting emotionally at what you're looking at. And so that's particularly interesting. Then we also have the capabilities of looking directly into your brain so we know exactly what's driving that behavior and the underpinnings behind that. We can also measure stress signals like respiration rates, heart rates. And so it's not that we're actually trying to change everything we know about behavioral economics, but we're trying to better understand the motivations behind them and trying to, more importantly, measure them. Because if we're able to measure them, then we can more certainly understand what's driving our behavior on a daily basis or in our experiments.

Right. And it makes it more applicable, which is the whole point of the podcast and everything. Right?

Exactly. And this is why we also want to do some of the work we do in the lab, outside of the lab, in the wild, because we also want to make sure that we are able to implement some of the results that we have in the real world. And so these, what we call the virtual room is a room in which we have a mock small version of a grocery store in which people can come in and they can look at real products. We can also do testing with real products, whether it's an electronic product, maybe some food products, some packaging, labeling, you name it. And we also have the capabilities of collecting. Collecting this type of information using our devices in natural environments. So we can study people when they're making decisions inside a grocery store, or maybe when they're looking and walking through a garden center, or maybe purchasing a car or going into the bank and deciding which type of loan they're going to get or which retirement plan makes more sense. And so it's equally important for us to conduct these type of experiments in a controlled environment in the laboratory. But it's equally important that we do this in the real world so we understand how the complexity of the real world might affect behavior in different ways that we are going to be able to measure.

Right.

And some of the eyeglasses that I was able to put on, they had their little pack that you're able to make it very mobile, so I could go walk around wearing them. They're very lightweight. And I know that, I think you said you use imotion software for those. And then there's a different type of. The glasses that you use are from a different company.

That's right. So the tobii the glasses are from, the. The manufacturers of the glasses are tobii. That's the company that does the actual hardware. And in addition to that, we can also have an EEG machine, and we can put all of the required hardware inside a very small backpack. So you can walk around with an EEG device and the glasses. So we know exactly what's driving your behavior. Obviously, there's a lot of contaminants there, because when you're moving, your brain is act over the place because you have to exercise motor skills and you're watching different colors. And so your occipital lobe is going very active, looking at all the different type of stimuli that you're looking at. But we're getting to the point in which we're actually able to maybe by aggregation, filter out some of that noise that happens in the brain to look at your stimuli. And the company that makes all of this possible is imotions. They're one of the leaders globally in terms of making it easy to synchronize multiple devices at the same time. It's almost seamless. So we are actually able to collect eye tracking and facial expressions and EEG and any of these other type of sensors simultaneously by using this software. So that really makes it such that we don't have to spend a lot of time programming or doing computer programming as opposed to concentrating what we do best, which is implement experiments and understand human behavior.

Absolutely. And then you're able to know that this all happened at the exact same time while they were doing something, they were looking at this, they were doing this. And you said, I think that the. When you launched the lab recently and got all this new software and all this new equipment, all this great stuff, that you were able to take the time of an experiment down from about three months to just a couple of days.

Yes. And that's huge because now we're running a lot more experiments which is advanced the, the pace at which we're actually making discoveries. And we're able to partner with people outside of our discipline and be able to make sure that we use the lab as much as possible. So we don't days where no experiments are being run. Right.

And so if I was a company, what sort of things would I be able to bring to the lab if I wanted to have some research done? I know we were talking about menu optimization and being able to track if you're wearing the glasses or if you're using the computer simulation that you're able to it can see where you're looking and then actually more accurate on decision making behavior based on your brain versus some other studies that had been out for before.

