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Interaction and UX designers have long known and used heuristics to guide the creation of a user-friendly interface. We know empirically that these principles work, and they make “common sense”. These heuristics themselves aim to make interface “intuitive”, but common sense and intuitive are concepts hard to define precisely.

交互和UX設計人員早就知道并使用啟發式技術來指導用戶友好界面的創建。 我們憑經驗知道這些原則行之有效，并且它們具有“常識”。 這些啟發式方法本身旨在使界面“直觀”，但是常識和直觀是很難精確定義的概念。

Research in neuroscience has proposed a new interpretation for how the brain works and how decisions are made, namely the Bayesian brain. When comparing the known heuristics and this new interpretation model, we find numerous similarities. Understanding the Bayesian brain should help designers go beyond vague concepts of “common sense” and “intuitive” to understand why their heuristics work or not.

神經科學研究已經提出了一種關于大腦如何運作以及如何做出決策的新解釋，即貝葉斯大腦。 當比較已知的啟發式方法和這種新的解釋模型時，我們發現了許多相似之處。 理解貝葉斯大腦應有助于設計師超越模糊的“常識”和“直覺”概念，以了解其啟發式方法為何起作用或不起作用。

我們的大腦是統計學家 (Our brain is a statistician)

The Bayesian brain is a way to describe how our perception works. We receive millions of stimuli each second from all our sensory nerves. Most of these signals, as numerous as they are, are still ambiguous. When we receive a certain amount/shape of light in our retina, multiple objects outside could produce this same pattern. However, most of the time, we don’t consider all the possible objects, we identify instantaneously the right object.

貝葉斯大腦是描述我們的感知如何運作的一種方式。 我們每秒從我們所有的感覺神經接收數百萬個刺激。 這些信號中的大多數(盡管數量眾多)仍然是模棱兩可的。 當我們在視網膜中接收到一定數量/形狀的光時，外面的多個物體可能會產生相同的圖案。 但是，在大多數情況下，我們不會考慮所有可能的對象，而是會立即識別出正確的對象。

The Bayesian brain theory explains how, behind the scenes, the brain operates: it uses probability and equations similar to Bayes theorem to give us only one a single answer. Even though we are aware of a single object, our brain has processed all the possibilities and determined the most likely one, using Bayes theorem, and presents this most likely possibility as the only one. Several experiences show the power of prediction of this theory, and how the physical neuron network can actually perform these kinds of calculations.

貝葉斯腦理論解釋了大腦在幕后如何運作：它使用類似于貝葉斯定理的概率和方程式，只給我們一個答案。 即使我們知道單個對象，我們的大腦也已經處理了所有可能性，并使用貝葉斯定理確定了最可能的可能性，并將這種最可能的可能性表示為唯一的可能性。 幾項經驗證明了該理論的預測能力，以及物理神經元網絡如何實際執行此類計算。

The Bayesian brain theory makes a distinction between the unconscious, statistical treatment of stimuli, which includes many alternatives with their own probability and our awareness of a situation, which is (should be) unique. Bayes theorem uses two factors to calculate the posterior probability / make an inference: the known probability of a given situation, and previous knowledge. The decision in this model is based on the resulting inference and a function of gain/loss.

貝葉斯大腦理論區分了無意識的，對刺激的統計處理(包括許多具有自己可能性的替代方法)和我們對情況的認識(這是(應該)獨特的)。 貝葉斯定理使用兩個因素來計算后驗概率/進行推論：給定情況的已知概率和先前知識。 該模型中的決策基于結果推斷和損益函數。

為什么我們的大腦使用概率對UX設計人員如此重要？ (Why is the fact that our brains use probability so important for UX designers?)

First, this emphasizes the fundamental ambiguity of all types of signals, including the interfaces we design. Optical illusions work by leveraging this signal ambiguity and the processing done by the brain to present to us the most likely solution(s) based on our previous experience. They show us how our perception is a brain construct rather than an exact reflection of reality. Optical illusions are only extreme cases of how we constantly transform our sensations into perceptions. Thus, designers need to reconcile themselves with the inherent ambiguity of all constructs (while trying to minimize it) and cease to blame “dumb” users for their misunderstanding.

首先，這強調了所有信號類型(包括我們設計的接口)的基本歧義。 視錯覺通過利用信號的模糊性和大腦進行的處理來發揮作用，從而根據我們以前的經驗為我們提供最可能的解決方案。 它們向我們展示了我們的感知是大腦的構造，而不是現實的精確反映。 錯覺只是我們不斷將感覺轉變為感知的極端情況。 因此，設計人員需要使自己與所有構造的固有歧義保持一致(同時嘗試將其最小化)，并不再因誤解而責怪“笨拙”的用戶。

Second, Bayes theorem gives us clues on how to minimize this fundamental ambiguity, and make the right interpretation stand out among all the possible ones. Luckily, these clues map exactly to the already known heuristics, but now we have an idea why they work so well.

