含有〈生物学〉标签的文章(34)

左撇子的可能优势

【2023-07-01】

@whigzhou: Charlotte Faurie 和 Michel Raymond在1996年提出了一種解釋左撇子的理論,認爲那是一種頻率依賴選擇(frequency-dependent selection)的結果,是說,當左撇子的比例低於某一均衡值時,左撇子會帶來某些優勢,很可能是戰鬥方面的優勢(我猜大概是讓對方感覺更别扭吧),

此後他倆又做了更多研究來驗證該理論,主要提供了三方面的支持證據:

1)首先是衆所周知的事實:男性左撇子比例顯著高於女性,(more...)

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9144
【2023-07-01】 @whigzhou: Charlotte Faurie 和 Michel Raymond在1996年提出了一種解釋左撇子的理論,認爲那是一種頻率依賴選擇(frequency-dependent selection)的結果,是說,當左撇子的比例低於某一均衡值時,左撇子會帶來某些優勢,很可能是戰鬥方面的優勢(我猜大概是讓對方感覺更别扭吧), 此後他倆又做了更多研究來驗證該理論,主要提供了三方面的支持證據: 1)首先是衆所周知的事實:男性左撇子比例顯著高於女性, 2)來自狩獵采集社會的群體閒比較,左撇子頻率的群體閒差異非常大,從3%到27%,而且與群體暴力程度相關,越暴力,左撇子越多,比如著名的雅諾馬米人,接近25%, 3)對抗性運動項目中左撇子比例明顯偏高,有些項目高達50%, 這是其中一篇論文(相關鏈接裏可以找到更多): pubmed.ncbi.nlm.nih.gov/23742683/ 【2024-01-14】 @whigzhou: 又新出炉一篇与左撇子有关的论文,据说左撇子与大五人格和镇痣倾向都显著相关,与经验开放性和神经质正相关,与尽责性、外向性和宜人性负相关,左撇子比例高的州,镇痣上更左倾 doi.org/10.1177/00332941241227521
食物与人类#2:吃还是不吃

食物与人类#2:吃还是不吃
辉格
2018年6月18日

常有人感叹人类食谱之广泛,简直能把什么东西都弄上餐桌,从某些角度看,确实如此,不过这里有几件容易混淆的事情,首先,人类食谱之广泛,主要归功于人类文化的巨大多样性,群体间的饮食习俗差异,以及个体间的口味嗜好差异,假如分解到单个群体或个人,其广度就远不如一本《食材大全》所显示的那么值得惊叹了。

其次,假如我们随便挑几个食俗不像因纽特人那么极端的群体,用赫芬达尔-赫希曼指数(Herfindahl-Hirschman Index)——这是经济学家度量供方离散度的标准方法——来测量食物来源离散度,那么人类得分确实不低,毕竟我们是杂食动物,可是,假如我们把衡量标准换成『有能力消化因而有可能吃多少种食物并从中获取营养』的话,那么得分最高的脊椎动物远不是人类,而是——你或许会吃惊——食草动物(注:除非特别说明,本文所谈论的动物仅限于脊椎动物)。

食草动物也吃肉

因为凡食肉动物和杂食动物吃的东西,食草动物几乎也都能吃,鹿经常被观察到在啃动物尸体,甚至同类的内脏,牛在吃草叶时也常有开点小荤的机会:草丛里的蜗牛,树上掉下来的雏鸟或鸟蛋,死老鼠……河马上岸吃草时甚至偶尔会主动猎杀动物,畜牧业者也早就懂得往牛羊饲料里添加屠宰下脚料。

反过来却不行,食草动物消化纤维素和对付植物毒素的能力太强大了,以至很多被它们当作主食的植物其他动物都吃不了,而食草动物很少吃其主食之外的东西,特别是肉食,并不是因为消化吸收上存在任何障碍,而是一种策略选择:基于它们在生理和技能上的相对优势,把时间和精力花在寻找、争夺和获取肉食上,几乎总是不划算的。

比如一头鹿,在一天中可用于觅食的那几个小时里,若面临两个选择:要么专心吃树叶(并时刻警惕着随时出没的老虎),要么漫(more...)

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7946
食物与人类#2:吃还是不吃 辉格 2018年6月18日 常有人感叹人类食谱之广泛,简直能把什么东西都弄上餐桌,从某些角度看,确实如此,不过这里有几件容易混淆的事情,首先,人类食谱之广泛,主要归功于人类文化的巨大多样性,群体间的饮食习俗差异,以及个体间的口味嗜好差异,假如分解到单个群体或个人,其广度就远不如一本《食材大全》所显示的那么值得惊叹了。 其次,假如我们随便挑几个食俗不像因纽特人那么极端的群体,用赫芬达尔-赫希曼指数(Herfindahl-Hirschman Index)——这是经济学家度量供方离散度的标准方法——来测量食物来源离散度,那么人类得分确实不低,毕竟我们是杂食动物,可是,假如我们把衡量标准换成『有能力消化因而有可能吃多少种食物并从中获取营养』的话,那么得分最高的脊椎动物远不是人类,而是——你或许会吃惊——食草动物(注:除非特别说明,本文所谈论的动物仅限于脊椎动物)。 食草动物也吃肉 因为凡食肉动物和杂食动物吃的东西,食草动物几乎也都能吃,鹿经常被观察到在啃动物尸体,甚至同类的内脏,牛在吃草叶时也常有开点小荤的机会:草丛里的蜗牛,树上掉下来的雏鸟或鸟蛋,死老鼠……河马上岸吃草时甚至偶尔会主动猎杀动物,畜牧业者也早就懂得往牛羊饲料里添加屠宰下脚料。 反过来却不行,食草动物消化纤维素和对付植物毒素的能力太强大了,以至很多被它们当作主食的植物其他动物都吃不了,而食草动物很少吃其主食之外的东西,特别是肉食,并不是因为消化吸收上存在任何障碍,而是一种策略选择:基于它们在生理和技能上的相对优势,把时间和精力花在寻找、争夺和获取肉食上,几乎总是不划算的。 比如一头鹿,在一天中可用于觅食的那几个小时里,若面临两个选择:要么专心吃树叶(并时刻警惕着随时出没的老虎),要么漫游林中寻找尚未过度腐烂的动物尸体,动物尸体能量密度高,消化成本低,一只野兔或许顶得上啃两天树叶的净收益,可同时,大张旗鼓的搜索尸体,扩大了活动范围,提高了自己的活跃度和曝光率,因而更可能被老虎吃掉,也增加了与食腐动物(比如狼)发生冲突的机会。 更要命的是,搜索尸体的结果远不如啃树叶那么确定,很可能连续几天一无所获,况且鹿又不像食腐动物那样具备远距离发现尸体所需要的灵敏嗅觉,也不像老虎那样能够大块吞肉,一次吃下一周所需,相比之下,树叶虽能量密度低,消化成本高,但收益十分确定,因其难消化,竞争者也少,而且竞争者都是无威胁的食草动物。 只有当尸体是沿途偶遇的,并且附近没有危险的竞争者,因而无须承担上述种种风险时,鹿才会去吃,这就好比偷窃,食肉动物是职业小偷,将生计建立在偷窃之上,并为此而发展了高度特化于偷窃的生理机制、行为模式和后天技能,食草动物没有这些优势,但若是有顺手牵羊的便宜机会出现,它们也不会漠然放过。 草饲与谷饲 所以,尽管食草动物拥有强大的纤维素消化和毒素处理能力,但只要在成本与风险无差异的条件下给它们选择,它们还是会偏爱高能量密度、低消化成本和低毒性的食物,野生条件下,成本风险无差异这个条件只是偶尔会满足,而在人工饲养时,由于这些成本和风险转移给了饲养者,而后者拥有的技术又将它们降至极低水平,因而可以轻松满足。 于是我们有了谷饲牛,与草饲相比,谷饲牛长肉快,产肉多,脂肪含量高,容易出雪花,同等产肉量所需土地面积仅为草饲的1/3,这些优点对于谷物充裕而草场相对稀缺(相对于加拿大、澳洲和阿根廷)的美国尤为显著,西欧谷物和草场都稀缺,所以更倾向于往饲料里添加屠宰下脚料,这也是为何疯牛病首先在西欧爆发的缘故。 有些情况下,谷饲不仅有好处,而且不可或缺,比如军马;若是只吃草,马一天至少要花八小时咀嚼草料(这是人工饲喂干草的情况,若自己在草场吃,需十几个小时),每公斤嚼3500-4500下,约需40分钟,而且吃完后三四小时内消化负担极重,其长达20米的小肠在此期间将分泌100多升消化液,随后50多升食糜进入一米多长的盲肠并在那里开始发酵。 这样,每天能用于行军(牵引或骑乘)和作战的时间就十分有限,最多四五个小时,这还得益于马的睡眠很短,每天不到三小时,外加两三个小时的伏坐休息,所以它们能在夜晚继续进食;但若能将部分草料换作谷物,比如燕麦,每公斤咀嚼次数便降至850次,只需十分钟,替换一半即可省下三小时进食时间,并大幅减轻消化负担。 正是谷饲,让优良役马在轻负荷条件下每天能工作多至8-10小时,从而让一些骑兵部队能以每天50-60公里的速度行军(如果能沿路获得补给的话),勉强超出罗马步兵自带给养的行军速度,至少马不再是行军速度的瓶颈。 最优觅食理论 关于特定动物吃什么,不吃什么,偏爱哪些食物,优先寻找哪些食物,当条件改变时食谱会如何改变,以及有关动物食性的其他种种问题,生物学家发展了一套被称为最优觅食理论(optimal foraging theory, OFT)的成本收益分析方法来寻找解释,该理论考虑的因素主要有:觅食的时间成本,失败的几率和自身的风险承受能力,各种食源的竞争强度和自身的竞争优势,在消化能力和消化成本上的相对优势,因暴露在觅食环境中而被捕食的风险,中毒风险,等等。 理解该理论的一个要点是,某种动物花最多时间和努力去寻找,因而事实上也吃得最多的,未必是(且常常不是)它最喜爱的食物,反之,它很少或根本不花精力去寻找某些食物,未必是它消化不了、不爱吃、或没能力获取,而常常是因为,在综合考虑上述因素之后,它“发现”,把时间精力投入在寻觅该食物上并不合算,要么失败风险高的难以承受,要么边际净收益低于将这份时间精力投入于其他食源的收益。 沿着这条思路,不同动物的食性差异,觅食相关的种种行为模式,以及人类饮食习俗的形成与变迁,都将得到更为深入也更系统化的理解。 食草与食肉 这是最鲜明的一组对比,但这对名称本身并未揭示出这一对比的要点所在,关键区别其实并不在于食物来自植物还是动物,而在于对待风险的策略差异:食草动物代表了策略光谱的稳妥保守一端,而像猫科这样的顶级食肉动物则代表了冒险激进一端。 捕猎是高风险活动,专以捕猎为生更是高风险生存策略;捕猎成功率往往很低,而且越是大型猎食者越倾向于大型猎物,而猎物越大,成功率越低,猫科之王老虎的成功率只有5-10%,北极熊10%,狼14%,非洲狮18%,体型苗条的猎豹成功率高的出奇,40-50%,但猎物经常被抢走;对于大型猎食者,连续几天空手而归的情况很平常,他们就像赌场里喜欢博大输赢的赌客,赢上一把够吃上一阵,但经常输个精光。 相比之下,草虽然营养密度低,摄食时间长,消化负担重,但分布广泛,供给充分,收益非常确定,一份付出一份回报,是勤恳吃苦耐劳者可以依靠的生计来源;但具备这些特征的食物未必来自植物,在海滩捡拾贝类,在蚁穴舔食蚂蚁或白蚁,在河流入海口捕捞洄游鱼群,都更像是采集而非捕猎,那些以此为生的动物,在生理特征和行为模式上更靠近食草动物。 比如在食蚁兽身上,你看不到食肉动物的典型特征:大脑发达,认知能力强,活跃好动,好奇心强,爱探索,爱玩耍,反倒有许多食草动物的特征:安静,不好动,重防御,以及高度特化的摄食与消化系统:能快速伸缩的超长舌头,高粘度的唾液,胃内用于碾碎昆虫的搓板状结构(类似鸟类的嗉囊),分泌的胃酸是甲酸而非常见的盐酸;类似的,以洄游鱼群为主食的人类族群,其文化与社会结构的各方面都更像农耕者而非狩猎者。 风险策略上的分化,起初可能只是因为所处环境不同,比如在空旷平坦的大草原上捕猎,比在温带森林中困难的多,因为最普遍的捕猎方式是偷偷靠近然后突然袭击,老虎和豹在扑袭之前通常会贴近猎物到十几米甚至几米以内,这一战术需要有足够多的掩蔽物,树丛、土丘、岩石、沟壑,或特别高的草,只有像猎豹这样速度优势极为显著的猎手才会在百米之外就发动进攻,或者像非洲野狗这样的团队捕猎者,能靠合作与耐力长途追逐猎物将其拖垮;总之,在那些捕猎难度过高的生态位(另一个例子是热带雨林的树冠层)中,动物更可能向保守稳妥的食草、食果或食虫发展,反之亦然。 然而一旦策略分化出现并长期持续,进化机制便会将这一差异扩大并固化下来,选择压力作用之下,主吃低营养密度食物的动物,重点发展消化和防御,而食肉动物则重点发展搜索、追踪、潜行、擒杀等捕猎技能,这些特征,相信大家都已耳熟能详,这里我仅以视觉为例略加说明。 同样是两只眼睛,食草动物更注重视野广度,以便全方位探知正在靠近的捕猎者,所以双眼分别朝向头部两侧,视野重叠少,比如牛的视野广度330度,重叠部分仅20-50度,马的视野350度,重叠65度,而食肉动物则高度依赖立体视觉和景深感知,因而两眼向前,形成双眼视觉,视野广度小,但重叠度大,比如猫的视野200度,重叠140度,这一差别,在食肉猛禽和其他鸟类的对比中也可看到。 随着时间推移,选择了不同觅食策略的动物逐渐被自然选择朝着不同方向改造,从生理构造,行为模式,到生存技能,都发生了与其主要食物对应的特化适应;这些改造是全方位和成套出现的,因为生理结构受着基本生化规律和长期积累的进化包袱的严格局限,其设计空间有限,要强化某方面性能,就不得不在其他方面作出牺牲,比如发达的消化系统往往对应着较小的大脑和较低的认知能力。 而且很多生理/行为特性是连锁的,一个改动将引发一系列相应改动,而食性改变往往是触发连串改动的初始启动因素,因而它总是我们认识一种动物生理、习性和行为模式——以及,对于人类,文化与社会结构——的最佳起点;比如在开阔草原吃草的动物都成群出没,这不是因为它们友爱互助,恰好相反,它们需要同类替它们挡子弹:在开阔地躲避捕食者的最好办法就是往同类群里扎;成群出没的习性极大提升了雄性间的性竞争强度,和交配关系中的雌雄比,继而导致雄性发达的第二性征和巨大的性器官。 重要的是,特化适应是个不断加速的正反馈过程,策略选择与生理/技能改变轮番相互加强:消化能力越提升,食草策略越受青睐,爪牙越锐利,立体视觉越好,捕猎越有优势,食肉策略越受青睐,反之,草叶在食谱中比例越高,对消化系统的选择压力越强,肉类比例越高,对爪牙和双眼视觉选择压力越强,如此循环,走上一条特化的不归路。 专食与杂食 物种(及更大类元)在特化道路上可能会走得很远,考拉几乎只吃桉树叶,而桉叶以营养低、难消化和毒性强而著称,桉叶精油是强效杀虫杀菌剂,只有考拉和一些负鼠有能力对付;对付桉叶的独特能力让考拉占据了一个极少竞争的生态位,但也失去了很多:考拉代谢率非常低,行动迟缓,反应迟钝,活动范围小,每天睡20个小时,清醒时间几乎全部用来嚼桉叶……幸好澳洲没有擅长爬树的大型食肉动物(比如豹)。 猫科则走向另一个极端,它们将捕猎禀赋发展到了极致,但由于几乎专吃肉食,其消化系统处理植物的能力严重退化,比如味觉系统丧失了甜味感受器,而后者是辨别植物营养价值的重要手段;无论朝哪个方向,高度特化都降低了物种的适应灵活性,当食物来源随环境条件而改变,或出现新的天敌或竞争者时,很难转向或掉头。 然而并非所有动物都沿食性特化道路走的很远,熊科和猪科都是高度杂食的,犬科和人科的食谱也相当广泛;杂食让这些动物保持了应对环境变化的适应灵活性,所以熊科里才会既有专吃肉食的北极熊,也有吃素——而且几乎只吃极难消化的竹子——的大熊猫,大熊猫从杂食向素食的转变只有两三百万年的历史(和人类转向肉食的时间差不多),这很好的展示了熊科的灵活性。 当然,大熊猫要是在这个特殊生态位下继续进化几百上千万年,或许也会像考拉一样走上高度特化的不归路,反过来说,杂食性可能恰恰体现了这些动物的祖先所走过的进化道路上,环境条件的摆动更频繁,幅度更大,从未提供充足时间让它们完成食性特化。 机会主义者 杂食性代表了一种觅食策略上的机会主义,在素食-肉食这一光谱上,它显然处于中间位置,不过,这个维度对我们理解该策略并没有多大帮助,我们最好从时间分配的角度看待它,即,在面临各种潜在的觅食机会时,将多少时间分配给自己熟悉且擅长处理的食物源,而多少分配给较为陌生的,新颖的,充满未知因素的,价值不明确的食物源。 让我用一个有点类似的生活问题来说明我的意思:我发现自己在超市买食品时经常面临一个两难:日复一日的买同样的食品,难免让人厌倦,感觉自己错过了太多美味,可是尝试新鲜事物的风险也很高,以我个人经验,其中大部分会让我失望,很多最后进了垃圾桶,所以必须作出权衡:将多少预算分配给新食品?多年前我还不会做饭时,在选择餐馆上也面临同样权衡:每十顿饭里几顿留给熟悉的饭馆,几顿用来探索新饭馆? 一旦我们转换到守旧-探索这一维度上,便发现,原来杂食动物才是策略上的激进分子,它们随时准备捕捉任何出现在面前的新机会,而不是一心专注于自己最熟悉擅长的食物源上;所谓机会主义,就是对特定食物源较少持有内在偏好或固有习惯,对新食源总是持开放态度,某一时刻作何选择,全看哪个机会在此时此刻的有着最高预期收益。 这听起来简单,实则对动物的某些禀赋极具挑战,诚然,杂食性对特定捕食技能——诸如鹰的锐眼,猫的柔韧性,鳄鱼的咬合力——没有那么强的选择压力,可是对综合感官和一般认知能力的要求却很高,因为它要求动物在不断面临新情境、新食源的条件下能够良好辨别物体种类与数量,评估其可食性、营养价值和中毒风险,以及面对不同竞争者时的获胜可能性,正因此,猪、熊、狗普遍有着较高的智力,鸟类中的杂食冠军乌鸦也以高智力出名,更别提人科了。 不妨再以人类职业倾向作类比,许多人偏爱一份稳定职业,有着相对固定的收入,就像食草动物,也有些人是命中注定的连续创业者,朝九晚五这种事情对他们是完全不可接受的,他们是食肉动物,但还有另一些人,他们对职业类型没有任何内在偏好,没有好机会时,也能朝九晚五安心打一份工,可一旦机会出现,比如诱人的跳槽机会,激动人心的创业念头,捞笔外快的良机,则决不会轻易放过,他们是杂食动物——一个并不像其名称所显示的那么中庸的类型。   参考资料 Richard Wrangham: Catching Fire (2009) Wikipadia: Optimal foraging theory Wikipadia: Cattle feeding Wikipadia: Bovine spongiform encephalopathy Wikipadia: Binocular vision Wikipadia: Koala Wikipadia: Eucalyptus Wikipadia: Cat senses Wikipadia: Giant panda How Horses Digest Feed https://aaep.org/horsehealth/how-horses-digest-feed Sleep Requirements of Horses https://ker.com/equinews/sleep-requirements-horses/ Grazing Management for Horses http://agriculture.vic.gov.au/agriculture/livestock/horses/feed-requirements-of-horses/grazing-and-feeding/grazing-management-for-horses Hunting success rates: how predators compare http://www.discoverwildlife.com/animals/hunting-success-rates-how-predators-compare Anteater Facts http://facts.net/anteater/ Lori Marino & Christina M. Colvin: Thinking Pigs https://animalstudiesrepository.org/cgi/viewcontent.cgi?referer=https://www.google.com.au/&httpsredir=1&article=1042&context=acwp_asie The Average Bear Is Smarter Than You Thought https://blogs.scientificamerican.com/thoughtful-animal/the-average-bear-is-smarter-than-you-thought/
可进化性

【2016-12-04】

@innesfry 发布了头条文章:《进化是不可避免的吗?

@innesfry:基因、代谢物、蛋白质和核酸序列拥有的拓扑性质,使进化成为可能。可进化性可能是复杂网络的一个基本特征,达尔文进化可能不仅仅是生物学的组织原理,而且是“物理定律”,是信息在复杂系统中组织的必然结果。

@whigzhou: 此文大意是,一个系统若要成为可进化的,其基因型集合与表现型集合之间的映射关系须满足两个条件:1)每个表现型平均对应足够多等效基因型,这意味着中性变异的概(more...)

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【2016-12-04】 @innesfry 发布了头条文章:《进化是不可避免的吗?》 @innesfry:基因、代谢物、蛋白质和核酸序列拥有的拓扑性质,使进化成为可能。可进化性可能是复杂网络的一个基本特征,达尔文进化可能不仅仅是生物学的组织原理,而且是“物理定律”,是信息在复杂系统中组织的必然结果。 @whigzhou: 此文大意是,一个系统若要成为可进化的,其基因型集合与表现型集合之间的映射关系须满足两个条件:1)每个表现型平均对应足够多等效基因型,这意味着中性变异的概率很高,2)同一表现型所对应的等效基因型在编码空间上相距较近,这意味着小步幅变异(相比大步幅变异)更可能是中性的。 @whigzhou: 反过来说,假如编码冗余率很低,或等效基因型在编码空间上随机分散,则系统是不可进化的 @whigzhou: 我觉得这两个条件还是蛮平凡的~ @茶博未:貌似还有一条:编码空间中各viable的等功能子集之间有足够多的相切(或邻近)区域? @whigzhou: 嗯嗯,可供随机游走的连接通道(我也不知道怎么表达准确),也就是他说的拓扑关系  
[译文]观察大脑的新工具

How the Brain Is Computing the Mind
大脑是如何计算意识的

作者:Ed Boyden @ 2016-02-12
译者:Veidt(@Veidt)
校对:混乱阈值(@混乱阈值)
来源:Edge,http://edge.org/conversation/ed_boyden-how-the-brain-is-computing-the-mind

The history of science has shown us that you need the tools first. Then you get the data. Then you can make the theory. Then you can achieve understanding.
科学的历史告诉我们,首先你需要合适的工具,然后去收集数据,之后你就可以创造理论了,最终你才能获得对事物的理解。

Ed Boyden is a professor of biological engineering and brain and cognitive sciences at the MIT Media Lab and the MIT McGovern Institute. He leads the Synthetic Neurobiology Group.
Ed Boyden是MIT媒体实验室和MIT麦戈文研究院的一名生物工程和大脑与认知科学方向的教授。他领导着MIT合成神经生物学研究小组。

HOW THE BRAIN IS COMPUTING THE MIND
大脑是如何计算意识的

How can we truly understand how the brain is computing the mind? Over the last 100 years, neuroscience has made a lot of progress. We have learned that there are neurons in the brain, we have learned a lot about psychology, but connecting those two worlds, understanding how these computational circuits in the brain in coordinated fashion are generating decisions and thoughts and feelings and sensations, that link remains very elusive. And so, over the last decade, my group at MIT has been working on technology, ways of seeing the brain, ways of controlling brain circuits, ways of trying to map the molecules of the brain.

我们如何才能真正地认识到大脑是如何计算着意识的?在过去百年中,神经科学研究在这方面获得了长足进步。我们已经了解到大脑中有着巨量的神经元,也对心理学有了许多认识,但想要把这两个领域联系起来,去理解这些大脑中的计算电路是如何通过合作来产生决策、思想、感觉和情感,则并非易事,人们目前对其中的关联仍知之甚少。正因此,在过去十年中,我在MIT领导的研究小组一直致力于研究相关方面的技术,以期找到观测大脑,控制脑内回路,以及在大脑内部定位分子的方法。

At this point, what I’m trying to figure out is what to do next. How do we start to use these maps, use these dynamical observations and perturbations to link the computations that these circuits make, and things like thoughts and feelings and maybe even consciousness?

目前,我正在试图弄清我们下一步应该做些什么。我们能够如何利用这些分子定位图——也就是一些动态的观测和扰动——来将脑内电路的计算过程与思想,感觉,甚至是意识这些东西联系在一起?