Absolutely. So one of the things that we're really interested in is evaluating what actually happens with what people, what drives attention. So take for example what Milton Friedman used to say, that the road to hell was paved of very good intentions. And so think about a company putting together an ad and in this ad they make these beautiful scenes of the beach and then you have people running all over the place. And then maybe you might introduce a cute little cat or a cute little dog, if you're a dog person. And all of a sudden that's driving your attention and maybe something is lost in the middle in terms of what's the right message you want to convey to your customers. And you invested millions and millions of dollars to put this ad together. But then it turns out their attention of your potential customers is Elsewhere because they were paying too much attention to the doggy. And so what these devices can bring to the table in this type of setup is that we know exactly what's driving the attention. So we can design, for example, the advertising to use this and leverage to the fact that now we know people are paying attention to the doggy. So we can make a message appear from what we know in the position of the screen that people are paying attention to. So we can maximize what we want the customer to see. And that's just one example. But it also can be maybe in the design of the website to see if it's easy for people to find the information you want them to see. Do they get to it relatively quickly? It might be usage, type of experiments. It might be the way in which you lay out information in a menu or in a way in which you lay out the products in your store or how you interact with your customers. Where do you place the price, information, how do you place it? And all of these things that may seem very like very small details, but as I feel, as you have remarkably put it in all of your podcasts, they tend to have a profound effect in whether people actually see, value and purchase your products.

Right.

Everything matters. And often those little things that you think don't matter, like whether it's a dog next to them or a cat, can be a huge influencer in the behavior. I really. I like that suggestion. I was thinking of. Maybe you make the ad, you run it, you see, okay, the dog is too distracting. Let's get rid of the dog. We're going to just. Now we're not going to have any animal. But I like what you were suggesting. Maybe you move the messaging there next to the dog, or there are different opportunities, and then you can retest and see, okay, now are people actually able to understand the message we're trying to get across? So if you're a financial institution and you're trying to do something about credit cards for vacations, and then people just get totally focused on this dog and they think, oh, man, I need to go buy dog food later. That's maybe not the best ad for you. But the credit card for vacations and then you get rid of the dog, now it's something that works remarkably better, and you're able to test, you know, side by side with different people to see which one's going to work better and help with that buying behavior.

Right?

Absolutely. One of the things I'm very guilty of is that when you've been looking at something For a long time, you become blind to the true message that is being conveyed. And so, for example, you're working on something and there might be something that you're not seeing because you've been working on it too hard. And I think we're all guilty of that. And sometimes, and sometimes we, we miss some of these things because we're so used to seeing them. And one of the things that we're guilty of is complexity in the sense that maybe the message is too complex, there's too much information. And how we. There's always too much, there's always right, too much overcrowding. And so how do we ensure that the message we want to put across is actually being perceived as simple as possible and communicating the benefits that we want our clients to, to see?

Absol. And that's where, like I always say on the podcast and with my clients, you know what, if you can only do one thing, if this is only going to do one thing, what's the one thing that you want them to do? And then we'd be able to test and see, are they doing that one thing or are they getting hung up somewhere else doing something different? I know you're working on optimization, you're talking about menus. So what's some of the work you're able to do with menu optimization?

So in terms of the menu, we use some of the concepts that you have, have been talking about. In terms of anchoring and such, we actually place very little, very salient incentives in the sense that you want to be able to maximize some of the benefits and minimize some of the cost. And the costs are not necessarily just in terms of the price, but how do you acquire information about what you're going to purchase? And so take for example, the concept of anchoring that you very much talk about in your podcast. So if you see, for example, a menu in a fancy restaurant or maybe a menu in a wine tasting setting, and you see that the first price you actually look at, it's about $100 bottle of wine. So you're immediately anchoring into that sort of price. And then when you go choose, you're not necessarily looking for that particular item, but then you anchor in a high price. And then this is how you can actually maybe try to move some of the products that might be in the range that you want them to be. But these concepts are very helpful because then you use some of the saliency. And so all of the work that we've done with eye tracking shows that because we live in Western society. And we read from left to right, up and down. This is the way in which people actually search for information in a computer screen, in a menu, and in any sort of information that they see. So using this information, we know that people are going to be doing the searching of what they are going to buy, potentially in a Z manner. And so you use this construction to understand how people are going to go through and sort of like guide them through what you want them to see in the menu.

