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TED, Laura Schulz: The surprisingly logical minds of babies (1)

Laura Schulz: The surprisingly logical minds of babies (1)

0:11Mark Twain summed up what I take to be one of the fundamental problems of cognitive science with a single witticism. He said, "There's something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment in fact. " (Laughter)

0:31Twain meant it as a joke, of course, but he's right: There's something fascinating about science. From a few bones, we infer the existence of dinosaurs. From spectral lines, the composition of nebulae. From fruit flies, the mechanisms of heredity, and from reconstructed images of blood flowing through the brain, or in my case, from the behavior of very young children, we try to say something about the fundamental mechanisms of human cognition. In particular, in my lab in the Department of Brain and Cognitive Sciences at MIT, I have spent the past decade trying to understand the mystery of how children learn so much from so little so quickly. Because, it turns out that the fascinating thing about science is also a fascinating thing about children, which, to put a gentler spin on Mark Twain, is precisely their ability to draw rich, abstract inferences rapidly and accurately from sparse, noisy data. I'm going to give you just two examples today One is about a problem of generalization, and the other is about a problem of causal reasoning. And although I'm going to talk about work in my lab, this work is inspired by and indebted to a field. I'm grateful to mentors, colleagues, and collaborators around the world.

1:58Let me start with the problem of generalization. Generalizing from small samples of data is the bread and butter of science. We poll a tiny fraction of the electorate and we predict the outcome of national elections. We see how a handful of patients responds to treatment in a clinical trial, and we bring drugs to a national market. But this only works if our sample is randomly drawn from the population. If our sample is cherry-picked in some way -- say, we poll only urban voters, or say, in our clinical trials for treatments for heart disease, we include only men -- the results may not generalize to the broader population.

2:37So scientists care whether evidence is randomly sampled or not, but what does that have to do with babies? Well, babies have to generalize from small samples of data all the time They see a few rubber ducks and learn that they float, or a few balls and learn that they bounce. And they develop expectations about ducks and balls that they're going to extend to rubber ducks and balls for the rest of their lives.And the kinds of generalizations babies have to make about ducks and balls they have to make about almost everything: shoes and ships and ceiling wax and cabbages and kings.

3:13So do babies care whether the tiny bit of evidence they see is plausibly representative of a larger population? Let's find out. I'm going to show you two movies, one from each of two conditions of an experiment, and because you're going to see just two movies, you're going to see just two babies, and any two babies differ from each other in innumerable ways. But these babies, of course, here stand in for groups of babies, and the differences you're going to see represent average group differences in babies' behavior across conditions. In each movie, you're going to see a baby doing maybe just exactly what you might expect a baby to do, and we can hardly make babies more magical than they already are. But to my mind the magical thing, and what I want you to pay attention to, is the contrast between these two conditions, because the only thing that differs between these two movies is the statistical evidence the babies are going to observe. We're going to show babies a box of blue and yellow balls, and my then-graduate student, now colleague at Stanford, Hyowon Gweon, is going to pull three blue balls in a row out of this box, and when she pulls those balls out, she's going to squeeze them, and the balls are going to squeak. And if you're a baby, that's like a TED Talk It doesn't get better than that. (Laughter) But the important point is it's really easy to pull three blue balls in a row out of a box of mostly blue balls. You could do that with your eyes closed. It's plausibly a random sample from this population. And if you can reach into a box at random and pull out things that squeak, then maybe everything in the box squeaks.So maybe babies should expect those yellow balls to squeak as well. Now, those yellow balls have funny sticks on the end, so babies could do other things with them if they wanted to. They could pound them or whack them. But let's see what the baby does.

5:11(Video) Hyowon Gweon: See this? (Ball squeaks) Did you see that? (Ball squeaks) Cool See this one? (Ball squeaks) Wow.

5:32Laura Schulz: Told you. (Laughs)

5:34(Video) HG: See this one? (Ball squeaks) Hey Clara, this one's for you. You can go ahead and play.

(Laughter)

5:55LS: I don't even have to talk, right? All right, it's nice that babies will generalize properties of blue balls to yellow balls, and it's impressive that babies can learn from imitating us, but we've known those things about babies for a very long time. The really interesting question is what happens when we show babies exactly the same thing, and we can ensure it's exactly the same because we have a secret compartment and we actually pull the balls from there, but this time, all we change is the apparent population from which that evidence was drawn. This time, we're going to show babies three blue balls pulled out of a box of mostly yellow balls, and guess what? You [probably won't] randomly draw three blue balls in a row out of a box of mostly yellow balls. That is not plausibly randomly sampled evidence. That evidence suggests that maybe Hyowon was deliberately sampling the blue balls. Maybe there's something special about the blue balls Maybe only the blue balls squeak. Let's see what the baby does.