There are a couple of things that we can do. One idea is simply to go get the data. A lot of people have the opposite po(more...)

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How the Brain Is Computing the Mind 大脑是如何计算意识的 作者:Ed Boyden @ 2016-02-12 译者:Veidt(@Veidt) 校对:混乱阈值(@混乱阈值) 来源:Edge,http://edge.org/conversation/ed_boyden-how-the-brain-is-computing-the-mind The history of science has shown us that you need the tools first. Then you get the data. Then you can make the theory. Then you can achieve understanding. 科学的历史告诉我们,首先你需要合适的工具,然后去收集数据,之后你就可以创造理论了,最终你才能获得对事物的理解。 Ed Boyden is a professor of biological engineering and brain and cognitive sciences at the MIT Media Lab and the MIT McGovern Institute. He leads the Synthetic Neurobiology Group. Ed Boyden是MIT媒体实验室和MIT麦戈文研究院的一名生物工程和大脑与认知科学方向的教授。他领导着MIT合成神经生物学研究小组。 HOW THE BRAIN IS COMPUTING THE MIND 大脑是如何计算意识的 How can we truly understand how the brain is computing the mind? Over the last 100 years, neuroscience has made a lot of progress. We have learned that there are neurons in the brain, we have learned a lot about psychology, but connecting those two worlds, understanding how these computational circuits in the brain in coordinated fashion are generating decisions and thoughts and feelings and sensations, that link remains very elusive. And so, over the last decade, my group at MIT has been working on technology, ways of seeing the brain, ways of controlling brain circuits, ways of trying to map the molecules of the brain. 我们如何才能真正地认识到大脑是如何计算着意识的?在过去百年中,神经科学研究在这方面获得了长足进步。我们已经了解到大脑中有着巨量的神经元,也对心理学有了许多认识,但想要把这两个领域联系起来,去理解这些大脑中的计算电路是如何通过合作来产生决策、思想、感觉和情感,则并非易事,人们目前对其中的关联仍知之甚少。正因此,在过去十年中,我在MIT领导的研究小组一直致力于研究相关方面的技术,以期找到观测大脑,控制脑内回路,以及在大脑内部定位分子的方法。 At this point, what I’m trying to figure out is what to do next. How do we start to use these maps, use these dynamical observations and perturbations to link the computations that these circuits make, and things like thoughts and feelings and maybe even consciousness? 目前,我正在试图弄清我们下一步应该做些什么。我们能够如何利用这些分子定位图——也就是一些动态的观测和扰动——来将脑内电路的计算过程与思想,感觉,甚至是意识这些东西联系在一起? There are a couple of things that we can do. One idea is simply to go get the data. A lot of people have the opposite point of view. You want to have an idea about how the brain computes, the concept of how the mind is generating thoughts and feelings and so forth. Marvin Minsky, for example, is very fond of thinking about how intelligence and artificial intelligence can be arrived at through sheer thinking about it. 的确有一些我们能做的事情。其中的一个主意就只是从这些分子定位图中获取数据。但有很多人持有相反的意见。他们希望获得关于大脑是如何进行计算的,意识是如何产生出思想和感觉的,以及诸如此类的一些观点和概念。例如,Marvin Minsky(译者注:马文·明斯基,计算机科学家,人工智能领域的奠基人之一)就非常热衷于通过纯粹的思考来解决智能和人工智能是如何实现的这个问题。【编注:这句原文的字面意思是『Marvin Minsky就非常热衷于思考如何能够通过纯粹的思考来解决智能和人工智能是如何实现的这个问题』,Minsky的工作重点好像不是这种二阶思考,疑似作者笔误。】 On the other hand, and it’s always dangerous to make analogies and metaphors like this, but if you look at other problems in biology like, what is life? how do species evolve? and so forth, people forget that there are huge amounts, centuries sometimes but at least decades of data that was collected before those theories emerged. 但另一方面,做出这样的类比和隐喻总是十分危险的。如果你去看看生物学中的其它一些问题,例如“什么是生命?”“物种是如何进化的?”以及类似的种种,人们在提这些问题的时候忘记了一个事实:那就是在理论出现之前,研究者们已经收集了大量的数据,数据的时间跨度有时长达数个世纪,至少也有几十年。 Darwin roamed the Earth looking at species, looking at all sorts of stuff until he wrote the giant tome, On the Origins of Species. Before people started to try to hone in on what life is, there was the tool development phase: people invented the microscope. 达尔文在他的环球旅行中观察了许多物种,他仔细观察着关于这些物种的一切,最终写出伟大的巨著《物种起源》。在人们开始尝试研究“生命是什么”这个问题之前,必经的一步是工具的发展:有人发明了显微镜。 People started looking at cells and watching them divide and so forth, and without those data, it would be very hard to know that there were cells at all, that there were these tiny building blocks, each of which was a self-compartmentalized, autonomous building block of life. 在那之后人们才开始观察细胞,观察它们的分裂和其它种种行为,如果没有这些数据,人们甚至很难发现细胞的存在,而生命正是由这些微小的,独立自治的“小积木”搭建而来的。 The approach I would like to take is to go get the data. Let’s see how the cells in the brain can communicate with each other. Let’s see how these networks take sensation and combine that information with feelings and memories and so forth to generate the outputs, decisions and thoughts and movements. And then, one of two possibilities will emerge. 在这里,我想采用的方法是从其中获取数据。让我们来看看大脑中的细胞是如何彼此交流信息的,看看这些细胞构成的网络如何获得感觉,并将这种信息与感情,记忆,还有其它类似的东西组合在一起来生成输出信号,决策,思想和动作。之后,我们将会看到两种可能性之一的出现。 One will be that patterns can be found, motifs can be mined, you can start to see sense in this morass of data. The second might be that it’s incomprehensible, that the brain is this enormous bag of tricks and while you can simulate it brute force in a computer, it’s very hard to extract simpler representations from those datasets. 一种可能性是,我们可以从中发现一些模式,挖掘出一些主旨,并开始从这堆乱糟糟的数据中寻找一些理论了。另一种可能性则是,我们仍然无法理解其中的奥妙,由于大脑中所包含的复杂机制是如此之多,虽然我们可以简单粗暴地在计算机中进行模拟,但想要从这些数据集中抽取出一些相对简单一点的模型仍然是非常困难的。 In some ways, it has to be the former because it’s strange that we can predict our behaviors. People walk through a city, they communicate, they see things, there are commonalities in the human experience. So that’s a clue; that’s a clue that it’s not an arbitrary morass of complexity that we’re not going to ever make sense of. 从某种角度说,第一种可能性应该是对的,虽然很奇怪,但人们的确已经获得了预测自身行为的能力。人们会在城市中穿行,会相互交流,会看到形形色色的事物,在人类的生存体验中存在诸多这样的共同之处。所以现在我们至少有了点线索,我们知道自己所面临的并不是一堆混乱到我们完全无法搞清楚其中意义的随机复杂性。 Of course, being a pessimist, we should still always hold open the possibility that it will be incomprehensible. But the fact that we can talk in language, that we see and design shapes and that people can experience pleasure in common, that suggests that there is some convergence that it’s not going to be infinitely complex and that we will be able to make sense of it. 当然,从悲观主义者的视角来看,我们仍然需要对第二种可能性抱以开放的态度,也就是我们的确可能无法理解这个问题。但人们能够使用语言交谈,能够辨别并设计不同形状,还能够获得共同的愉悦体验,这些事实都表明我们所要研究的对象是存在一些收敛性质的,至少我们所面对的不是无穷无尽的复杂性,而我们也的确能够从中找到一些规律。 Biology and brain science are not fundamental sciences in the sense that physics is. In physics, there are particles and there are forces, and you could write down a very short list of those things. But if you’re thinking about the brain and the brain is going to have these cells called neurons, and the neurons have all these molecules that generate their electrical functions and their chemical exchanges of information, those are encoded for by the genome. 生物学和脑科学并不是像物理一样的基础科学。在物理学中有质点和各种力的概念,你可以很容易地将所有这些概念列在一张很短的清单上。但想想大脑吧,大脑中有一些被称为神经元的细胞,这些神经元又是由许多不同的分子构成的,神经元正是靠这些分子来产生电信号并通过化学递质交流信息,而所有这些分子则都被编码在了基因组中。 In the genome, we have, depending on who you ask, 20,000- to 30,000-odd genes, and those genes produce gene products like proteins, and those proteins generate the electrical potentials of neurons and they specify at least some parts of the wiring. The way that I look at it is we’re going to want to understand the brain in terms of these fundamental building blocks, and we can always try to ignore some detail, this concept of the abstraction layer. 基因组中大约有2万到3万个基因(研究者们在基因的具体数目这个问题上存在一些分歧),这些基因能够生产出像蛋白质这样的基因产物,而其中一些蛋白质又生成了神经元中的高低电位,因而它们也指定了神经电路中至少某些部分的构成方式。关于大脑,我认为目前我们所要了解的是这些基础的组成部件,而我们总是可以尝试去忽略掉一些细节,从抽象层上去理解其中的概念。 Can we ignore everything below a certain level of description and just focus on the higher level concepts? But modern neuroscience is now almost 130 years old, since the neuron was discovered, and so far, the attempts to ignore below certain levels of description have not yielded universally accepted and explanatory theories of how our brains are computing our thoughts or feelings or movements. 我们真的能够忽略掉某个特定描述层次之下的一切,而仅仅把注意力集中在更高层次的概念上吗?自从神经元被发现至今,现代神经科学的发展已经有近130年历史了,但那些尝试忽略掉某些特定描述层次以下的微观机制的努力,至今还没能产生出能够被广泛接受并具有解释力的理论,来回答大脑是如何计算出我们的思想、感受或是动作的这些问题。 The way that we approach things is pretty radically different from the past. The premise that I launched my research group at MIT on was that we needed new technology. The reason people are shying away from these very, very detailed measurements of brain function, getting the deep data, was because we didn’t have the tools. The history of science has shown us that you need the tools first. Then you get the data. Then you can make the theory. Then you can achieve understanding. No theory with no technology. It’s very difficult to know that you’ve solved it. 而我们团队目前处理问题的方式与之前的则有着非常明显的区别。我在MIT成立这个研究小组所基于的一个前提就是我意识到我们需要新的技术。人们之所以会回避这些对于大脑功能非常细节化的测量,原因在于我们并没有获得合适的工具。科学的历史告诉我们,首先你需要合适的工具,然后才能去收集数据,之后你就可以创造理论了,而最终你将获得对事物的理解。没有合适的技术就无法创造出好的理论。因为你很难确定自己的理论是否真的解决了问题。 Before Newton’s Laws, there were lots of people like Kepler and Galileo who were watching the planets, and they had decades and decades of data. Why don’t we have that for the brain? We need tools for the brain like the telescope and the microscope, and now, we need to collect the data, ground truth data, if you will, where we can see all those cells and molecules in action, and then, we’re going to see a renaissance in our ability to think of and learn about the brain at a very detailed level, but to extract true insight from these datasets. 在牛顿定律之前,很多人都曾经观察过行星的运动(例如开普勒和伽利略),而他们已经积累了数十年的数据。在针对大脑的研究中,我们为什么不做相同的事情呢?在对大脑的研究中,我们首先需要找到像天文学中的望远镜和生物学中的显微镜一样的有效工具,之后我们所要做的就是收集真实的基础数据,如果你愿意的话,我们现在已经能从数据中看到所有的那些细胞和分子的运动,之后,我们将能够欣喜地看到自己获得了从非常细节的层次上思考和学习大脑的能力,同时也能够从那些收集到的数据集中获得一些真正的洞见。 Let’s think for a second about the hypothesis that biology is not a fundamental science. If you think about books like The Structure of Scientific Revolutions, this and other attempts to explain the path of science, we often have these models: here’s my hypothesis, somebody comes along and disproves it, and if it’s a big enough disproof, you get a revolution. 让我们花点时间想想“生物学不是一门基础科学”这个假说。想象一下托马斯·库恩的《科学革命的结构》这本书,还有其他一些试图解释科学发展之路径的著作,在这些书中我们通常会看到这样的模式:首先我提出了一个假说,然后有人出来对这个假说提出反对意见,如果这个反对意见足够重大,那么这就可以被称之为一项“革命”。 But let’s think about biology: suppose I want to figure out how a gene in the genome relates to an emergent property like intelligence or behavior or a disease like Alzheimer's. There are so many genes in the genome, most hypotheses are probably wrong just by chance. What are the chances that you got the exact gene that’s most important for something? And even if you did, how do you know what other genes modulate it? It’s an incredibly complicated network. 但让我们想想生物学吧:假设我想要找到基因组中的某个基因是如何与某个重要属性(例如智力、行为或者是阿尔茨海默症这样的疾病)发生关联的。基因组中的基因数量如此之多,从概率上看,大多数假说大概都是错误的。对于某种属性,你能准确地找到对它而言最重要的那个基因的概率有多大?而即使你找到了这个基因,你又如何知道有哪些其它的基因会对它发挥调控作用?这个网络的复杂程度简直令人难以置信。 If you started thinking of how different genes of the genome, how their products interact to generate functions in cells or in neurons or networks, it’s a huge combinatorial explosion. Most hypotheses about what a gene is doing, or especially what a network of genes is doing, much less a network of cells in the brain, they’re going to be incorrect. That’s why it’s so important to get these ground truth descriptions of the brain. 而如果你开始思考基因组中的不同基因所生产出的基因产物之间是如何通过互动在细胞中,或者神经元和神经网络中,产生不同的功能的,那么你将面临一个组合大爆炸了。关于某个基因的功能是什么,尤其是某个基因网络的功能是什么,人们所提出的绝大多数假说都将被证明是错误的,更不用提大脑中的某个细胞网络的功能是什么了。这就是为什么我们需要获得真实的关于大脑的基础性描述的原因。 Why can't we map the circuits and see how the molecules are configured, and turn on or off different cells in the brain and see how they interact? Once you have those maps, we can make much better hypotheses. I don’t think the maps of the brain equal the understanding of the brain, but the maps of the brain can help us make hypotheses and make them less assumption-prone, make them less likely to be wrong. 为什么我们不能绘制出大脑中的神经电路并看看其中的分子是如何装配的,然后通过打开或者关闭大脑中的不同细胞来看看它们是如何交互的呢?一旦你能够绘制出这些电路图,我们就能够提出比现在好得多的假说了。我认为这种将大脑比作一张神经电路图的观点并不等于对大脑的正确理解,但将大脑比作神经电路图的做法的确能够帮助我们提出更好的假说,并让这些假说变得不那么依赖于前提假设,同时也降低它们的错误概率。 One thing that I hope a circuit description of the brain will help us understand about humanity is, as we know from psychology, there are countless unconscious processes that happen. One of the most famous such experiments is you can find regions of the brain or even single cells in the brain that will be active even seconds before people feel like they’re making a consciously-willed decision. That leads to what you might maybe slightly jokingly say, we have free will but we’re not conscious of it. Our brains are computing what we’re going to do, and that we’re conscious after the fact is one interpretation of these studies. 我希望这种关于大脑的神经电路式描述能够帮助我们理解人性,而其中一个方面就是我们已经从心理学中所了解到的无数无意识过程的发生。在这方面最著名的实验之一就是人们发现大脑中的某些区域或者甚至是某些细胞会在人们感受到自己正在做出一个意识清醒的决定的数秒之前就开始变得活跃。这让我们能够半开玩笑地说,人们的确拥有自由意志,只是自己还没意识到而已。对这些研究结果的一种解读方式是,大脑已经计算出了我们会在接下来做什么,而我们是在之后才意识到这一点。 What I suggest though is that if we peek under the hood, if we look at what the brain is computing, we might find evidence for the implementation or the mechanisms of feelings and thoughts and decisions that are completely inaccessible if we only look at behavior, or if we only look at the kinds of things that people do, whereas if you find evidence that something you’re about to do, something you’re about to consciously decide, your brain already has that information in advance. Wouldn’t it be interesting to know what’s generating that information? Maybe there are free will circuits, quote, unquote, in the brain that are generating these decisions. 但我想要建议的是,如果我们试着去一窥面纱之下的风景,去看看大脑到底在计算些什么的话,我们也许能够找到一些关于感受、思想和决策的实现方法或机制的证据,而仅仅通过观察人们的行为或是人们会做哪些事情是完全无法获得这些证据的,因为当你意识到你将会做某件事情,或者是清醒地做出某个决定的时候,你的大脑已经提前获得了这些信息。了解是什么产生了这些信息难道不是一件很有趣的事情吗?也许在大脑中存在着生成“ 自由意志”的神经电路来负责产生这些决策呢。 We know all sorts of other things that occur, feelings that our brains are generating, and we have no idea about what’s causing them. There are very famous examples where somebody who has an injury to a part of their brain that is responsible for conscious vision, but you tell them when you see something, I want you to have a certain feeling, or when you see something, I want you to imagine a certain kind of outcome, and people will have that occur even though they’re not consciously aware of what they’re seeing. 我们还知道很多大脑中会发生的其它事情,例如大脑会产生感受,但我们完全不知道是什么导致了这些事情的发生。在这些方面有些著名的例子,例如有些人大脑中负责有意识的视力的部分受到了损伤,但如果你告诉他们“当你看见某种东西的时候,我希望你能产生某种特定的感受”,或者“当你看见某种东西的时候,我希望你能够想象某种特定的结果”,那么他们就真的会产生这种感受或是想象出这种特定的结果,即使他们并不能清醒地意识到自己看见了什么。 There is so much processing that we have no access to, and yet, it’s so essential to the human condition for and decisions and thoughts, and if we can get access to the circuits that generate them, that might be the fastest route to understanding those aspects of the human condition. 至今我们仍然没有任何途径去研究大脑中很大一部分处理过程,然而它们对于人类的决策和思维至关重要,一旦我们能够找到办法去研究那些生成它们的神经电路,那么这也许将成为理解人类状态中的这些方面最快捷的途径。 I’ve been thinking a lot over the last decade primarily about the technology that helped us figure out what we need to understand about the brain in terms of circuits and how they work together. But now that those tools are maturing, I’m thinking a lot about how we use these tools to understand what we all care about. 在过去十年中,我花了很多时间去思考如何发展那些能够帮助我们从神经电路和它们共同工作的机制方面去理解大脑的技术。现在这些工具开始慢慢成熟了,我会花更多时间去思考我们能够如何利用这些工具去理解我们共同关心的那些问题。 Up until now, we mostly have been giving our tools out to other neuroscientists to use. We’ve been focusing very much on technology invention, and other groups have been discovering profound things about the brain. I’ll just give you a couple of examples. 目前为止,我们主要还是在将这些工具提供给其他的一些神经科学家使用。我们主要关注的是技术的研发,而其它一些研究小组则致力于探索关于大脑的一些意义重大的事情。这里我将举两个例子。 There’s a group at Caltech and they use one of our technologies, a technology that makes neurons activatable by pulses of light. They put these molecules into neurons deep, deep in the brain, and when you shine light, those neurons are electrically active, just like when they’re normally being used. They found that there are neurons deep in the brain that trigger aggression or violence in mice, so they would activate these neurons and the mice would attack whatever was next to them, even if it was just a rubber glove. 加州理工大学的一个研究小组使用了我们的一种技术,这种技术能够通过光脉冲让神经元处于可激活状态。他们将这些分子放置在大脑非常深处的神经元中,当你发出光信号时,这些神经元就会处于电活跃状态,就像它们平时发挥作用时一样。他们发现大脑深处的某些神经元会触发小白鼠的攻击性或暴力倾向,于是他们就激活了这些神经元,而之后小白鼠就会攻击它们身旁的一切东西,即使是一只橡胶手套。 I find it fascinating to think about something as ethically charged, as essential to the human condition, as involved with our justice system and all sorts of stuff, as violence. You can find a very small cluster of neurons that, when they’re activated, are sufficient to trigger an act of aggression or violence. So of course, now, the big question is what neurons connect to those? Are they violence detectors? Oh, here is the set of stimuli that makes us now decide, oh, I should go attack this thing next to me even if it’s just a glove. 我发现思考诸如暴力之类概念是一件非常令人着迷的事情,它们在道德上受到谴责,但对人类具有重要影响,并且被包含在我们的司法系统中。你能够找到一小簇神经元,当它们被激活时,就足以触发攻击性或是暴力行为。那么当然,现在最大的问题就是哪些神经元是与它们相关的?这些神经元能够用于探测暴力行为的发生吗?“噢,这儿有一组让我们马上作出决定的刺激信号,噢,我应该去攻击我身边的这个东西了,即使它是一只手套。” And then, of course, where do these neurons project? What are they driving? Are they driving an emotion, and downstream of that emotion comes the violent act? Or are they just driving a motor command: go attack the glove next to you? For the first time, you can start to activate very specific sets of cells deep in the brain and have them trigger an observable behavior, but you can also ask, what are these cells getting, what are these cells sending messages to, and looking at the entire flow of information. 然后,理所当然的问题就是这些神经元是在哪里得到表现的?它们驱动的又是什么?是它们驱动了某种情感,然后这种情感顺流而下的发展导致了暴力行为的发生吗?或者说它们只是驱动了某种机械指令:攻击你身边的那只手套!?有史以来第一次,你能够去激活大脑深处的那些特定的细胞组,并且触发它们的某种可观测的行为,但同时你还可以发问,这些细胞获得了什么,它们在向哪些对象发送消息,而你能够看到这其中完整的信息流。 I’ll give you another example that is fascinating. One of my colleagues at MIT, Susumu Tonegawa, trained mice on a learning task, so that certain neurons in the brain become activatable by light. They used some genetic tricks to do that. 还有另一个令人着迷的例子。我在MIT的一位同事,利根川进(译者注:日本生物学家,因“发现抗体多样性的遗传学原理”获1987年诺贝尔生理学或医学奖)用一个学习任务来训练小白鼠,使小白鼠脑内的某些特定神经元处于可被光信号激活的状态。他们使用了一些基因技巧来进行这个实验。 Now, what happens is those mice can be doing something else much later, they shine light on the brain, and those neurons, the ones that had been activated earlier when they were learning, they get reactivated and the mice make a memory recall. It’s like they were there in the earlier place and time. 而之后所发生的事情是,那些小白鼠在神经元处于该状态很久之后可能正在做着某些别的事情,但一旦研究者们在小白鼠的脑内点亮光信号,那些之前在它们进行学习任务时就已经被激活的神经元会被重新激活,而那些小白鼠则经历了一次记忆唤醒的过程,就像它们还处在之前的时间和地点一样。 That’s interesting because for the first time, they can show that you can cause the recall of a specific memory, and now they are doing all sorts of interesting things. For example, you can activate those cells again, and let’s say that’s a happy memory; let’s say it’s associated with pleasure or a reward. 这个例子的有趣之处在于,这些研究者们第一次证明了人们的确可以唤醒某段特定的记忆,而现在他们仍然在做着各种有趣的事情。例如,你能够再一次激活那些神经元,我们假设那代表着一段快乐的回忆,或者说它与愉悦感或是奖励是联系在一起的。 They have shown that that can have antidepressant effects, that you can have an animal recall, a memory when you shine light on certain neurons, now the memory that is recalled triggers happy emotions; this is how they interpreted it. And that can counteract other stressors or other things that make the animal normally feel not so good. 这些研究者们已经证明了这种记忆唤醒能够产生抗抑郁的效果,他们对此的解释是,你能够通过用光信号照射某些特定的神经元来唤醒动物的某段记忆,这段被唤醒的记忆会触发动物的一些欢快的情感,这些情感能够抵抗某些压力源或其它一些通常会让动物产生不良感受的东西。 Literally, hundreds and hundreds of groups are using this technology that we developed for activating neurons by light to trigger things that are of clinical and maybe even sometimes philosophical interest. 毫不夸张地说,现在已经有数以百计的研究小组采用了我们开发的这种通过光信号来激活神经元的技术,他们使用这种技术来触发一些具有临床意义,有时甚至具有哲学意义的东西。

