1 00:00:00,743 --> 00:00:05,990 [no audio yet] 2 00:00:05,990 --> 00:00:08,012 In this video, we'll differentiate 3 00:00:08,012 --> 00:00:11,440 between stereotypes, prejudice, and discrimination; 4 00:00:11,440 --> 00:00:14,513 and we'll discuss several important social psychological concepts 5 00:00:14,513 --> 00:00:17,132 and hypotheses related to each, 6 00:00:17,132 --> 00:00:20,019 including what causes them to arise in the first place. 7 00:00:21,081 --> 00:00:24,364 Let's go over a bit of terminology to kick things off. 8 00:00:24,364 --> 00:00:28,020 A stereotype is a belief (which can be positive or negative ) 9 00:00:28,020 --> 00:00:30,491 about the characteristics of members of a group 10 00:00:30,491 --> 00:00:34,675 that is applied generally to most members of that group. 11 00:00:34,675 --> 00:00:38,733 Believing that Asians are good at math, for example, is positive; 12 00:00:38,733 --> 00:00:40,398 it's not necessarily derogatory, 13 00:00:40,398 --> 00:00:44,433 but it's nonetheless a stereotype that you have about Asians. 14 00:00:44,433 --> 00:00:48,569 Now, stereotypes (these beliefs) can lead to prejudice, which in contrast, 15 00:00:48,569 --> 00:00:50,903 can only ever be negative. 16 00:00:50,903 --> 00:00:54,504 Prejudice involves drawing negative conclusions about 17 00:00:54,504 --> 00:00:56,056 a person, a group of people, 18 00:00:56,056 --> 00:01:00,001 or a situation prior to evaluating the evidence. 19 00:01:00,001 --> 00:01:01,979 These baseless conclusions are typically 20 00:01:01,979 --> 00:01:06,200 the result of those stereotypes that you hold about that group. 21 00:01:06,200 --> 00:01:07,883 Also, in contrast to stereotypes, 22 00:01:07,883 --> 00:01:11,022 prejudice involves emotion; it’s an attitude. 23 00:01:11,022 --> 00:01:14,639 Being prejudiced against a person or a group of people involves 24 00:01:14,639 --> 00:01:16,908 feeling negatively toward them. 25 00:01:16,908 --> 00:01:19,290 Now, because of these negative emotions 26 00:01:19,290 --> 00:01:22,079 and these negative conclusions that you're coming to, 27 00:01:22,079 --> 00:01:24,664 prejudice often leads to discrimination, 28 00:01:24,664 --> 00:01:29,017 which is negative behavior towards members of an out-group. 29 00:01:29,017 --> 00:01:31,670 And by the way, an out-group is a group 30 00:01:31,670 --> 00:01:35,591 that we don't belong to or one that we view as fundamentally different from us; 31 00:01:35,591 --> 00:01:37,240 whereas an in-group, in contrast, 32 00:01:37,240 --> 00:01:41,945 refers to a group that we DO identify with or see ourselves as belonging to. 33 00:01:41,945 --> 00:01:44,350 So I might be using that terminology quite a bit– 34 00:01:44,350 --> 00:01:45,973 important to know. 35 00:01:45,973 --> 00:01:47,067 So just to summarize, 36 00:01:47,067 --> 00:01:51,659 stereotypes are beliefs, prejudice is an attitude, 37 00:01:51,659 --> 00:01:54,445 and discrimination is a behavior. 38 00:01:54,445 --> 00:01:57,583 Let's go over an example that puts all of this together. 39 00:01:57,583 --> 00:02:01,904 Let's say, for example, that you believe older adults are incompetent, 40 00:02:01,904 --> 00:02:05,437 and that's a stereotype that you have about older adults. 41 00:02:05,437 --> 00:02:09,111 (And I'll note that I'm not endorsing this stereotype or any other stereotype 42 00:02:09,111 --> 00:02:11,151 that I use as an example in this video, 43 00:02:11,151 --> 00:02:14,203 but we have to have some kind of an example to work with here.) 44 00:02:14,203 --> 00:02:16,777 So let's say you work at, I don't know, a tech company 45 00:02:16,777 --> 00:02:18,963 and you're looking to hire an assistant. 46 00:02:18,963 --> 00:02:20,835 If an elderly gentleman applies, 47 00:02:20,835 --> 00:02:23,505 you might walk into that interview with the gentleman, 48 00:02:23,505 --> 00:02:27,908 assuming he won't be a good fit or that he'd be difficult to train. 49 00:02:27,908 --> 00:02:30,710 Now, we would call this premature conclusion; 50 00:02:30,710 --> 00:02:33,749 this negative attitude toward this gentleman [is] prejudice. 51 00:02:33,749 --> 00:02:36,623 Finally, you may decide not to hire the gentleman at all 52 00:02:36,623 --> 00:02:39,772 because of your stereotype, because of your prejudice. 