6:56(Video) HG: See this? (Ball squeaks) See this toy? (Ball squeaks) Oh, that was cool. See? (Ball squeaks)Now this one's for you to play. You can go ahead and play. 7:17(Fussing) (Laughter) 7:25LS: So you just saw two 15-month-old babies do entirely different things based only on the probability of the sample they observed. Let me show you the experimental results. On the vertical axis, you'll see the percentage of babies who squeezed the ball in each condition, and as you'll see, babies are much more likely to generalize the evidence when it's plausibly representative of the population than when the evidence is clearly cherry-picked. And this leads to a fun prediction: Suppose you pulled just one blue ball out of the mostly yellow box. You [probably won't] pull three blue balls in a row at random out of a yellow box, but you could randomly sample just one blue ball That's not an improbable sample. And if you could reach into a box at random and pull out something that squeaks, maybe everything in the box squeaks. So even though babies are going to see much less evidence for squeaking, and have many fewer actions to imitate in this one ball condition than in the condition you just saw, we predicted that babies themselves would squeeze more, and that's exactly what we found. So 15-month-old babies, in this respect, like scientists, care whether evidence is randomly sampled or not, and they use this to develop expectations about the world: what squeaks and what doesn't, what to explore and what to ignore.

8:49Let me show you another example now, this time about a problem of causal reasoning. And it starts with a problem of confounded evidence that all of us have, which is that we are part of the world. And this might not seem like a problem to you, but like most problems, it's only a problem when things go wrong.Take this baby, for instance. Things are going wrong for him. He would like to make this toy go, and he can't I'll show you a few-second clip. And there's two possibilities, broadly: Maybe he's doing something wrong, or maybe there's something wrong with the toy. So in this next experiment, we're going to give babies just a tiny bit of statistical data supporting one hypothesis over the other, and we're going to see if babies can use that to make different decisions about what to do.

9:42Here's the setup. Hyowon is going to try to make the toy go and succeed. I am then going to try twice and fail both times, and then Hyowon is going to try again and succeed, and this roughly sums up my relationship to my graduate students in technology across the board. But the important point here is it provides a little bit of evidence that the problem isn't with the toy, it's with the person. Some people can make this toy go, and some can't. Now, when the baby gets the toy, he's going to have a choice. His mom is right there, so he can go ahead and hand off the toy and change the person, but there's also going to be another toy at the end of that cloth, and he can pull the cloth towards him and change the toy So let's see what the baby does.

10:29(Video) HG: Two, three. Go! (Music) LS: One, two, three, go! Arthur, I'm going to try again. One, two, three, go! YG: Arthur, let me try again, okay? One, two, three, go! (Music) Look at that. Remember these toys? See these toys? Yeah, I'm going to put this one over here, and I'm going to give this one to you.You can go ahead and play. LS: Okay, Laura, but of course, babies love their mommies. Of course babies give toys to their mommies when they can't make them work. So again, the really important question is what happens when we change the statistical data ever so slightly. This time, babies are going to see the toy work and fail in exactly the same order, but we're changing the distribution of evidence This time, Hyowon is going to succeed once and fail once, and so am I. And this suggests it doesn't matter who tries this toy, the toy is broken. It doesn't work all the time. Again, the baby's going to have a choice. Her mom is right next to her, so she can change the person, and there's going to be another toy at the end of the cloth. Let's watch what she does.

12:06(Video) HG: Two, three, go! (Music) Let me try one more time. One, two, three, go! Hmm.

12:18LS: Let me try, Clara. One, two, three, go! Hmm, let me try again One, two, three, go! (Music) HG: I'm going to put this one over here, and I'm going to give this one to you. You can go ahead and play. (Applause) 13:03LS: Let me show you the experimental results. On the vertical axis, you'll see the distribution of children's choices in each condition, and you'll see that the distribution of the choices children make depends on the evidence they observe. So in the second year of life, babies can use a tiny bit of statistical data to decide between two fundamentally different strategies for acting in the world: asking for help and exploring. I've just shown you two laboratory experiments out of literally hundreds in the field that make similar points, because the really critical point is that children's ability to make rich inferences from sparse data underlies all the species-specific cultural learning that we do. Children learn about new tools from just a few examples. They learn new causal relationships from just a few examples. They even learn new words, in this case in American Sign Language.