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I studied chemistry and electrical engineering and physics in college, and decided that I cared about understanding the brain. To me, that was the big unknown. This will seem kind of cheesy, but I started thinking about how our brains understand the universe, and the universe, of course, gives us things like the laws of physics upon which are built chemistry and biology, upon which is built our brain. It’s kind of a loop. I was trying to think about what to do in a career; I thought, what’s the weak point in the loop? And it seemed like the brain was very unknown. 我曾在大学里学过化学,电气工程和物理学,而我最终认定自己最牵挂的是对于大脑的理解。对我来说,那是一个大大的未知领域。下面这段话可能看起来有点肉麻,但当时我开始思考我们的大脑是如何去理解宇宙的,正是宇宙给了我们物理定律,而化学和生物又是建立在这些物理定律的基础上的。这某种程度上构成了一个环。而我试图去思考在我的职业生涯中应该做些什么,我当时所想的是,在这些环节中最弱的一个是什么?看起来大脑是存在最多未知的地方。 I was very impressed by people who would go build technology to tackle big problems, sometimes very simple technology. All the chemists in the 1700s and 1800s who built ways of looking at pressure and volume and stoichiometry, without that, it’s inconceivable that we would have things like the Periodic Table of the Elements and quantum mechanics and so forth. 那些愿意去创造技术以解决重大问题的人们给我留下了深刻的印象,而有时那些技术其实非常简单。例如所有在18世纪和19世纪尝试用各种方法测量压力、体积和其他化学量的科学家们,没有他们的工作,难以想象我们今天能够拥有元素周期表,量子力学和其它的一些科学工具。 What stuck out in my mind was you need to have that technological era, and that then gives you the data that you want, that then yields the most parsimonious and elegant representations of knowledge. And for neuroscience, it seemed like we had never gone through that technological era. There were bits and pieces, don’t get me wrong, like electrodes and the MRI scanner, but never a concerted effort to be able to map everything, record all the dynamics, and to control everything. And that’s what I wanted to do. 当时我脑子里一个挥之不去的念头就是,你只有经历过一个那样的技术时代,才能获得自己想要的数据,然后才能从中产生出最精细最优雅的知识。对于神经科学来说,看起来我们还从未经历过一个那样的技术时代。别误会我的意思,当然我们已经拥有了一些零散的技术手段,例如电极技术和核磁共振扫描仪,但我们从未同心协力去努力获得定位大脑内部发生的一切,记录脑内的所有动态过程,并控制大脑的所有活动的能力。而那正是我想要做的事情。 At the time I started graduate school at Stanford, I went around telling everybody I wanted to build technologies for the brain and to bring the physical sciences into neuroscience. A lot of people thought it was a bad idea, frankly, and I think the reason why was at the time, many people who are physicists and inventors were trying to build tools for studying the brain. But they were thinking forwards from what was fun for them to do, and not backwards from the deep mysteries of the brain. 当时我刚开始在斯坦福大学的研究生生涯,我告诉身边的所有人我希望为探索大脑开发技术手段并将神经科学变成一门自然科学。老实说,当时有很多人都不认为这是个好主意,我觉得他们这么想的原因是,在当时,有许多物理学家和发明家都在试图为研究大脑创造工具,但是他们所想的都是向前看,去研究那些对他们而言有趣的事情,而并没有回过头去探索那些埋藏在大脑深处的谜题。 The key insight that I got during graduate school was if you don’t think backwards from the big mysteries of the brain, and you only think forwards from what you find fun in physics, the technologies you built might not be that important. They might not solve a big problem. What I learned was we have to take the brain at face value. We have to accept its complexity, work backwards from that, and survey all the areas of science and engineering in order to build those tools. 我在研究生阶段所获得的最重要的洞见就是,如果你不能回过头去思考那些关于大脑的谜题,而只是向前去思考那些让你在物理学中觉得有趣的东西,那么你所创造出来的技术可能就不那么重要,它们可能无法被用来解决大的问题。我所学到的是我们需要直面大脑本来的面目,要想创造出那些真正有用的工具,我们就需要接受大脑的复杂性,回过头来以此为目的去调研所有的科学和工程领域。 During the first decade that I’ve been a Professor at MIT, we have mostly been building tools. We built tools for controlling the brain, tools for mapping the detailed molecular and circuit structure of the brain, and tools for watching the brain in action. 在我成为MIT的一名教授之后的首个十年中,我们的主要精力都放在创造工具上。我们创造了用于控制大脑的工具,能够绘制大脑中具体的分子和神经电路结构的工具,还有用于观测大脑活动的工具。 Right now, we’re at a turning point; we’re ready to start deploying these tools systematically and at scale. Don’t get me wrong, the tools still need improvements to be equal to the challenge of studying the brain, but for small organisms like worms and flies and fish, or for small parts of mammalian brains, we’re ready to start mapping them and trying to understand how they’re computing. 现在我们来到了一个重要的转折点上,我们已经做好准备去系统化地大规模部署这些工具来研究大脑了。但请不要误解我的意思,这些工具目前仍然需要得到改进才能足以胜任研究人类大脑这一巨大的挑战性任务,但对于一些较小的有机体,例如蠕虫,蝇类和鱼类,以及哺乳类动物大脑中的一些较小部分,我们已经做好准备去绘制它们的结构并尝试去理解它们是如何进行计算的了。 The work progresses through primarily philanthropic as well as government grant funding. We have been very lucky that there has been a bit of an increase in people interested in funding high risk, high reward things. That’s one reason why I’m at the MIT Media Lab, and you might ask why is a neuroscience Professor in the School of Architecture at MIT? 这些工作的推进主要由慈善基金和政府资助基金提供资金上的支持。我们非常幸运,越来越多的人开始对资助这类高风险,高回报的研究项目感兴趣。而那也是我在MIT媒体实验室工作的原因之一,可能你想问为什么一个神经科学教授会在MIT的建筑学院任职。 As we were discussing earlier, neuroscientists long had a deep distrust of technology, that technologies often didn’t work, the brain was so complicated that the tools could only solve toy problems. When I was looking for a professor job, the search was hit-or-miss. 正如我们之前所讨论过的,神经科学家们长久以来都对技术怀有一种深深的不信任感,他们认为技术通常都起不了什么作用,而大脑是如此复杂,那些被创造出来的工具只能解决一些玩具般的小问题。当我在寻求教职的时候,找工作的过程不确定性很高。 My collaborator, Karl Deisseroth and I had already published a paper showing we could activate neurons with light, a technology that we’ve called ever since “optogenetics,” “opto” for light and “genetics” because it’s a gene that we borrow from a plant to make the neurons light-sensitive. 我和我的合作者Karl Deisseroth当时已经发表了一篇论文表明我们能够通过光信号来激活神经元,这项技术后来一直被我们称作“光基因”(optogenetics),”opto”代表“光”,而”genetics”则代表这是我们从一种植物中提取出的能够让神经元对于光信号敏感的基因。 But a lot of people at the time were still deeply skeptical: is this the real deal or is this yet more not-quite working technology that will be a footnote? I went to the Media Lab to complain about how political and complicated academia was, and I was very lucky; they were wrapping up a failed job search and they said, "Why don’t you come here?" And so I went, and we’ve been incubating a lot of neurotechnology there since then. 但当时很多人仍然对这项技术抱着深深的怀疑态度:这真的是一项重大的技术突破,还是又一种没什么用的仅仅会在将来成为一项脚注的技术?我去MIT的媒体实验室向他们抱怨学术圈的政治和勾心斗角,而这时我的运气来了,当时他们正在总结一次并不成功的求职,于是他们对我说,“要么你到我们这儿来吧?”于是我就去了他们实验室,从那以后我们就开始在这个实验室里培育一大堆的神经技术。 When I first got to Media Lab, a lot of people were deeply puzzled about what I would do there. Was I going to switch into, "classical publicly-perceived Media Lab technology," like would I have developed ways of having cell phones diagnose mental illness or other things like that? I wanted to get to the ground truth of the brain. 当我刚到MIT媒体实验室的时候,很多人都完全搞不清楚我会在那里做些什么。他们怀疑我会不会转向开发一些“经典的受到公众认可的‘媒体实验室技术’”,比如开发一些方法通过手机来诊断精神疾病,或者诸如此类的一些东西。而我所想做的是获得关于大脑的一些基础事实。 In some ways, the Media Lab was a perfect place to start. We could incubate these ideas, these tools out of the cold light of day until they were good enough that neuroscientists could see their value. And that took several years. 从某些角度上看,媒体实验室对我来说的确是一个完美的起点。我们可以避开人们的冷眼,专注于培育创意和技术,直到它们变得足够好,能够让那些神经科学家们看到它们的价值。而这一过程持续了好几年。 It was about a three-year period until this started to get mainstream acceptance, and then, there was another three-year period where people said, wow, how do we get more technology, and that led to initiatives like the Obama BRAIN Initiative, which is an attempt to get widespread technology development throughout neuroscience. 让我们的这些技术受到学界主流的认可花了大约三年时间,而又过了三年时间后有人开始问,哇,太棒了,我们怎么才能获得更多的这类技术?而这导致了之后的一些诸如奥巴马总统的BRAIN计划之类的项目,该计划试图在整个神经科学领域发展一些能够被广泛应用的技术。 The BRAIN Initiative started at the instigation of the Kavli Foundation. They were hosting a series of brainstorms about what nanoscientists and neuroscientists could do together, and Paul Alivisatos and George Church and Rafael Yuste and many people at that border were at these early sessions. BRAIN始于Kayli基金会的大力推动。他们举办了一系列的头脑风暴式的会议以讨论纳米科学家们能够和神经科学家们一同做些什么,Paul Aliyisatos,George Church, Refael Yuste还有其他一些相关领域的科学家们参加了这些早期的会议。 And in late 2012, I was invited to one of these sessions where many inventors were invited and we started talking about maybe brain activity mapping is great and all, but the technologies might be much more broad than that; you might need more than just maps. 2012年末,我应邀参加其中的一次会议。这次会议邀请了许多技术的发明者,我们开始谈论也许绘制出大脑活动的电路图是个伟大的主意之类的话题,但涉及其中的技术范围可能会更宽,因为我们需要的可能不仅仅是一些电路图。 You might need ways to control the brain, ways to rewire the brain. 我们可能需要一些能够控制大脑的方法,还有重连大脑电路的方法。 That was an interesting turning point because it went from activity mapping to broadly technology, and four or five months later, Obama announced this BRAIN initiative which, somewhat recursively, stands for Brain Research for Advancing Innovative Neurotechnologies, and they are now devoting tens to hundreds of millions of dollars a year, depending upon which year, to try to get more technology made to help understand the brain. 那次会议是一个很有趣的转折点,因为从此之后,我们的工作从绘制大脑的电路图拓展到了更宽的技术领域,又过了四五个月,奥巴马总统宣布了他的BRAIN计划,这个计划的首字母缩写看起来像个递归——致力于推动创新神经科技的大脑研究(Brain Research for Advancing Innovative Neurotechnologies),目前人们在这项计划中每年投入高达数千万甚至数亿美元的资金以获得更多能够帮助我们理解大脑的技术。 The BRAIN initiative now is run by different government agencies. They have their own priorities, so, for example, DARPA is very interested in short-term human prosthetics, for example, no surprise there. The National Science Foundation is interested in more basic science, and so forth. The different agencies have their own agendas now. 整个BRAIN计划目前由多个不同的政府机构负责运营。这些机构都有着自己的优先任务,例如,DARPA【编注:全称Defense Advanced Research Projects Agency,美国国防部所辖研究机构】最感兴趣的是短期的人类大脑修复技术(这一点毫不令人惊讶),而国家科学基金会则对于更基础的科学课题更感兴趣,如此种种。而不同的机构现在也都有了他们自己的日程表。 IARPA is involved. They are trying to do a hard push for short-term mammalian brain circuit mapping based upon existing technology, and sort of a small part of that more on the technology development side. Most of the money is on the application side. But we have some new tools that we think can be very, very helpful. IARPA【编注:全称Intelligence Advanced Research Projects Activity,是美国国家情报总监辖下一个研究部门】也同样参与了进来。他们正在努力推进一个通过使用已有的技术绘制哺乳类动物大脑神经电路图的短期计划,其中的一小部分主要是关于技术开发的,而主要的资金则投入到了技术应用上。我觉得我们开发的一些新工具能够在其中派上很大用场。

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Companies are great if you can work hard and be smart and solve the problem. But if you’re tackling something like the brain, or the biggest challenges in biology in general, a lot of it’s serendipity. A lot of it is the chance connections when you bring multiple fields together, when you connect the dots, when you kind of engineer the serendipity and make something truly unpredictable, and that’s hard to do if you have closed doors. That’s hard to do if you don’t allow open, free collaboration. 如果你工作足够努力并且有足够的聪明才智去解决问题,那么企业对你来说会是个不错的去处。但如果你的研究对象是大脑,则可以说是生物学史上最大的挑战,要想获得成功就必须依赖于一些意外的收获了。当你试图将多个领域的知识结合在一起,将点连成线,当你试图去驾驭偶然性并且做出一些真正不可预测的成果时,联系和接触的机会非常重要,如果你关起门来闭门造车,如果你不能允许开放而自由的合作,要想获得成功就太难了。 Our group is very big; I think we’re the second biggest research group at all of MIT. But we work with probably about 100 groups, people who are genomics experts and chemistry experts and people making nanodiamonds and all sorts of stuff. The reason is that the brain is such a mess and it’s so complicated, we don’t know for sure which technologies and which strategies and which ideas are going to be the very best. And so, we need to combinatorially collaborate in order to guarantee, or at least maximize the probability that we’re going to solve the problem. 我们的研究团队规模很大,我想它应该是整个MIT第二大的研究团队了。但我们还与大约100个其它的研究小组进行合作,这些小组中有染色体方面的专家,有化学专家,还有些人的工作是制造纳米金刚石,可以说研究什么的都有。这么做的原因在于,大脑是如此混乱而复杂的一个系统,我们并不能肯定哪些技术,哪些研究策略,哪些主意是最好的。所以,我们需要组合式的合作模式来保证问题被解决,或者至少是将解决问题的概率最大化。 You want to have academia for that serendipitous ability to connect dots and collaborate, and you want companies when it’s time to push hard and just get the thing done and scale up and get it out the door. What I would hope to engineer in the coming maybe decade or so are hybrid institutions where we can have people go back and forth because you might need to have an idea that would go back and forth a bit until it matures. 当你需要一些驾驭偶然性的能力以连点成线并推进合作时,学术圈的氛围是最合适的;但当你需要施加压力来搞定某件事情,并将技术推广以得到广泛应用时,企业又成了最合适的地方。在未来的也许十年中我希望能够做到的是建立起一个混合型的研究机构,这样我们就能够让研究者们在学术和企业的氛围之间迅速地切换,因为我们未来的研究思路可能也需要在两种模式间切换直到它变得足够成熟。 I’ll give you an example. We’re building new kinds of microscopes and new kinds of nanotechnologies to record huge amounts of data from the brain. One of our collaborators was estimating that soon some of these devices we’re making might need some significant fraction of the bandwidth of the entire internet in order to record all the brain data that we might be getting at some point. Now, we need some electronics, right? We need electronics to store all the data and computers to analyze the data. But that’s an industrial thing. 让我给你举个例子。我们正在制造一些新型的显微镜和一些新型的纳米技术以记录大脑中的海量数据。我们的一位合作者曾估计,我们正在制造的这些仪器可能很快就需要整个因特网带宽中不小的一部分以记录我们在某些关于大脑的研究过程中获得的所有数据。现在我们需要电子技术了,对吧?我们需要电子技术以记录所有这些数据,同时还需要足够强大的计算机来对这些数据进行分析。但这就是一个更适合让企业来解决的问题了。 It’s much easier to get that done in a company than in academia because people in industry can turn the crank and make incredible computers, so we started a collaboration. A small startup here in Cambridge, Massachusetts, does these computers with us. Now we’re working on the nanotechnologies, and that fusion of two different institutional designs allows us to rapidly move faster than companies alone or academics alone. These new hybrid models are going to be essential to balance the need for luck and the need for skill and ability. 在企业中搞定这类事情要比在学术界容易得多,因为在企业中人们能够开足马力制造出拥有令人难以置信的计算能力的电脑,所以我们启动了意向合作。在马萨诸塞州剑桥市的一家创业公司和我们合作开发了这些电脑。现在我们又开始研发纳米技术了,将企业和学术这两种类型的机构融合在一起则让我们的研究进程推进得比单独依靠企业或是单独依靠学术界要快得多。在对运气的需求与对技术和能力的需求间取得平衡来说,这类混合型机制将是必不可少的。 The thing that I’m excited about also is how do we get rid of the risk in biology and medicine? Most medicines, most strategies for treating patients, they are found in large part by luck. How do we get rid of the risk? We talked a bit about how there are fundamental sciences like physics, and then, you have higher order sciences like biology. Medicine also might have different scientific methods for different kinds of disease. We have made huge inroads against bacteria and viruses because of antibiotics, because of vaccines. 现在让我感到兴奋的是我们如何能够消除一些生物学和医学研究中的风险。目前多数的药物和治疗策略的发现,在很大程度上都是依靠运气。我们能够如何消除风险?我们之前曾经谈论过一点关于物理这样的基础科学的话题,而之后,我们又有了更高阶的科学领域,例如生物学。医学也同样可能在对待不同类型的疾病时使用不同类型的科学方法。由于有了抗生素和疫苗,我们在对抗细菌和病毒的战斗中获得了巨大的进展。 Why have these been so successful? It’s because we’re trying to help our body fight a foreign invader, right? But if you look at the big diseases, the ones that nobody has anybody clue what to do about, there are brain disorders, a lot of cancers, autoimmune conditions, these are diseases where it’s our body fighting ourselves, and that’s much harder because you can’t just give a drug that wipes out the foreign invader because the foreign invader is you. 为什么我们在这方面做得如此成功?这是因为我们在尝试帮助我们的身体对抗某种来自外界的入侵者,对吧?但其它的一些重大疾病,那些没人知道该怎么对付的疾病,例如大脑的功能紊乱,各种类型的癌症,还有自体免疫病,这些疾病实质上都是我们的身体在与自身进行对抗,要解决这些疾病就困难多了,因为如果入侵者就是你自身的话,你就无法为身体提供一种药物去清除这个入侵者。 How do we understand how to de-risk the tough parts of medicine? We have to think about drug development and therapeutic development from a different point of view. The models that give us new antibiotics and new vaccines and so forth might not be quite right for subtly shifting the activity levels of certain circuits in the brain, for subtly tuning the immune system to fight off a cancer but not so much that you’re going to cause an autoimmune attack, right? 我们该如何化解这些医学难点所蕴含的风险?我们必须从另外一个角度去思考药物和治疗方法的开发。那些引导我们研发出新的抗生素,疫苗和其它一些药物的模型也许在精细地切换大脑中的某些特定神经电路的活跃程度方面并不适用。它们或许也无法既精细地调整免疫系统以击败特定癌症,同时又避免引发对自身免疫系统的攻击,对吧? One thought is, well, if it’s your body fighting yourself, what you want is very deep knowledge about the building blocks of those cells and how they’re configured in the body. The basic premises behind ground truthing the understanding of the brain might be also right what we need in order to de-risk medicine, in order to understand how cells and organs and systems go awry in these intractable disorders. That’s something I’ve been thinking a lot about recently as well: how do we de-risk the goal and methodology and path towards curing diseases? 有一种想法是:如果身体在和自身作战,那就需要深入了解关于那些细胞的基础构成单元以及它们是如何在身体中配置成形的。对大脑的基础事实真正理解的一些基本前提也许在我们降低医学方面的风险,以及理解细胞,器官和组织是如何在顽疾中功能失调方面同样适用。这同样是我最近经常思考的一个问题:我们如何在治疗疾病方面降低那些蕴藏在目标,方法和实现路径之中的风险? There was just a study released about how taking a drug from idea to market can cost $2.5 billion now. And if you look at the really tough diseases like brain diseases, like cancers and so forth, the failure rate to be approved for human use is over 90 percent. 最近发表的一项研究成果显示现在研发一种药物从最初的想法开始到最终被推向市场可能需要花掉25亿美元。而如果你看看那些真正严重的疾病,例如大脑疾病和癌症等,治疗这类疾病的药物最终无法被批准投入使用的概率超过了90%。 This got me thinking that maybe this is the same kind of intellectual problem as why we don’t understand how brain circuits compute thoughts and feelings. We have these large 3D systems, whether it’s a brain circuit or a cancer or the immune system, and knowing how to tweak those cells, make them do the right thing, means finding the subtle differences that make those cells different from the normal cells in our body. I’ve been thinking a lot about how we can try to take these tools that we’ve been developing for mapping the brain, for controlling the brain, for watching the brain in action and applying it to the rest of medicine. 这些事实让我想到也许这与为什么我们无法理解大脑的神经电路是如何计算出思想和感受是同一类的问题。我们现在已经拥有了这些大型的3D系统,不论是大脑电路,癌症或是免疫系统,我们都能得到它们的3D图像,而了解如何通过牵引这些细胞让它们去做正确的事情则意味着找到这些细胞区别于正常细胞的细微不同之处。我花了很多时间思考如何使用这些我们开发的工具,将它们用于绘制大脑神经电路图,控制大脑,观察大脑的活动的工具,并将它们应用在其它医学领域。