Well, that makes sense. So I sat down and did your sort of eye calibration test and then ran through a simulation which they are going to send me the video and I can post it up. I'll put it on social media where you can find me as the brainy biz. And also I'll put it up on a blog post, things like that, @the brainybusiness.com. but I was scanning, watching a dot move across the screen. And then I thought it was interesting that then it was the Z pattern to go over here and move back. But then when you're watching my eye tracking movements and facial expressions and everything that's happening in the video, you have this calibration of how quickly my eye typically would move and follow something and how it moves around the screen.

Right.

I think it's fascinating to see, number one, how quickly our eye movements are. I don't know that we realize as human beings the complexity of how we actually look at stuff. But when you start looking at for the first time how you gaze at a computer screen or anything else, it's rapid movements that we digest a lot of information in a very, very fast manner. So it's very fast, very random, it seems. But actually there's some logic to it. Like you said, there are some patterns that we can look at.

What I thought was fascinating, so I talked about this on the episode, the first in the series on the senses about sight. And I was talking about how frequently our eyes move and that it's two to three times per second. And it was in one of the simulations on the computer that I was looking at, it was two different packages of chicken. And you can see there that when I'm looking at the one it said in 2.8 seconds, my focus came back to this package of chicken, the thing they were looking at, studying 10 times. So it came back 10 times in 2.8 seconds, which means it had to have time to leave, come back, leave, come back. So the software is so quick to be able to catch those little tiny movements and Then it's also tracking. Am I smiling while my eyes are darting around? Am I making a disgust face, which I know I've talked about on the show? Am I backing up? Am I moving forward? Are my pupils dilating? And I was just calibrated for the sitting in front of the screen version versus I didn't have the EEG on and any of the other body sensory stuff going on. And there's so much data that's there. It's just amazing.

Absolutely. These devices can track up to 600 data points per second. And so it's amazing the wealth and amount of information that we collect from it. And talking about data, making sense of this data, that's the future, you know, because in a control environment, we have some sort of behavior, but when you put it together with all the sensors collected simultaneously, we can really make inferences about behavior. You did not have the eeg, but we actually have a recent paper in which we do not need to ask a person whether they like something or not, whether they're going to buy it or not. We actually place an incentivized task in which people receive $5 and they could decide to keep the $5 or buy something. And it turns out that we can very accurately, with almost 84% accurate, predict whether the person is going to buy the product or not just by looking at the activity in the brain, particularly in the frontal lobe. So we're getting to the point that we do not necessarily need to ask people what they do. And we can do, because if I know what triggers you, can you picture yourself going into a retail setting and then watching some of the stuff that actually sets you in a good mood, and you can alter the mood of a particular individual and show them the products that might actually be customizable to them based on what they actually pay attention to, and show them the promotions that go along with those products to see that they can save some money. So it maximizes the opportunity for firms to actually make the most profits.

Right. And that's where you can tell, if you're listening to this, the complexity and all the concepts that have to come into play when you're looking at these. So in that case, you've got priming and anchoring and adjustment and colors and visual and sounds and music and all these other things that are going to be happening. And so when you're walking through and you're having to deal with it. I know. We were also talking about really being aware of something that you might inadvertently trigger. We were talking about a Different study where you really didn't want reciprocity to come into play. So what is it that you have to do to put these sort of control measures when you're building out the experiment? And that's something that's just so helpful to have a research lab like you have here at Texas A and M that you're able to think about all those concepts. You really know everything that's there. We're able to communicate what are the goals, what do we really want to know and what do we want to make sure isn't noise that's going to mess up and sort of dilute what we're trying to figure out out this.