14:07I want to close with just two points

Laura Schulz: The surprisingly logical minds of babies (1) Laura Schulz: Der erstaunlich logische Verstand von Babys (1) Laura Schulz: La sorprendente mente lógica de los bebés (1) Laura Schulz : L'esprit étonnamment logique des bébés (1) ローラ・シュルツ赤ちゃんの驚くべき論理的思考 (1) Laura Schulz: Zaskakująco logiczne umysły niemowląt (1) Laura Schulz: As mentes surpreendentemente lógicas dos bebés (1) Лаура Шульц: Удивительно логичный ум младенцев (1) 劳拉·舒尔茨：婴儿惊人的逻辑思维（1） 勞拉舒茲：嬰兒驚人的邏輯思維（1）

0:11Mark Twain summed up what I take to be one of the fundamental problems of cognitive science with a single witticism. 0: 11Mark Twain resumió lo que considero que es uno de los problemas fundamentales de la ciencia cognitiva con un solo ingenio. 0: 11Mark Twain a résumé ce que je considère comme l'un des problèmes fondamentaux de la science cognitive avec un seul mot d'esprit. He said, "There's something fascinating about science. Dijo: "Hay algo fascinante en la ciencia. One gets such wholesale returns of conjecture out of such a trifling investment in fact. Uno obtiene tales retornos de conjeturas de una inversión tan insignificante de hecho. On obtient en fait de si gros retours de conjectures sur un investissement aussi insignifiant. Obtém-se grandes retornos de conjecturas com um investimento de fato tão insignificante. Такая пустяковая инвестиция в реальность дает такую оптовую отдачу от предположений. " (Laughter)

0:31Twain meant it as a joke, of course, but he's right: There's something fascinating about science. 0: 31Tain lo entendió como una broma, por supuesto, pero tiene razón: hay algo fascinante en la ciencia. From a few bones, we infer the existence of dinosaurs. De unos pocos huesos, inferimos la existencia de dinosuars. From spectral lines, the composition of nebulae. Desde las líneas espectrales, la composición de las nebulosas. A partir des raies spectrales, la composition des nébuleuses. Van spectraallijnen, de samenstelling van nevels. From fruit flies, the mechanisms of heredity, and from reconstructed images of blood flowing through the brain, or in my case, from the behavior of very young children, we try to say something about the fundamental mechanisms of human cognition. Desde las moscas de la fruta, los mecanismos de la herencia y desde las imágenes reconstruidas de la sangre que fluye a través del cerebro, o en mi caso, desde el comportamiento de niños muy pequeños, tratamos de decir algo sobre los mecanismos fundamentales de la cognición humana. In particular, in my lab in the Department of Brain and Cognitive Sciences at MIT, I have spent the past decade trying to understand the mystery of how children learn so much from so little so quickly. Because, it turns out that the fascinating thing about science is also a fascinating thing about children, which, to put a gentler spin on Mark Twain, is precisely their ability to draw rich, abstract inferences rapidly and accurately from sparse, noisy data. I'm going to give you just two examples today One is about a problem of generalization, and the other is about a problem of causal reasoning. And although I'm going to talk about work in my lab, this work is inspired by and indebted to a field. Et même si je vais parler du travail dans mon laboratoire, ce travail est inspiré et redevable à un domaine. I'm grateful to mentors, colleagues, and collaborators around the world.

1:58Let me start with the problem of generalization. Generalizing from small samples of data is the bread and butter of science. We poll a tiny fraction of the electorate and we predict the outcome of national elections. We see how a handful of patients responds to treatment in a clinical trial, and we bring drugs to a national market. But this only works if our sample is randomly drawn from the population. If our sample is cherry-picked in some way -- say, we poll only urban voters, or say, in our clinical trials for treatments for heart disease, we include only men -- the results may not generalize to the broader population.

