The Cognitive Limits of Lifelong Learning

關(guān)于終身學(xué)習(xí)的認(rèn)知局限

Edoardo Campanella 愛德華多·坎帕內(nèi)拉

As new technologies continue to upend industries and take over tasks once performed by humans， workers worldwide fear for their futures. But what will really prevent humans from competing effectively in the labor market is not the robots themselves， but rather our own minds， with all their psychological biases and cognitive limitations.

In today's fast-changing labor market， the most in-demand occupations– such as data scientists， app developers， or cloud computing specialists – did not even exist five or ten years ago. It is estimated that 65% of children entering primary school today will end up in jobs that do not yet exist.

Succeeding in such a labor market requires workers to be agile lifelong learners， comfortable with continuous adaptation and willing to move across industries. If one profession becomes obsolete – a change that can happen virtually overnight – workers need to be able to shift nimbly into another.

Lifelong learning is supposed to provide the intellectual flexibility and professional adaptability needed to seize opportunities in new and dynamic sectors as they emerge，as well as the resilience to handle shocks in declining industries. Training centers， the logic goes， simply need to identify the competencies that companies will look for in the future and design courses accordingly.

Yet， in the eurozone， only about 10% of the labor force undertook some type of formal or informal training in 2017， and the share declined sharply with age. If lifelong learning is the key to competing in the labor market，why are people so reluctant to pursue it？

Lifelong learning is viewed as extremely costly in terms of time， money， and effort， and the returns are regarded as highly uncertain， especially amid technological disruption. Such views may be reinforced by the feelings of depression and hopelessness that often arise when workers lose their jobs or face career crossroads.

Human beings experience a decline in cognitive performance relatively early in life， with fluid intellectual abilities – associated with working memory， abstract reasoning， and the processing of novel knowledge – beginning to decline around age 20. After middle age， these abilities deteriorate substantially， making the acquisition of new skills increasingly challenging. Only our crystalized cognitive abilities， related to communication and management skills， improve later in life.

This reflects centuries of evolution. In almost any society，age is associated with wisdom， experience， and growing social status. Youth was the time for learning the fundamentals of the profession that one would practice throughout adulthood. Once in that job， a worker would refine their skills as they gained experience， but they would probably not have to learn new competencies from scratch.

Today's training programs are ineffective partly because they usually target fluid intellectual abilities. For companies， the conclusion seems to be that retraining a workforce is too challenging， so when new skills are needed， it is better to pursue alternatives like automation，offshoring， and crowdsourcing.

The assumption that workers， regardless of their age and educational background， will independently do what it takes to keep up with technological change is a fallacy that risks creating an army of unemployed. Such an approach can be expected only of the most highly educated and qualified workers – those whose jobs are usually not even at risk from automation.

This may change in the future， because younger generations are growing up with the expectation of lifelong learning. But， in the meantime， policymakers should take steps to mitigate the complicated mental processes at the root of many people's professional inertia.

As we develop robots with increasingly human-like capabilities， we should take a closer look at our own. Only by learning to overcome – or at least evade –our cognitive limitations can we have long and fruitful careers in the new global economy.

隨著新技術(shù)不斷顛覆各行各業(yè)并接管曾由人類完成的工作，全世界的勞動者都為自己的未來感到擔(dān)憂。但真正令人類無法在勞動力市場展開有力競爭的并不是機器人本身，而是我們自己的頭腦，其存在種種心理偏見和認(rèn)知局限。

在當(dāng)今變化迅速的勞動力市場，那些最受歡迎的職業(yè)在五年或十年前甚至還不存在，例如數(shù)據(jù)科學(xué)家、應(yīng)用程序開發(fā)員或云計算專家。據(jù)估計，在今天上小學(xué)的孩子中，有65%的人最終將從事現(xiàn)在還不存在的工作。

要想在這樣的勞動力市場上取得成功，勞動者需要成為敏捷的終身學(xué)習(xí)者，樂于不斷地適應(yīng)，并愿意跨行業(yè)流動。如果一個職業(yè)遭到淘汰——這種變化幾乎一夜之間就可能發(fā)生，勞動者就需要能夠敏捷地轉(zhuǎn)入另一個職業(yè)。

終身學(xué)習(xí)當(dāng)能提供在具有活力的新行業(yè)出現(xiàn)時抓住其中機遇所需的智識靈活性和職業(yè)適應(yīng)力，以及應(yīng)對衰退行業(yè)中所出現(xiàn)沖擊的恢復(fù)力。從道理上講，培訓(xùn)中心只需確定未來公司需要哪些能力，并相應(yīng)地設(shè)計課程。

然而，在歐元區(qū)，2017年只有約10%的勞動力接受過某種正式或非正式培訓(xùn)，并且這一比例隨年齡增長急劇下降。如果終身學(xué)習(xí)是在勞動力市場展開競爭的關(guān)鍵，那為什么人們?nèi)绱瞬辉高@樣做呢？

人們認(rèn)為，終身學(xué)習(xí)在時間、金錢和精力方面需要付出極高的成本，而回報非常不確定，尤其是在技術(shù)頻頻造成顛覆的情況下。勞動者在失去工作或面臨職業(yè)生涯的十字路口時常常會感到沮喪和絕望，這可能令上述觀點得到加強。

人類在較年輕的時候就會經(jīng)歷認(rèn)知能力衰退，與工作記憶、抽象推理和新知識處理有關(guān)的流動智力在20歲左右開始衰退。中年以后，這些能力大幅下降，從而令新技能的獲取變得越來越具挑戰(zhàn)性。只有與溝通和管理技能有關(guān)的固定認(rèn)知能力會隨年齡增長得到提升。

這反映了千百年的進化。在幾乎任何一個社會中，年齡都與智慧、經(jīng)驗和不斷提高的社會地位相關(guān)聯(lián)。從前，青年時期是一個人學(xué)習(xí)與他整個成年階段將會從事的職業(yè)有關(guān)的基本知識的時候。一旦開始從事那項工作，勞動者就會隨著經(jīng)驗的積累提高自身技能，但他們可能不必從零開始學(xué)習(xí)新的能力。

如今的培訓(xùn)項目之所以效果不佳，原因之一是它們通常針對的是流動智力。對企業(yè)來說，結(jié)論似乎是：重新培訓(xùn)員工太具挑戰(zhàn)性，因此，在需要新技能的時候不如另尋他策，比如自動化、離岸和眾包等。

無論年齡和教育背景如何勞動者都會自主拼盡全力跟上技術(shù)變革的臆斷是一種謬論，可能會帶來一支失業(yè)大軍。只有受教育程度和素質(zhì)最高的勞動者會這樣做，而那些人的工作崗位通常不受自動化威脅。

這在未來可能會發(fā)生改變，因為較年輕的一代又一代人從小就抱有對終身學(xué)習(xí)的預(yù)期。但與此同時，決策者應(yīng)該采取措施緩和造成許多人職業(yè)惰性的復(fù)雜心理過程。

在我們研發(fā)能力與人類越來越像的機器人的同時，我們應(yīng)該對我們自身的能力進行更仔細(xì)的審視。只有學(xué)會克服或者至少是避開認(rèn)知局限，我們才能在新的全球經(jīng)濟中擁有漫長且富有成效的職業(yè)生涯。（李莎譯自世界報業(yè)辛迪加網(wǎng)站7月4日文章）