Is the foundation of what we do in the lab comes back to establishing causality. Because the fact that two things move together doesn't necessarily mean that one causes the other. And so in the introduction of their book the Y Axis, which actually I highly recommend, John List and Uri Genesi set up this example. And they say, oh, there's this firm that hired us to look at the sales revenue and the number of promotions that we make. And they find actually they have some data to back up that the higher the number of promotions, the higher the sales. And so wait a minute, but this is kind of correlational. How do we know that it is just the ads that are changing the sales? And so it turns out in their story that these guys are putting together these ads around Christmas time, which happens to be that sales are up. And so you cannot robustly establish the fact that it is the promotions that are driving sales. And then they say, wait a minute. So we ran these data and show that if we put on the Y axis the number of drownings and we put on the X axis the number of ice cream purchases or ice cream sales, they're highly correlated. They're directly correlated. And so should we infer by that that if you eat more ice cream, you have a high probability of drowning? Probably not. And probably what's happened there in the middle is that in the summer we swim more because it's warm, and in the summer we also eat more ice cream. So these things move together, but it doesn't necessarily mean that one causes the other.

Right?

So when it comes to the laboratory and laboratory experiments, we're really, really very careful about controlling for every particular detail. So we can establish that causality. And this is important because once we understand what drives something, then we can go back into the wild, into the real world and observe how this type of behavior happens when there are many Other variables interacting at the same time.

Right. And then you can send somebody out with the backpack and you only need a couple of them because you're testing for something very specific when it's in that more expensive sort of test situation. Exactly right.

Exactly.

Awesome. So what are some of the concepts that you're most interested in studying in 2019?

So in 2019, I think we're interested in looking at self control. And this is a very special time of the year. You already alluded to some pointers in your self control and goal setting podcast, which I really, really liked. It was very eloquently put. And I think that if people are listening to you, the window of opportunity is actually closing down because in about a few days, by February 1st, 80% of Americans are going to give up in their goals. And in part, I think it's because we set up the go to be too high and we want to accomplish too much. So I always go back to your comment there about if you can do one thing and one thing only, what can you do better that is sustainable over time? So perhaps it is that, oh, wait a minute, I'm going to change my diet, but I'm going to go from eating 12 or 16 ounce steak to eating just salad. Wait a minute, that's too drastic. There has to be a better way. So what if I just give up one soda can per day and a couple of pieces of bread? So now we're talking about close to about 310 calories per day that you're saving. And maybe that's something that is sustainable over the years. So think about how can you simplify these type of concepts. And so we're going to continue to do work in the area of self control. That's something that we have been very much interested in and we're going to continue with that. We're also going to do some work related to cheating and what drives the behavior for people to cheat and lie for themselves when they stand to gain something, and cheating and lying on behalf of others for the gain or the losses of other people. And that's something that is very interesting to us because it has a lot of potential applications. If you think about there are trillions and trillions of dollars that are lost in the economy every year because people misreporting taxes, for example. And there are many other ways in which people perceive these big corporations or the government to be not real people. So if you go out there and you have your pro car hard and you're spending some money in your Business trip. And well, if it's a large corporation, you might not think that this is not a real person. Right. So it's okay if I eat a little bit more than I should or you misuse some of these resources. And when it comes to large corporations or the government, there's this perception. So you want to understand what really drives that sort of behavior. So the other thing we want to be able to do is looking at what drives people to become more generous. And we know that there are some confounding factors. Some people are generous because they're genuinely generous, but we also are driven by the behavior that makes us feel good. So my give to others because it makes me feel good. And in that particular case, I am gaining more than actually just helping others. But I'm personally feeling that warm glow of feeling good with myself because I know I'm helping other people, whether it's with money or with my time or my resources. And so we want to be able to maybe see what are some of the drivers and to see if we can disentangle this type of situation. In particular, we're looking at fundraising campaigns that look into promote given by matching funds versus given a non restricted gift, just like a seed money and what the difference is. There's a big debate in the literature as to which of the two schemes works better. I can tell you that one of them, through the matching gift, might reduce a little bit of the unwillingness of people to give. So the free writing issue, but then there are also some other complexities. And so we're working to try to see if we can really nail down what are the mechanisms behind this type of behavior. And finally, I hope to do more stuff related to competition. And we want to know what drives competition. We want to make sure that we have participation in the labor force by individuals who are the high ability individuals. And in many cases we see a lot of entrepreneurs, a lot of people that might not necessarily have that competitive edge. And if we look at the number of female CEOs in Fortune 500 companies, for example, there's only 6% representation and I think that's too low. If you look at the number of women actually in STEM fields, it's much, much lower compared to male counterparts. So we're trying to see if there are ways in which we can make women become more competitive, particularly high ability women, and select into these type of fields and try to go after dispositions. And one of the approaches to doing this is by changing the environment, changing the way in which people are Wired to think about women. And so that, for example, when you see somebody playing baseball, a girl playing baseball, you actually see them as a girl. And then you might actually hear phrases like, oh, wait a minute, you're kicking like a girl. And you start to see some ads that are designed to change that way, which we look at girls. Well, that's very costly. But more importantly, it takes a lot of time to rewire everybody around. And so we're asking the question, are there any other ways, easier ways that we can make promote competition, particularly among women? So, so take for example, the role of CEOs that we were talking about. Only 6% of women are CEOs.