2:37So scientists care whether evidence is randomly sampled or not, but what does that have to do with babies? Well, babies have to generalize from small samples of data all the time They see a few rubber ducks and learn that they float, or a few balls and learn that they bounce. And they develop expectations about ducks and balls that they're going to extend to rubber ducks and balls for the rest of their lives.And the kinds of generalizations babies have to make about ducks and balls they have to make about almost everything: shoes and ships and ceiling wax and cabbages and kings. Et ils développent des attentes à propos des canards et des balles qu'ils vont étendre aux canards en caoutchouc et aux balles pour le reste de leur vie.Et le genre de généralisations que les bébés doivent faire sur les canards et les balles qu'ils doivent faire à propos de presque tout: chaussures et les navires et la cire de plafond et les choux et les rois. И у них формируются ожидания относительно уток и мячей, которые они будут распространять на резиновых уток и мячи до конца своей жизни. И те обобщения, которые малышам приходится делать относительно уток и мячей, им приходится делать практически обо всем: о туфлях и кораблях, о потолочном воске, о капусте и королях.

3:13So do babies care whether the tiny bit of evidence they see is plausibly representative of a larger population? Let's find out. I'm going to show you two movies, one from each of two conditions of an experiment, and because you're going to see just two movies, you're going to see just two babies, and any two babies differ from each other in innumerable ways. But these babies, of course, here stand in for groups of babies, and the differences you're going to see represent average group differences in babies' behavior across conditions. In each movie, you're going to see a baby doing maybe just exactly what you might expect a baby to do, and we can hardly make babies more magical than they already are. But to my mind the magical thing, and what I want you to pay attention to, is the contrast between these two conditions, because the only thing that differs between these two movies is the statistical evidence the babies are going to observe. We're going to show babies a box of blue and yellow balls, and my then-graduate student, now colleague at Stanford, Hyowon Gweon, is going to pull three blue balls in a row out of this box, and when she pulls those balls out, she's going to squeeze them, and the balls are going to squeak. Nous allons montrer aux bébés une boîte de balles bleues et jaunes, et mon étudiant alors diplômé, maintenant collègue à Stanford, Hyowon Gweon, va tirer trois balles bleues d'affilée hors de cette boîte, et quand elle les tire balles, elle va les presser, et les balles vont grincer. And if you're a baby, that's like a TED Talk It doesn't get better than that. А если вы ребенок, то это как TED Talk - лучше не бывает. (Laughter) But the important point is it's really easy to pull three blue balls in a row out of a box of mostly blue balls. You could do that with your eyes closed. It's plausibly a random sample from this population. Вероятно, это случайная выборка из этой популяции. And if you can reach into a box at random and pull out things that squeak, then maybe everything in the box squeaks.So maybe babies should expect those yellow balls to squeak as well. Now, those yellow balls have funny sticks on the end, so babies could do other things with them if they wanted to. They could pound them or whack them. Ils pourraient les pilonner ou les frapper. Их можно было колотить или бить. But let's see what the baby does.

5:11(Video) Hyowon Gweon: See this? (Ball squeaks) Did you see that? (Ball squeaks) Cool See this one? (Ball squeaks) Wow.

5:32Laura Schulz: Told you. (Laughs)

5:34(Video) HG: See this one? (Ball squeaks) Hey Clara, this one's for you. You can go ahead and play.

(Laughter)

5:55LS: I don't even have to talk, right? All right, it's nice that babies will generalize properties of blue balls to yellow balls, and it's impressive that babies can learn from imitating us, but we've known those things about babies for a very long time. The really interesting question is what happens when we show babies exactly the same thing, and we can ensure it's exactly the same because we have a secret compartment and we actually pull the balls from there, but this time, all we change is the apparent population from which that evidence was drawn. This time, we're going to show babies three blue balls pulled out of a box of mostly yellow balls, and guess what? You [probably won't] randomly draw three blue balls in a row out of a box of mostly yellow balls. Vous ne tirerez probablement pas au hasard trois boules bleues à la suite d'une boîte de boules principalement jaunes. Вы [вероятно, не будете] случайным образом вытягивать три синих шара подряд из коробки, состоящей в основном из желтых шаров. That is not plausibly randomly sampled evidence. Это неправдоподобно случайно выбранное доказательство. That evidence suggests that maybe Hyowon was deliberately sampling the blue balls. Maybe there's something special about the blue balls Maybe only the blue balls squeak. Let's see what the baby does.

