1 00:00:00,760 --> 00:00:03,240 Paying close attention to something: 2 00:00:03,280 --> 00:00:04,520 not that easy, is it? 3 00:00:05,520 --> 00:00:10,536 It's because our attention is pulled in so many different directions at a time, 4 00:00:10,560 --> 00:00:14,640 and it's in fact pretty impressive if you can stay focused. 5 00:00:16,360 --> 00:00:20,416 Many people think that attention is all about what we are focusing on, 6 00:00:20,440 --> 00:00:25,240 but it's also about what information our brain is trying to filter out. 7 00:00:26,320 --> 00:00:29,040 There are two ways you direct your attention. 8 00:00:29,600 --> 00:00:31,160 First, there's overt attention. 9 00:00:31,640 --> 00:00:35,776 In overt attention, you move your eyes towards something 10 00:00:35,800 --> 00:00:37,360 in order to pay attention to it. 11 00:00:38,360 --> 00:00:40,336 Then there's covert attention. 12 00:00:40,360 --> 00:00:44,376 In covert attention, you pay attention to something, 13 00:00:44,400 --> 00:00:45,960 but without moving your eyes. 14 00:00:47,040 --> 00:00:48,680 Think of driving for a second. 15 00:00:50,960 --> 00:00:53,976 Your overt attention, your direction of the eyes, 16 00:00:54,000 --> 00:00:55,656 are in front, 17 00:00:55,680 --> 00:00:57,456 but that's your covert attention 18 00:00:57,480 --> 00:01:00,560 which is constantly scanning the surrounding area, 19 00:01:01,600 --> 00:01:03,480 where you don't actually look at them. 20 00:01:05,519 --> 00:01:07,456 I'm a computational neuroscientist, 21 00:01:07,480 --> 00:01:10,576 and I work on cognitive brain machine interfaces, 22 00:01:10,600 --> 00:01:13,640 or bringing together the brain and the computer. 23 00:01:14,720 --> 00:01:16,320 I love brain patterns. 24 00:01:16,720 --> 00:01:18,416 Brain patterns are important for us 25 00:01:18,440 --> 00:01:21,936 because based on them we can build models for the computers, 26 00:01:21,960 --> 00:01:23,376 and based on these models 27 00:01:23,400 --> 00:01:27,616 computers can recognize how well our brain functions, 28 00:01:27,640 --> 00:01:29,240 and if it doesn't function well, 29 00:01:30,080 --> 00:01:34,000 then these computers themselves can be used as assistive devices 30 00:01:34,760 --> 00:01:35,960 for therapies. 31 00:01:36,480 --> 00:01:38,120 But that also means something, 32 00:01:39,360 --> 00:01:41,856 because choosing the wrong patterns 33 00:01:41,880 --> 00:01:43,776 will give us the wrong models 34 00:01:43,800 --> 00:01:45,456 and therefore the wrong therapies. 35 00:01:45,480 --> 00:01:46,680 Right? 36 00:01:47,640 --> 00:01:49,296 In case of attention, 37 00:01:49,320 --> 00:01:50,600 the fact that we can 38 00:01:51,800 --> 00:01:55,296 shift our attention not only by our eyes 39 00:01:55,320 --> 00:01:56,640 but also by thinking -- 40 00:01:57,440 --> 00:02:01,520 that makes covert attention an interesting model for computers. 41 00:02:02,280 --> 00:02:05,736 So I wanted to know what are the brainwave patterns 42 00:02:05,760 --> 00:02:09,440 when you look overtly or when you look covertly. 43 00:02:10,440 --> 00:02:12,200 I set up an experiment for that. 44 00:02:12,960 --> 00:02:15,696 In this experiment there are two flickering squares, 45 00:02:15,720 --> 00:02:19,080 one of them flickering at a slower rate than the other one. 46 00:02:20,600 --> 00:02:24,416 Depending on which of these flickers you are paying attention to, 47 00:02:24,440 --> 00:02:28,400 certain parts of your brain will start resonating in the same rate 48 00:02:29,200 --> 00:02:30,640 as that flickering rate. 49 00:02:32,000 --> 00:02:34,936 So by analyzing your brain signals, 50 00:02:34,960 --> 00:02:38,000 we can track where exactly you are watching 51 00:02:38,760 --> 00:02:40,320 or you are paying attention to. 52 00:02:43,000 --> 00:02:47,216 So to see what happens in your brain when you pay overt attention, 53 00:02:47,240 --> 00:02:50,496 I asked people to look directly in one of the squares 54 00:02:50,520 --> 00:02:51,800 and pay attention to it. 55 00:02:52,760 --> 00:02:58,056 In this case, not surprisingly, we saw that these flickering squares 56 00:02:58,080 --> 00:03:00,016 appeared in their brain signals 57 00:03:00,040 --> 00:03:02,400 which was coming from the back of their head, 58 00:03:03,560 --> 00:03:06,960 which is responsible for the processing of your visual information. 