53 00:02:39,772 --> 00:02:42,604 In this case, the behavior of not hiring him 54 00:02:42,604 --> 00:02:45,223 would be discrimination. 55 00:02:45,223 --> 00:02:48,649 Now, stereotypes and prejudice can be either explicit 56 00:02:48,649 --> 00:02:51,851 (meaning, we're consciously aware of having this bias) 57 00:02:51,851 --> 00:02:55,870 or implicit (meaning, it's there, but we aren't aware of it). 58 00:02:55,870 --> 00:02:59,857 Research shows that explicit prejudice is in decline, which is encouraging; 59 00:02:59,857 --> 00:03:03,844 however, implicit prejudice really isn't much. 60 00:03:03,844 --> 00:03:07,749 That is, people report being very anti-bias nowadays, 61 00:03:07,749 --> 00:03:11,000 but their behavior still tells us a different story. 62 00:03:11,000 --> 00:03:14,168 Let's take a look at a few examples to illustrate. 63 00:03:14,168 --> 00:03:18,265 Starting with the realm of gender, we can look to some of my own data. 64 00:03:18,265 --> 00:03:20,000 In one study, I searched through 65 00:03:20,000 --> 00:03:23,104 the language used by students evaluating their teachers 66 00:03:23,104 --> 00:03:28,708 in over 14 million reviews posted to a popular instructor evaluation website, 67 00:03:28,708 --> 00:03:33,696 RateMyProfessors.com, which you've perhaps used in the past. 68 00:03:33,696 --> 00:03:37,448 I was specifically interested in stereotypes about intelligence, 69 00:03:37,448 --> 00:03:41,954 so I searched through uses of the words “genius” and “brilliant.” 70 00:03:41,954 --> 00:03:46,225 So let's take a look at the results. There's a lot of information here. 71 00:03:46,225 --> 00:03:48,490 Let me help you interpret these graphs. 72 00:03:48,490 --> 00:03:50,101 These are graphs for uses of 73 00:03:50,101 --> 00:03:54,220 the words “genius” on the left and “brilliant” on the right. 74 00:03:54,220 --> 00:03:56,715 The x-axis on both of these graphs 75 00:03:56,715 --> 00:04:00,072 represents uses per millions of words of text, 76 00:04:00,072 --> 00:04:02,829 which might sound a little complicated, but really isn't. 77 00:04:02,829 --> 00:04:04,264 There's a ton of text here, 78 00:04:04,264 --> 00:04:07,613 so to keep the numbers on the x-axis from being enormous 79 00:04:07,613 --> 00:04:09,397 and just visually unappealing, 80 00:04:09,397 --> 00:04:15,170 I used this uses per millions of words of text, but the interpretation is basically the same. 81 00:04:15,170 --> 00:04:17,338 The further to the right you go on the x-axis 82 00:04:17,338 --> 00:04:20,590 (the higher the number), the more this word was used. 83 00:04:20,590 --> 00:04:22,392 So that's how you can interpret that. 84 00:04:22,392 --> 00:04:26,762 The y-axis here displays all of the different fields such as philosophy, 85 00:04:26,762 --> 00:04:30,115 music, mathematics, psychology; 86 00:04:30,115 --> 00:04:31,951 so you can look for your own field 87 00:04:31,951 --> 00:04:35,990 or just pause the video and look through them in general, if you're curious, 88 00:04:35,990 --> 00:04:39,092 And fields that are higher up on the y-axis were the ones 89 00:04:39,092 --> 00:04:41,976 in which the words were used the most often. 90 00:04:41,976 --> 00:04:47,095 The blue dots here on the slide represent reviews of male professors, 91 00:04:47,095 --> 00:04:51,881 whereas the orange dots represent reviews of female professors. 92 00:04:51,881 --> 00:04:55,098 Before I give you the punch line, what do you notice here? 93 00:04:55,098 --> 00:04:59,557 Well, what I found is that every field for which we have data, 94 00:04:59,557 --> 00:05:02,720 students describe their male professors as genius and brilliant 95 00:05:02,720 --> 00:05:05,840 significantly more often than they do their female professors. 96 00:05:05,840 --> 00:05:09,189 And in no field was this effect reversed, 97 00:05:09,189 --> 00:05:12,874 even for fields where women were the statistical majority. 98 00:05:12,874 --> 00:05:15,944 And this points to a stereotype in favor of men's intelligence 99 00:05:15,944 --> 00:05:18,294 and against women's intelligence. 100 00:05:18,294 --> 00:05:19,480 You might be wondering: 101 00:05:19,480 --> 00:05:22,700 Does this reflect an overall bias against women, 102 00:05:22,700 --> 00:05:26,586 or is the stereotype specific to intellectual ability? 