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I can tell you about a collaboration that we have with George Church. George’s group for about fifteen years now has been trying to work on a technology called in situ sequencing, and what that means is can you sequence the genetic code and also the expressed genes, the recipes of cells, right there inside the cells? 我可以向你描述一下我们和George Church之间的一项合作。George的团队已经在一项名为“就地排序”的技术研究上花了大约十五年的时间,这项技术意味着你能够直接在细胞内部对遗传序列和那些被表达出来的基因——也就是细胞自身的配方——进行测序。 Now, why is that important? It’s important because if you just sequence the genome, or you sequence the gene expression patterns after grinding up all the cells, you don’t know where the cells are in three-dimensional space. If you’re studying that brain circuit and here is how information is flowing from sensation into memory regions towards motor areas, you’ve lost all the three-dimensionality of the circuit. You just have ground up the brain into a soup, right? 为什么这项技术如此重要?因为如果你只是对基因组进行测序,或者在破坏了细胞结构之后再将这些基因表达的模式进行测序,你就无法知道这些细胞在三维空间中的位置。如果你正在研究某个大脑神经电路,而恰好感觉中的信息正是经由这些细胞流入记忆区进而流向运动区,那么你就丢失了这一神经电路中的所有三维信息,因为你已经把大脑搅成了一锅粥。 Or for a tumor, we know that there are cells that are by the blood vessels, there are stem cells, there are metastasizing cells; if you just grind up the tumor and sequence the nucleic acids, you again have lost the three-dimensional picture. A couple years ago, George’s group published a paper where they could take cells in a dish and sequence the expressed genes. 或者举个肿瘤的例子,我们知道有些肿瘤细胞分布在血管附近,有些是肿瘤干细胞,还有些肿瘤细胞能够转移,如果你将肿瘤打碎并且将其中的核酸进行排序,你同样会丢失它的三维图像。几年前,George的研究团队发表了一篇论文表明他们能够在保持细胞完整性的同时对已经表达的基因进行测序。 That is, you have DNA in the nucleus, that expresses in terms of RNA, which is the recipe of that cell, and the RNA then drives all the downstream production of proteins and other biomolecules. The RNA is sort of in-between the genome and the mature phenotype of the cell. It's kind of the recipe. George’s group was sequencing the RNA. I thought that was amazing: you could read out the recipe of a cell. 也就是说,细胞核中有DNA,DNA通过转录会生成RNA,而RNA则是细胞的配方,在之后它会驱动下游的蛋白质和其它生物分子的生产过程。RNA可以看作某种基因组和细胞的成熟表现型之间的中间产物,它也是一种配方。当George的团队对RNA进行测序时,我觉得这有些不可思议,因为现在我们居然已经能够读懂细胞的配方了! Now, there was a tricky part: it didn’t work well in large 3D structures like brain circuits or tumors. Our group had been developing a way of taking brain circuits and tumors and other complex tissues and physically expanding them to make them bigger. What we do to make the brain or a tumor bigger is we take a piece of brain tissue and we chemically synthesize throughout the cells, in-between the molecules, around the molecules, in that piece of brain, a web of a polymer that’s very similar to the stuff in baby diapers. And then, when we add water, the polymer swells and pushes all the molecules apart, so it becomes big enough that you can see it even using cheap optics. 现在棘手的问题来了:这种技术在大脑神经电路和肿瘤这样的大型3D结构上的表现并不好。我们团队已经在研发了一种能在物理上将大脑神经电路,肿瘤和其它此类复杂组织进行放大的方法。我们用来放大大脑神经电路(或是肿瘤)的方法是获取其中的一小块脑组织,然后通过化学方法在这块脑组织的细胞内部分子之间和分子外部进行合成,最终得到一块像婴儿的纸尿裤一样的网状聚合物。然后我们在其中加入水,这块聚合物会膨胀,并将所有的分子推散,这样它就变得足够大了,即使用一些便宜的光学设备也能看清楚其中的结构。 One of my dreams is you could take a bacterium or a virus and expand it until you can take a picture on a cell phone. Imagine how that could help with diagnostics, right? You could find out what infection somebody has just by making it bigger, take a picture and you’re done. 我的梦想之一就是有一天我们可以将一个细菌或是病毒放大到你能够用手机给它拍照的程度。想想这能在多大程度上帮助人们进行诊断吧,在判断某个病人到底是被什么感染了这个问题时,你只需要将感染物不断地放大,然后给它拍张照片就搞定了。 We started talking with George: what if we can take our sample and expand it and then run their in situ sequencing method—because sequencing, of course, is really complicated. You need room around the molecules to sequence them. This is very exciting to me, if we can take stuff and expand it and then use George’s technology to read out the recipes of the cells, we could map the structure of life in a way. 于是我们去和George谈了这件事情:如果我们能够将我们所采集的样本放大,然后再对放大后的样本使用“就地测序”的方法——测序这项工作本身真的非常复杂,因为分子之间要有足够的空间。这对我来说是件令人兴奋的事情,如果我们能够将这些组织进行采样,然后将它们放大,再使用George发明的技术去读取这些细胞的“配方”,那么我们就能以某种方式绘制出生命的结构。 We can see how all the cells look in a complex brain circuit, or in a tumor, or in an organ that’s undergoing autoimmune attack like in type 1 diabetes. That’s one of the things that excites me most is this in situ sequencing concept. If we can apply it to large 3D structures and tissues, we might be able to map the fundamental building blocks of life. 我们可以看看在一个复杂的大脑神经电路中所有细胞到底是什么样子的,对于一个肿瘤或是一个正在遭受类似I型糖尿病这类自免疫攻击的器官,我们也可以做到同样的事情。这就是“就地测序”这一概念所能做到的最令我激动的事情之一。如果我们能够将这项技术应用到大型的3D结构和组织中,也许就能绘制出生命基本单元的样子。 Our current collaboration with George’s group has been focused very much on small pieces of tissue that we have: mouse brains probably, other model organisms in use in neuroscience. But we know that if they work in those systems, they’ll probably work in human tissues as well. 在目前与George的团队的合作当中,我们的关注点还主要集中在一些比较小的组织切片上:例如老鼠的大脑和其它一些在神经科学中常用的模式生物。但我们知道如果他们的技术在这样的系统中是有效的,那么这些技术大概在人体组织中也同样能发挥作用。 Imagine we get a cancer biopsy from somebody, we use our group’s technology to expand it physically, making everything big enough to see, and then, we can go in and use George’s in situ sequencing technology to read out the molecular composition. 想像一下,假如我们从某位患者身上获得了一块活体癌症组织,然后使用我们小组开发的技术将它在物理上进行放大,让其中所有东西都大到能够被观测到,那么我们就可以进入组织内部,使用George的“原地测序”技术读取其中的分子构成。 When we first published the idea of expanding something, a lot of people were very skeptical about it. It’s a very unconventional way of doing things. To convince people that it works, we went down [the following] line of reasoning: a design method. 当我们首次公开发表这项在物理上将某个活体组织进行放大的技术思路时,很多人都对此深表怀疑。因为这是一种非常不合传统的做法。为了让人们相信这种技术是可行的,我们采用了如下的论证路线,它是一种设计方法。 When we synthesized the baby diaper-like polymers inside the cells, we would anchor through molecular bonds specific molecules to the polymer, and then we would wipe up all the rest. We can use enzymes and so forth to chop up the rest. 当我们在细胞内部合成出那些像婴儿纸尿裤一样的网状聚合物时,我们会将整个分子键结构中的一些特定分子保留在聚合物的网状结构上,而去除掉其它的分子。我们可以使用一些酶和类似的化合物将其它的分子切掉。 That way, when we expand the polymer, our molecules that we care about are anchored and move apart, but the rest of the structure has been destroyed or chopped up so that it does not impede the expansion. That’s a key design element. 通过这种方式,当我们在放大网状聚合物时,我们所关心的那些分子都被原封不动地单独保留了下来,但剩下的那些结构则会被销毁或切除,这样它们就不会妨碍放大的过程。这就是其中关键的设计元素之一。 One way to think of this is—chemistry is a way of doing fabrication massively in parallel. So suppose that I want to see two things that are close together, like my two hands here. But of course, lenses cannot see very, very small things, right, thanks to diffraction. So what if we took my two hands and anchored them to these expandable polymers and then destroyed everything else? There might be a lot of junk here we don’t care about. 可以这样看——化学技术是一种并行地进行大规模制造的方法。假设我想要看清两件紧紧贴在一起的东西,就像我的两只放在一起的手。当然,由于衍射现象的存在,普通的镜头是无法看清非常非常小的东西的。但如果我把两只手都固定在这些可放大的网状聚合物上,然后将其它所有的东西都毁掉呢?因为其中可能包含了一大堆我们完全不关心的垃圾。 We add water and the polymer swells, moving my hands along with it until they’re far apart enough that we can see the gap between them. That’s the core idea of what we call expansion microscopy where we take the molecules in a cell or the molecules in a tissue, a brain circuit or a tumor, and we anchor those molecules to a swellable polymer. When we add water, the molecules we care about, the ones we’ve anchored—that we’ve nailed to the polymer, as it were, have moved apart until they’re far apart enough that we can see them using cheap, scalable, and easily deployed optics like you could find on an inexpensive microscope or even a webcam. 我们向网状聚合物中加水,然后它会膨胀,我的两只手也会随着它的膨胀发生移动,一直到它们的距离远到我们能够看清其中的缝隙。这就是被这项我们称为“放大显微术”的核心思路,我们从一个细胞或者一块组织——比如大脑的神经电路或者肿瘤——中选定一些分子,然后将它们固定在一块可膨胀的网状聚合物上,当我们向其中加水,那些我们所关心的被固定在聚合物上的原本贴在一起的分子就会互相分离,直到我们可以通过使用一些廉价的,可扩展并且容易部署的光学仪器——比如低端的显微镜,甚至是网络摄像头——将它们看清。 After we published our paper on expanding tissues, a lot of people started to apply them. For example, suppose you wanted to figure out how the cells are configured in a cancer biopsy. You can take the sample and if you look at it under a microscope, you can’t see the fine structures, but if you blow it up and make it bigger, maybe you could see the shape of the genome; maybe you could see that one cell is extending a tiny tendril, too tiny to see through other means, and maybe that’s the beginning of metastasis. 在我们发表了关于这项放大生物组织技术的论文之后,有许多人都开始将这项技术投入应用。举个例子,假如你想知道在一块活体癌症组织中细胞是如何构成的,你可以取下一块样本,如果你用一架显微镜去观察它,你根本无法看清其中的精细结构,但如果你能够将它放得更大,也许你就能看清其中基因组的形状了,也许你还能看见某个细胞在扩张一个细小的卷须状结构,但这个结构实在太小了,通过其它的任何方法你都无法看清它,而那可能正是一次癌细胞转移过程的开始。 A lot of people are trying to use our technology now for seeing things that you just can’t see any other way, and we’re finding a lot of interest not just from brain scientists because now you have a way of mapping brain circuits with nanoscale precision in 3D, but also from other brain-like problems: tumors and organs and development and so forth where you want to look at a 3D structure but with nanoscale precision. 现在有很多人在尝试使用我们的技术来看清那些他们无法通过其它方式看清的结构,而我们发现不仅仅只有脑科学家对它感兴趣——这项技术为脑科学家们提供了一种在纳米级精度上绘制3D大脑神经电路图的方法,而其它一些研究与大脑问题具有共性的课题的科学家们也对此感兴趣:在肿瘤和某些器官的发展过程和其它一些类似的课题中,人们也希望能够在纳米级的精度上看清3D结构。 We’ve spun out a small company to try to make kits and maybe provide this as a service so that people can use this widely. Of course, we’ve also put all the recipes on the Internet so people can download them, and hundreds and hundreds of groups have already started to play with these kinds of tools. 我们成立了一家小公司来尝试为这项技术制作一些工具套件,甚至是将它作为一项服务提供给需要的人以让这项技术能够被广泛地使用。当然,我们同样也在因特网上公开了这项技术的所有“配方”,人们可以下载它们。已经有数以百计的研究小组开始在他们的工作中使用这些工具。 We want to make the invisible visible, and it’s hard to see a 3D structure like a circuit that might store a memory or a circuit in the brain that might be processing an emotion, with the nanoscale resolution that you need to see neural connections and the molecules that make neurons do what they do. 我们希望能让那些原来看不到的结构被看清,要清晰地看到大脑中某个可能存储了记忆或是正在处理某种感情的神经电路的3D结构是非常困难的,你需要在纳米级的分辨率下才能看到神经元之间的连接和那些促使神经元发挥作用的分子结构。 The fundamental limit on how fine we can see things is related to a technical parameter called the mesh size; that is basically the spacing between the polymer chains. We think that the spacing between the polymer chains is about a couple nanometers; that is, around the same size as a biomolecule. If we can push all the molecules away from each other very evenly, it’s like drawing a picture on a balloon and blowing it up: you might be able to see all the individual particles and building blocks of life, but you know what? 决定我们能够在多高的清晰度下看清东西的基础限制是与一项被称为“网格尺寸”的技术参数相关的,这个参数的含义其实就是网状聚合物构成的链式结构之间的孔隙大小。我们认为这个空隙的大小大约是几纳米,也就是说,这和一个生物分子的大小差不多。如果我们能够将所有的分子按照非常接近的比例彼此推开,这就有点像在一个气球上画了一幅画,然后再将气球吹大,之后你就有可能看清所有的那些颗粒和组成生命的基础成分了。 We have to validate the technology down to that level of resolution. So far, we have validated it down to about a factor of ten bigger than that, in order of magnitude. But if we can get down to single molecule resolution, you could try to map the building blocks of living systems. We haven’t gotten there yet. 但你需要知道的是,我们还需要在生物分子级的分辨率上对这项技术进行验证,到目前为止,我们已经在比这高一个数量级的分辨率上成功地验证了这项技术。如果我们能够在单个分子的分辨率上验证这项技术,我们就能够绘制出活系统中的那些基础成分了,但目前我们还没能做到这一点。 I’ve been amazed at how fast neurotechnology has started to move. Ten years ago, we had relatively few tools for looking at and controlling the brain, and now, ten years later, we have our optogenetic tools for controlling brain circuits, this expansion method for mapping the fine circuitry, and also, we have developed 3D imagining methods that basically work the way that our eyes work to reconstruct 3D images of brain high speed electrical dynamics. 我对近来神经技术的发展速度感到吃惊。十年前,我们只有相对很有限的工具来控制大脑,而十年后的今天,我们已经拥有了像“光基因”这样的工具来控制大脑的神经电路,还有这种通过放大技术来绘制精细的神经电路的方法。此外,我们还开发了3D成像的方法来观测大脑内部的高速电子动态,其工作原理和我们的眼睛重建3D图像的方法是相同的。 In the coming fifteen years, two things are going to happen and a third thing, might happen. One thing that will happen is that our ability to map the fine details of neural circuits and see high speed dynamics and control it will probably be perfected; that might happen as soon as five years from now but definitely within fifteen years, I would predict that. 在接下来的十五年中,我认为会发生两件重大的事情,另外还有第三件事情也可能会发生。第一件事情是,我们绘制神经电路的精确细节,观测其中的高速动态,以及对它进行控制的能力将会得到完善,这些也许最快在今后的五年中就会发生,并且在十五年内几乎一定会发生,我可以肯定地这样预测。 The second thing is that we’re going to have some detailed-enough maps of small neural circuits that maybe we could even make computational models of their operation. For example, there is a small worm called C. elegans that has 302 neurons; maybe we can map all of them and their molecules and their dynamics and perhaps we can make a computational model of that worm. Or maybe a slightly larger brain: the larval zebrafish has 100,000 neurons, mice have 100 million—ballpark—and humans have 100 billion. You can see there are some multistage logarithmic jumps there that we have to make. 第二件事情是我们将绘制出一些细节足够丰富的小型神经电路图像,也许我们甚至可以据此开发出一些有关它们工作方式的计算模型。例如,有一种叫做秀丽隐杆线虫的蠕虫拥有302个神经元,也许我们能够绘制出它的所有神经电路图,以及其中的分子结构和电子动态,那么我们也许可以建立这种蠕虫的计算模型。如果扩展到大一点的大脑,斑马鱼拥有大约十万个神经元,而老鼠则拥有大约1亿个神经元,人类的神经元数目大约是一千亿。你可以从这里看出,在大脑规模从小到大的过程中,我们需要做很多次多级的对数跳跃。 The speculative thing is that we might have some tools that might let us look at human brain functions much, much more accurately. Right now, we have so few tools for looking at the human brain, there is functional MRI which lets you look at blood flow that is downstream of brain activity, but it’s very indirect and it’s very crude. The time resolution is thousands of times slower than in brain activity, and the spatial resolution, each little block that you see in these brain scans contains tens to hundreds of thousands of neurons, and we know that even nearby neurons can be doing completely different things. 而那件不太确定的事情则是我们也许会拥有一些能够让我们以远高于当前的精确程度观察人类大脑功能的工具。现在,能用来观察人类大脑的工具实在太少了,我们有一些功能性的核磁共振(MRI)设备能让我们观察某种大脑活动所引发的血液流动,但这种方式太间接了,同时也太不精确。这种工具的时间分辨率比大脑活动要慢上数千倍,从空间分辨率上说,你从MRI的扫描图像上看到的每个小方格都包含了数以百万计的神经元,而我们知道,即使是相邻的神经元也可能正在做着完全不同的事情。 What we most need right now, I would say, is a method for imaging and controlling human brain circuits with single cell, single electrical pulse precision, and the jury is out on how that could happen. There’s lots of brainstorming. I haven’t seen any technology generated so far that can probably do it although there’s lots of interesting speculation. That’s something I would love to see happen and we have started to work on some ideas that might allow you to do it. 我想说,我们当前最需要的,是一种能够在单个细胞,单个电子脉冲的精度上对大脑电路进行控制和成像的方法,而不确定的是这将会如何发生。我们已经进行过了很多次头脑风暴,但至今为止,虽然有许多有趣的可能性,我却并没有看出任何一种现有的技术能有很大的可能性做到这一点。我希望能够看到这件事情在不远的将来发生,而且我们已经开始将一些有前景的想法付诸实践了。 There’s a lot of speculation about whether there are quantum effects that are necessary for brain computations. At body temperature, it’s very likely that quantum effects, if any, are going to be very, very short-lived, maybe much shorter than the kinds of computations that are happening in the brain. It’s quite possible that if such effects are important, we would need far more powerful tools to see them, or perhaps you can explain all of the biophysics of neurons known to date, for the most part, with completely classical models. 关于大脑的计算过程中是否会用到量子效应这一问题有很多猜测。在人的体温之下,似乎量子效应即便存在也会非常非常短暂,其存续时间相对于发生在大脑之内的计算过程要短得多。如果此类效应的确是重要的,那么我们很可能就需要比当前强大得多的工具来观测它们。但实际上我们也可能完全能够通过一些经典模型来解释目前我们所知的绝大部分关于神经元的生物物理现象。 The thing that I loved about working on the quantum computation project, this was with Neil Gershenfeld back in the day, was this greater philosophy of how information and physics are linked. There are many theories of fundamental physical principles of computation; there is even the phrase, “it from bit,” where people talk about the fundamental thermodynamic limits of how information processing occurs in physical systems. 我之所以愿意投入时间在量子计算研究项目上,主要是早先在与Neil Gershenfeld一起工作的时候,受到了关于信息和物理学是如何紧密联系在一起的这一伟大哲学思想的影响。在计算的基础物理原则方面已经有了很多理论。当人们会谈论在物理学系统中发生的信息处理过程所受到的基础热力学限制时甚至有这样的谚语:“万物皆比特(it from bit)”。 For example, there are so many bits associated with a black hole, there is, based upon temperature, a fundamental amount of information that might be encoded in a specific transition. The brain for the most part is operating, because it’s at body temperature and all that, far above those physical fundamental limits in terms of information processing. 例如,与一个黑洞相关的比特数非常之多,在给定的温度下,一个基础量的信息可能会被编码到某个特定的转换过程中。而大脑在大多数情况下都在工作,因为它处于人体体温的环境下,而在这种温度下的信息处理则远远超过了那些物理上的基础限制。 On one level, the most parsimonious models of the brain are analogue because we know that there are different amounts of transmitters being released at synapses, we know that the electrical pulses that neurons compute can vary in their height and in their duration. 从某个层面上说,那些关于大脑的最简化模型都是模拟的(而非数字的),因为我们知道,大脑中的各突触所释放出的传导物质是不同的,我们也知道神经元所计算的不同电脉冲的强度和持续时间区别很大。 Of course, if you dig deep enough, you could say, well, you could just count the neurotransmitters, you could count the ions, and it becomes digital again, but that’s a much more detailed level of description that might not be the most parsimonious level because you had to count and localize every single sodium ion and potassium ion and chloride ion. Hopefully, we don’t have to go that far. But if we need to, we would probably have to build new technologies to do that. 当然,如果你功课做得够深,那你可以去数一下那些神经递质的数目,还有电脉冲中离子的数目,那么这个问题就又成为了数字的(而非模拟的)了,但那是一个细致得多的描述水平,而并不处在最简化层次上,因为你需要计数和定位每一个钠离子,钾离子和氯离子。希望我们不需要走得那么远,但如果真的有必要,我们还是很可能去创造一些新的技术来做到这些事情。 My co-inventor, Karl Deisseroth, and I both won Breakthrough Prizes in Life Sciences for our work together on optogenetics, this technology where we put molecules that are light sensitive into neurons and then we can make them activatable or silence-able with pulses of light. 我和我的合作者Karl Deisseroth由于我们在“光基因”技术上的合作成果共同获得了《生命科学》杂志所颁发的突破奖,在这项技术中,我们将一些光敏分子植入神经元中,然后我们就可以通过光脉冲来让它们在可激活状态和静息状态之间切换。 Our groups have sent these molecules out to literally thousands of basic as well as clinically interested neuroscientists, and people are studying very basic science questions like how is a smell represented in the brain? But they’re also trying to answer clinically relevant questions like where should you deactivate brain cells to shut down an epileptic seizure? I’ll give you an example of the latter since there is a lot of disease interest. 我们的小组已经将这样的分子提供给数千名基础神经科学家和临床神经科学家,其中有些科学家研究的是非常基础的科学问题,例如气味是如何在大脑中被表达出来的。还有一些科学家则试图回答一些临床相关的问题,例如应该在什么地方让大脑细胞停止活动以停止一次癫痫病的发作。下面我会给你举一个后者的例子,因为有很多人都对我们的技术在疾病研究方面的应用感兴趣。 People have been trying to shut down the over excitable cells during seizures for literally decades, but it’s so difficult because which part of the brain and which cells and which projections? It’s such a big mess, right, the brain? So a group at UC Irvine has been using our technologies to try to turn off different brain cells or even to turn on different brain cells, and what they’re finding is that some cells, if you activate them, can shut down a seizure in a mouse model. But still, who would have thought that activating a certain kind of cell would be enough to terminate a seizure? There is no other way to test that, right, because how do you turn on just one kind of cell? 人们在过去数十年中一直在尝试去关闭那些在癫痫病发作时过度活跃的细胞,这非常困难,因为很难弄清大脑中究竟是哪个部分的哪些细胞的哪些投射过于活跃了。要知道大脑看起来就是一团乱麻。一个来自加州大学尔湾分校的研究小组使用我们的技术试图关闭和激活大脑内部的不同细胞,而他们的研究成果表明的确存在某些细胞,通过激活它们可以在一个鼠脑模型中停止癫痫的发作。 What they did was there are certain classes of cell called interneurons, and they tend to shut down other cell types in the brain. What this group did is they took a molecule that we had first put into neurons about a decade ago, a molecule that, kind of like a solar panel, when you shine light on it, will drive electricity into the neuron. They delivered the gene for this molecule so that it would only be on in those interneurons, none of the other cells nearby, just the interneurons. And then, when they shine light, these interneurons will shut down their neighboring cells, and they showed you could terminate a seizure in a mouse model of epilepsy. 某些类型的细胞被称为中间神经元,它们所做的事情是尝试去关闭大脑内部其它类型的细胞。这个小组采用了我们十年前第一次植入神经元时所使用的一种分子,这种分子有点像一块太阳能电池板,当你将它置于光照下,它就会驱动神经元内部的电信号。他们将这种分子的基因植入了那些中间神经元,并保证除了这些中间神经元之外,附近的其它细胞内部都不存在这种分子。然后当他们点亮光线,这些中间神经元就会关闭它们相邻的细胞,他们的工作成果表明你能够在一只老鼠癫痫病发作时通过这种方法停止病症的发作。 That’s interesting because now, if you could build a drug that would drive those cells, maybe that would be a new way of treating seizures, or you could try to directly use light to activate those cells and build a sort of prosthetic that would be implanted in the brain and activate those cells near a seizure focus, for example. 这一结果十分有趣,因为现在你可以制造一种药物来驱动那些细胞,也许这会成为一种治疗癫痫病的新途径,或者你也可以尝试直接使用光来激活那些细胞,并制造出某种能够被植入大脑的假体,并通过它来激活癫痫病发作的核心区附近的那些细胞。 People are exploring both ideas. Could you use our optogenetic tools to turn on and off different cell types in the brain to find better targets, but then, treat those targets with drugs? Or could you use light to activate cells and directly sculpt their activity in real-time in a human patient? The latter, of course, is much higher risk, but it’s fun to think about for sure. And there are a couple companies that are trying to do that now. 以上两种思路都正处于人们的探索之中。人们是否能够使用我们的“光基因”工具来打开和关闭大脑内部不同类型的细胞功能以更好地发现目标,然后再使用药物对这些目标有的放矢呢?或者是否能够使用光来激活病人大脑中的某些细胞并且直接实时控制它们的行为呢?第二种做法无疑会带来很高的风险,但考虑这种可能性确实非常有趣。目前的确有一些公司在尝试这么做。 When we were talking about the Breakthrough Prize, I thought about the little speech I gave—they give you thirty seconds, but I thought about it for several weeks because I feel like there is such a push to cure things, a push to find treatments, but in some ways, by forcing it to go too fast, we might miss the serendipitous insights that are much more powerful. 谈到《生命科学》杂志的突破奖,我想到了我在获奖时所发表的一段简短讲话——他们只给了我三十秒,但我却考虑了几个星期,因为我觉得人们太过于急着去治疗疾病,找到好的疗法,但在某种情况下,如果我们强行地迅速推进这些事情,就很可能错过一些实际上要强大得多的只有通过机缘巧合才能发现的深刻洞见。 I’ll give you an example: in 1927, the Nobel Prize in Medicine was given to this guy who came up with a treatment for dementia. What this person did is, he would take people with dementia and he would deliberately give them malaria. Remember this is the greatest idea of its time, right? 让我来举个例子:1927年的诺贝尔医学奖颁发给了一位发现了一种痴呆症疗法的科学家。而他所做的事情则是故意让那些患有痴呆症的病人感染疟疾。记住,这可是那个时代最伟大的点子。 Now, why did it work? Well, malaria causes a very high fever. At that time, dementia was often caused by syphilis, and so, the high fever of malaria would kill the parasite that causes syphilis. Now, in 1928, one year later, antibiotics started to come online, and of course, antibiotics have been a huge hit and syphilis-related dementia is almost unheard of nowadays. 那么问题来了,为什么这种做法能够起效?其实是因为疟疾会导致非常严重的发热。而在当时,痴呆症则通常是由梅毒引起的,通过这种方法,疟疾所带来的高烧就能够杀死那些引起梅毒的寄生虫。而在一年后的1928年,抗生素开始得到普及,当然,抗生素所带来的影响的确非常深远,由梅毒所引起的痴呆症在最近几乎完全销声匿迹了。 The rush to get a short-term treatment, I worry, can sometimes cause people to misdirect their attention from getting down to the ground truth mechanisms of knowing what’s going on. It’s almost like people often talk about we’re doing all this incremental stuff, we should do more moon shots, right? I worry that medicine does too many moon shots. Almost everything we do in medicine is a moon shot because we don’t know for sure if it’s going to work. 我所担心的是,人们急于在短期内去寻求某种疗法的风潮有时会错误地将人们的注意力从脚踏实地去研究基础事实并弄清其中的机制上转移开。这就像人们所经常谈论的,我们总是做着这些循序渐进的事情,我们难道不应该把更多的精力花在探月这样的事情上吗?我担心医学界会关注太多这类“探月”式的大目标。我们目前在医学上所做的所有事情都是一次“探月”,因为我们并不能肯定我们所做的事情能够见效。 People forget. When they landed on the moon, they already had several hundred years of calculus so they have the math; physics, so they know Newton’s Laws; aerodynamics, you know how to fly; rocketry, people were launching rockets for many decades before the moon landing. When Kennedy gave the moon landing speech, he wasn’t saying, let’s do this impossible task; he was saying, look, we can do it. We’ve launched rockets; if we don’t do this, somebody else will get there first. 人们都是健忘的。当人类第一次踏上月球时,微积分已经发明了几百年了,所以他们拥有足够好的数学工具;而在物理上,人类也已经知道了牛顿定律;在空气动力学上,人们已经知道了如何飞行;在火箭技术上,登月前人们已经积累了数十年的发射火箭的经验。当肯尼迪发表登月演说时,他并不是在说,让我们来完成这项不可能完成的任务吧,他所说的是,看吧,我们能做到这件事情。美国人发射了登月火箭,如果我们不做这件事情,将会有别人捷足先登。 Moon shot has gone almost into the opposite parlance; rather than saying here is something big we can do and we know how to do it, it’s here is some crazy thing, let’s throw a lot of resources at it and let’s hope for the best. I worry that that’s not how “moon shot” should be used. I think we should do anti-moon shots! “探月”这个字眼现在的意思已经和当初完全颠倒过来了,现在它的意思已不再是“这是件大事,而且我们知道如何将它完成”,而变成了“这件事情很疯狂,让我们多投入些资源然后祈祷吧”。我所担心的正是对探月精神的这种误用,我觉得我们现在应该做的是反对这种“探月精神”,脚踏实地做更多的基础研究! (编辑:辉格@whigzhou) *注:本译文未经原作者授权,本站对原文不持有也不主张任何权利,如果你恰好对原文拥有权益并希望我们移除相关内容,请私信联系,我们会立即作出响应。

——海德沙龙·翻译组,致力于将英文世界的好文章搬进中文世界——

不会有太大差别

【2016-07-04】

@whigzhou: 常有人说,人类个体间99.5%的DNA是相同的,所以我们在遗传上不会有太大差别,这么说的人对数字不太敏感,0.5%的单核苷酸差异意味着每200个碱基中就有一个是不同的,每个基因平均2000个碱基对,平均可能摊上10个差异,实际上没那么多,那是因为编码段受自然选择约束,变异率低于非编码段,

@whigzhou: 但这一极简单计算即表明,不同个体的每个基因都*有机会*是不同的,只要他们各自的种系发生历史(more...)

