1 00:00:00,080 --> 00:00:04,560 Speaker 1: How do refrigerators keep food cold? Who really invented the radio? 2 00:00:04,680 --> 00:00:08,000 Speaker 1: What was the worst video game of all time? On 3 00:00:08,119 --> 00:00:11,520 Speaker 1: tex Stuff, we answer these questions and more. You can 4 00:00:11,560 --> 00:00:15,120 Speaker 1: get brand new episodes of tech Stuff every Wednesday on Spotify, 5 00:00:15,400 --> 00:00:21,040 Speaker 1: Google Play, iTunes, and anywhere else you get podcasts. Welcome 6 00:00:21,079 --> 00:00:23,680 Speaker 1: to House to Works Now. I'm your host, Lauren voc Obaum, 7 00:00:23,800 --> 00:00:30,240 Speaker 1: a researcher and writer. Here it has to Works. Every week, 8 00:00:30,280 --> 00:00:32,519 Speaker 1: I'm bringing you three stories from our team about the 9 00:00:32,560 --> 00:00:36,360 Speaker 1: weird and wondrous advances we've seen in science, technology, and culture. 10 00:00:36,960 --> 00:00:39,760 Speaker 1: This week, studies have shown that the way you walk 11 00:00:39,920 --> 00:00:44,560 Speaker 1: can predict your personality and even your behavior, and unrelated 12 00:00:44,960 --> 00:00:48,199 Speaker 1: courses have officially joined the ranks of non human animals 13 00:00:48,240 --> 00:00:51,880 Speaker 1: that can communicate with us using simple symbols. But First, 14 00:00:52,000 --> 00:00:55,120 Speaker 1: Senior writer Jonathan Strickland brings us new research into whether 15 00:00:55,200 --> 00:00:58,440 Speaker 1: bicycle helmets equipped with air bags are really safer than 16 00:00:58,480 --> 00:01:02,000 Speaker 1: traditional helmets. At Stanford put them to the test. Let's 17 00:01:02,000 --> 00:01:08,839 Speaker 1: see how they held up. First. In case you aren't aware, 18 00:01:09,240 --> 00:01:12,680 Speaker 1: there are bike helmet airbags. Typically, they're packed inside a 19 00:01:12,720 --> 00:01:15,479 Speaker 1: pouch that you wear around your neck. The pouch has 20 00:01:15,520 --> 00:01:18,920 Speaker 1: sensors on it to detect changes in orientation and acceleration 21 00:01:19,160 --> 00:01:22,760 Speaker 1: to indicate a crash. At that point, a triggering mechanism 22 00:01:22,800 --> 00:01:25,600 Speaker 1: inflates the bag, which swoops up over the back of 23 00:01:25,600 --> 00:01:29,000 Speaker 1: your head to protect you. An early bike helmet airbag 24 00:01:29,120 --> 00:01:32,880 Speaker 1: called hoof Ding received support from a Swedish insurance company 25 00:01:32,920 --> 00:01:36,160 Speaker 1: called Folksom, which said, according to their studies, the air 26 00:01:36,160 --> 00:01:39,759 Speaker 1: bag was by far more effective than traditional bike helmets 27 00:01:39,920 --> 00:01:45,000 Speaker 1: at preventing traumatic brain injuries. Stanford's results support this conclusion. 28 00:01:45,520 --> 00:01:50,560 Speaker 1: Researchers suspended mannequin heads containing sensory equipment upside down and 29 00:01:50,600 --> 00:01:53,120 Speaker 1: then dropped them onto a hard surface. Some of the 30 00:01:53,120 --> 00:01:56,400 Speaker 1: mannequins were wearing deployed air bag helmets, while others wore 31 00:01:56,480 --> 00:02:00,920 Speaker 1: traditional bike helmets. According to researcher David Amarillo, the air 32 00:02:00,960 --> 00:02:04,200 Speaker 1: bag version had quote the potential to reduce the acceleration 33 00:02:04,240 --> 00:02:07,640 Speaker 1: of impact by a factor of five end quote. Further, 34 00:02:07,760 --> 00:02:10,080 Speaker 1: the study says that airbag helmets can achieve up to 35 00:02:10,120 --> 00:02:13,480 Speaker 1: an eight fold reduction in the risk of concussion compared 36 00:02:13,520 --> 00:02:17,320 Speaker 1: to standard helmets. Airbags are softer and larger than normal 37 00:02:17,360 --> 00:02:21,280 Speaker 1: bike helmets and