103 00:05:26,586 --> 00:05:28,616 Well, I was curious about this as well, 104 00:05:28,616 --> 00:05:31,857 but if you look at the data for the terms “excellent” and “amazing,” 105 00:05:31,857 --> 00:05:34,224 the gender bias goes away entirely. 106 00:05:34,224 --> 00:05:35,963 It appears that students believe 107 00:05:35,963 --> 00:05:38,795 that their female professors can be excellent and amazing, 108 00:05:38,795 --> 00:05:43,516 but they believe it's mainly the male professors who are genius and brilliant. 109 00:05:43,516 --> 00:05:45,884 Again, this is evidence of implicit bias 110 00:05:45,884 --> 00:05:47,329 because students are likely 111 00:05:47,329 --> 00:05:49,506 not consciously aware of this discrepancy. 112 00:05:49,506 --> 00:05:52,186 They're simply going on line to review their professors 113 00:05:52,186 --> 00:05:54,751 and they're not giving their stereotypes any thought. 114 00:05:54,751 --> 00:05:58,890 So explicitly, students would likely say they don't hold a bias, 115 00:05:58,890 --> 00:06:02,142 yet implicitly, they respond in this way. 116 00:06:02,142 --> 00:06:03,552 This is a common theme 117 00:06:03,552 --> 00:06:08,744 in modern research on stereotypes, prejudice, and discrimination. 118 00:06:08,744 --> 00:06:10,987 Now that's gender. What about race? 119 00:06:10,987 --> 00:06:15,005 One study found that doctors were only 60% as likely to suggest 120 00:06:15,005 --> 00:06:19,152 a top-rated diagnostic test for Black heart patients 121 00:06:19,152 --> 00:06:21,504 than for White heart patients. 122 00:06:21,504 --> 00:06:23,053 There's also evidence to suggest 123 00:06:23,053 --> 00:06:26,355 that White men are offered greater financial opportunities. 124 00:06:26,355 --> 00:06:29,358 As one example, a study found that White men were offered 125 00:06:29,358 --> 00:06:32,245 the best deals at used car dealerships. 126 00:06:32,245 --> 00:06:36,622 White men paid $109 on average less than White women, 127 00:06:36,622 --> 00:06:40,302 $318 less than Black women, 128 00:06:40,302 --> 00:06:47,757 and a whopping $935 less for a used car on average than Black men. 129 00:06:47,757 --> 00:06:51,470 Now, these are just two examples out of thousands that I could tell you about, 130 00:06:51,470 --> 00:06:53,022 but again, it's likely the case 131 00:06:53,022 --> 00:06:56,642 that these doctors and car salesmen aren't EXPLICITLY biased, 132 00:06:56,642 --> 00:07:01,676 but their behavior provides evidence of IMPLICIT bias. 133 00:07:01,676 --> 00:07:04,513 Okay, so let's finish with a brief discussion 134 00:07:04,513 --> 00:07:09,483 of what leads to the development and perpetuation of some of these things 135 00:07:09,483 --> 00:07:11,704 (stereotypes, prejudice, and discrimination), 136 00:07:11,704 --> 00:07:13,873 starting with stereotypes. 137 00:07:13,873 --> 00:07:17,025 A factor that we've learned about before is confirmation bias, 138 00:07:17,025 --> 00:07:20,462 the tendency to seek out evidence that supports our beliefs 139 00:07:20,462 --> 00:07:25,381 and to deny, dismiss, or distort evidence that contradicts them. 140 00:07:25,381 --> 00:07:28,918 Say, for example that you believe women to be bad drivers. 141 00:07:28,918 --> 00:07:30,984 If you're out driving for an hour, 142 00:07:30,984 --> 00:07:36,107 you might encounter several bad drivers, some male, some female. 143 00:07:36,107 --> 00:07:39,393 If you don't have a stereotype against male drivers, though, 144 00:07:39,393 --> 00:07:43,776 you might not think much of them when they speed or make dangerous moves. 145 00:07:43,776 --> 00:07:47,015 But the second a female driver cuts you off, for example, 146 00:07:47,015 --> 00:07:50,299 you feel vindicated as though you've found additional evidence 147 00:07:50,299 --> 00:07:52,286 or proof for your belief. 148 00:07:52,286 --> 00:07:54,157 And this reinforces your stereotype 149 00:07:54,157 --> 00:07:59,081 even though, in truth, many people are bad drivers regardless of their gender. 150 00:07:59,081 --> 00:08:01,503 Now, if we used System 2 thinking 151 00:08:01,503 --> 00:08:03,782 (which we've learned about in a previous video) 152 00:08:03,782 --> 00:08:07,386 to evaluate these kinds of assumptions and the data that we base them on, 153 00:08:07,386 --> 00:08:11,869 we might realize that those assumptions are erroneous, but we usually don't. 154 00:08:11,869 --> 00:08:14,854 This is because we are cognitive misers. 