1 00:00:01,920 --> 00:00:06,400 Speaker 1: Welcome to brain Stuff production of I Heart Radio, Hey 2 00:00:06,440 --> 00:00:10,039 Speaker 1: brain Stuff, Lauren bob o blam here. The world is 3 00:00:10,080 --> 00:00:14,880 Speaker 1: full of faces. Faces in wall outlets, faces in lamp switches, 4 00:00:15,200 --> 00:00:19,720 Speaker 1: faces in cheese graters. Sometimes these faces have religious significance, 5 00:00:19,920 --> 00:00:21,599 Speaker 1: or like the woman who found an image of the 6 00:00:21,640 --> 00:00:24,439 Speaker 1: Virgin Mary and her grilled cheese, or the cheeto that 7 00:00:24,480 --> 00:00:29,479 Speaker 1: looks convincingly like Jesus. The phenomenon of seeing faces where 8 00:00:29,480 --> 00:00:32,680 Speaker 1: they're not supposed to be, in clouds, on buildings in 9 00:00:32,840 --> 00:00:36,159 Speaker 1: tacos is so common and widespread that it has a 10 00:00:36,280 --> 00:00:42,440 Speaker 1: name paraidolia. In Greek, paraidolia translates as beyond form or image, 11 00:00:42,760 --> 00:00:46,239 Speaker 1: and it means finding meanings or patterns where there aren't any, 12 00:00:46,479 --> 00:00:49,519 Speaker 1: like hearing a heartbeat in white noise, or believing that 13 00:00:49,560 --> 00:00:53,000 Speaker 1: a seat cushion is mad at you. It's easy to 14 00:00:53,040 --> 00:00:57,120 Speaker 1: dismiss paraidolia as at best of fun optical illusion or 15 00:00:57,320 --> 00:01:01,720 Speaker 1: at worst a psychotic delusion. But some scientists now believe 16 00:01:01,840 --> 00:01:05,479 Speaker 1: that our uncanny ability to find faces and everyday objects 17 00:01:05,800 --> 00:01:08,840 Speaker 1: points to a new understanding of how our brains process 18 00:01:08,920 --> 00:01:12,800 Speaker 1: the outside world. Instead of taking in visual cues and 19 00:01:12,880 --> 00:01:15,600 Speaker 1: then making sense of them. As an apple, a tree 20 00:01:15,800 --> 00:01:18,400 Speaker 1: or a face. It might be the other way around. 21 00:01:19,080 --> 00:01:22,560 Speaker 1: What if our brains are actually telling our eyes what 22 00:01:22,720 --> 00:01:26,679 Speaker 1: to see. We spoke with Kang Lee, a professor of 23 00:01:26,680 --> 00:01:29,839 Speaker 1: applied psychology and human development at the University of Toronto. 24 00:01:30,680 --> 00:01:34,400 Speaker 1: Lee has spent decades studying how infants, children and adults 25 00:01:34,480 --> 00:01:38,320 Speaker 1: process faces, and relatedly, he gave a popular Ted talk 26 00:01:38,360 --> 00:01:41,760 Speaker 1: on how to tell kids are lying. Lee explained that 27 00:01:41,800 --> 00:01:45,000 Speaker 1: we're basically programmed to see faces as a product of 28 00:01:45,080 --> 00:01:49,520 Speaker 1: millions of years of evolution. Quote as soon as we're born, 29 00:01:49,760 --> 00:01:52,800 Speaker 1: we start to look for faces. One reason is that 30 00:01:52,840 --> 00:01:56,880 Speaker 1: our ancestors needed to avoid predators or find prey, all 31 00:01:56,920 --> 00:02:00,200 Speaker 1: of which have faces. And a second reason is that 32 00:02:00,320 --> 00:02:04,080 Speaker 1: humans are very social animals. When we interact with each other, 33 00:02:04,320 --> 00:02:06,200 Speaker 1: we need to know if the other person is a 34 00:02:06,200 --> 00:02:10,880 Speaker 1: friend or foe. Since the ability to quickly recognize and 35 00:02:10,960 --> 00:02:13,679 Speaker 1: respond to different faces could be a matter of life 36 00:02:13,720 --> 00:02:17,000 Speaker 1: and death. There's a much higher cost for not seeing 37 00:02:17,000 --> 00:02:20,280 Speaker 1: the lions face in the underbrush than for mistaking an 38 00:02:20,280 --> 00:02:23,680 Speaker 1: orange and black flower for a lion's face. The brain 39 00:02:23,800 --> 00:02:26,760 Speaker 1: is better off making a false positive if it means 40 00:02:26,760 --> 00:02:31,799 Speaker 1: that you're primed to recognize real danger too. Okay, So 41 00:02:31,919 --> 00:02:36,240 Speaker 1: if evolution has programmed our brains to prioritize faces, how 42 00:02:36,280 --> 00:02:39,960 Speaker 1: exactly does it all play out under the hood. The 43 00:02:39,960 --> 00:02:42,960 Speaker 1: conventional understanding of how we see things is that the 44 00:02:43,040 --> 00:02:47,639 Speaker 1: eyes taken