Way too low.

That's way too low. But what happens is that this position label has traditionally been something that is male dominated. And so there is some sort of a glass ceiling, if you may, or there's some sort of a barrier that. Wait a minute, when you think about a CEO, what comes to mind is this middle age male and it has certain characteristics. So what if we actually just change some of the names? So we're studying a level playing field and we change the titles and we're seeing that even very subtle changes like that actually promote the participation of women. So there might be something that we can do about it without necessarily having to transform the whole society and make just small changes and see. Very nice, nice. Very good results.

Awesome. Well, I am definitely looking forward to all the research that's going to be coming out of A M. And I think actually, interestingly enough, this episode is potentially going to be coming out on February 1st. As far as that first where you're talking about with the resolutions episode. So if you're listening now and you were maybe about to give up on a resolution or you had already, you can revisit. I'm pretty sure it's episode 29 on resolutions. And you can revive, revamp and maybe read some of this research which I'll have linked in the show notes from Dr. Palma and the team here at the Human Behavior Lab at Texas A and M University. So any other important things that you think people should know or stuff you want to say?

Absolutely. I think that the last thing I want to mention is all the work we're actually doing with food, because that's something that, let's face it, we do it when we actually at least three times a day, most of us. And the way in which we interact with food is paramount to everything we do. So we have to have a right balance into what we actually eat. And how that translates into our nutrition and our propensity for chronic diseases. And we are going through a very strong period in which there is an obesity epidemic in the country, and part of it has been driven by access to food. And so we're trying to find ways in which we can help people make better choices without prescribing what they need to do. This is a very important feature of what we do. We're not telling people what they should do. We're just trying to help accomplish the goals that they want to set for themselves. And so it's interesting to understand, though, how our brains are wired in terms of what we really like and what can we do to change that. So take, for example, sugar. Sugar. It's something that has been ingrained in our brains since before we were a complex society. So we were hunters and gatherers. Our survival depended on us finding sugar, because finding sugar meant that we were able to store calories that would be the difference between living and dying. And so our brains rewarded us when we found these sugary foods, almost in the exact same way as it reward people when they consume drugs. So if you look at the patterns of brain activation when individuals are ingesting cocaine, it's the exact same pattern as when individuals actually consume sugar, because our survival depended on that. That's how important sugar was for us. So what happened was that the agricultural system started to increase their efficiency, but guess what? Our brains did not follow through at the same pace as the agricultural system. And so our brains are still in the stage in which we are being rewarded for consuming sugar. But now we don't really need to spend six to eight hours a day trying to gather a little fruit to survive. And so our brains haven't kept up at the same pace in terms of the amount of time it requires to change the brain structure, as it did with the efficiency of the agricultural system. So I think that in this case, what I'm trying to say is that we owe it to ourselves and to society to try to understand how we can better overcome this type of phenomenons, recognizing that it's not anybody's fault. I mean, it's your brain that is driving you to consume these type of food. So is there anything that we can do to help individuals and help ourselves go beyond February 1st when it comes to these type of goals?