其次，貝葉斯定理為我們提供了有關如何最大程度地減少這種基本歧義以及使正確的解釋在所有可能的解釋中脫穎而出的線索。 幸運的是，這些線索正好映射到了已知的啟發式方法，但是現在我們有了一個想法，為什么它們如此有效。

Third, we can now define more clearly what has long been elusive: the concept of “intuitive” itself and understand that there might not be any kind of universal intuitive system, but that what we consider as “intuitive” is mostly previously learned rules, so well internalized that we consider them as “natural”.

第三，我們現在可以更清楚地定義長期以來難以捉摸的東西：“直覺”本身的概念，并了解可能沒有任何一種通用的直覺系統，但是我們認為“直覺”主要是以前學過的規則，內部化程度很高，我們認為它們是“自然的”。

減少歧義 (Minimizing ambiguity)

The Bayesian theorem produces accurate inferences based on two factors: the known probability of a given situation and the prior probability of the alternative that is considered (based on our previous experience).

貝葉斯定理基于兩個因素產生準確的推論：給定情況的已知概率和所考慮的替代方法的先驗概率(基于我們的先前經驗)。

As a designer, we need to use each of these aspects, so they are all aligned in the same direction:

作為設計師，我們需要使用以下所有方面，因此它們都朝著相同的方向排列：

- The given situation is the stimuli: the visual/audio/text signals must all convey the same message: green checkmark with a high pitch sound: positive/red cross with a loud low pitch sound = negative.

-給定的情況是刺激：視覺/音頻/文本信號都必須傳達相同的消息：高音高的綠色復選標記：高音低音的正/紅叉=負。

Of course, not all situations are so simple, but even things that simply can be messed up. Some card payment terminals emit a low pitch sound while displaying a “payment accepted” message, resulting in confusion in the client, and the need for the cashier to reassure them that all went well. This might seem minor, but I bet the few seconds wasted everywhere this type of terminal is used add up to significant productivity loss.

當然，并非所有情況都如此簡單，但即使是簡單的事情也可能被弄亂。 一些卡支付終端會在顯示“已接受付款”消息時發出低調的聲音，從而導致客戶產生混亂，并且需要收銀員向他們保證一切進展順利。 這看似微不足道，但我敢打賭，使用這種類型的終端到處都浪費了幾秒鐘的時間，這會大大降低生產率。

- Prior probability represents what we call in design mental models. They are the results of the successful inferences made by the user in the past, often associated with a causality system. We learned to associate green with something valid or positive and red with errors or wrong way signal.

-先驗概率代表我們在設計思維模型中所說的。 它們是用戶過去進行的成功推斷的結果，通常與因果關系系統相關聯。 我們學會了將綠色與有效或肯定的事物關聯起來，將紅色與錯誤或錯誤的信號關聯起來。

These should be a well-known quantity for designers: through competitive research, we know other existing systems and interfaces; through user research, we know which tools our users manipulate, what their mental models are, etc. The design should leverage this known quantity to make the new interface or system familiar, reinforcing previously learned patterns.

對于設計師來說，這些應該是眾所周知的數量：通過競爭研究，我們知道其他現有的系統和接口； 通過用戶研究，我們知道用戶使用了哪些工具，他們的心理模型是什么，等等。設計應利用這一已知數量來使新界面或系統熟悉，從而增強以前學習的模式。

Then, the user must make a decision. According to the decision-making theory using Bayesian interpretation, we make our decision based on the inference made using Bayes theorem and a gain/loss function.

然后，用戶必須做出決定。 根據使用貝葉斯解釋的決策理論，我們基于使用貝葉斯定理和損益函數得出的推論做出決策。

Perception-Action loop according to Bayesian perspective — Source: Ernst & Bulthoff, 2004; quoted in S. Dehaene, 2012根據貝葉斯觀點的知覺-行動循環-來源：Ernst＆Bulthoff，2004； 引用于S.Dehaene，2012年

This maps perfectly onto other UX heuristics: clearly state the outcome of an action and keep the user informed on the system status. Having clear information on the system status and outcome will help the user make better, quicker decisions.

這完美地映射到其他UX啟發式方法：清楚地說明操作的結果，并使用戶了解系統狀態。 具有有關系統狀態和結果的清晰信息將幫助用戶做出更好，更快的決策。

直覺是什么意思？ (What does intuitive mean?)

When we act seamlessly, without even thinking, is when we say that things are “intuitive”. Based on what we said previously, we have a better grasp of what makes things/interfaces feel intuitive or not.

當我們什至沒有思考就無縫地行動時，就是說事情是“直覺的”。 根據我們之前所說的，我們可以更好地理解使事物/界面感覺直觀與否的原因。

We make quick, almost imperceptible decisions when all the mentioned factors align perfectly: signals, mental model, know status and outcome, because our Bayesian brain has calculated a very high probability for the right alternative, and very low probability for all other possibilities.