can absorb energy more effectively, distributing the 38 00:02:21,320 --> 00:02:24,800 Speaker 1: impact in a TED talk in April two thousand sixteen, 39 00:02:24,880 --> 00:02:27,919 Speaker 1: Camarilla said that more kids got a concussion from cycling 40 00:02:28,000 --> 00:02:32,160 Speaker 1: accidents than through any other sports activity. According to the CDC, 41 00:02:32,600 --> 00:02:35,079 Speaker 1: this is true, though you have to keep in mind 42 00:02:35,160 --> 00:02:38,919 Speaker 1: that more kids ride bicycles than play contact sports. It 43 00:02:38,960 --> 00:02:42,480 Speaker 1: doesn't necessarily mean that cycling is more dangerous than full 44 00:02:42,560 --> 00:02:46,960 Speaker 1: contact football. But Camarilla's point was that kids do sometimes 45 00:02:47,000 --> 00:02:51,519 Speaker 1: receive concussions in bike accidents, and that, more importantly, traditional 46 00:02:51,560 --> 00:02:54,760 Speaker 1: bike helmets don't do a good job at preventing concussions. 47 00:02:55,160 --> 00:02:57,800 Speaker 1: Camarilla said that the purpose of bike helmets is to 48 00:02:57,840 --> 00:03:01,840 Speaker 1: prevent skull fractures, which is the same thing as a concussion. 49 00:03:02,280 --> 00:03:06,600 Speaker 1: A concussion occurs when an accelerative force makes the brain stretch, twist, 50 00:03:06,760 --> 00:03:09,320 Speaker 1: or bump into the inside of the skull. It's an 51 00:03:09,320 --> 00:03:12,400 Speaker 1: internal injury that results in chemical changes in the brain, 52 00:03:12,680 --> 00:03:15,639 Speaker 1: and it can have serious effects. So a traditional helmet 53 00:03:15,639 --> 00:03:18,920 Speaker 1: couldn't protect your skull from breaking while failing to prevent 54 00:03:18,960 --> 00:03:22,359 Speaker 1: a concussion. The way we test bike helmets in the 55 00:03:22,440 --> 00:03:25,520 Speaker 1: US is within the context of skull fractures. If we 56 00:03:25,560 --> 00:03:29,600 Speaker 1: take concussions into consideration, airbags suddenly look much more attractive, 57 00:03:29,919 --> 00:03:32,160 Speaker 1: but they're triggy to test. Not only do you need 58 00:03:32,200 --> 00:03:35,880 Speaker 1: to prove they're better at preventing cyclists from receiving concussions, 59 00:03:35,880 --> 00:03:38,800 Speaker 1: but also that the triggering mechanism will deploy the air 60 00:03:38,840 --> 00:03:43,000 Speaker 1: bag quickly, safely, and under the right circumstances. It's still 61 00:03:43,040 --> 00:03:46,320 Speaker 1: a long road ahead before we see bike helmet airbags 62 00:03:46,400 --> 00:03:54,240 Speaker 1: as a legal alternative to standard helmets. Next up, staff 63 00:03:54,320 --> 00:03:56,840 Speaker 1: editor Christopher Hassiotis has a story for us that he 64 00:03:56,920 --> 00:03:59,880 Speaker 1: worked on with one of our freelance writers, jess Lyn Shields. 65 00:04:00,440 --> 00:04:02,840 Speaker 1: It turns out that horses can be taught to communicate 66 00:04:02,880 --> 00:04:06,760 Speaker 1: with us using symbols more easily, even than the researchers 67 00:04:06,800 --> 00:04:13,160 Speaker 1: involved had hoped. Humans and horses have lived and worked 68 00:04:13,200 --> 00:04:16,200 Speaker 1: together for thousands of years. Although we've succeeded in teaching 69 00:04:16,200 --> 00:04:18,760 Speaker 1: these creatures to take direction from us while we ride 70 00:04:18,800 --> 00:04:20,800 Speaker 1: on their backs, carry us into battle like it's no 71 00:04:20,880 --> 00:04:23,679 Speaker 1: big deal, and drag farm machinery around behind them, horses 72 00:04:23,680 --> 00:04:26,599 Speaker 1: have never been considered among the smarter animals. This is 73 00:04:26,640 --> 00:04:29,320 Speaker 1: due in part to our notion that another animal's intelligence 74 00:04:29,360 --> 00:04:32,039 