Visual Thinking Helps in Digesting Big Data
視覺思考有助消化大數據
By Benedict Carey

For the past year or so, genetic scientists at the Albert Einstein College of Medicine in New York have been collaborating with a specialist from another universe: Daniel Kohn, a painter and conceptual artist.
過去這一年左右的時間，紐約的亞伯特．愛因斯坦醫學院的基因科學家一直在和來自另一個領域的專家合作：畫家兼概念藝術家丹尼爾．科恩。

Mr. Kohn has no training in computers or genetics, and he’s not there to conduct art therapy classes. His role is to help the scientists with a signature 21st-century problem, namely, Big Data.
科恩沒有任何電腦或基因學方面的訓練，他也不是來開藝術治療課程的。他的角色是幫助科學家處理21世紀的一個典型問題，也就是大數據。

Advanced computing produces waves of abstract digital data that in many cases defy interpretation. To extract some order from this chaos, analysts need to continually reimagine the ways in which they represent their data, which is where Mr. Kohn comes in.
先進電腦運算產生的抽象數位數據浪潮，在很多情況下都無法解讀。為了從這些混亂中整理出一些秩序，分析家需要不斷重新想像傳達數據的方法，這就是科恩來此的目的。

Scientists working in a little-known branch of psychology called perceptual learning have shown that it is possible to fast-forward a person’s gut instincts both in physical fields and more academic ones. The idea is to train specific visual skills, usually with computer-game-like modules that require split-second decisions. Over time, a person develops a “good eye” for the material, and with it an ability to extract meaningful patterns instantaneously.
心理學中鮮為人知的分支「知覺學習」領域的科學家已經證明，在物理場和更學術的領域中，要快速發展人的本能直覺是可行的。這個構想是訓練特殊視覺技巧，通常要用到類似電玩遊戲這類需要瞬間做決定的模組。隨著時間，人培養出判斷素材的「好眼力」，而眼力有助瞬間整理出有意義的模式。

Take learning to fly, a disorienting and sometimes terrifying experience that requires hundreds of hours in the air and in the classroom, many of them devoted to learning how to read an instrument panel. In the 1980s, a cognitive scientist named Philip Kellman wondered if there was a better, and quicker, way. The dials on the instrument panel are easy enough to read on their own, one at a time, but reading all of them at once, at a glance, is another skill altogether. It’s more about reflexes, and gut feeling, than reasoning.
以學習飛行為例，這是讓人失去方向感且時而驚恐的經驗，必須花幾百小時在空中和教室學習，其中許多時候是花在學習如何判讀儀表板。在1980年代，認知科學家菲利浦．凱爾曼想知道是否有更好且更快的方法。若一次只看一個，儀表板上的指針本身皆易於判讀，但若一眼便同時全部讀取，則完全是另一種技術。這和反射及直覺有關，甚於推理。

Dr. Kellman designed a lesson during which a student sees a panel on a computer screen and decides quickly what the dials are saying, collectively. Below the panel are seven choices, including “straight climb,” “descending turn” and “level turn.” A chime sounds if the answer is correct. A burp sounds if the answer is wrong, and the correct answer is highlighted. Then up comes the next screen, with another instrument panel, and then another.
凱爾曼博士設計了一項課程，學生可在電腦螢幕上看儀表板，並快速判定所有指針的整體意義。在儀表板下有7個選項，包括「直線爬升」、「下降轉彎」和「水平轉彎」。答對會發出叮咚聲；答錯則發出噗聲並標示正確答案。接著顯示下一個畫面，另一個儀表板畫面出現，接著又是另一個。

In 1994, Dr. Kellman, now a professor at the University of California, Los Angeles, tested this perceptual learning module, as he calls it, on amateur pilots. After one hour of training, novices could read the panel as accurately and quickly as pilots with an average of 1,000 flying hours, he found.
凱爾曼現為洛杉磯加州大學教授，1994年曾對業餘機師測試這個他所謂的知覺學習模組。經過一小時訓練後，他發現這些初學者判讀儀表板的正確及快速程度，和有1000小時飛行經驗的機師相當。

Dr. Kellman and others have used variations on this method to quickly ramp up instincts in other complex fields.
凱爾曼和其他學者利用這個方法的變項，快速提升其他複雜領域所需的直覺反應。

The most important question when dealing with reams of digital data is not whether perceptual skills will be centrally important. The question is when, and in what domain, analysts will be able to build a reliable catalog of digital patterns that provide meaningful “clues” to the underlying reality.
處理大量數位數據時最重要的問題，並非知覺技巧是否具中心重要地位，而是何時以及在何種領域，分析家能夠建立可信的數位模型目錄，為潛藏其下的現實提供有意義的線索。

When that happens, scientists will gain a means to build a prototype for applying perceptual-learning techniques. Given the importance of defusing terrorist plots and mining health and economic data, digital instinct-building is likely to become crucial.
做到這一點時，科學家將有方法建立應用知覺學習技能的原型。由於揭穿恐怖分子陰謀和挖掘健康與經濟數據十分重要，數位直覺的建立可能會變得至關緊要。

For now, it is easier to invite a visually creative expert over to the lab, to see what he or she can add.
目前，邀請視覺創意專家到實驗室來，看看他或她能幫上什麼忙，是比較容易的。

“It’s not just about bigger machines to crunch more data, and it’s not even about pattern recognition,” Mr. Kohn, the painter, said. “It’s about frameworks of recognition; how you choose to look, rather than what you’re trying to see. Scientists often think of visual images like graphs as the end result of their analysis. I try to get them to think visually.”
畫家科恩說：「這不只是用大機器去咀嚼更多數據，這甚至和模式認知無關，這是認知架構的問題，你如何選擇去看勝過於你想看到什麼。科學家常把圖表這類視覺影像當作他們分析的最終結果，我試著讓他們以視覺思考。」

原文參照：
http://www.nytimes.com/2015/03/29/sunday-review/learning-to-see-data.html

2015-04-14聯合報/G5版/UNITED DAILY NEWS 莊蕙嘉譯 原文參見紐時週報十版下