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【2016-07-04】 @whigzhou: 常有人说,人类个体间99.5%的DNA是相同的,所以我们在遗传上不会有太大差别,这么说的人对数字不太敏感,0.5%的单核苷酸差异意味着每200个碱基中就有一个是不同的,每个基因平均2000个碱基对,平均可能摊上10个差异,实际上没那么多,那是因为编码段受自然选择约束,变异率低于非编码段, @whigzhou: 但这一极简单计算即表明,不同个体的每个基因都*有机会*是不同的,只要他们各自的种系发生历史上所面临的选择压力不同,就很可能不同,而且每个基因可能有多个不同之处。 @whigzhou: 理解这一点的最佳类比是程序代码,把DNA想象成一个软件的源代码,其中98.5%是注释,剩下1.5%是有用编码,现在为它建立一亿个副本,拷贝时平均每200个字符随机出现一个拷贝错误,然后将无法正常工作的副本剔除,剩下能工作的副本会表现出多大功能差异? @whigzhou: 没写过代码的同学可以考虑做菜,让使用同样主料的两道菜味道变得十分不同,需要在调料上有多大不同?从寡淡无味变成巨咸无比需要几克盐?从不辣变成超辣需要几克辣椒汁?制造难以忍受的麻味需要几克花椒汁?  
[译文]特里夫斯的灿烂人生

Trivers’ Pursuit
罗伯特·特里夫斯:一生的追寻

作者:Matthew Hutson @ 2016-1-5
译者:Drunkplane(@Drunkplane-zny)
校对:慕白(@李凤阳他说)
来源:Psychology Today,https://www.psychologytoday.com/articles/201601/trivers-pursuit

Renegade scientist Robert Trivers is lauded as one of our greatest thinkers—despite irking academia with blunt talk and bad manners.

尽管罗伯特·特里夫斯直率的言谈和粗鲁的举止让学界恼怒,这位离经叛道的科学家仍被誉为最伟大的思想家之一。

To call Robert Trivers an acclaimed biologist is an understatement akin to calling the late Richard Feynman a popular professor of physics. As a young man in the 1970s, Trivers gave biology a jolt, hatching idea after idea that illuminated how evolution shaped the behavior of all species, including fidelity, romantic bonds, and willingness to cooperate among humans. Today, at 72, he continues to spawn ideas. And if awards were given for such things, he certainly would be on the short list for America’s most colorful academic.

把已故的理查德·费曼称为“一位受欢迎的物理学教授”,那是低估了他,同样地,如果把罗伯特·特里夫斯称为“一位广受赞誉的生物学家”也不够恰当。1970年代,当时不过是一个年轻人的特里夫斯就大大促进了生物学的研究,阐述了一个又一个想法,揭示了进化是如何塑造所有物种的行为,包括人类在性方面的忠贞、恋爱和合作的意愿。今天,他72岁,新的想法仍然不断从他脑中诞生。如果要为“想法”颁奖的话,他一定能进入“美国最有想法学者”短名单。

He was a member of the Black Panthers and collaborated with the group’s founder. He was arrested for assault after breaking up a domestic dispute. He faced machete-wielding burglers who broke into his home and stabbed one in the neck. He was imprisoned for 10 days over a contested hotel charge. And two men once held guns to his head in a Caribbean club that doubled as a brothel.

他曾是黑豹党一员,并曾同该组织的创立者合作。他曾因为在家庭纠纷中动手打人而被拘捕。他曾直面挥舞着弯刀的破门而入者,并在其中一人的脖子上扎了一刀。他曾因为一笔有争议的酒店费用而坐了十天牢。他还曾在加勒比一个俱乐部被人用枪顶着头——那个俱乐部同时也是妓院。

Fisticuffs aside, what propelled Trivers into the academic limelight were five papers he wrote as a young academic at Harvard—including research on altruism, sex differences, and parent-offspring conflict. This work won him the 2007 Royal Swedish Academy of Sciences Crafoord Prize in Biosciences, the Nobel for evolutionary theory. The award came with half a million dollars and a ceremony attended by the queen.

除拳脚之外,让特里夫斯在学术圈声名大噪的是他年轻时在哈佛写就的五篇论文——包括关于利他主义、性别差异和亲子冲突的研究。这些成就为他赢得了2007年瑞典皇家科学院颁发的克拉福德生物学奖——进化理论的诺贝尔奖。奖金为50万美元,女王亦出席了颁奖典礼。

Steven Pinker has called him “one of the great thinkers in the history of Western thought.” Yet Trivers has not led the life of your typical contemplative academic. Mental breakdowns, public feuds, and near-death experiences have peppered his career, distracting him from his work even as they’ve nourished it.

史蒂文·平克曾称特里夫斯是“西方思想史上伟大的思想家之一”。然而特里夫斯不是你印象中那种典型的喜欢沉思的学者。精神崩溃、公开与人结怨和险些丧命的经历都让他的生涯显得与众不同,他的工作因此受到影响也因此获益。

No one is quite sure what to make of him, but all agree he is both brilliant and volatile, a sort of Steve Jobs without the colossal second coming. In a new memoir, Wild Life, he contrasts his existence with the “often solitary and intensely internal” one he sees in most scientists. “[That] kind of life,” he writes, “never appealed to me.”

没人确信该怎么评价他,但所有人都同意,他绝顶聪明,绝不安分,就像史蒂夫·乔布斯,但没有经历过乔布斯式卷土重来。在新回忆录《狂野生活》中,他对比了自己的生活同他在大多数科学家中所看到的“往往孤寂的、极其注重内心的”的生活,“那样的生活,”他写道,“从来不曾吸引我。”

To begin, Trivers’ revolutionary 1970s papers presented no new data. Trivers simply offered entirely novel ways of looking at what was already there, along with new avenues for moving science forward. His dissertation was so strong that when he showed up before the evaluating committee, which included such luminaries as E. O. Wilson and Ernst Mayr, they skipped the charade of making him defend it and simply offered their congratulations.

刚开始时,特里夫斯于1970年代发表的那几篇革命性论文中并没有提出新的数据。特里夫斯仅仅提供了一种全新的视角来看待既已存在的知识,一条推动科学进步的崭新道路。他的论证强而有力,以至当他面对评审委员会时——其中包括著名科学家爱德华·威尔逊和厄内斯特·迈尔——他们(more...)

