Don’t Focus on the Most Expressive Face in the Audience

by?Amit Goldenberg?and?Erika Weisz

Imagine yourself pitching an idea to a group of people. As you speak, you quickly scan the audience, your attention jumping from face to face. Are people smiling? Or do they look confused, bored, maybe even angry?

想象一下你自己在向一群人宣傳某種想法。當(dāng)你說話的時(shí)候，你快速地掃視聽眾，你的注意力從一張臉跳到另一張臉。人們是否在笑？還是看起來很困惑、很無聊，甚至是怒氣沖沖？

Facial expressions impart vital clues about people's emotions. Whether you're a junior employee or a C-suite executive, making these split-second judgments about how your audience is feeling is a critically important skill. But even the most emotionally intelligent among us can struggle to understand exactly how these split-second judgments are made, and more importantly, whether or not they are accurate. And this becomes even more complicated when you start trying to read social cues not just in a single person, but in a group of people.

面部表情可以透露人們情緒方面的重要線索。無論你是初級員工還是最高層管理者，對你聽眾的感受做出這些瞬間判斷是一項(xiàng)至關(guān)重要的技能。可是，即便是我們之中情商最高的人，也難以確切明白這些瞬間判斷是如何做出的，更重要的是，這些判斷是否準(zhǔn)確。而當(dāng)你開始嘗試不只是在一個(gè)人身上，而是在一群人身上解讀社交線索時(shí)，情況就愈加復(fù)雜了。

Research shows?that when looking at a group, people tend to focus on faces expressing stronger emotions — whether those emotions are positive or negative — and pay less attention to faces conveying less intense emotions. In the context of public speaking, this attentional bias can shape speakers’ impressions of how they’re being received: since people pay more attention to their more-emotionally-expressive audience members, they tend to conclude that an audience’s overall reaction is more intense than it actually is.

研究表明，當(dāng)人們在注視一群人時(shí)，更傾向于關(guān)注表達(dá)強(qiáng)烈情緒的面孔——無論這些情緒是正面的是還是負(fù)面的——而不太注意表達(dá)較少情緒的面孔。在公共演講的背景下，這種注意力偏差可能會(huì)影響到演講者對自己受歡迎程度的印象：既然人們更留意情感表現(xiàn)力更豐富的聽眾，他們通常會(huì)得出的結(jié)論是，聽眾的整體反應(yīng)比實(shí)際情況更強(qiáng)烈。

To better understand how these biases present (as well as how you can start to overcome them), we — together with our colleagues Timothy Sweeny, Mina Cikara and James Gross — conducted a?series of studies?exploring this tendency to amplify groups’ emotions. In one experiment, participants were shown images of groups of up to 12 people. The faces in the images were calibrated to each display a certain amount of emotionality, enabling us to calculate the “objective” average emotional state of group. We then asked the participants to estimate the groups’ average emotional state and compared their responses to the actual levels of emotion depicted in each image.

為了更好地理解這些偏差如何呈現(xiàn)（以及你如何克服），我們與同事蒂莫西·斯威尼(Timothy Sweeny)、米娜·契卡拉(Mina Cikara)和詹姆斯·格羅斯(James Gross)一道進(jìn)行了一系列研究，探究這種放大群體情緒的傾向。在一項(xiàng)實(shí)驗(yàn)中，參與者被展示了最多12人一組的圖像，圖像中的面孔都被進(jìn)行了校正，以使每張面孔都顯示出一定程度的情緒，這樣我們能夠計(jì)算群體“客觀的”平均情緒狀態(tài)。然后，我們要求參與者估計(jì)各組的平均情緒狀態(tài)，并將他們的回應(yīng)與每張圖像描繪的實(shí)際情緒水平進(jìn)行比較。

As expected, we found that participants consistently overestimated the emotionality of the groups. But we also found two interesting new results:

正如所料，我們發(fā)現(xiàn)，參與者自始至終都高估了群體情緒。不過，我們還發(fā)現(xiàn)了兩個(gè)有趣的新結(jié)果：

First, the larger the group, the more our participants overestimated its emotional state. Because the degree of emotionality was randomly distributed among the faces (just like real-world groups will have a random distribution of more and less emotionally expressive people), larger groups had a greater likelihood of containing highly emotional faces than smaller groups did. And since peoples’ attention tends to get stuck on those highly emotional faces, they ended up rating the larger groups as more emotional on average.