visual stimuli from the outside world a light, colors, 45 00:02:47,680 --> 00:02:51,480 Speaker 1: shapes movement, and send that information to the visual cortex, 46 00:02:51,680 --> 00:02:53,800 Speaker 1: located in a region of the brain known as the 47 00:02:53,800 --> 00:02:57,880 Speaker 1: occipital lobe. After the occipital lobe translates the raw data 48 00:02:57,919 --> 00:03:01,040 Speaker 1: into images, those images are sent to the frontal lobe, 49 00:03:01,200 --> 00:03:04,840 Speaker 1: which does the high level processing. We look at a cliff, 50 00:03:05,000 --> 00:03:07,880 Speaker 1: and our brains then have to determine is that a 51 00:03:08,000 --> 00:03:12,760 Speaker 1: rock outcropping or is it a giant head. That conventional 52 00:03:12,800 --> 00:03:16,600 Speaker 1: model is what Lee calls bottom up processing, in which 53 00:03:16,639 --> 00:03:19,760 Speaker 1: the brain's role is to passively take an information and 54 00:03:19,919 --> 00:03:23,280 Speaker 1: make sense of it. If the brain sees faces everywhere, 55 00:03:23,480 --> 00:03:26,600 Speaker 1: it's because the brain is responding to face like stimuli, 56 00:03:27,240 --> 00:03:31,120 Speaker 1: basically any cluster of spots and spaces that roughly look 57 00:03:31,240 --> 00:03:34,720 Speaker 1: like two eyes, a nose, and amount. But Lee and 58 00:03:34,760 --> 00:03:38,320 Speaker 1: other researchers began to question the bottom up of processing model. 59 00:03:38,880 --> 00:03:41,600 Speaker 1: They wondered if it wasn't the other way around, a 60 00:03:41,760 --> 00:03:45,080 Speaker 1: top down process In which the brain is calling the shots. 61 00:03:46,040 --> 00:03:49,200 Speaker 1: Alice said, we wanted to know whether the frontal lobe 62 00:03:49,240 --> 00:03:52,360 Speaker 1: actually plays a very important role in helping us to 63 00:03:52,400 --> 00:03:56,200 Speaker 1: see faces instead of the face imagery coming from the outside. 64 00:03:56,560 --> 00:04:00,240 Speaker 1: The brain generates some kind of expectation from the frontal lobe, 65 00:04:00,400 --> 00:04:03,080 Speaker 1: then goes back to the occipital lobe and finally to 66 00:04:03,120 --> 00:04:08,760 Speaker 1: our eyes, and then we see faces. That question is 67 00:04:08,760 --> 00:04:12,840 Speaker 1: what made Lee think about paradolia. Had read those stories 68 00:04:12,880 --> 00:04:16,200 Speaker 1: of people seeing images of Jesus, Elvis, and angels in 69 00:04:16,240 --> 00:04:19,520 Speaker 1: their toast and tortillas and wondered if he could build 70 00:04:19,560 --> 00:04:24,400 Speaker 1: an experiment around it. So Lee recruited a bunch of 71 00:04:24,440 --> 00:04:27,400 Speaker 1: regular people, hooked them up to an fm R I scanner, 72 00:04:27,680 --> 00:04:30,800 Speaker 1: and showed them a series of grainy images, some of 73 00:04:30,839 --> 00:04:34,440 Speaker 1: which contained hidden faces and some of which were pure noise. 74 00:04:35,040 --> 00:04:38,279 Speaker 1: The participants were told that exactly half of the images 75 00:04:38,400 --> 00:04:41,520 Speaker 1: contained a face, which was not true, and we're asked 76 00:04:41,520 --> 00:04:45,200 Speaker 1: with each new image, do you see a face? As 77 00:04:45,240 --> 00:04:48,520 Speaker 1: a result of this prodding, participants reported seeing a face 78 00:04:48,680 --> 00:04:51,280 Speaker 1: thirty four percent of the time, and there was nothing 79 00:04:51,480 --> 00:04:55,520 Speaker 1: but static. What was most interesting to Lee were the 80 00:04:55,560 --> 00:04:58,200 Speaker 1: images coming back from the real time f m R 81 00:04:58,240 --> 00:05:02,280 Speaker 1: I scan. When part disipants reported seeing a face, the 82 00:05:02,320 --> 00:05:05,440 Speaker 1: face area of their visual cortex lit up even when 83 00:05:05,440 --> 00:05:08,120 Speaker 1: there was no face in the image at all. That 84 00:05:08,279 --> 00:05:11,000 Speaker 1: told Lee then another part of the brain must be 85 00:05:11,040 --> 00:05:15,240 Speaker 1: telling the visual cortex to see a face. In a 86 00:05:15,320 --> 00:05:19,919 Speaker 1: paper provocatively titled seeing Jesus in Toast Neural and Behavioral 87 00:05:19,960 --> 00:05:23,920 Speaker 1: Correlates a Face Paraidolia, Lee and his colleagues reported that 88 00:05:23,960 --> 00:05:27,640 Speaker 1: when the brain was properly primed to see faces, then 89 00:05:27,800 --> 00:05:30,360 Speaker 1: the expectation to see a face was coming from the 90 00:05:30,400 --> 00:05:35,479 Speaker 1: frontal lobe, specifically an area called the inferior frontal gyrus. 