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Trivers' Pursuit 罗伯特·特里夫斯:一生的追寻 作者:Matthew Hutson @ 2016-1-5 译者:Drunkplane(@Drunkplane-zny) 校对:慕白(@李凤阳他说) 来源:Psychology Today,https://www.psychologytoday.com/articles/201601/trivers-pursuit Renegade scientist Robert Trivers is lauded as one of our greatest thinkers—despite irking academia with blunt talk and bad manners. 尽管罗伯特·特里夫斯直率的言谈和粗鲁的举止让学界恼怒,这位离经叛道的科学家仍被誉为最伟大的思想家之一。 To call Robert Trivers an acclaimed biologist is an understatement akin to calling the late Richard Feynman a popular professor of physics. As a young man in the 1970s, Trivers gave biology a jolt, hatching idea after idea that illuminated how evolution shaped the behavior of all species, including fidelity, romantic bonds, and willingness to cooperate among humans. Today, at 72, he continues to spawn ideas. And if awards were given for such things, he certainly would be on the short list for America’s most colorful academic. 把已故的理查德·费曼称为“一位受欢迎的物理学教授”,那是低估了他,同样地,如果把罗伯特·特里夫斯称为“一位广受赞誉的生物学家”也不够恰当。1970年代,当时不过是一个年轻人的特里夫斯就大大促进了生物学的研究,阐述了一个又一个想法,揭示了进化是如何塑造所有物种的行为,包括人类在性方面的忠贞、恋爱和合作的意愿。今天,他72岁,新的想法仍然不断从他脑中诞生。如果要为“想法”颁奖的话,他一定能进入“美国最有想法学者”短名单。 He was a member of the Black Panthers and collaborated with the group’s founder. He was arrested for assault after breaking up a domestic dispute. He faced machete-wielding burglers who broke into his home and stabbed one in the neck. He was imprisoned for 10 days over a contested hotel charge. And two men once held guns to his head in a Caribbean club that doubled as a brothel. 他曾是黑豹党一员,并曾同该组织的创立者合作。他曾因为在家庭纠纷中动手打人而被拘捕。他曾直面挥舞着弯刀的破门而入者,并在其中一人的脖子上扎了一刀。他曾因为一笔有争议的酒店费用而坐了十天牢。他还曾在加勒比一个俱乐部被人用枪顶着头——那个俱乐部同时也是妓院。 Fisticuffs aside, what propelled Trivers into the academic limelight were five papers he wrote as a young academic at Harvard—including research on altruism, sex differences, and parent-offspring conflict. This work won him the 2007 Royal Swedish Academy of Sciences Crafoord Prize in Biosciences, the Nobel for evolutionary theory. The award came with half a million dollars and a ceremony attended by the queen. 除拳脚之外,让特里夫斯在学术圈声名大噪的是他年轻时在哈佛写就的五篇论文——包括关于利他主义、性别差异和亲子冲突的研究。这些成就为他赢得了2007年瑞典皇家科学院颁发的克拉福德生物学奖——进化理论的诺贝尔奖。奖金为50万美元,女王亦出席了颁奖典礼。 Steven Pinker has called him “one of the great thinkers in the history of Western thought.” Yet Trivers has not led the life of your typical contemplative academic. Mental breakdowns, public feuds, and near-death experiences have peppered his career, distracting him from his work even as they’ve nourished it. 史蒂文·平克曾称特里夫斯是“西方思想史上伟大的思想家之一”。然而特里夫斯不是你印象中那种典型的喜欢沉思的学者。精神崩溃、公开与人结怨和险些丧命的经历都让他的生涯显得与众不同,他的工作因此受到影响也因此获益。 No one is quite sure what to make of him, but all agree he is both brilliant and volatile, a sort of Steve Jobs without the colossal second coming. In a new memoir, Wild Life, he contrasts his existence with the “often solitary and intensely internal” one he sees in most scientists. “[That] kind of life,” he writes, “never appealed to me.” 没人确信该怎么评价他,但所有人都同意,他绝顶聪明,绝不安分,就像史蒂夫·乔布斯,但没有经历过乔布斯式卷土重来。在新回忆录《狂野生活》中,他对比了自己的生活同他在大多数科学家中所看到的“往往孤寂的、极其注重内心的”的生活,“那样的生活,”他写道,“从来不曾吸引我。” To begin, Trivers’ revolutionary 1970s papers presented no new data. Trivers simply offered entirely novel ways of looking at what was already there, along with new avenues for moving science forward. His dissertation was so strong that when he showed up before the evaluating committee, which included such luminaries as E. O. Wilson and Ernst Mayr, they skipped the charade of making him defend it and simply offered their congratulations. 刚开始时,特里夫斯于1970年代发表的那几篇革命性论文中并没有提出新的数据。特里夫斯仅仅提供了一种全新的视角来看待既已存在的知识,一条推动科学进步的崭新道路。他的论证强而有力,以至当他面对评审委员会时——其中包括著名科学家爱德华·威尔逊和厄内斯特·迈尔——他们跳过了答辩环节,直接向他表示祝贺。 Yet he published little follow-up work. A scientist can build a whole career milking a single small concept, but Trivers has been known to put forth a big new idea and then essentially drop the mic. 之后他几乎没有发表后续研究。一名科学家可以以一个小概念为基础建构自己的全部事业,但特里夫斯通常是提出一个全新的、有爆炸力的想法后,然后就不再就此发言了。 Trivers’ first paper, on the evolution of reciprocal altruism, described a theoretical model showing how altruism among strangers could naturally develop—people cooperate with the expectation of similar treatment from others. This model explained a wide variety of feelings and behaviors, from friendship to moralistic aggression. 特里夫斯的第一篇论文是关于互惠利他主义(reciprocal altruism)的,论文描述了一个关于陌生人之间的利他主义是如何自然发展的理论模型——人们带着“我怎样对人,人就怎么对我”的期许相互合作。这个模型解释了从友谊到道德侵略(moralistic aggression)等许多不同的感受和行为。 The emotion of gratitude, for instance, evolved to motivate us to return favors, encouraging cooperation. Guilt motivates us to repair relationships we’ve harmed. Anger makes us avoid or punish those who have harmed us. And gossip makes us mindful of our reputations. Trivers suggested that complex strategies of cheating, detecting cheating, and the false accusation of cheating (itself a form of cheating) pushed the development of intelligence and helped increase the size of the human brain. 举例来讲,之所以进化出“感激”这种情绪,是因为它会激励我们投桃报李,鼓励合作。负罪感会促使我们修复受损的关系。愤怒会让我们避开或惩罚那些伤害了我们的人。而闲言碎语则让我们在意自己的名声。特里夫斯认为,欺骗、发现欺骗和对欺骗的不实指控(其本身也是种欺骗)构成了复杂的策略,推动我们智力的发展并助力人类大脑尺寸的增长。 Next, in Trivers’ second paper, he hypothesized that a single factor drives sex differences across all species. He argued that differences in parental investment—the energy and resources invested in an offspring—lead the sex that invests more (females, in most species) to focus on mate quality and the sex that invests less (males) to seek quantity. 接着,特里夫斯在他的第二篇论文中提出一个假说:一个单一因素便导致了所有物种的性别差异。他认为亲代投资(为后代投入的能量和资源)的差别区分了“投资多的性”(对大多数物种而言是雌性)和“投资少的性”(雄性),前者关注配偶的质量而后者追求数量。 So in humans we expect choosiness in females and aggression between males as they vie for females. The theory has tremendous explanatory power, from justifying the brightly colored feathers of male birds to illuminating why sexual jealousy is a leading (and, until recently, legally defensible) cause of homicide—men prize their mate’s fidelity above all. 因此在人类中我们便观察到女性的挑剔和男人之间在追逐女性时所表现出的攻击性。这个理论有力地解释了雄鸟身上鲜艳的羽毛,以及为何性嫉妒是杀人案的首要(直到现在也是法律上站得住脚的【编注:在美国一些州,当场捉奸并杀死奸夫的丈夫往往可以愤激作为辩护理由并得以脱罪】)动机——在男人看来,伴侣的忠贞高于一切。【编注:此处有所夸大,亲代投资理论本身并不能单独解释性嫉妒】 In another paper, Trivers conceptualized offspring not as passive recipients of parental investment, but as independent actors, generating the theory of parent-offspring conflict. A child wants disproportionate attention and resources for him- or herself, but a parent wants to spread the goods equally between all offspring. 在另一篇论文里,特里夫斯将后代视为独立的行为主体,而不仅仅是亲代投资的被动接受者,从而引出了“亲子冲突”(parent-offspring conflict)这一理论。子女想要为自己争取到比例过当的关注和资源,但家长则希望在后代之间平分好处。 And so we have kids who bawl until they get what they want, siblings who maintain lifelong rivalries, and parents who try to instill equality no matter how selfish the kids’ tendencies. It was for these three papers, plus another two, on insect colonies and on parents’ ability to vary the sex ratio of their offspring, that he won the Crafoord. 于是,子女们闹个不停直到他们得到想要的,兄弟姐妹们终其一生相互竞争,而父母们不管小孩多么自私仍坚持贯彻平等主义。这三篇论文加上另外两篇有关昆虫巢群和亲代改变子代性别比例之能力的论文,为特里夫斯赢得了克拉福德奖。 In each paper, he found a simple, clear idea, and took it as far as it would go, wrapping diverse and widespread phenomena together in one neat package. You might not have made the connections before, but once you see them, they’re quite clear. 在每篇论文里,他都建立一个简洁、清晰的概念,并最大限度地发展这个概念,将多种多样、涵盖广泛的各种现象融为一炉。你也许以前并没有发现这些现象间的联系,但一旦你注意到,这些联系就显得十分清楚。 “Trivers has answered some of the most profound questions about the human condition,” Pinker  told me. “Namely, why are our relationships with other people such complicated mixtures of cooperation and conflict? He did so with a simple, though nonobvious, analysis of the patterns of overlap and nonoverlap of our long-term genetic interests.” “特里夫斯回答了关于人类境况的一些最本质的问题,”平克对我说。“即为什么我们同他人的关系是如此复杂,既有合作又有冲突?他以一种简明——虽然不那么一目了然——的方式分析了重叠或不重叠的基因利益,从而回答了这个问题。” According to David Haig, a geneticist at Harvard and a longtime friend and collaborator of Trivers, “Bob has a great ability to see questions as simple and not be distracted by details.” Richard Dawkins praises him for applying economic ideas to biology “with greater clarity of mind than any biologist since R. A. Fisher,” the knighted geneticist. 据戴维·海格——哈佛遗传学家、特里夫斯的多年好友和合作者——所言,“鲍勃【罗伯特的昵称】有一种能力,可以单刀直入地看问题而不被细节干扰。” 理查德·道金斯赞扬他将经济学的观点引入生物学,“思路极清晰,罗纳德·费希尔(就是后来被册封骑士的那位遗传学家)之后的生物学家难以望其项背。” In their own books, E. O. Wilson and Richard Dawkins drew heavily on Trivers’ papers, although he has not always had positive things to say about his popularizers. “Richard wrote a beautiful book,” Trivers says about The Selfish Gene. “I was not about to take the time to do it.” 威尔逊和道金斯在自己的书中都大量引用了特里夫斯的论文,让特里夫斯的观点在学界人尽皆知,但特里夫斯本人对这两位却并不总是好言相向。“理查德的书写得漂亮,”特里夫斯如此评价《自私的基因》,“我不会花时间去做这种事。” But as for Wilson and Sociobiology, “He played the old Harvard game of becoming the father of a field by becoming the father of the name of a field.” (Wilson told me his own work on the sociobiology of insects actually influenced Trivers.) 但对威尔逊和《社会生物学》,特里夫斯说,“他为一个领域发明一个名字然后便成了该领域的开山鼻祖,这不过是老套的哈佛把戏罢了。”(威尔逊告诉我他对昆虫的社会生物学研究成果其实影响过特里夫斯。) After writing papers addressing how we treat strangers, friends, lovers, parents, and children, Trivers offered a no-less-powerful theory on how we deal with ourselves. In a sentence in the foreword to Dawkins’ book, he proposed that self-deception evolved to facilitate the deception of others. Trivers says he’d planned to flesh out the theory but didn’t get around to it because he was “smoking too much strong herb.” 在撰写了有关我们如何对待陌生人、朋友、爱人、父母和小孩等论文之后,特里夫斯又就我们如何对待自我提出了一个同等重要的理论。在为道金斯的新书【编注:《自私的基因》第一版】写的序中,他提出,自我欺骗机制(self-deception)之所以进化出来,是为了方便我们欺骗他人。特里夫斯说他本打算丰富下该理论但终未动手,因为他“抽了太多够劲的大麻。” Trivers also made a mark with the 2006 textbook Genes in Conflict, for which he and Austin Burt spent 15 years integrating thousands of papers on genetic competition within organisms. A reviewer for NatureGenetics called it “meticulously assembled, thought-provoking, and sometimes deliciously speculative.” 特里夫斯于2006年撰写的教科书《基因冲突》让他再一次名声大噪。为了这本书,他和奥斯汀·伯特花了15年时间,将数千篇关于有机体内基因竞争的论文进行了整合。《自然—遗传学》的一名评审者称,这本书在整合方面不遗余力,引人思考,一些地方还包含了有趣的猜测。 According to Trivers, “We created an entire field, the evolutionary dynamics of within-individual genetic conflict. So first, I worked on social theory between individuals, then I dropped one level lower.” Proudly showing me its color inserts, he pointed to what appeared to be a drumstick. “Looks like a piece of chicken, right? No, it’s the only transmissible cancer known. That’s a dog dick. He punches it into a female, the cancerous tissue breaks off and starts growing inside her pumpum.” 特里夫斯说:“我们创造了一个完整的领域:个体内部基因冲突的进化动力学。首先,我研究关于不同个体的社会学,然后我深入到更基础的一个层次。”特里夫斯自豪地给我展示一张彩色插图,指着上面一个像鸡腿一样的东西问我,“看起来像是鸡的一部分,对吧?但其实不是,这是唯一已知的会传播的癌症。那是狗的屌。他把这玩意插入母狗体内,癌症组织便分裂,然后在母狗的屄里开始生长。” My early emails with Trivers attested to his mercurial nature. He lavished praise for a hypothesis I’d suggested, then scolded me for failing to answer a question he’d written. After some back and forth, he agreed to an interview and last spring met me at the train station in New Brunswick—he’s currently a professor at Rutgers, the State University of New Jersey. 我同特里夫斯早期往来的电子邮件见证了他善变的性格。他曾对我提出的某项假设大加赞赏,然后又因为我答不上他提出的问题而骂我。在几个回合后,他同意接受我的采访,并在去年春天于新泽西新不伦瑞克市的火车站与我见面——他现在已是新泽西州立罗格斯大学的一名教授。 Wearing a wool hat with a weed leaf on it, he grumbled at my not finding the right station exit. He warmed up as we drove to his disorganized apartment—a mattress remained in the middle of the floor from a visit by his son. One wall displayed photos of his family, a former girlfriend and her family, and a lizard. We cracked open beers, and he soon offered me a puff of his joint as we got down to business. 见到他时,他戴着一顶毛线帽,上面粘着一片大麻叶子,他抱怨我没有找对车站出口。在开车前往他的公寓的途中,我们逐渐变得热络起来。他的公寓乱糟糟的,他儿子来看他时留下的一个床垫还躺在地板中央。公寓的一面墙上贴满他家人的照片,包括一位前女友及其家人,还有一只蜥蜴。我们开了啤酒,不一会当我们聊到正题时他已经开始给我递大麻烟卷了。 The son of a diplomat, Trivers grew up in Maryland, Denmark, and Germany. At age 12, he knew he wanted to be a scientist and took a liking to astronomy, then to math. He spent two months mastering a calculus textbook and another two months mastering the next volume. 他是一名外交官的儿子,在美国马里兰州、丹麦和德国长大。12岁时,他想成为一名科学家并先后对天文学和数学产生了兴趣。他花了两个月时间钻研一本微积分教材,又花了两个月时间把下一本学完。 He studied pure math as a Harvard freshman, but as a sophomore he realized it wasn’t likely to yield many applications, so he briefly looked to physical science. He didn’t have a knack for physics, however, and hadn’t learned much chemistry or biology. (His college roommates once showed him pictures of a hippo and a rhino and asked him to identify which was which. He picked wrong.) 大一时,他在哈佛学习纯数学,但到了大二他意识到这可能没有太多实际用处,于是又跑去学习物理,但只是浅尝辄止。同样他也没有多少化学和生物学知识。(他的大学室友曾把河马和犀牛的照片拿给他选,结果他选错了。) “So, I literally said, ‘Well, if it’s not truth I’m going to devote myself to, then it’s justice.’” He identified with the civil rights movement and decided to become a lawyer. Unfortunately that meant plodding through a major in U.S. history, which he found to be “an exercise in self-deception and self-glorification.” “所以我当时曾说,‘如果我不能献身真理,那就献身正义。’”他受民权运动的感召并决意成为一名律师。不幸的是这意味着要修完枯燥的美国历史课程,这在他看来就是“练习自我欺骗和自我美化。” During his junior year at Harvard, Trivers had a mental breakdown. After five weeks of mania—little of which he remembers besides insomnia and feelings of grandiosity—he checked himself into the hospital and stayed for 11 weeks. Doctors diagnosed him with bipolar disorder. 特里夫斯在哈佛念大三时曾有过一次精神崩溃,在五个星期的躁狂症之后(在这其间的一切他几乎都不记得了,除了失眠和自大的感觉),他把自己送进了医院并在里面呆了11周。医生诊断他患了躁郁症。 When he returned to school, he thought it might be a good idea to take courses in psychology—though not abnormal psych because, as he likes to say, “I had a special advantage in it.” But he soon decided psychology in its then state was not a real science. 当他重返学校时,他认为修心理学课程可能是个不错的主意——这心理动机不算太意外——因为正如他自己喜欢说的,“这方面我有特别的优势。”不过他很快认定当时的心理学还算不上一门真正的科学。 The field at the time had three strands: First was work on conditioning, pioneered by Ivan Pavlov and B. F. Skinner. Skinner “was stupid enough to think you could build up a whole theory and system of logic about human psychology based entirely on learning,” Trivers says, “and specifically the kind of stimulus-response learning that’s studied in the lab.” Trivers didn’t see how, for example, the brain could pick up the complexities of language this way without some genetic scaffolding. 心理学当有三个分支:首先是对条件反射的研究,由巴甫洛夫和斯金纳开创。斯金纳“太蠢了,以至于认为你可以仅仅通过学习便建立起一整套关于人类心理的理论和逻辑体系,”特里夫斯说,“尤其是通过那种在实验室里被当作研究对象的刺激-反应式学习。”举个例子,特里夫斯就不认为,抛开遗传因素,大脑能够通过这种方式领会语言的复杂性。 Then there’s Freud, who had some keen insights into self-deception, Trivers says, “but he wedded them to a completely corrupt view of human development” characterized by the anal, oral, and Oedipal stages.“He just invents it out of whole cloth while snorting too much cocaine.” 第二个分支便是弗洛伊德,他对自我欺骗有着某种深刻的洞见,特里夫斯说,“但他将这种洞见同一种朽烂不堪的人类成长观嫁接到了一起”,其观点的标签便是肛欲期、口欲期和恋母期。“他不过是在嗑了太多白粉后凭空发明了这些概念。” Third was social psychology, which Trivers saw as too dependent on self-reports. “You cannot build up a science based on a whole series of correlations between how people answer questionnaires,” he says. “By definition it can’t work, if only because we don’t know most of what’s causing us to do things, and second, we don’t necessarily tell the truth.” Trivers considered psychology “a joke.” 第三个分支是社会心理学,特里夫斯认为其太依赖自我报告了。“你不能以人们如何回答问卷之间的相关性为基础,建立起一门学科。”特里夫斯说。“显然这不管用,首先我们大部分时候并不知道是什么让自己去做一件事,其次,我们也不一定会说实话嘛。”特里夫斯认为心理学就是个“笑话”。 So he stuck to justice and applied to law school. He selected the progressive law school at Yale, with Virginia as a backup, but neither accepted him, in part because of his mental health. “But for his mental illness,” says William von Hippel, a friend and collaborator at the University of Queensland, “he would not be the famous scientist that he is. He’d be a well-to-do lawyer.” 所以他转而追求正义并申请了法学院。他选择了耶鲁的进步主义法学院,并把弗吉尼亚法学院作为备选项,但都被拒绝了——部分因为他的心理健康状况。“要不是他的心理疾病,”特里夫斯在昆士兰大学时的同事和朋友威廉·范希波尔说,“他不会是今天这个著名科学家。他会是一个有钱的律师。” Suddenly without a clear path, Trivers heard about a job writing children’s books. He took his writing sample, an account of his breakdown, to Jerome Bruner, the Harvard psychologist running the project. “I was hired. Strange, eh?” He was assigned to write about biology, a topic he knew nothing about (hippo or rhino) and to work under the wing of the naturalist and bird expert William Drury. 突然不知通过什么方式,特里夫斯听说了一份为小孩子写书的工作。他带上他写的样稿(讲述他自己精神崩溃的事),去见杰罗姆·布鲁纳——当时主持该项目的哈佛心理学家。“我被雇佣了,怪不?”他被分配到博物学和鸟类学家威廉·特鲁里的麾下,题目有关生物学,一个他一无所知的主题(还记得河马和犀牛吧)。 Together they would sit in the woods imitating bird sounds so they could watch avian courting, clashes, and cooperation. Under Drury’s tutelage, Trivers decided to become an evolutionary biologist. Upon discovering evolutionary logic, he says. “I knew I had found where I wanted to be.” He has called Drury “the man who taught me how to think.” 他俩会一起坐在树林下模仿鸟类的叫声,观察它们求偶、打架以及合作。在特鲁里的指导下,特里夫斯决意成为一名进化生物学家。在发现进化的逻辑之后,他说“我知道我已找到我想追求的东西。”他称特鲁里为“那个教会我如何思考的人。” Trivers headed back to Harvard to earn a Ph.D. in biology, studying under Ernest Williams, a herpetologist. Trivers decided to study lizards in Jamaica and became enamored with the island—not least because he finds dark-skinned women attractive and says that at that time a white man couldn’t roam Boston with a black woman on his arm. 特里夫斯之后回到哈佛大学攻读博士学位,师从厄内斯特·威廉斯,一位爬虫学家。特里夫斯决定去牙买加研究蜥蜴并从此爱上了这个岛国。(其中一个重要原因是,他发现深色皮肤的女人很有吸引力,他还说,那时白人男性无法同黑人女性并肩徜徉在波士顿街头)。 “So I always felt free down there in a way that I never felt here,” he says. He has lived in Jamaica on and off since 1968 and frequently falls into Jamaican patois, speckling his speech with its slang (pumpum, raashuol). “所以在那里我时常感到在这里(美国)从来没感受过的自由,”他说。自1968年起他便时常回到牙买加居住并且经常讲牙买加方言,他的句子从此不时点缀些牙买加俚语(pumpum屄、raashuol屌)。 He has many tales to tell of Jamaica. One is a memorable stickup in an East Kingston club. That story begins when he visited the establishment after a hiatus, curious to see if things had gotten as bad as he’d heard. 关于牙买加,特里夫斯有很多故事可讲。其中之一便是在东金斯敦俱乐部里被持枪抢劫,这事可谓终身难忘。这个故事要从他闲来无事走进这家俱乐部讲起,他是个好奇的人,想看看事情是不是真有听说的那么糟。 When he entered, two men put guns to his head as three more gunmen stood by. They pulled the money from his pocket and pushed him against a wall next to a man bleeding from the head. When the next victim arrived, Trivers dashed out the door. After reporting the robbery to police, he learned that they and the community had sanctioned the ambush as a form of extrajudicial punishment for the johns. 当他走进去,两个男人拿枪顶着他的头,旁边还站着三个持枪者。他们拽出他兜里的钱,把他推到墙上,旁边就是个满头是血的人。当下一个受害者进来时,特里夫斯夺路而逃。在向警察报告了这起抢劫案后,他得知警察和这个社区是认可这类袭击的,并将其视作对嫖客的法外惩罚。 But as a white man, whose death would have caused major scrutiny for the area, he was a surprise inconvenience. The robbers had let him flee. According to Trivers, one woman who saw him running down the road later said to him, “Massah, me nebber know white man could fly, until I see you go by.” 但是白人是烫手山芋,他的死会引起对这个地区的大规模监视,所以抢匪们放他跑了。据特里夫斯回忆,一个看到他逃命的女人后来对他说,“妈呀,我原来都不知道白人还会飞,看到你我才信了。” Trivers also nurtured a family in Jamaica. He has two Jamaican ex-wives, five children, and eight grandchildren. One daughter is now the principal of a charter school in Harlem. 特里夫斯还在牙买加组建了一个家庭。他有两个牙买加前妻、五个儿女和八个孙辈。其中一个女儿现在是哈林区一所特许学校的校长。 After finishing his Ph.D. in 1972, Trivers joined Harvard’s faculty. In 1977, he sought tenure, but the decision was pushed back three years because of his mental health issues. Instead of waiting, he decamped to the University of California at Santa Cruz with his wife and son in tow. 1972年博士毕业后,特里夫斯留在哈佛任教。1977年,他谋求终身教职,但因为心理健康的问题连续三年被驳回。他没有继续等待,而是带着妻子和儿子到了加州大学圣克鲁斯分校。 In Santa Cruz, Trivers met Huey Newton, then a Ph.D. student and the leader of the Black Panthers. They became close, and in 1979 Trivers joined the party—for which he says he’s done “an illegal thing or two.” Trivers still refers to himself as “my black ass,” which he picked up from Newton, who told him: “Bob, everyone’s ass is black if you look closely enough.” 在圣克鲁斯,特里夫斯遇到了休伊·牛顿,一名在读博士,也是黑豹党的领导人。两人走得很近,在1979年特里夫斯加入了这个党。特里夫斯说他自己曾为黑豹党“干过那么一两件非法的事情。”特里夫斯如今还以“我这个黑屁眼”自称,这是他从牛顿那学来的。牛顿曾对他说:“鲍勃,所有人的屁眼都是黑的,如果你离近点看的话。” Together they wrote an article for the magazine Science Digest about self-deception in the pilots of Air Florida Flight 90, which had crashed into the Potomac River upon takeoff in 1982, killing 78. A friend of Trivers, the Harvard butterfly expert Bob Silberglied, had died in the crash. 他俩一起在《科学文摘》上发表了一篇文章,论述1982年1月13日佛罗里达航空90次航班空难中飞行员的自我欺骗行为。当时飞机在起飞时坠入波托马克河,共造成78人丧生,包括他的朋友、哈佛的蝴蝶专家罗伯特·希尔博格里德。 Trivers was also drawn to the cockpit conversation replayed on TV. “You could hear the fear and rationality of the copilot,” he says, “and the overconfidence of the pilot, who showed fear only when they were in the air and it was too late.” 特里夫斯被电视上播放的驾驶舱录音所吸引。“听得出来,副机长怀有担忧,很理性,”他说,“而机长过于自信,他在飞机离地以后才表现出担忧,但已经太迟了。” In their article, they analyze the NTSB transcript line by line. The copilot repeatedly expresses concern about snow accumulating on the wings, the need for more de-icing, and what he believes are faulty instrument readings. The pilot brushes him off. Finally, 49 minutes after their last de-icing, they take off. Without sufficient velocity, they pull up, and a few seconds later they stall. The plane grazes a bridge and plunges into the Potomac. 在那篇文章中,他们逐行分析了全国运输安全委员会的报告。副机长当时反复表达了对机翼积雪的担忧,认为需要再除除冰,还有仪器读数也不正常。机长没理他。最终在最后一次除冰后49分钟,他们起飞了。在没有足够速度的情况下,他们就开始爬高,几秒钟后引擎熄火。飞机擦过一座桥梁,一头栽进波托马克河。 “We imagine that presenting a falsely positive front may often have been advantageous to the pilot prior to Flight 90,” Trivers and Newton wrote, “giving him the illusion that skill plus overconfidence works in all encounters.” “我们猜想,在飞90次航班之前,对这名机长来说,虚假的积极乐观一直都是有利的,”特里夫斯和牛顿写道,“这给了他一种幻觉,似乎技术加上过度自信就能应付任何情况。” The two began writing a book titled Deceit and Self-Deception, but the publishing house closed. Newton, Trivers recalls, “was a master at propagating deception, he was a master at seeing through other people’s deception, he was a master at beating people’s self-deception out of them, and like all the rest of us, he fell down when it came to his own self-deception.” In an interview with The Black Panther newspaper, he called Newton a “heavyweight mind,” in comparison to the many “light- and middleweight minds” he found at Harvard. 两人开始写一本名为《欺骗与自我欺骗》的书,但出版社倒闭了。牛顿“是个宣传欺骗的大师”,特里夫斯回忆道,“一个一眼洞悉别人骗术的大师,他精于把他人从自我欺骗中打回原形,然后他像其他所有人一样,当他从自己的自我欺骗中走出来时,他垮掉了。”有一次《黑豹》报采访了他,他说牛顿是“重量级的思想者”,许多他在哈佛接触过的人相较之下只能算是“轻量级或中量级的思考者”。 Trivers’ most detailed exploration of self-deception didn’t come until his 2011 book The Folly of Fools, where he explains that we fool ourselves in all realms of life—when overestimating our looks or abilities, when justifying our righteousness, when defending our power or privilege, when constructing false historical narratives. It’s all part of advancing our own agendas. 直到2011年《愚人愚道》出版,特里夫斯才对自我欺骗进行了详细论述,书中他解释说我们在生活的各个领域愚弄自己——高估自己的能力和相貌、为自己的正直感找正当的理由、保卫自己的权力或特权、构建虚假的历史叙事。这些都是为了达到自己某种目的而做的部分努力。 “What I’ve done is found disciplines,” Trivers says. As to self-deception, “I lost a lot by being sooo slow to develop suuch an important idea. Had I written the paper in ’78 like I was supposed to, there would have been a whole science now.” “我做的工作是建立范式,”特里夫斯说。对自我欺骗理论,“这个理论太太太重要了,而我太太太晚才发展出这个理论以至于我损失了太多。我本该在1978年就写下论文,我要是那样做了,现在肯定已经发展出完整的学科了。” In 1994, he moved to Rutgers to be closer to his children. There, he has continued to publish on evolution and human behavior. One area of interest has been body symmetry in Jamaican children as a measure of genetic ability to withstand stressors during development. In 2005, he co-authored a paper showing that more symmetrical Jamaican teenagers were rated better dancers. The study was featured on the cover of the prestigious journal Nature. 1994年他前往罗格斯大学,这样可以跟他的孩子们近一些。在那里他继续就进化和人类行为发表文章。当时他的一个兴趣所在是身体的对称性,他将牙买加小孩身体的对称性视作一把尺子,度量在发育过程中适应压力的遗传能力。2005年,他合作撰写的一篇论文指出,身体更为对称的牙买加青少年在舞蹈方面表现更好。这项研究被声名卓著的《自然》杂志选作封面报道。 Later, however, another researcher had trouble replicating the findings, and Trivers took a closer look at the data. He found irregularities and concluded that William Brown, a postdoc and the paper’s lead author, had fabricated data. Trivers sought retraction from the journal, but Brown and Lee Cronk, a fellow Rutgers professor who had worked on the paper, denied any wrongdoing or mistakes. 然而另一名研究者在之后验证这项发现的可重复性时遇到了问题,特里夫斯也仔细检查了数据。他发现了不合常规的地方,并得出结论:论文的第一作者、博士后威廉·布朗编造了数据。特里夫斯试图从杂志上撤回论文,但布朗和另一位罗格斯大学的同事李·克朗克却否认存在任何不端行为或错误。 (Von Hippel said Cronk’s position is a classic case of self-deception, because a Nature paper was more important to his résumé than it was to Trivers’.) Trivers self-published a book, The Anatomy of a Fraud, to back up his case. Rutgers conducted its own investigation and came to the same conclusions as Trivers. (范希波尔说克朗克的行为是自我欺骗的典型案例,因为一篇发表于《自然》的论文对他的履历的重要性要远胜于对特里夫斯履历的重要性。)特里夫斯自己出版了一本书《解剖骗子》来支持自己的立场。罗格斯大学展开了调查并得出了同特里夫斯一致的结论。 In 2012, he stood in Cronk’s office and called him a “punk” for continuing to deny the allegations. Cronk claims to have felt threatened, and Trivers was banned from campus for five months. (Cronk declined to comment for this article.) Nature finally retracted the paper in 2013, five years after the initial request. “For me to produce a fraudulent result, know about it, and not do everything to expose it and prove it is anathema to the essence of my identity,” Trivers says. 2012年,特里夫斯跑到克朗克的办公室,为他继续否认指控而叫他“废物”。克朗克宣称他受到威胁,于是特里夫斯被禁止出现在校园,为期五个月。(克朗克拒绝为本文就此事发表评论。)《自然》终于在2013年将论文撤回,距初次发表已有五年时间。“对我来说,知道自己伪造了一个结果却不竭尽全力去揭露它,是对我人格本质的诅咒,” 特里夫斯说。 Trivers’ latest dustup with Rutgers began at the end of 2013, when he was assigned to teach a course on human aggression and he protested that he didn’t know the material. After much back and forth, he showed up in class and told his students the backstory. The university suspended him with pay for bringing students into the dispute, then withheld his pay for three months. 特里夫斯同罗格斯大学最近的一次纷争始于2013年底,当时他被分配去教一门关于人类攻击行为的课,而他抗议自己并不熟悉这个领域。在几轮较量后,他最后还是出现在了教室里,他告诉学生发生了什么。大学先是以将学生卷入纷争为由让他带薪停课,之后又扣了他三个月工资。 “I am one of the most accomplished scientists they have ever had, period,” Trivers says in a characteristic but not inaccurate self-assessment. “Why not treat him well?” he asks. He has taken a dim view of the university and looks forward to a conscious uncoupling. “Honesty is not their strong suit,” he says. “Remember, we’re talking about New Jersey.” “我是他们拥有过的成就最高的人之一”,他这话带着特里夫斯的风格,但这个自我评价却不能说不准确。“怎么就不能对他(指特里夫斯自己)好点呢?”他问道。他对罗格斯大学的前景感到悲观并主动寻求离开。“诚实不是他们的强项,”他说,“记住,毕竟我们说的是新泽西州。” Trivers also had a talk at Harvard canceled once when he made a perceived threat against Alan Dershowitz in The Wall Street Journal letters pages over their conflicting views on Israeli-Lebanese relations. He admits to writing many “strongly worded” letters to people. And he notes: “If I ask you a direct question and you don’t give me a direct answer, I will wheel on you and say, ‘Yes but what about the question I asked you?!’” 因为对以色列-黎巴嫩关系的相左认识,特里夫斯曾在《华尔街日报》的读者来信版面里猛烈抨击艾伦·德肖维茨,这让后者感觉受到了人身威胁,特里夫斯在哈佛的一次讨论会也因此取消。他承认自己给人写过许多“措辞激烈”的信。他还补充说:“如果我直截了当地问你一个问题,而你不直截了当地回答,那我就要穷追猛打,‘是的,可是我刚才问你的那个问题呢?!’” When I asked Trivers how much blame he should take for the drama that surrounds him, he says, “I know I’m a hard man.” But he doesn’t see himself as violent. When he was kicked off campus for calling Cronk a punk, Rutgers sent him to a psychologist for threat evaluation. 当我问特里夫斯,对于这些围绕你的这些争议,你自己负有多少责任,他回答“我知道自己是个不好相处的人。”但他并不认为自己暴力。当他因为叫克朗克废物而被踢出校园时,罗格斯大学给他找了个心理学家进行威胁评估。 “After an hour and a half, the psychologist says to me: ‘You know something, Dr. Trivers? You’re not a danger to anyone, including any of your colleagues. Your problem is you call stupid people stupid, and if they have power over you, you get blowback.’” Trivers told me this not a minute after framing an off-the-record comment with: “Please, I will get violent if I see this in print, and I’m not joking.” “一个半小时后,这位心理学家对我说:‘你知道吗,特里夫斯博士?对任何人你都不是一个威胁,包括你的大学同事。你的问题是你管笨蛋叫笨蛋,如果他们能奈何得了你,你就有得好受。”特里夫斯在告诉我这些之前没多一会儿的时候曾说过,他的某句评论可不能传出去。他是这么说的:“拜托,我要是看见这句话印出来的话,我肯定会动手打人的,我不开玩笑。” But this hard man is trying to change. He relies on strategies he developed years ago for managing his emotions, including something resembling prayer. He put religion aside at around age 13, “because math was a hell of a lot more interesting than ‘begat begatbegat.’ And there was this little contradiction between religion and 13-year-old girls.” 不过这位不好相处的人也在试着改变。他依靠一套自己多年前开发出的办法来管理情绪,其中一种办法类似于祈祷。他13岁时便抛弃了宗教,“因为比起什么‘以父之名’,数学要他妈有趣得多。而且宗教这玩意还和13岁的女孩子有矛盾【编注:这里特里夫斯大概是在吹嘘他13岁时就懂得泡妞了】。” Now, he wishes he hadn’t neglected it so much. He doesn’t believe in a god who listens: “How does God have any time left for my moaning and groaning? It’s insane.” Instead, it’s more a meditation. “I pray to keep my anger under control, to be more compassionate, for forgiveness, but I regard myself as talking to different parts of my own psyche.” 现在,他后悔自己当时如此地忽视宗教。他并不相信有一个会倾听的神:“神怎么会有时间来听我抱怨?这太扯了。”他的祈祷更接近冥想。“我祈祷我的愤怒得到控制,自己更加悲悯,我祈祷得到宽恕,但我总感觉,我这是在和自己灵魂的不同部分对话。” Trivers sees himself doing another five to ten years of research, but he describes his current contributions as more humble. He pumps out papers on lizards and knee symmetry in runners, which he admits, were “designed to fly me to Jamaica at someone else’s expense.” 特里夫斯认为自己还能做上5到10年的研究,但他认为自己目前的贡献远不如前。关于蜥蜴和跑步运动员膝盖的对称性,他撰写了大量论文,对此他说“用处也不过是能让我花别人的钱飞来牙买加罢了。” Yet one recent idea emerging from his interest in self-deception appears to have real significance: Research shows that older adults are biased toward paying attention to and remembering the positive over the negative and that they don’t dwell in negative moods, a phenomenon called the aging positivity effect. 然而他对自我欺骗的关注最近孕育了一个新观念,这一观念可能具有巨大的价值:研究者们发现年长些的人总是偏向关注和记忆正面的事情而忽略负面的,他们不会长时间陷在负面情绪里,这一现象被称作“衰老的正面效应”。 There’s been no functional explanation, and it would seem that such a bias could be dangerous by blinding people to hazards. But Trivers notes that positive moods improve immune function, and older adults have a greater need for a strong immune system to fight off tumors and other ills. So maybe we’ve evolved to cheer ourselves up as we age just to boost immunity. 对这一效应现在还没有有效的解释,而这一对正面事物的偏执会让人们对危害视而不见,因而可能造成危险。不过特里夫斯注意到,积极的情绪会增强人体的免疫机能,而年长的人需要一个强健的免疫系统来对抗肿瘤和其他疾病。所以,也许我们就是这样进化的:越老就越充满正能量,从而提高我们的免疫力。 He suggested the idea to von Hippel, who didn’t buy it. Why would natural selection shape old age, after we can no longer reproduce? But, Trivers argued, you can still help raise your grandchildren, who carry your genes. 他把这一理念跟范希波尔提起,但后者一开始并不买账。自然选择为什么在我们失去生育能力后,还让我们老当益壮?但特里夫斯争辩说,在你老了之后你仍可以帮助养育孙辈,他们身上仍然携带了你的基因。 Von Hippel ran a test that found that in older adults, a greater positivity bias correlated with stronger immune function. So they published the findings in 2014 in Psychology & Aging. Now they’re working on a longitudinal study to see if positivity predicts later immune function. 范希波尔做了验证,发现专注正面事物的年长者确实拥有更强健的免疫功能。于是在2014年,他俩在《心理学与衰老》上发表了这一发现。现在他们正合作一项纵向研究,以验证积极的心态是否会带来免疫力。 Trivers refrains from making grand predictions about the future of evolutionary theory, but he has certain interests. David Haig’s work on genetic conflict excites him, as does von Hippel’s work on aging. And he’s just applied for a yearlong fellowship at Harvard to study honor killings. “How in the world,” he wonders, “do you select for, if indeed you do, murdering your own daughter?” 特里夫斯不会预测进化理论会有如何广阔的前景,但对这一理论他颇有兴趣。David Haig关于基因冲突的研究、范希波尔关于衰老的研究都让他兴奋。而且他刚刚申请了哈佛为期一年的研究员职位,以研究“荣誉谋杀”现象。“在这个世界上,人怎么会选择——如果真的是自己选择的话——亲手杀死自己的女儿呢?”特里夫斯对此感到疑惑。 He also has a lifetime interest in homosexuality—another genetic conundrum—and plans to write a review paper. “I enjoy trying to think through those kinds of problems,” he says. “As a theoretician you’re attracted, or you ought to be, to precisely those phenomena that seem to contradict your theory, and the deeper the better.” 他对同性恋现象——另一个遗传学的谜题——也抱有持续的热情,并打算写一篇综述论文。“我很享受思考这些问题,”他说,“作为一个建立理论的人,你被,或者说你理应被那些与你的理论相悖的现象所吸引,越是痴迷就越好。” Eating dinner at a Thai restaurant with Trivers, I mentioned that a colleague of his had painted him to be something of a badass. As evidence I noted the time he stabbed the home invader in the neck. “That’s a badass?” he inquired between slurps of soup. “That ain’t a badass. That’s someone protecting his f*cking life. I came an inch from being killed, man.” 和特里夫斯在一家泰国餐馆吃饭时,我提到他的一位同事曾把他描述成一个混蛋。作为证据,我强调了那次他曾捅伤一位非法闯入者的脖子。“那叫混蛋?”他一边喝汤一边质问,“那不叫混蛋,那他妈是保命。我差点就被干死了,老兄!” Fair enough. But hurting his case, he went on to describe his response to the criminals’ lenient sentences. “I chased down both of them, because I had to,” he says. “Since the police aren’t disciplining them, I will.” One morning he spotted one of the men and pulled his car over. 言之有理。不过接下去他描述他对轻判罪犯的反应,可就要为他减分了。“我对这俩家伙穷追不舍,因为我不得不这么做,”他说,“既然警察不去规训他们,那我来。”一天早上,他认出了罪犯中的一个,然后停下车。 “‘Listen,’ I say, ‘If you want to rob me, you rob me at the roadside. Don’t rob me in my own home. That’s where my children live, that’s where my guests are. I will kill you three times over. In fact...’” As he started to get out of his car, Trivers says the man ran backward. (Helpfully, Trivers boxed in boarding school at Andover; but still, during one separate altercation, he ended up with an ice pick to his hand.) “‘听着,’我说,‘你要是想抢我,那你就在路边抢我。别在我家抢。那是我孩子生活的地方,是我客人到访的地儿。你再那么干,信不信我让你死透?实际上……’”特里夫斯说,当他准备下车时,那家伙倒退着跑开了。(特里夫斯在安多弗的寄宿学校练过拳击,不过,在另外一次争执中,他最后还是操起了碎冰椎。) Today, Trivers retains vitriol for those who don’t see the legitimacy in his work and the research it’s spawned. According to von Hippel, people reject evolutionary psychology for ideological reasons. Those on the right fear that it absolves us of responsibility, while those on the left fear that accepting inherited differences hinders the goal of social equality. 今天,特里夫斯仍然对那些看不出他所做工作及其孵化出的研究的合理性的家伙们冷嘲热讽。据范希波尔说,人们拒绝进化心理学是处于意识形态的理由。右派担心进化心理学会解除我们身上的责任,而左派担心承认天生差异会对阻碍实现社会平等这一目标。 Trivers says that many feminists and cultural anthropologists regard him as “the devil.” In return, he calls them “feebleminded” and “stone nuts.” More genes are expressed in the brain than in any other tissue, he notes, and to ignore the partnering of nurture with nature is “ludicrous, if you have any serious interest in reality or science.” 特里夫斯说,许多女权主义者和文化人类学家将他视为“魔鬼”。而作为回击,他管他们叫“玻璃心”和“石化脑”。他指出,比起其他组织,在大脑里得到表达的基因更多,忽视后天习得和先天遗传的共同作用是“可笑的,要是你对事实和科学还有一丝严肃态度的话。” Trivers feels grateful for everything evolutionary biology has given him. It’s taken him around the world to wild and often unwelcoming places, and it’s given him the tools to analyze what he’s seen, from lizards to lovers’ quarrels to leftist movements. “In short,” Trivers writes in his memoir, “I signed on to a system of thought that allowed me to study life and live it, sometimes very intensively.” 特里夫斯对从进化心理学那里得到的一切都心存感激。进化心理学带他走向世界各地,去到荒僻、甚至往往不友好的地方;给他分析所见所闻的工具,从蜥蜴到情侣争吵再到左翼运动。“一言以蔽之,”特里夫斯在回忆录里写道,“我献身于一个思想体系,它让我可以研究和体味生命,而且这一过程有时还颇为激烈。” (编辑:辉格@whigzhou) *注:本译文未经原作者授权,本站对原文不持有也不主张任何权利,如果你恰好对原文拥有权益并希望我们移除相关内容,请私信联系,我们会立即作出响应。