首先，群體越大，我們的參與者越高估其情緒狀態(tài)。由于情緒的程度是在面孔中隨機(jī)分布的（就像情緒表現(xiàn)力更強(qiáng)與更弱的人會(huì)隨機(jī)分布在真實(shí)世界里一樣），較大群體比較小群體更有可能包含高度情緒化的面孔。由于人們的注意力通常停留在那些高度情緒化的面孔上，平均而言，他們最終認(rèn)定較大群體更為情緒化。

Second, participants'overestimation of groups' emotions was slightly greater for negative expressions, such as anger, than it was for positive expressions, such as happiness.?Prior research suggests?that people's attention is naturally drawn more to faces expressing negative emotions than to faces conveying positive ones, but our study found that this effect holds for groups as well as for individuals. People's ability to judge a group's emotional state isn't just skewed towards more intense emotions — it is specifically biased toward more negative evaluations.

第二，在參與者對群體情緒的高估中，對消極表情（如憤怒）的高估略多于積極表情（如快樂）。之前的研究表明，人們的注意力自然會(huì)更多地被表達(dá)負(fù)面情緒的面孔所吸引，而不是被表達(dá)正面情緒的面孔所吸引?？墒?，我們的研究發(fā)現(xiàn)，這種效應(yīng)不僅適用于個(gè)人，還適用于群體。人們判斷一個(gè)群體情緒狀態(tài)的能力不僅僅偏向更強(qiáng)烈的情緒——它還特別偏向于更負(fù)面的評估。

To further understand the mechanics of this tendency to overestimate groups’ emotions, we conducted a second study in which we asked participants to evaluate a group’s emotions while tracking their gaze with an eye-tracking apparatus. We found that as participants scanned an image of a group, their gaze would consistently get stuck on more emotional faces, leading them to overweight those faces when estimating the group's average emotional state.

為了進(jìn)一步了解這種高估群體情緒傾向的機(jī)制，我們進(jìn)行了第二項(xiàng)研究，其間我們要求參與者評估一組人的情緒，同時(shí)用眼球追蹤裝置追蹤他們的目光。我們發(fā)現(xiàn)，參與者在掃視一組圖像時(shí)，目光始終停留在更情緒化的面孔上，這導(dǎo)致他們在估計(jì)該群體的平均情緒狀態(tài)時(shí)會(huì)過于加大這些面孔的權(quán)重。

Our research is early and we want to be careful in prescribing takeaways. But interestingly, this latter finding points to a potential remedy for the bias towards overestimating groups’ emotions: Because focusing on emotional faces tends to overly amplify our perceptions of a group’s emotionality, intentionally scanning more evenly across both emotional and non-emotional faces may lead to a more accurate perception of your audience. We also suspect that the tendency to amplify strong emotional responses may be especially salient in virtual contexts, since you may be even more likely to miss weaker emotional signals on a screen than in person (thought this is a speculation that would require further research to confirm).

我們的研究尚在初期。我們希望小心謹(jǐn)慎地給予人啟示。但有趣的是，后一項(xiàng)發(fā)現(xiàn)為高估群體情緒偏差琢磨出了一個(gè)潛在解決辦法：由于關(guān)注情緒化面孔通常會(huì)過度放大我們對一個(gè)群體情緒的感知，所以有意更均勻地掃視情緒化和非情緒化的面孔，可以讓你更準(zhǔn)確地感知聽眾。我們還懷疑，放大強(qiáng)烈情緒反應(yīng)的傾向在虛擬環(huán)境中可能尤為突出，因?yàn)楹兔鎸γ娴那樾蜗啾?，你更有可能錯(cuò)過屏幕上較弱的情緒信號（這只是一種推測，需要進(jìn)一步的研究來證實(shí)）。

So next time you pitch an idea, give a talk, or even just enter a room and start getting a sense of the atmosphere, try actively looking at everyone, rather than letting your focus get drawn to just one or two highly emotional faces. While it won’t completely eradicate your natural attention biases, it should leave you with a more accurate estimation of how your audience really feels.

所以，下一次你宣傳某種想法、作報(bào)告或者只是進(jìn)入一個(gè)房間并開始感受氣氛的時(shí)候，試著主動(dòng)地審視每個(gè)人，而不是讓你的注意力只被一兩張高度情緒化的面孔所吸引。雖然這不能完全消除你的自然注意力偏差，但它應(yīng)該會(huì)讓你對聽眾的真實(shí)感受有更準(zhǔn)確的估計(jì)。

阿米特·戈登堡是哈佛商學(xué)院(Harvard Business School)談判組織與市場部(NegotiationOrganizations & Markets)的助理教授。戈登堡博士的研究重點(diǎn)是情緒在社交互動(dòng)中的角色。

埃麗卡·魏斯是哈佛大學(xué)(Harvard University)的心理學(xué)博士后研究員。她于2018年獲得了斯坦福大學(xué)(Stanford University)心理學(xué)博士學(xué)位。她的研究探尋的是如何利用社會(huì)心理學(xué)技術(shù)來鼓勵(lì)人們相互產(chǎn)生共情。

劉雋 | 編輯