Speaker 1: can be demonstrated through how readily it learns to communicate 75 00:04:32,040 --> 00:04:35,080 Speaker 1: with us about what it wants. But between humans and horses, 76 00:04:35,160 --> 00:04:38,479 Speaker 1: communication has traditionally been viewed as one sided. You tell 77 00:04:38,520 --> 00:04:41,039 Speaker 1: the horse to turn left, the horse turns left or 78 00:04:41,120 --> 00:04:43,000 Speaker 1: runs back to the barn with you on its back 79 00:04:43,040 --> 00:04:46,120 Speaker 1: if it doesn't feel like turning left, and that's not 80 00:04:46,200 --> 00:04:49,600 Speaker 1: really a conversation. However, a new study published in the 81 00:04:49,640 --> 00:04:53,320 Speaker 1: journal Applied Animal Behavior Science finds that horses actually can 82 00:04:53,360 --> 00:04:55,960 Speaker 1: be trained to use symbols in order to tell humans 83 00:04:56,080 --> 00:04:59,120 Speaker 1: what they want, a cognitive step beyond knowing that a 84 00:04:59,200 --> 00:05:01,839 Speaker 1: nuzzle might get them an apple or a carrot. A 85 00:05:01,839 --> 00:05:04,680 Speaker 1: group of Norwegian researchers worked with twenty three horses, all 86 00:05:04,720 --> 00:05:07,479 Speaker 1: accustomed to wearing blankets in the cold. The horses, not 87 00:05:07,520 --> 00:05:11,080 Speaker 1: the researchers. These researchers trained them to indicate whether they 88 00:05:11,160 --> 00:05:14,039 Speaker 1: like their blanket put on or removed by pointing to 89 00:05:14,080 --> 00:05:16,880 Speaker 1: one of three different symbol boards posted on a fence. 90 00:05:17,279 --> 00:05:19,640 Speaker 1: The horses were given carrot pieces as a reward for 91 00:05:19,680 --> 00:05:24,040 Speaker 1: displaying quote unquote right behavior. But unlike most horse training, 92 00:05:24,240 --> 00:05:27,239 Speaker 1: the behavior the trainers were looking for didn't involve correctly 93 00:05:27,279 --> 00:05:31,400 Speaker 1: completing humans serving tasks like stop, turn, change gate, or 94 00:05:31,440 --> 00:05:34,840 Speaker 1: come this way. Instead, the horses received rewards when they 95 00:05:34,880 --> 00:05:37,680 Speaker 1: correctly communicated to the trainers whether they were feeling hot 96 00:05:37,839 --> 00:05:40,240 Speaker 1: or cold at the moment. They could do this by 97 00:05:40,240 --> 00:05:42,839 Speaker 1: pointing with their noses to one of the three icons, 98 00:05:42,880 --> 00:05:46,360 Speaker 1: which essentially meant one, take the blanket off, to put 99 00:05:46,360 --> 00:05:48,840 Speaker 1: the blanket on, and three, please don't mess with my 100 00:05:48,880 --> 00:05:53,039 Speaker 1: current blanket situation. Everything's great at first. The researchers rewarded 101 00:05:53,040 --> 00:05:56,560 Speaker 1: the horses with carrots when they demonstrated correct behaviors, for instance, 102 00:05:56,600 --> 00:05:59,400 Speaker 1: any choice the horse made. Wrong responses, on the other hand, 103 00:05:59,400 --> 00:06:02,200 Speaker 1: were ignored, like touching the blanket on symbol when the 104 00:06:02,240 --> 00:06:05,400 Speaker 1: horse already was wearing a blanket. The horses received training 105 00:06:05,400 --> 00:06:07,400 Speaker 1: for ten to fifteen minutes a day and for no 106 00:06:07,440 --> 00:06:10,200 Speaker 1: more than fourteen days, and in this time all twenty 107 00:06:10,240 --> 00:06:13,840 Speaker 1: three horses in the study master the communication method. In fact, 108 00:06:14,080 --> 00:06:17,440 Speaker 1: on average, members of the herd learned to recognize, understand, 109 00:06:17,560 --> 00:06:20,640 Speaker 1: and choose between the symbols in just ten days. The 110 00:06:20,680 --> 00:06:23,440 Speaker 1: horses didn't outperform some of the other smarty pants animals 111 00:06:23,440 --> 00:06:26,240 