155 00:08:14,854 --> 00:08:18,040 That is, we seek to use only minimal cognitive resources 156 00:08:18,040 --> 00:08:20,593 when explaining the world around us. 157 00:08:20,593 --> 00:08:23,028 Evaluating our stereotypes takes effort, 158 00:08:23,028 --> 00:08:25,348 and because we generally don't go to more effort 159 00:08:25,348 --> 00:08:27,431 than we deem absolutely necessary, 160 00:08:27,431 --> 00:08:31,465 we don't evaluate or re-evaluate them at all. 161 00:08:31,465 --> 00:08:33,537 Now, what causes prejudice? 162 00:08:33,537 --> 00:08:37,372 First, we have in-group bias, which refers to the tendency 163 00:08:37,372 --> 00:08:43,146 to favor individuals from within our group over those from outside our group. 164 00:08:43,146 --> 00:08:46,117 Evidence from developmental psychology suggests that this bias 165 00:08:46,117 --> 00:08:50,551 is innate, with young infants showing strong preferences, for example, 166 00:08:50,551 --> 00:08:54,670 for others who share their preferences (such as their favorite snack) 167 00:08:54,670 --> 00:08:58,474 and infants disliking others who do not share their preferences 168 00:08:58,474 --> 00:09:02,605 (for example, if the other person shows that they like a different snack more). 169 00:09:02,605 --> 00:09:06,236 Think of the implications for racism, sexism, and so on. 170 00:09:06,236 --> 00:09:10,051 Another factor is called the ultimate attribution error, 171 00:09:10,051 --> 00:09:11,676 which refers to the assumption 172 00:09:11,676 --> 00:09:14,180 that behaviors among individual members of a group 173 00:09:14,180 --> 00:09:17,554 are due to their internal dispositions. 174 00:09:17,554 --> 00:09:20,407 Out-group members’ flaws are due to internal factors 175 00:09:20,407 --> 00:09:25,483 such as their personality or their race, whereas in-group members flaws aren't. 176 00:09:25,483 --> 00:09:28,459 This might sound a lot like the fundamental attribution error, 177 00:09:28,459 --> 00:09:31,355 which we've learned about before, but it is a bit different. 178 00:09:31,355 --> 00:09:33,285 Think of the ultimate attribution error 179 00:09:33,285 --> 00:09:37,202 as more of a narrow case of the fundamental attribution error 180 00:09:37,202 --> 00:09:39,267 applied specifically to attributions 181 00:09:39,267 --> 00:09:43,694 about an individual in relation to the group to which they belong. 182 00:09:43,694 --> 00:09:45,000 Along similar lines, 183 00:09:45,000 --> 00:09:48,352 out-group homogeneity refers to the tendency to view 184 00:09:48,352 --> 00:09:52,836 all individuals outside our group as highly similar to one another. 185 00:09:52,836 --> 00:09:54,356 Here, think of the implications 186 00:09:54,356 --> 00:09:57,442 for identifying a suspect in a police lineup, for example, 187 00:09:57,442 --> 00:10:02,314 but also consider this bias in relation to the ultimate attribution error. 188 00:10:02,314 --> 00:10:04,681 It's a very bad combination to assume 189 00:10:04,681 --> 00:10:08,136 that out-group members flaws are due to inherent factors 190 00:10:08,136 --> 00:10:10,143 such as their personalities or their race, 191 00:10:10,143 --> 00:10:11,943 and to simultaneously assume 192 00:10:11,943 --> 00:10:15,997 that out-group members are all highly similar to one another. 193 00:10:15,997 --> 00:10:19,866 Finally, scapegoating refers to the act of blaming an out-group 194 00:10:19,866 --> 00:10:25,872 when the in-group experiences frustration or is blocked from obtaining some kind of a goal. 195 00:10:25,872 --> 00:10:30,493 People scapegoat because it preserves a positive self-concept. 196 00:10:30,493 --> 00:10:35,329 If you believe the reason you can't get a job is because immigrants are taking them all, 197 00:10:35,329 --> 00:10:38,192 well, then you don't have to come to terms with the reality 198 00:10:38,192 --> 00:10:42,580 that you simply aren't qualified or competent enough for that line of work. 199 00:10:42,580 --> 00:10:46,431 Now, this list of causes here is by no means all-inclusive 200 00:10:46,431 --> 00:10:50,116 but should give you a good idea of the general psychological phenomena 201 00:10:50,116 --> 00:10:53,311 that lead to the formation and perpetuation of stereotypes, 202 00:10:53,311 --> 00:10:55,661 prejudice, and discrimination. 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