——海德沙龙·翻译组,致力于将英文世界的好文章搬进中文世界——

[译文]为何人类阴道那么大

Why Is the Human Vagina So Big?
为什么人类阴道那么大

作者:Holly Dunsworth @ 2015-12-03
译者:小册子(@昵称被抢的小册子)
校对:林翠(@cwlinnil)
来源:The Evolution Institute,https://evolution-institute.org/blog/why-is-the-human-vagina-so-big/

We are obsessed with penis and testicle size. Yet, we can barely say “vagina” and when we do we’re usually talking about the vulva.

我们总是着迷于阴茎和睾丸的尺寸,却极少谈及“阴道”,就算我们提到了,一般也只是讨论外阴。

Everyone’s come across some article somewhere on-line that is thrilled to share how big human penises really are, for primates, and to explain why they evolved to be so big. It’s not really the length, but the girth. Alan Dixson is your go-to on this. He’s conservative in his assessment of the literature on penis size and even he concedes that human penis “circumference is unusual when compared to the penes of other hominoids (apes)” (p. 65 in Sexual Selection and the Origins of Human Mating Systems).

每个人都见过,网上的一些文章在兴奋不已地告诉你,人类的阴茎在灵长类中有多大,为什么会进化成这么大。其实所谓的大,不是指长度,而是指茎围。说到这个话题,你去问Alan Dixson就准没错。他对有关阴茎大小的文献一贯持比较谨慎的态度,但连他也承认,人类的阴茎“周长和其他人猿的阳具相比是个异数” (见《性选择与人类繁衍系统起源》第65页)

A favorite explanation for the big phallus is female mate choice, that females selectively make babies with males who have larger and, presumably, more pleasurable semen delivery devices. This is backed up by studies. When life size projections of naked men are shown to female subjects, they say they find the ones with bigger ones to be more attractive. [This is exactly how mate choice works where I live, how about you?]

对阳具大型化的一个比较受欢迎的解释是雌性交配偏好,也就是说雌性倾向于选择与有着较硕大、想来也较受用的“精液注射器”的雄性交配产子。这一理论得到了一些研究的支持。有研究让女性受访对象看真实大小的裸男幻灯片,她们纷纷表示阳具伟岸的男性比较有吸引力。(这完全符合我日常所见的择偶选择,你呢?)

Other explanations include male competition. If you can deliver your package to the front yard but the other guy can deliver to the front door, his is more likely to be carried inside the house first. Or, if he can steal away what you just delivered, then, again, his package has yours beat. Thanks to his big penis he’s more likely to pass on his winning penis genes than you are to pass on your loser penis genes. Loser.

雄性竞争也是一种解释。如果你只能把包裹投递到院子里,但另一个人可以把包裹放到屋门前,那他的包裹先进屋的几率就高一些。又或者,如果他可以偷走你刚送的包裹,那他的包裹也一样打败了你的。因为器材比较大,他延续他那“赢茎”基因的可能性比你延续你那“输茎”基因的可能性也就比较大。于是你就完蛋了。

All this is just terribly fun to write about and I’m not even going nuts (gah) like they do. And they do. They really do(more...)

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Why Is the Human Vagina So Big? 为什么人类阴道那么大 作者:Holly Dunsworth @ 2015-12-03 译者:小册子(@昵称被抢的小册子) 校对:林翠(@cwlinnil) 来源:The Evolution Institute,https://evolution-institute.org/blog/why-is-the-human-vagina-so-big/ We are obsessed with penis and testicle size. Yet, we can barely say “vagina” and when we do we’re usually talking about the vulva. 我们总是着迷于阴茎和睾丸的尺寸,却极少谈及“阴道”,就算我们提到了,一般也只是讨论外阴。 Everyone’s come across some article somewhere on-line that is thrilled to share how big human penises really are, for primates, and to explain why they evolved to be so big. It’s not really the length, but the girth. Alan Dixson is your go-to on this. He’s conservative in his assessment of the literature on penis size and even he concedes that human penis “circumference is unusual when compared to the penes of other hominoids (apes)” (p. 65 in Sexual Selection and the Origins of Human Mating Systems). 每个人都见过,网上的一些文章在兴奋不已地告诉你,人类的阴茎在灵长类中有多大,为什么会进化成这么大。其实所谓的大,不是指长度,而是指茎围。说到这个话题,你去问Alan Dixson就准没错。他对有关阴茎大小的文献一贯持比较谨慎的态度,但连他也承认,人类的阴茎“周长和其他人猿的阳具相比是个异数” (见《性选择与人类繁衍系统起源》第65页) A favorite explanation for the big phallus is female mate choice, that females selectively make babies with males who have larger and, presumably, more pleasurable semen delivery devices. This is backed up by studies. When life size projections of naked men are shown to female subjects, they say they find the ones with bigger ones to be more attractive. [This is exactly how mate choice works where I live, how about you?] 对阳具大型化的一个比较受欢迎的解释是雌性交配偏好,也就是说雌性倾向于选择与有着较硕大、想来也较受用的“精液注射器”的雄性交配产子。这一理论得到了一些研究的支持。有研究让女性受访对象看真实大小的裸男幻灯片,她们纷纷表示阳具伟岸的男性比较有吸引力。(这完全符合我日常所见的择偶选择,你呢?) Other explanations include male competition. If you can deliver your package to the front yard but the other guy can deliver to the front door, his is more likely to be carried inside the house first. Or, if he can steal away what you just delivered, then, again, his package has yours beat. Thanks to his big penis he’s more likely to pass on his winning penis genes than you are to pass on your loser penis genes. Loser. 雄性竞争也是一种解释。如果你只能把包裹投递到院子里,但另一个人可以把包裹放到屋门前,那他的包裹先进屋的几率就高一些。又或者,如果他可以偷走你刚送的包裹,那他的包裹也一样打败了你的。因为器材比较大,他延续他那“赢茎”基因的可能性比你延续你那“输茎”基因的可能性也就比较大。于是你就完蛋了。 All this is just terribly fun to write about and I’m not even going nuts (gah) like they do. And they do. They really do. And all over the Internet they do: “Evolution of human penis” gets 53,000 hits just on scholar.google alone, and about 832,000 on Google. 写这些东西真是好玩死了,而我又不会像他们那样疯狂,咔咔。他们真是挺疯的,不骗你。男人在网上有这么疯:搜索“人类阴茎的进化”,仅仅在谷歌学术上就得到了53,000个结果,在整个谷歌上则得到了832,000个。 But doesn’t it make sense that for a penis to be somewhat useful it has to be somewhat correlated to vagina size? 但是,如果阴茎要发挥功能,难道不是应该和阴道的大小联系起来才说的通吗? I’m talking about all penises in the universe and all vaginas too. Sure there’s variation, but a penis can’t be too wide. It helps to be long, probably, but it can’t be too long. 我在讨论的是地球上所有的阴茎,和地球上所有的阴道。当然它们会有差异,但阴茎也不能太粗。长应该是有好处,但也不能太长。 So neither pleasure nor psychology need matter at all, just function associated with some sort of fit. Pleasure and psychology are never invoked to explain penis morphology in other animals. If anything, it’s the cornucopia of horrifying, not pleasing, animal penises that begs for evolutionary explanations. 其实肉体欢愉和心理需要都根本不重要,重要的只是与大小匹配相关的功能。肉体欢愉和心理感受从来就没有被拿来解释其他动物的阴茎形态。如果非要从进化论的角度看,就要去解释太多种并不讨喜,反而可怕的动物阴茎了。 Wouldn’t you explain the size and shape of the key by the size and shape of the lock? So wouldn’t it be a little more scientifically sound to hypothesize that the human penis is sized and shaped like that because it fits well into the human vagina? 你在解释钥匙的大小和形状的时候,不是以锁的大小和形状为参照的吗?因此,人类阴茎之所以是如此的大小和形状,是为了要匹配人类的阴道,这种猜想在科学上不是比较合理么? Sure, it gets chicken-and-eggy or turtles-all-the-way-downy, but c’mon. Isn’t it a bit obvious that the privates that fit inside the other privates are probably correlated? You’d think that even the people who have never had intercourse would default to this explanation for the evolution of the human penis. 当然,这会演变成一个鸡先蛋先,又或者是龟下有龟的问题【译注:”It’s turtles all the way down”来自一则古老的轶事,体现逻辑上的无限递归,后来成为一句玩笑话,用来表达表面立于不败之地,而实则回避逻辑问题的境况】。不过,拜托,互相契合的灵长类体征具有相关性,这不是很明显吗?你应该会同意,就算没有性经验的人,不需多想也会接受这个有关人类阴茎进化的理论吧。 But we’re rarely, if ever, told that human penises are relatively girthy because human vaginas are. It’s always about male competition or female preference. 但我们极少,甚至从没有听人提过,人类的阴茎之所以比较粗大,是因为人类的阴道比较粗大。大家总是在研究那些雄性竞争和雌性偏好的理论。 Sure, we may be a little weird compared to our close relatives for not having a baculum (penis bone), and maybe that’s the sort of thing you want to explain for whatever reason, but does human penis size and shape need a uniquely human story? 人类阴茎没有骨骼,和人类的近亲相比这也许显得有些奇怪,你可能会出于种种原因想为这一现象找个解释。但人类阴茎的大小和形状有别于其它动物,真的需要一个独特的解释吗? Assuming it’s correlated to the vagina like it probably is in many other species,* then no it doesn’t… unless the size and shape of the human vagina has an exceptional story. 有不少其他动物的阴茎大小和形状很可能和它们的阴道相关,假设人类也是如此,那除非人类阴道的形态有异于其它动物,否则人类阴茎的形态不应该有什么特别。

journal.pone_.0000418.g002 [水禽雌雄生殖器官协同变异的图例。标星型的为雄性生殖器,箭头指向的是女性生殖器。图片来自“水禽雌雄生殖形态的协同进化”。DOI编码: 10.1371/journal.pone.0000418]

Does it? We wouldn’t know. There are zero (look!) articles titled “Why is the human vagina so big?” 究竟有没什么特别呢,我们不知道。从来都没有文章(你自己看!)以“为何人类的阴道那么大”为题。 Until right now. 直到现在才有。 Here we go. If we were going to answer it the same way we’ve long explained the human penis, and other animal penis shapes, then we’ve got a few ideas… 来吧,如果以我们长期以来解释人类和其他动物阴茎形态的思路,来回答这一问题的话,我们可以有如下一些解释…… Because walking upright made the vagina conspicuous and males thought a bigger vagina was better. Because big vaginas outcompete small ones at catching sperm. Because of male pleasure from coitus with a big vagina. Because of heat dissipation or thermoregulation. Because of a tradeoff with brain size. 因为直立行走让阴道外露,雄性认为阴道大一点比较好。因为大点的阴道比小点的更易于捕捉精子。因为大一点的阴道令雄性性交更舒服。因为有利于散热和调节体温。因为这是针对脑量增大的折衷方案。 And of course, we’d need to demonstrate that the human vagina is in fact larger, relative to body size, than the vaginas of other primates. Regardless, a sound answer to the question of vagina size and shape focuses on childbirth, wouldn’t you say? She’s got to be big enough to push out a baby and, for humans, it’s a great big baby. 当然,我们需要证明人类阴道相对于身体的比例,比起其它灵长类动物来说的确要更大。无论如何,难道你不觉得要回答人类阴道大小形状的问题,重点应该放在分娩上吗?阴道得足够大才能把婴儿生出来呀,人类婴儿的个头可大得很。 #169-3

[红毛猩,大猩猩,黑猩猩与人类的对比。这些数值偏离了灵长类总体的回归分析。数据来自Dunsworth等人,2012年,PNAS第 109(38):15212-15216]

So if there’s an exceptionally human story for the great big human penis, that exceptional story originates not in a woman’s orgasms, not in her pornographic thoughts or her lustful eyes, but in her decidedly unsexy “birth canal.” 所以如果人类阴茎硕大有什么特别原因的话,那原因既不是来自女人的性高潮,也不是来自女人的淫思欲眼,而是来自于那毫无性感可言的“产道”。 And I dug up a nice little note to explain this to us all written by Dr. Bowman, a gynecologist, back in 2008 for the Archives of Sexual Behavior which is magnificent. It starts out giving the only vagina-size-based, not to mention childbirth-based, explanation for human penises that I can find in the literature (which is thankfully cited by Dixson in his book mentioned above). But it still manages to bring the explanation beyond the vagina and onto another proud triumph: “In sum, man’s larger penis is a consequence of his larger brain.” 我找到了妇科医生Bowman于2008年发表在《性行为档案》上的一篇文章,该文很好地向大家解释了上述问题。文章开始以阴道大小和分娩需要为切入点来解释人类阴茎的大小,这是我能找到的以阴道大小来解释阴茎大小的唯一文献,从分娩需要着眼就更不用说了。这要感谢Dixson在前文提到的书中引用了这一资料。Bowman在文中还站在一个比阴道更高的层次,去解释这个问题:“总而言之,人类阴茎巨大是脑量增加的后果”。 After you clean up the coffee you just spat onto your computer screen, you can read it all for yourself by clicking on the link up there (or emailing me for the pdf). 你可以先把喷到电脑上的咖啡抹干净,然后点击上面的链接,通读一下那篇文章(想要pdf格式的可以发邮件给我)。 Guess who didn’t read it? That study in PNAS, mentioned above, that showed women naked penises, got a high attractive score for the big ones, and thinks that’s evidence for mate choice now, today, let alone back when (I’m going to speculate that) women had a tiny bit less of it. 你猜谁没有读过那篇文章?就是文章开头提到的那项研究的研究者【编注:指本文第三节提到的“让女性受访对象看真实大小的裸男幻灯片”的那项研究】,他们把阴茎赤裸裸地呈现给女人看,让那些大家伙拿到高分,然后认为拿到了交配偏好在当代的证据,至于过去女人眼福稍浅(我只是猜的)的年代是什么情况,就更不用废话了。 Point is, the literature rages on with the special explanations for the big penis with nary a big vagina in sight. 重点是,各路文章热火朝天地为大阴茎找了各种特别的原因,却对大阴道视而不见。 But you heard it here, at least. 但起码你在这里听说了。 Childbirth is why the human vagina is so big and, consequently, why the male penis is so big. It’s pretty straightforward. Yet we’re still left scratching our heads as to why the penis question endures. 分娩导致了人类阴道如此巨大,进而导致人类阴茎如此巨大。这很直观。然而大阴茎的话题经久不衰,这很令我们挠头。 Is evolutionary science averse to big vaginas? 难道进化研究是反大阴道的么? Does nobody love a big vagina? 难道就没人喜欢大阴道么? Because that’s just ridiculous. Everybody came from one. 这很荒谬,每个人都是从那儿出来的呀。

******

P.S. Unfortunately a few scholar.google searches led me to find no cross-species comparisons of mammalian vagina lengths or any vaginal measures. It may be out there, but I haven’ t found it. I found some measures for bitches… DOGS! And some heifers… COWS! So I’ve got to compile some data if I’m to do this properly. Baby size might be a way to do this. P.S. 我在谷歌学术搜索了一下,可惜没有找到不同哺乳动物阴道长度或尺码的对比。也许有,但我找不到。我倒是找到了狗娘的尺码……是真的母狗啦!还有牛逼的尺码……也是真的母牛啦!所以如果要认真对比的话,我得收集整理一些数据才行。通过分析婴儿的大小可能也是一个办法。 P.P.S. p. 73 in Dixson has Figure 4.3 with nine primate species’ penile and vaginal lengths plotted. Thanks Patrick C. for reminding me where I’d seen something like this and where to point readers! P.P.S. Dixson书中第73页的图表4.3,列出了九种灵长类动物的阴茎与阴道的长度。谢谢Patrick C.提醒我曾经看到过这样的图表,让我可以告诉读者上哪里找。 (编辑:辉格@whigzhou) *注:本译文未经原作者授权,本站对原文不持有也不主张任何权利,如果你恰好对原文拥有权益并希望我们移除相关内容,请私信联系,我们会立即作出响应。

——海德沙龙·翻译组,致力于将英文世界的好文章搬进中文世界——

[译文]长寿、祖母假说与配偶关系

Got a great relationship? You may want to thank your prehistoric grandmother
拥有美妙的关系?你可能想感谢你远古的祖母

作者:Jo Setchell @ 2015-09-08
译者:淡蓝 ([email protected])
校对:沈沉(@你在何地-sxy)
来源:THE CONVERSATION,https://theconversation.com/got-a-great-relationship-you-may-want-to-thank-your-prehistoric-grandmother-47181

I went to a cross-cultural wedding last weekend. The guests travelled across continents to be there, spoke mutually incomprehensible languages and came from different traditions. However, they all shared a common understanding of the relationship between the bride and the groom. Pair bonds are, after all, universal in human societies, despite being rare in other mammals. And we don’t exactly know why.

上周末我参加了一场跨文化的婚礼。源自不同的文化传统、说着彼此都听不懂的语言的婚礼嘉宾们穿越各大洲来到这里。虽然如此,对新郎和新娘的关系,他们却有着共识。在其他哺乳动物中罕见的配偶式结对,却实实在在地在全人类社会中普遍存在。而我们却不太清楚这是为什么。

Before the wedding breakfast, I chatted with a relaxed couple who had left their kids with their grandparents for the day. This is not unusual; UK grandparents babysit on average 76 times a year – and we often take it for granted. But now a new study finally gives grandparents the credit they deserve by arguing that long-term relationships actually evolved thanks to grandmothers helping out with kids in prehistoric times.

婚礼早餐之前,我与一对十分放松闲适的夫妇聊了会。那天他俩把孩子交给了他们的祖父母照看。这种做法应该不在少数;在英国,祖父母们每年平均照顾孙辈76次——而我们常常也觉得这是理所当然(more...)

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Got a great relationship? You may want to thank your prehistoric grandmother 拥有美妙的关系?你可能想感谢你远古的祖母 作者:Jo Setchell @ 2015-09-08 译者:淡蓝 ([email protected]) 校对:沈沉(@你在何地-sxy) 来源:THE CONVERSATION,https://theconversation.com/got-a-great-relationship-you-may-want-to-thank-your-prehistoric-grandmother-47181 I went to a cross-cultural wedding last weekend. The guests travelled across continents to be there, spoke mutually incomprehensible languages and came from different traditions. However, they all shared a common understanding of the relationship between the bride and the groom. Pair bonds are, after all, universal in human societies, despite being rare in other mammals. And we don’t exactly know why. 上周末我参加了一场跨文化的婚礼。源自不同的文化传统、说着彼此都听不懂的语言的婚礼嘉宾们穿越各大洲来到这里。虽然如此,对新郎和新娘的关系,他们却有着共识。在其他哺乳动物中罕见的配偶式结对,却实实在在地在全人类社会中普遍存在。而我们却不太清楚这是为什么。 Before the wedding breakfast, I chatted with a relaxed couple who had left their kids with their grandparents for the day. This is not unusual; UK grandparents babysit on average 76 times a year – and we often take it for granted. But now a new study finally gives grandparents the credit they deserve by arguing that long-term relationships actually evolved thanks to grandmothers helping out with kids in prehistoric times. 婚礼早餐之前,我与一对十分放松闲适的夫妇聊了会。那天他俩把孩子交给了他们的祖父母照看。这种做法应该不在少数;在英国,祖父母们每年平均照顾孙辈76次——而我们常常也觉得这是理所当然的。但是,现在一项新研究终于承认了爷爷奶奶们应得的功劳,研究认为,长期夫妻关系的进化产生,实际上多亏了远古时代祖母们对孩子们的照看。 The greatness of grandparents 祖父母的伟大之处 The question of why humans form pair bonds – the biological term for the strong affinity that develops between partners (often a male-female pair but not always) – is in fact one of the biggest puzzles in evolutionary anthropology. Humans are apes, yet our closest living relatives – chimpanzees and bonobos – have no such long-term relationships between male-female pairs. 人类为何会形成配偶式结对——生物学术语,指伴侣之间(常常是雌雄配对,但并不全然如此)发展出的强亲和关系——事实上是进化人类学上的最大谜题之一。人类是一种猿,可我们的现存近亲——黑猩猩和倭黑猩猩——的雌雄伴侣之间却不存在这种长期关系。 In the late 1990s, anthropologists put forward the “grandmother hypothesis” to explain why human females stop reproducing at a similar age to other great apes, but live markedly longer lives. Chimpanzees live into their 30s or 40s, but human females often live decades beyond their child-bearing years. 1990年代末,人类学家提出了“祖母假说”,以解释为何人类女性停止生育的年龄与其他大猿相仿,却明显更加长寿。黑猩猩可以活到30多或40多岁,人类女性却能在育龄后再活数十年。 The grandmother hypothesis was based on observations of the Hadza people, in Tanzania. Hadza people live by hunting and gathering food, like our ancestors, although, they are of course modern people. 祖母假说基于对坦桑尼亚哈扎族人的观察而提出。尽管哈扎族人象我们祖先一样,靠狩猎和采集食物而生,但他们当然也是现代人。 Older Hadza women dig up tubers to feed youngsters who aren’t strong enough to it themselves. The grandmother hypothesis suggests that this help allows daughters to have their next baby sooner than they would otherwise. Over time, grandmothers who lived longer and helped more had more grandchildren, who shared their genes for longer life and care of their grandchildren. Thus, these genes became increasingly common in the population and human lifespan increased. 年老的哈扎族妇女靠挖掘植物块茎来喂养不够强壮、不能自食其力的年幼者。祖母假说认为,这种帮助让女儿们能更快地孕育下一个宝宝,否则间隔时间会更久。随着时间推移,更长寿并能提供更多帮助的祖母们就拥有了更多的孙辈,这些孙辈会共享她们的长寿基因并再去照顾自己的孙辈。这样一来,这些基因在人口中变得越来越普遍,人类寿命就此增加了。 The evolution of partnership 伴侣关系的演变 The new study, published in the Proceedings of the National Academy of Sciences, has used computer simulations to link this hypothesis to the evolution of pair-bonding in humans. The authors argue that long-term romantic relationships evolved due to a combination of people living longer and men remaining fertile longer than women. This situation led to a surplus of older men competing for younger, fertile women. 发表在《美国国家科学院院刊》上的一项新研究,用计算机模拟将这一假说与人类固定配偶关系的进化联系了起来。作者们认为,长期浪漫关系之所以进化出来,是因为人类越来越长寿,并且男性保有生殖能力的时间比女性更长。这种状况使得有更多相对较老的男性为年轻的育龄女性而相互竞争。 In fact, the study shows that the ratio of fertile males to fertile females in humans is twice as big as it is in chimpanzees, making humans very unusual mammals. This excess of males makes us more like birds. And birds are well-known for their pair-bonds. 事实上,这项研究显示,人类的育龄男女比,要比黑猩猩群体中的同一比例大两倍,这让人类成为十分不同寻常的哺乳动物。男性过多,使得我们更像鸟类,而鸟类的配偶关系是众所周知的。 Where many males compete for relatively few females, a male who develops a strong bond with one female will have more surviving offspring than males who seek numerous partners. The authors suggest that this created increasing incentives for men to “guard” their mate against rival males. 在数量更多的男性为相对较少的女性而彼此竞争时,与那些寻求众多伴侣的男性相比,同某一女性发展出强结合的男性将会拥有更多的成活后代。作者们认为,这就造成了很大的激励,促使男性去“守卫”他们的伴侣,赶走竞争对手。 While mate-guarding is not necessarily the same thing as pair-bonding, the authors argue that both involve a trade-off between paying attention to the current partner and seeking a new one. Of course, although the study concentrates on male strategy, females are not passive in this scenario – it takes two to bond. 当然,守卫伴侣与配偶关系未必是同一回事,作者们认为,两者有共同点,即都涉及在专心于当前伴侣和寻找新伴侣之间的权衡取舍。当然,尽管这项研究集中于男性的策略,女性在这一情景中也不是被动的——配偶结合需要两个人。 So, to put the wedding celebrations into their evolutionary context, perhaps it was the caring grandparents who led to the special relationship that we celebrated. A toast to the bride and groom … and one to their parents. 因此,把婚礼庆典放到进化论中来说,也许是因为那些曾经照看孙子的爷爷奶奶们,才造就了今天我们来庆祝的这种特殊关系吧。来吧,让我们为新娘和新郎干一杯……也为新人的父母干一杯。 (编辑:辉格@whigzhou) *注:本译文未经原作者授权,本站对原文不持有也不主张任何权利,如果你恰好对原文拥有权益并希望我们移除相关内容,请私信联系,我们会立即作出响应。