Speaker 1: that can learn to communicate with us through symbolic vocabularies 112 00:06:26,240 --> 00:06:29,760 Speaker 1: and gestures, dolphins, apes, and pigeons among them, But this 113 00:06:29,839 --> 00:06:32,120 Speaker 1: study adds to what we know about how horses think, 114 00:06:32,240 --> 00:06:34,240 Speaker 1: and it could change how all of us interact with 115 00:06:34,279 --> 00:06:39,840 Speaker 1: our equine friends for the better. Finally, this week, I've 116 00:06:39,880 --> 00:06:43,120 Speaker 1: got another story for you from our freelancer Jesselyn Shields. 117 00:06:43,120 --> 00:06:45,960 Speaker 1: Had tip to Jesselyn. A couple of different teams of 118 00:06:46,000 --> 00:06:49,400 Speaker 1: researchers have posed the question, what does your walk say 119 00:06:49,440 --> 00:06:53,360 Speaker 1: about you? The answer, it turns out, is probably quite 120 00:06:53,360 --> 00:06:59,479 Speaker 1: a lot. Healing movement is complex, yet we constantly draw 121 00:06:59,600 --> 00:07:02,640 Speaker 1: pretty accurate conclusions about people based on how they move. 122 00:07:03,000 --> 00:07:05,520 Speaker 1: If you've ever, for example, watch John to volta strut 123 00:07:05,560 --> 00:07:07,600 Speaker 1: to the Bags at the end of Saturday Night Fever, 124 00:07:08,080 --> 00:07:10,240 Speaker 1: you know, the other hour and fifty seven minutes of 125 00:07:10,320 --> 00:07:14,360 Speaker 1: character development in that movie is kind of superfluous. That 126 00:07:14,400 --> 00:07:17,240 Speaker 1: walk tells you all you need to know. The act 127 00:07:17,280 --> 00:07:20,760 Speaker 1: of walking, those seemingly simple for many people, is actually 128 00:07:20,840 --> 00:07:24,000 Speaker 1: a highly coordinated series of movements that each require their 129 00:07:24,040 --> 00:07:27,800 Speaker 1: own timing and path. Not only that, everyone has a 130 00:07:27,840 --> 00:07:31,160 Speaker 1: distinct style of walking. Think about John Wayne's swagger or 131 00:07:31,280 --> 00:07:34,400 Speaker 1: Naomi Campbell's stride, and if you know someone well enough, 132 00:07:34,640 --> 00:07:37,560 Speaker 1: you can probably identify them by their movements even from 133 00:07:37,600 --> 00:07:41,120 Speaker 1: far away. Think about if your mom and sister were 134 00:07:41,120 --> 00:07:43,600 Speaker 1: walking side by side to block ahead of you, it's 135 00:07:43,680 --> 00:07:45,840 Speaker 1: likely you'd be able to tell them apart based on 136 00:07:45,960 --> 00:07:49,080 Speaker 1: movement alone. It's even possible you'd be able to discern 137 00:07:49,160 --> 00:07:51,640 Speaker 1: from that distance if your sister is in a good mood, 138 00:07:51,840 --> 00:07:54,520 Speaker 1: or if your mom is distracted by something. But science 139 00:07:54,520 --> 00:07:57,920 Speaker 1: suggests that locomotion may be able to predict something deeper 140 00:07:58,040 --> 00:08:01,320 Speaker 1: than someone's temporary mood. A new paper published in the 141 00:08:01,360 --> 00:08:05,200 Speaker 1: Journal of Nonverbal Behavior presents a technique for making assumptions 142 00:08:05,240 --> 00:08:10,080 Speaker 1: about a person's entire personality based on their walk. The researchers, 143 00:08:10,160 --> 00:08:12,840 Speaker 1: a team out of the University of Portsmouth, had twenty 144 00:08:12,920 --> 00:08:16,440 Speaker 1: nine study participants first take the Big Five Inventory, which 145 00:08:16,480 --> 00:08:19,320 Speaker 1: is a commonly used personality test that can help predict 146 00:08:19,360 --> 00:08:24,280 Speaker 1: patterns in five areas a person's thought and behavior, their conscientiousness, 147 00:08:24,320 --> 00:08:29,560 Speaker 1: their social skills or agreeableness, their propensity to worry or neuroticism, 148 00:08:29,600 --> 00:08:33,600 Speaker 1: their creativity and intellect or openness, and their extra version, 149 00:08:33,679 --> 00:08:37,880 Speaker 1: their sociability and assertiveness. They then had each participant walk 150 00:08:37,880 --> 00:08:40,880 Speaker 1: on a treadmill and they recorded and analyzed their gaits. 