91 00:05:36,400 --> 00:05:40,120 Speaker 1: Lee explained, the inferior frontal gyrus is a very interesting area. 92 00:05:40,520 --> 00:05:43,560 Speaker 1: It's related to generating some kind of idea and an 93 00:05:43,560 --> 00:05:47,120 Speaker 1: instructing our visual cortex to see things. If the idea 94 00:05:47,200 --> 00:05:49,360 Speaker 1: is a face, then it would see a face. If 95 00:05:49,400 --> 00:05:52,000 Speaker 1: the idea is Jesus, I'm pretty sure the cortex is 96 00:05:52,040 --> 00:05:54,600 Speaker 1: going to see Jesus. If the idea is Elvis, then 97 00:05:54,640 --> 00:05:58,520 Speaker 1: it's going to see Elvis. The Jesus in Toast paper 98 00:05:58,680 --> 00:06:03,159 Speaker 1: one Lee teen ig Noble Prize, a cheeky award handed 99 00:06:03,160 --> 00:06:06,839 Speaker 1: out by the humorous science magazine Annals of improbable research, 100 00:06:07,440 --> 00:06:11,200 Speaker 1: but Lie says the Paradolia experiment proved the top down 101 00:06:11,240 --> 00:06:14,440 Speaker 1: processing plays a critical role in how we experience the 102 00:06:14,440 --> 00:06:17,680 Speaker 1: world around us. He said, a lot of things we 103 00:06:17,720 --> 00:06:20,080 Speaker 1: see in the world aren't coming from our site, but 104 00:06:20,120 --> 00:06:24,719 Speaker 1: are coming from inside our minds. Lee has also run 105 00:06:24,760 --> 00:06:28,200 Speaker 1: research on babies and racial bias. He found that the 106 00:06:28,360 --> 00:06:31,840 Speaker 1: very youngest babies were able to recognize differences between faces 107 00:06:31,920 --> 00:06:35,159 Speaker 1: of all races, but lost that ability as they grew older. 108 00:06:36,040 --> 00:06:39,520 Speaker 1: By nine months, they could only differentiate between faces that 109 00:06:39,560 --> 00:06:43,000 Speaker 1: were their same race. The rest started to blur together. 110 00:06:44,080 --> 00:06:46,599 Speaker 1: The reason is that they had only been exposed to 111 00:06:46,800 --> 00:06:50,039 Speaker 1: same race faces, in most case mom and dad, for 112 00:06:50,080 --> 00:06:53,719 Speaker 1: the first nine months of their life. From his research, 113 00:06:53,960 --> 00:06:57,680 Speaker 1: Lee now believes that racial biases are not biological. We 114 00:06:57,920 --> 00:07:00,600 Speaker 1: simply learned to trust people that look like the faces 115 00:07:00,640 --> 00:07:04,480 Speaker 1: we saw when our brains were first developing. Unfortunately, this 116 00:07:04,560 --> 00:07:07,600 Speaker 1: can develop later into different kinds of biases based on 117 00:07:07,640 --> 00:07:12,520 Speaker 1: societal messaging and stereotypes. Lee said. The reason that there 118 00:07:12,520 --> 00:07:16,680 Speaker 1: are racial biases is because of early experiences. If we 119 00:07:16,840 --> 00:07:20,960 Speaker 1: created a diverse visual and social experience for children, then 120 00:07:21,000 --> 00:07:25,080 Speaker 1: they would be less likely to have biases. The good 121 00:07:25,120 --> 00:07:28,360 Speaker 1: news is that parents and educators can combat racial bias 122 00:07:28,400 --> 00:07:31,680 Speaker 1: by exposing infants and toddlers to faces of all races 123 00:07:32,040 --> 00:07:35,440 Speaker 1: and identifying them by things like names, professions, or other 124 00:07:35,600 --> 00:07:40,160 Speaker 1: personally identifying qualifiers or interests, not as a white person 125 00:07:40,440 --> 00:07:48,000 Speaker 1: or a black person. Today's episode was written by Dave 126 00:07:48,040 --> 00:07:50,280 Speaker 1: Ruse and produced by Tyler Clain. For more on this 127 00:07:50,360 --> 00:07:52,880 Speaker 1: amounts of other topics, visit how stuff works dot com. 128 00:07:52,880 --> 00:07:55,520 Speaker 1: Brain Stuff is production of iHeart Radio. For more podcasts 129 00:07:55,520 --> 00:07:58,360 Speaker 1: to my heart Radio, visit the iHeart Radio app, Apple podcasts, 130 00:07:58,400 --> 00:07:59,960 Speaker 1: or wherever you listen to your favorite show