Absolutely. And since the A in A and M is for agriculture course, food is important for you here at Texas A and M. So. Well, thank you so much for being on the podcast and for letting me Come down and tour. Like I said, there's gonna be videos available and all sorts of great stuff on the podcast. And if you wanted to get in touch with myself and Dr. Palma, we'll make sure to have links in the show notes for everybody there as well.

Thank you very much for the opportunity of being here. I know that this is a very special treat for us and being at A and M, we're one of the land grant universities, which essentially means that it's equally important for us to have the formal education, it's equally important for us to do the research discovery. But at the same time, it's equally important that we place this research in the hands of people who are doing the discovery, who are actually the entrepreneurs that are creating the businesses. And in that you're a pioneer, you're doing some beautiful work. You're taking otherwise dry material that maybe nobody will be interested in reading and making accessible to people so that they actually can implement what's going on in the research and put it into a plot. Everyday work that benefits humanity. So I admire you because of that and thank you for the opportunity of being in your podcast.

Absolutely. Thank you so much. Appreciate it and have a great day everybody.

So what got your brain buzzing as you learned about the Human behavior lab with Dr. Marco Palma today? For me, this has mostly been a fun walk down memory lane and it's been nice to take some time to reflect on how different different things were back when I did this tour in January of 2019. This aired originally as episode 33 of the podcast, coming out as a refresh in episode 374 more than five years later. It really is amazing to reflect on how much has happened since then. I've been down to the lab multiple times now and we launched a certificate in Applied Behavioral Economics. As I mentioned at the beginning of the episode episode, that program is fully virtual and open to students all around the world. I've taught and met students from everywhere, Peru, Germany, New Zealand, Canada and Poland to name a few, along with of course, the many U.S. participants. And you're welcome to apply links for you in the show notes. And if you are interested in being our newest student, of course there are links for you in the show notes to learn more. We've done some research together and this connection gave me opportunities to do projects for some great companies. I've interviewed other interesting faculty from A and M on the show. Those episodes are linked in the show notes as well. I got to work with imotions on a project that was featured in my first book, what your customer wants and can't tell you and interviewed them on the show to discuss what we learned. Oh, and I wrote and published two other books. I've done guest lectures in person at A M as well as with several other programs and courses around around the world that have my books as required reading and sometimes the main textbook for their classes. How cool is that? There are countless factors that went into these years and the amazing things that have happened because of my connection to Dr. Palma, Jeff and the whole Human Behavior Lab and Texas A and M. In case I don't say it enough, thank you. Thank you for everything and to you listening. I often use this as an example of the value of following up and putting constant kindness in the world. This all happened because Jeff wrote a five star review on the podcast which I did a shout out for and we connected and had an email exchange that led to a meeting and a visit and the rest, as they say, is history. I talk more about that story in episode 100 of the podcast which is about my top insight from a hundred episodes on the show. It is of course linked for you in the show notes along with other related past episodes, most of which which of course weren't out when this first aired back in early 2019. You'll also find links to related books and articles and information on the certificate program in Applied Behavioral Economics. I hope to see you in an upcoming class soon. It's all waiting for you in the app you're listening to and@the brainybusiness.com 374 and just like that, episode 374 on the Human Behavior Lab with Dr. Mark Marco Palma is done. Join me Friday for a brand new episode with Eve Boudreau to discuss AI and Behavioral science. It's going to be a lot of fun. You don't want to miss it. Until then, thanks again for listening and learning with me and remember to be thoughtful.

Thank you for listening to the Brainy Business Podcast. Melina offers offers virtual strategy sessions, workshops and other services to help businesses be more brain friendly. For more free resources, visit thebrainybusiness.com.