59 00:03:08,280 --> 00:03:10,616 But I was really interested 60 00:03:10,640 --> 00:03:13,800 to see what happens in your brain when you pay covert attention. 61 00:03:14,480 --> 00:03:18,376 So this time I asked people to look in the middle of the screen 62 00:03:18,400 --> 00:03:20,280 and without moving their eyes, 63 00:03:21,120 --> 00:03:23,840 to pay attention to either of these squares. 64 00:03:25,120 --> 00:03:26,736 When we did that, 65 00:03:26,760 --> 00:03:30,696 we saw that both of these flickering rates appeared in their brain signals, 66 00:03:30,720 --> 00:03:31,920 but interestingly, 67 00:03:32,640 --> 00:03:36,176 only one of them, which was paid attention to, 68 00:03:36,200 --> 00:03:37,856 had stronger signals, 69 00:03:37,880 --> 00:03:40,136 so there was something in the brain 70 00:03:40,160 --> 00:03:42,696 which was handling this information 71 00:03:42,720 --> 00:03:48,920 so that thing in the brain was basically the activation of the frontal area. 72 00:03:50,440 --> 00:03:53,416 The front part of your brain is responsible 73 00:03:53,440 --> 00:03:56,320 for higher cognitive functions as a human. 74 00:03:57,160 --> 00:04:01,600 The frontal part, it seems that it works as a filter 75 00:04:02,640 --> 00:04:07,016 trying to let information come in only from the right flicker 76 00:04:07,040 --> 00:04:08,680 that you are paying attention to 77 00:04:09,400 --> 00:04:13,360 and trying to inhibit the information coming from the ignored one. 78 00:04:15,400 --> 00:04:20,696 The filtering ability of the brain is indeed a key for attention, 79 00:04:20,720 --> 00:04:23,496 which is missing in some people, 80 00:04:23,520 --> 00:04:26,000 for example in people with ADHD. 81 00:04:26,640 --> 00:04:31,656 So a person with ADHD cannot inhibit these distractors, 82 00:04:31,680 --> 00:04:36,440 and that's why they can't focus for a long time on a single task. 83 00:04:37,600 --> 00:04:39,136 But what if this person 84 00:04:39,160 --> 00:04:42,696 could play a specific computer game 85 00:04:42,720 --> 00:04:45,600 with his brain connected to the computer, 86 00:04:46,440 --> 00:04:48,560 and then train his own brain 87 00:04:49,360 --> 00:04:51,800 to inhibit these distractors? 88 00:04:53,680 --> 00:04:56,160 Well, ADHD is just one example. 89 00:04:57,200 --> 00:05:00,456 We can use this cognitive brain machine interfaces 90 00:05:00,480 --> 00:05:02,680 for many other cognitive fields. 91 00:05:03,760 --> 00:05:05,536 It was just a few years ago 92 00:05:05,560 --> 00:05:11,280 that my grandfather had a stroke and he lost complete ability to speak. 93 00:05:12,640 --> 00:05:15,976 He could understand everybody, but there was no way to respond, 94 00:05:16,000 --> 00:05:18,480 even not writing because he was illiterate. 95 00:05:20,000 --> 00:05:22,520 So he passed away in silence. 96 00:05:24,800 --> 00:05:27,136 I remember thinking at that time, 97 00:05:27,160 --> 00:05:31,056 what if we could have a computer 98 00:05:31,080 --> 00:05:32,440 which could speak for him? 99 00:05:33,840 --> 00:05:36,056 Now, after years that I am in this field, 100 00:05:36,080 --> 00:05:38,400 I can see that this might be possible. 101 00:05:40,240 --> 00:05:43,096 Imagine if we can find brainwave patterns 102 00:05:43,120 --> 00:05:46,560 when people think about images or even letters, 103 00:05:47,720 --> 00:05:50,656 like the letter A generates a different brainwave pattern 104 00:05:50,680 --> 00:05:52,400 than the letter B, and so on. 105 00:05:52,960 --> 00:05:56,640 Could a computer one day communicate for people who can't speak? 106 00:05:57,640 --> 00:05:59,080 What if a computer 107 00:05:59,960 --> 00:06:04,520 can help us understand the thoughts of a person in a coma? 108 00:06:05,840 --> 00:06:07,456 We are not there yet, 109 00:06:07,480 --> 00:06:10,216 but pay close attention. 110 00:06:10,240 --> 00:06:11,936 We will be there soon. 111 00:06:11,960 --> 00:06:13,456 Thank you. 112 00:06:13,480 --> 00:06:16,880 (Applause)