當所有上述因素完美契合時，我們會做出快速，幾乎不可察覺的決策：信號，心理模型，狀態和結局，因為我們的貝葉斯大腦計算出正確選擇的可能性很高，而其他所有可能性的可能性卻很小。

Something is not intuitive when the multiple alternatives processed by our brain cease to operate at an unconscious level and make their way into our awareness. When does that happen? When several alternatives have similar probabilities. Then our well-trained Bayesian brain lets us become aware of several possibilities, the most likely ones, creating confusion.

當大腦處理的多種選擇停止在無意識的水平運行并進入我們的意識時，有些事情就不直觀了。 什么時候發生？ 當幾個備選方案具有相似的概率時。 然后，我們訓練有素的貝葉斯大腦使我們意識到幾種可能性，最有可能的情況是造成混亂。

Thus, for things to be intuitive, there needs to be one clear winner among all the alternatives. However, as the probability is not only a function of the stimuli, but also of previous knowledge, there is no way to create an interface that would be universally intuitive. We shouldn’t say that things or interfaces ‘are’ intuitive by themselves. We can only say that they ‘feel’ intuitive for this category of users. Each person has her own previous experience, that might influence differently her perception of a given interface.

因此，為了使事情直觀，所有替代方案中都必須有一個明確的贏家。 但是，由于概率不僅是刺激的函數，而且還是以前的知識的函數，因此無法創建通用直觀的界面。 我們不應該說事物或界面本身就是“直觀的”。 我們只能說他們對這類用戶“感覺”直觀。 每個人都有自己的先前經驗，這可能會不同地影響她對給定界面的看法。

As an example, there is no answer to the question whether Apple or Microsoft is more intuitive: users of each system have learned patterns of usage, which became “intuitive” to them. Making the switch to a system with different patterns becomes very costly. If you eliminate the notion of a naturally intuitive system and replace it with learned patterns, then there is no intrinsic superiority of a system compared to another.

例如，沒有答案是蘋果還是微軟更直觀：每個系統的用戶都已經學會了使用模式，這對他們來說是“直觀的”。 切換到具有不同模式的系統變得非常昂貴。 如果您消除了自然直觀系統的概念并將其替換為學習過的模式，那么與其他系統相比，該系統就沒有內在的優勢。

As a designer, this means that you need to know the audience you’re designing for intimately enough that you know their mental models. This is typically achieved in one of two ways:

作為設計師，這意味著您需要充分了解要設計的受眾，以了解他們的思維模式。 通常通過以下兩種方式之一來實現：

- Designers who are so immersed in a given “culture” that they share most patterns with their users. These designers will use the same patterns in their design, without even thinking about it. They might be great designers in a specific context, but they won’t perform so well in another, because they not only ignore the patterns of this new context, they don’t have an explicit understanding/conceptualization of what make things “intuitive” for some people and them and not others (these are the designers who say that users are dumb).

-如此沉迷于給定“文化”以至于與用戶共享大多數模式的設計師。 這些設計師將在設計中使用相同的模式，甚至無需考慮它。 他們可能在特定的環境中是出色的設計師，但在其他環境中表現不佳，因為他們不僅忽略了這種新環境的模式，而且對使事物“直觀”的內容沒有明確的理解/概念化。對于某些人和他們而不是其他人(這些設計師說用戶很愚蠢)。

- Designers who research and identify the mental models of their expected users. Of course, they also have their own learned patterns and are not completely immune to the illusion of things being intuitive, but they reflect on their own learned habits, acknowledge their preferred patterns, so they can more easily distance themselves and learn other ways of doing things. They don’t trust their “intuition” so much and prefer hard evidence.

-研究和確定預期用戶心理模型的設計師。 當然，他們也有自己的學習模式，并不能完全避免直觀事物的幻覺，但是他們會反思自己的學習習慣，承認自己的偏好模式，因此他們可以更輕松地與自己保持距離并學習其他做事方式東西。 他們不太相信自己的“直覺”，而是喜歡有力的證據。

For more on the Bayesian brain, see:

有關貝葉斯大腦的更多信息，請參見：

• a series of lecture by Stanilas Dehaene, College de France, 2012 (in French)

  Stanilas Dehaene的系列講座，法國學院，2012年(法語)

• Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths, Fei Xu, A tutorial introduction to Bayesian models of cognitive development , Cognition, Volume 120, Issue 3, September 2011, Pages 302–321.

  Amy Perfors，Joshua B. Tenenbaum，Thomas L. Griffiths，徐飛， 認知發展的貝葉斯模型教程簡介 ，《認知》，第120卷，第3期，2011年9月，第302-321頁。

• Marc O.Ernst, Heinrich H.Bülthoff, Merging the senses into a robust percept, Trends in Cognitive Science, Volume 8, Issue 4, April 2004, Pages 162–169.

  馬克·厄恩斯特(Marc O.Ernst)，海因里希·H·伯索夫(Heinrich H.Bulthoff)，《 將感官融合為一個強有力的感知》 ，《認知科學趨勢》，第8卷，第4期，2004年4月，第162-169頁。

翻譯自: https://uxdesign.cc/how-neuroscience-supports-ux-heuristics-76d94d977d90

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