151 00:08:41,360 --> 00:08:43,760 Speaker 1: They found that larger movements in the upper body compared 152 00:08:43,800 --> 00:08:46,359 Speaker 1: with the lower body were a strong predictor of aggression 153 00:08:46,960 --> 00:08:49,679 Speaker 1: UH and that large amounts of pulit movement are predictors 154 00:08:49,720 --> 00:08:53,680 Speaker 1: of social traits extra version and agreeableness. But this goes 155 00:08:53,720 --> 00:08:56,560 Speaker 1: beyond knowing from a glance whether your significant other is 156 00:08:56,559 --> 00:09:00,320 Speaker 1: in a bad mood. This research helps mathematically explain previous 157 00:09:00,360 --> 00:09:03,680 Speaker 1: research or distinctive gates were helpful in predicting impending crimes 158 00:09:03,679 --> 00:09:06,760 Speaker 1: and footage from security cameras. The University of Ports with 159 00:09:06,840 --> 00:09:09,920 Speaker 1: Team proposes that of security personnel were trained in recognizing 160 00:09:09,920 --> 00:09:13,120 Speaker 1: aggressive gates, they might be able to prevent even more 161 00:09:13,160 --> 00:09:16,680 Speaker 1: crimes before they happen. Other research has also indicated the 162 00:09:16,679 --> 00:09:19,599 Speaker 1: people's behaviors can be predicted by how they move. The 163 00:09:19,679 --> 00:09:24,040 Speaker 1: journal Plos Computational Biology published a computational model that can 164 00:09:24,080 --> 00:09:27,160 Speaker 1: generally predict the path and timing of different parts of 165 00:09:27,200 --> 00:09:30,600 Speaker 1: people's movements. The model can also use a person's movements 166 00:09:30,600 --> 00:09:34,439 Speaker 1: to make educated guesses about their personality traits like aggressiveness 167 00:09:34,559 --> 00:09:38,920 Speaker 1: or mood like grouchiness. By modeling what's normal, the researchers 168 00:09:38,960 --> 00:09:42,040 Speaker 1: created a framework for identifying movements that differ from the 169 00:09:42,080 --> 00:09:45,079 Speaker 1: norm and are unique to a particular person or to 170 00:09:45,320 --> 00:09:49,000 Speaker 1: someone who's just acting really weird. In the future, could 171 00:09:49,040 --> 00:09:52,440 Speaker 1: this technology be used to prevent robberies or diffuse potentially 172 00:09:52,480 --> 00:09:55,360 Speaker 1: violent situations and would that be a good thing or 173 00:09:55,400 --> 00:09:58,360 Speaker 1: could lead to unfair targeting of innocent people whose brains 174 00:09:58,360 --> 00:10:01,280 Speaker 1: and movements happen to fall outside a norm. We'll keep 175 00:10:01,320 --> 00:10:09,200 Speaker 1: an eye out for updates. That's our show for this week. 176 00:10:09,480 --> 00:10:12,120 Speaker 1: Thank you so much for tuning in. Subscribe now for 177 00:10:12,160 --> 00:10:15,040 Speaker 1: more of the latest and strangest science news, and send 178 00:10:15,080 --> 00:10:16,880 Speaker 1: us links to anything you'd like to hear us cover, 179 00:10:17,320 --> 00:10:21,040 Speaker 1: plus your favorite bizarre historical fact or of the sickest 180 00:10:21,040 --> 00:10:23,400 Speaker 1: burn you've ever heard from history. You can send us 181 00:10:23,400 --> 00:10:26,440 Speaker 1: an email at now podcast at how stuff works dot com, 182 00:10:26,480 --> 00:10:29,240 Speaker 1: and of course, for lots more stories like these, head 183 00:10:29,240 --> 00:10:31,880 Speaker 1: on over to our home planet Now dot how stuff 184 00:10:31,880 --> 00:10:45,600 Speaker 1: works dot com