6:56(Video) HG: See this? (Ball squeaks) See this toy? (Ball squeaks) Oh, that was cool. See? (Ball squeaks)Now this one's for you to play. You can go ahead and play. 7:17(Fussing) (Laughter) 7:25LS: So you just saw two 15-month-old babies do entirely different things based only on the probability of the sample they observed. Let me show you the experimental results. On the vertical axis, you'll see the percentage of babies who squeezed the ball in each condition, and as you'll see, babies are much more likely to generalize the evidence when it's plausibly representative of the population than when the evidence is clearly cherry-picked. And this leads to a fun prediction: Suppose you pulled just one blue ball out of the mostly yellow box. You [probably won't] pull three blue balls in a row at random out of a yellow box, but you could randomly sample just one blue ball That's not an improbable sample. Вы [вероятно, не будете] вытаскивать три синих шара подряд из желтой коробки наугад, но вы можете случайным образом выбрать только один синий шар. Это не невероятная выборка. And if you could reach into a box at random and pull out something that squeaks, maybe everything in the box squeaks. So even though babies are going to see much less evidence for squeaking, and have many fewer actions to imitate in this one ball condition than in the condition you just saw, we predicted that babies themselves would squeeze more, and that's exactly what we found. So 15-month-old babies, in this respect, like scientists, care whether evidence is randomly sampled or not, and they use this to develop expectations about the world: what squeaks and what doesn't, what to explore and what to ignore.

8:49Let me show you another example now, this time about a problem of causal reasoning. And it starts with a problem of confounded evidence that all of us have, which is that we are part of the world. And this might not seem like a problem to you, but like most problems, it's only a problem when things go wrong.Take this baby, for instance. Things are going wrong for him. He would like to make this toy go, and he can't I'll show you a few-second clip. And there's two possibilities, broadly: Maybe he's doing something wrong, or maybe there's something wrong with the toy. So in this next experiment, we're going to give babies just a tiny bit of statistical data supporting one hypothesis over the other, and we're going to see if babies can use that to make different decisions about what to do.

9:42Here's the setup. 9:42Вот и вся установка. Hyowon is going to try to make the toy go and succeed. I am then going to try twice and fail both times, and then Hyowon is going to try again and succeed, and this roughly sums up my relationship to my graduate students in technology across the board. Затем я попытаюсь дважды и оба раза потерплю неудачу, а затем Хёвон попытается снова и преуспеет, и это примерно подводит итог моим отношениям с моими аспирантами в области технологий по всем направлениям. But the important point here is it provides a little bit of evidence that the problem isn't with the toy, it's with the person. Но важным моментом здесь является то, что это дает небольшое свидетельство того, что проблема не в игрушке, а в человеке. Some people can make this toy go, and some can't. Now, when the baby gets the toy, he's going to have a choice. His mom is right there, so he can go ahead and hand off the toy and change the person, but there's also going to be another toy at the end of that cloth, and he can pull the cloth towards him and change the toy So let's see what the baby does.

10:29(Video) HG: Two, three. Go! (Music) LS: One, two, three, go! Arthur, I'm going to try again. One, two, three, go! YG: Arthur, let me try again, okay? One, two, three, go! (Music) Look at that. Remember these toys? See these toys? Yeah, I'm going to put this one over here, and I'm going to give this one to you.You can go ahead and play. LS: Okay, Laura, but of course, babies love their mommies. Of course babies give toys to their mommies when they can't make them work. So again, the really important question is what happens when we change the statistical data ever so slightly. This time, babies are going to see the toy work and fail in exactly the same order, but we're changing the distribution of evidence This time, Hyowon is going to succeed once and fail once, and so am I. And this suggests it doesn't matter who tries this toy, the toy is broken. It doesn't work all the time. Again, the baby's going to have a choice. Her mom is right next to her, so she can change the person, and there's going to be another toy at the end of the cloth. Let's watch what she does.

12:06(Video) HG: Two, three, go! (Music) Let me try one more time. One, two, three, go! Hmm.

12:18LS: Let me try, Clara. One, two, three, go! Hmm, let me try again One, two, three, go! (Music) HG: I'm going to put this one over here, and I'm going to give this one to you. You can go ahead and play. (Applause) 13:03LS: Let me show you the experimental results. On the vertical axis, you'll see the distribution of children's choices in each condition, and you'll see that the distribution of the choices children make depends on the evidence they observe. So in the second year of life, babies can use a tiny bit of statistical data to decide between two fundamentally different strategies for acting in the world: asking for help and exploring. I've just shown you two laboratory experiments out of literally hundreds in the field that make similar points, because the really critical point is that children's ability to make rich inferences from sparse data underlies all the species-specific cultural learning that we do. Children learn about new tools from just a few examples. They learn new causal relationships from just a few examples. They even learn new words, in this case in American Sign Language.

14:07I want to close with just two points