——海德沙龙·翻译组,致力于将英文世界的好文章搬进中文世界——

[译文]为何最大动物的精子最小

Why do the largest animals have the tiniest sperm? A brief investigation.
为什么全球最大动物的精子却是最小的?一份简要调查报告。

作者:Brian Resnick @ 2015-11-20
翻译:Drunkplane(@Drunkplane)
校对:慕白(@李凤阳他说)
来源:Vox.com,http://www.vox.com/science-and-health/2015/11/20/9768864/largest-animals-have-the-tiniest-sperm

Stefan Lüpold is a sperm guy. The Swiss evolutionary biologist did his masters work on sexual selection and bat genitalia, his PhD on the evolution of bird sperm, and a postdoctorate on how fruit fly sperm compete. “I’m fascinated by the almost unlimited diversity in both size and shape of sperm,” he writes me in an email from Zurich, describing his chosen sub-sub-discipline.

Stefan Lüpold 是个精子达人。这位瑞士进化生物学家硕士时研究的是性选择和蝙蝠的生殖器,博士时研究的是禽类精子的进化,博士后时则研究果蝇的精子如何竞争。“精子大小和形状近乎无穷的多样性让我着迷。”他从苏黎世给我发来电邮,讲述了他所选择的这一学科分支。

I’ve emailed him because he’s recently found evidence to answer a perplexing question: Why are sperm so weird?

我给他发邮件是因为他最近的发现能回答一个令人费解的问题:精子为什么这么诡异?

Mice, for instance, have sperm that’s twice as long as elephants’. The world’s longest spe(more...)

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Why do the largest animals have the tiniest sperm? A brief investigation. 为什么全球最大动物的精子却是最小的?一份简要调查报告。 作者:Brian Resnick @ 2015-11-20 翻译:Drunkplane(@Drunkplane) 校对:慕白(@李凤阳他说) 来源:Vox.com,http://www.vox.com/science-and-health/2015/11/20/9768864/largest-animals-have-the-tiniest-sperm Stefan Lüpold is a sperm guy. The Swiss evolutionary biologist did his masters work on sexual selection and bat genitalia, his PhD on the evolution of bird sperm, and a postdoctorate on how fruit fly sperm compete. "I’m fascinated by the almost unlimited diversity in both size and shape of sperm," he writes me in an email from Zurich, describing his chosen sub-sub-discipline. Stefan Lüpold 是个精子达人。这位瑞士进化生物学家硕士时研究的是性选择和蝙蝠的生殖器,博士时研究的是禽类精子的进化,博士后时则研究果蝇的精子如何竞争。“精子大小和形状近乎无穷的多样性让我着迷。”他从苏黎世给我发来电邮,讲述了他所选择的这一学科分支。 I've emailed him because he's recently found evidence to answer a perplexing question: Why are sperm so weird? 我给他发邮件是因为他最近的发现能回答一个令人费解的问题:精子为什么这么诡异? Mice, for instance, have sperm that's twice as long as elephants'. The world's longest sperm belongs to a fruit fly. And across the animal kingdom, sperm take on extremely odd and varied shapes and sizes. The "tadpole" shape we most associate with sperm is not at all common outside of mammals. Rat and mice sperm can have hook-like attachments on their heads. "In some species they seem to allow sperm to connect by their heads and form so-called sperm trains," Lüpold says. "These groups of sperm seem to swim faster than individual sperm." Fascinating! 举例来讲,老鼠精子的长度是大象精子的两倍。世界上最长的精子属于一种果蝇。纵观整个动物王国,精子的形状和大小极其古怪和多变。“蝌蚪”状这一我们最为熟悉的形状在哺乳动物以外根本不常见。老鼠精子的头部会有钩子形状的附属物。“一些物种的精子似乎可以通过头部连接在一起,形成所谓的精子列车。” Lüpold说,“这种精子群似乎比单个的精子游得更快。”多么神奇! From an evolutionary perspective this raises an intriguing question: Why are sperm so varied among different species when they all have the exact same purpose — fertilizing eggs? 从进化的角度看,这带来一个引人入胜的问题:所有的精子都有一个相同的目的——让卵子受精,但为什么不同物种的精子相差如此巨大?

Screen Shot 2015-11-19 at 5.54.26 PM[Four wildly different shapes of sperm, as illustrated in the text Sperm Competition and the Evolution of Animal Mating Systems (only 700 pages long!). A) Silverfish sperm, B) sponge sperm. C) molluscan sperm.,D) gyrinid beetle sperm.] 【四种截然不同的精子形状,来自《精子竞争和动物交配系统的进化》一书(也就700页而已啦!)A) 蠹虫的精子 B) 海绵的精子 C)软体动物的精子 D) 一种豉甲的精子】

Lüpold has a theory. Analyzing the sperm of 100 species of mammals, he and a co-author found a pattern amid the chaos: the larger the species, the smaller the sperm. The results were recently published in Proceedings B, a journal of the Royal Society of London. Lüpold有个理论。他和一位合著者分析了近百种哺乳动物的精子后,从这千头万绪中发现了一个规律:物种个体越大,精子越小。最近其成果已发表在伦敦皇家学会的《Proceedings B》杂志上。 Why would evolution favor such a pattern? Lüpold explains that longer sperm has some advantages — they are better at "elbowing" aside the competition. But it also takes a lot of energy to make long sperm, which larger animals can't afford. So it's a trade-off: 为什么进化倾向于这样一种规律?Lüpold 解释说,更长的精子具有某些优势——它们更能将竞争者“排挤”开;但是制造长精子会消耗大量能量,这是大型动物无法负担的,所以这是一种权衡取舍。
If there were no constraints on sperm production and assuming that longer sperm are advantageous, each male would probably produce lots of impressively big sperm. But in nature there are always constraints because resources and energy are not unlimited. For a testis of a given size, producing bigger sperm thus means it cannot produce as many of them (producing big sperm takes more resources, energy and time). 如果在精子生产方面没有限制,并且假设更长的精子确实更有优势,那么每个雄性也许会造出大量个头大得吓人的精子。但是自然界中总是存在种种限制,因为资源和能量不是无限的。对给定大小的睾丸来说,生产更大的精子意味着它生产的精子数量会减少(生产大精子会消耗更多资源、能量和时间)。 So, whether investing more in sperm size or in sperm number to maximize sperm competitiveness really depends on the circumstances, for example the size of the female reproductive tract. In large species, the female reproductive tract is massive compared to the tiny sperm, so sperm can easily be lost or diluted in it. Males have to compensate by transferring more sperm. Simply making longer sperm really wouldn’t make a difference in an elephant. They would have to be incredibly large. So males are better off making lots of tiny sperm. 所以,为了将精子的竞争力最大化,是在精子尺寸还是精子数量上“投资”取决于环境条件,比如雌性的生殖道。对大体型的物种来说,雌性的生殖道相较微小的精子来说太大了,所以精子很容易在其中迷失或被稀释掉。雄性只能通过投送更多的精子来弥补损失。简单通过制造更长的精子对大象来说于事无补,精子得大到离谱才行【编注:是指大到离谱才能产生阻挡其他精子的效果】。所以对雄性来讲,更好的策略是制造大量的小精子。
This inverse correlation between animal size and sperm size might be a consistent pattern across the animal kingdom. Almost all animals with sperm longer than a 10th of a millimeter, he explains, weigh less than one or two pounds. "Our results certainly suggest a unifying pattern that is likely to explain much of the diversity in mammalian sperm size and possibly beyond," he says, while noting more research is still needed. 动物的体型尺寸和精子尺寸之间这种负相关关系也许是动物王国里的普遍规律。几乎所有精子尺寸长于十分之一毫米的动物,体重都不超过一两磅。“我们的研究成果明确揭示了一个统一的模式,基本可以解释哺乳动物精子的多样性,也许还不止于此。”Lüpold说,然而他同时也表示还需要做更多的研究。 The mammal with the longest sperm? It's not the human. That distinction belongs to the honey possum, a very small (they grow to 3.5 inches long ) marsupial that lives in western Australia. They are adorable. Their sperm is 350 micrometers (.014 inches) long. 拥有最长精子的哺乳动物?不是人类。这一殊荣属于长吻袋貂,一种生活在澳大利亚西部的非常小(它们能长到八九厘米长)的有袋动物。它们很是可爱。它们的精子有0.356毫米长。 (编辑:辉格@whigzhou) *注:本译文未经原作者授权,本站对原文不持有也不主张任何权利,如果你恰好对原文拥有权益并希望我们移除相关内容,请私信联系,我们会立即作出响应。

——海德沙龙·翻译组,致力于将英文世界的好文章搬进中文世界——

[译文]基因作用的可加性

Fifty years of twin studies
双胞胎研究五十年

作者:Stephen Hsu @ 2015-5-21
译者:demo
来源:Information Processing,http://infoproc.blogspot.co.uk/2015/05/fifty-years-of-twin-studies.html

The most interesting aspect of these results is that for many traits there is no detectable non-additivity. That is, gene-gene interactions seem to be insignificant, and a simple linear genetic architecture is consistent with the results.

以下结果中最有意思的一点在于,很多人类的复杂性状都没有检测出非可加性(非线性)。也就是说,基因和基因之间的作用似乎微不足道,而一个简单的线性遗传结构就可以解释这些结果。

Meta-analysis of the heritability of human traits based on fifty years of twin studies
Nature Genetics (2015) doi:10.1038/ng.3285

基于五十年双胞胎研究的人类表型遗传率的整合分析

《自然遗传学》(2015年)
Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of ge(more...)

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Fifty years of twin studies 双胞胎研究五十年 作者:Stephen Hsu @ 2015-5-21 译者:demo 来源:Information Processing,http://infoproc.blogspot.co.uk/2015/05/fifty-years-of-twin-studies.html The most interesting aspect of these results is that for many traits there is no detectable non-additivity. That is, gene-gene interactions seem to be insignificant, and a simple linear genetic architecture is consistent with the results. 以下结果中最有意思的一点在于,很多人类的复杂性状都没有检测出非可加性(非线性)。也就是说,基因和基因之间的作用似乎微不足道,而一个简单的线性遗传结构就可以解释这些结果。
Meta-analysis of the heritability of human traits based on fifty years of twin studies Nature Genetics (2015) doi:10.1038/ng.3285 基于五十年双胞胎研究的人类表型遗传率的整合分析 《自然遗传学》(2015年) Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. 尽管关于人类复杂性状的研究已进行了一个世纪,但基因和环境对人类表型的作用孰轻孰重,以及它们的具体性质如何,都还存在争议。 We report a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. 我们在此发表一项关于双胞胎相关性的整合分析,涵盖几乎所有已发表的双胞胎复杂性状研究,包括2748篇论文中研究的14,558,903对(部分重复研究)双胞胎、其所得出的17,804项表型的方差分量。估算出的遗传率在功能群内呈现群集分布,对于全部性状来说,报告的遗传率为49%。 For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. 对于多数(69%)性状,观察到的双胞胎相关性可以用一个简单到吝啬的模型解释;在这个模型中,双胞胎的相似之处完全归结于可加的遗传差异。这些数据不支持共同的环境因素或者非可加的遗传差异对于复杂性状有显著影响。 This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. 这项研究提供了目前最为全面的一份关于人类性状的个体差异分析,对以后的基因定位研究具有指导意义。
See also Additivity and complex traits in mice: 另见(作者早先的博文)《小鼠的复杂性状与可加性》:
You may have noticed that I am gradually collecting copious evidence for (approximate) additivity. Far too many scientists and quasi-scientists are infected by the epistasis or epigenetics meme, which is appealing to those who "revel in complexity" and would like to believe that biology is too complex to succumb to equations. ("How can it be? But what about the marvelous incomprehensible beautiful sacred complexity of Nature? But But But ...") 你可能已经注意到,我逐渐在搜集(近似于)可加性的丰富证据。有太多科学家和民科染上了流行的遗传上位或者表观遗传的观念;这些观念对于那些“为复杂而陶醉”、相信生物学太过复杂不可能用简单方程来表达的人非常有吸引力。(他们会说“怎么可能呢?可是自然中那些美妙不可方物、神圣不可侵犯的复杂性呢?可是这个可是那个呢?”) I sometimes explain things this way: There is a deep evolutionary reason behind additivity: nonlinear mechanisms are fragile and often "break" due to DNA recombination in sexual reproduction. Effects which are only controlled by a single locus are more robustly passed on to offspring. ... 我有时候会这样解释: 遗传的可加性背后有很深的进化上的原因:非线性的机制过于脆弱,常常会在有性生殖DNA重组中“断开”。而仅由单个位点控制的性状则更易于被传给后代。 Many people confuse the following statements: "The brain is complex and nonlinear and many genes interact in its construction and operation." "Differences in brain performance between two individuals of the same species must be due to nonlinear (non-additive) effects of genes." The first statement is true, but the second does not appear to be true across a range of species and quantitative traits. 很多人会把下面的两个陈述混淆: “大脑是复杂且非线性的,有很多基因在它的构成和功能中相互作用。” “同一物种的不同个体之间大脑性能的差异一定是由于非线性(非可加性)的基因作用。” 第一个说法是正确的,但第二个在很多物种和可量化的性状中似乎都不成立。
On the genetic architecture of intelligence and other quantitative traits (p.16): (作者早先的学术论文)《智力及其他可量化表型的遗传结构》(第16页):
... The preceding discussion is not intended to convey an overly simplistic view of genetics or systems biology. Complex nonlinear genetic systems certainly exist and are realized in every organism. However, quantitative differences between individuals within a species may be largely due to independent linear effects of specific genetic variants. 前面讨论的用意并非要给遗传学或者系统生物学一个过于简化的看法。复杂、非线性的遗传系统肯定存在,而且在任何有机体中都有实现。然而,一个物种中不同个体间的定量差异,在很大程度上可能取决于某些基因差异的独立线性效果。 As noted, linear effects are the most readily evolvable in response to selection, whereas nonlinear gadgets are more likely to be fragile to small changes. (Evolutionary adaptations requiring significant changes to nonlinear gadgets are improbable and therefore require exponentially more time than simple adjustment of frequencies of alleles of linear effect.) 上面说过,线性作用在自然选择中最容易进化出来,而非线性的小把戏则更可能被很小的变化破坏。(非线性机制作出大量改变而得到的进化适应不太可能出现,因此相比于仅需要简单调整基因频率的线性机制来说,它们需要更多时间。) One might say that, to first approximation, Biology = linear combinations of nonlinear gadgets, and most of the variation between individuals is in the (linear) way gadgets are combined, rather than in the realization of different gadgets in different individuals. 有人可能会说,做个简单的近似,生物学等于非线性机制的线性组合,而且大部分个体间差异是来自各种机制被(线性)组合的方式,而不是这些机制本身在个体间的差异。 Linear models work well in practice, allowing, for example, SNP-based prediction of quantitative traits (milk yield, fat and protein content, productive life, etc.) in dairy cattle. ... 线性的模型在实践中有广泛用途,比方说用奶牛的单核酸多态性(SNP)来预测可量化的表型(产奶量、奶制品的脂肪和蛋白含量、生产时限等等)。…
(编辑:辉格@whigzhou) *注:本译文未经原作者授权,本站对原文不持有也不主张任何权利,如果你恰好对原文拥有权益并希望我们移除相关内容,请私信联系,我们会立即作出响应。

——海德沙龙·翻译组,致力于将英文世界的好文章搬进中文世界——

适应模式、复杂性和进化速度

一种广为流行说法是:在几十年、几百年、甚至几千年的尺度上,谈论(生物学意义上的)进化是没有意义的,这么短时间内不可能发生足够显著因而足以用来说明点什么的遗传改变,进化只能在地质年代的尺度上(比如至少几万年)谈论。

具体说就是:假如我们观察到一种环境条件、文化形态或生活方式上的改变,其持续时间只有几百年,那就不能:1)据此而推测有关群体已在生物学意义上发生了适应性改变;2)并据此而推测这些改变可能带来的后续影响。

但这是错误的,理论上,只要时间跨度超出一个世代,并且选择压力(表现为繁殖成效差异)足够大,有意义的遗传改变便可发生。

那么繁殖率差异可以大到什么程度呢?看一下不同族群的生育率和人口增长率,便可得到一个直观的印象:北美再洗礼派社群的总合生育率(TFR)高达4到8,年增长率高于3%,而波罗的海三国的生育率皆远低于替代水平,人口正在急剧下降。

类似差异也存在于同一族群的不同阶层或不同文化/职业群体中,比如欧洲女博士的生育率比未受过高等教育的女性低很多,美国高收入阶层的生育率也比低收入者低很多。这是人类的情况,其他生物的选择压力可以比这大得多,其世代周期也可以短得多。

高达几倍十几倍的繁殖率差异,在几个世代之内便可显著改变某些遗传特性在种群内的分布,只要这些特性是有意义的,那么从进化角度谈论这些改变的后果,也就是有意义的。

上述错误观念的流行(即便那些对进化理论有着极好理解的学者,也常犯这个错误),可能是因为人们误解了生物种群在面临选择压力时,作出适应性改变的方式,或者说适应器的构造模式,假如他们对适应的理解仅仅限于某些类型,那么适应/进化速度确实会很慢。

下面我们考察一下适应可能会以哪些方式发生,然后对比一下它们对进化速度有何影响。

1)阳性变异vs阴性变异

这对概念(以及后面几对概念)是我杜撰的,反正我明确说明了含义,取个名字只是为了说起来方便。

阳性变异是指那些导致一种新功能产生的遗传变异,比如一个复制错误恰好将DNA的一段非编码序列变成了编码序列,于是产生(more...)

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一种广为流行说法是:在几十年、几百年、甚至几千年的尺度上,谈论(生物学意义上的)进化是没有意义的,这么短时间内不可能发生足够显著因而足以用来说明点什么的遗传改变,进化只能在地质年代的尺度上(比如至少几万年)谈论。 具体说就是:假如我们观察到一种环境条件、文化形态或生活方式上的改变,其持续时间只有几百年,那就不能:1)据此而推测有关群体已在生物学意义上发生了适应性改变;2)并据此而推测这些改变可能带来的后续影响。 但这是错误的,理论上,只要时间跨度超出一个世代,并且选择压力(表现为繁殖成效差异)足够大,有意义的遗传改变便可发生。 那么繁殖率差异可以大到什么程度呢?看一下不同族群的生育率和人口增长率,便可得到一个直观的印象:北美再洗礼派社群的总合生育率(TFR)高达4到8,年增长率高于3%,而波罗的海三国的生育率皆远低于替代水平,人口正在急剧下降。 类似差异也存在于同一族群的不同阶层或不同文化/职业群体中,比如欧洲女博士的生育率比未受过高等教育的女性低很多,美国高收入阶层的生育率也比低收入者低很多。这是人类的情况,其他生物的选择压力可以比这大得多,其世代周期也可以短得多。 高达几倍十几倍的繁殖率差异,在几个世代之内便可显著改变某些遗传特性在种群内的分布,只要这些特性是有意义的,那么从进化角度谈论这些改变的后果,也就是有意义的。 上述错误观念的流行(即便那些对进化理论有着极好理解的学者,也常犯这个错误),可能是因为人们误解了生物种群在面临选择压力时,作出适应性改变的方式,或者说适应器的构造模式,假如他们对适应的理解仅仅限于某些类型,那么适应/进化速度确实会很慢。 下面我们考察一下适应可能会以哪些方式发生,然后对比一下它们对进化速度有何影响。 1)阳性变异vs阴性变异 这对概念(以及后面几对概念)是我杜撰的,反正我明确说明了含义,取个名字只是为了说起来方便。 阳性变异是指那些导致一种新功能产生的遗传变异,比如一个复制错误恰好将DNA的一段非编码序列变成了编码序列,于是产生了一个新基因,而阴性变异则是导致一个原有功能的失效。 一个适应性变异既可能是阳性的,也可能是阴性的,比如高纬度族群的肤色变浅是一种适应,这是丧失某些色素合成功能的结果。 很明显,阴性变异发生几率远高于阳性变异(破坏一个既有功能总是比创造一个新功能容易得多),所以,当一种适应通过阴性变异而发生时,其速度会更快。 2)二值特性vs多值特性 有些遗传特性要么有要么无,而另一些则有一个较宽的取值范围。导致多值性的原因有很多,这里只举一种较纯粹的情况:有些基因会在DNA上存在多个副本,每个副本又有多种等位体,假如某种基因有5个副本,每个副本有效和无效两种等位体,同时其所对应的性状取决于该基因所编码蛋白质的浓度,那么该性状的取值范围便是0-10。 性状的值域越宽,其分布看起来就越像是连续的(尽管根本上说它仍是离散的);值域较宽的性状被称为量化性状([[quantitative traits]]),由其副本组合改变所造成的变异,叫副本数变异([[copy-number variations]],CNVs)。 显然,经由副本数变异的适应,比经由单基因阳性变异的适应,要容易而迅速的多,首先,副本数减少只需要一次让某个副本失效的变异即可,其次,副本数增加只需要一次导致编码段重复的复制错误即可,第三,对于有性生物,多副本基因的数量变异可经由有性繁殖过程中的重组和交换而实现,因而更加容易发生。 3)单基因调控vs多基因调控 除了副本数变异,多值性也可以通过多基因调控实现,有些性状(比如肤色)的产生和调控机制十分复杂,许多基因参与其中,其中每个基因的变异都可能影响结果,而且这种影响通常不是致命的,即,其中一个的失效或改变只是让结果有所不同,而不是让结果在二值(或少数几个取值)之间翻转,这样,性状也就表现出多值性。 上述副本数变异其实就是多基因调控的一个特例,只不过前者参与同一调控机制的多个基因是同源且高度相似的,因而被视为同一基因的多个副本。 多基因调控的性状,其适应速度快于单基因调控性状,原理同上。 4)单向调控vs多向拮抗 有些性状虽然是多基因调控的,但参与调控的各基因,其作用都指向同一个方向,比如在肤色调控中,假如所有参与基因都在帮助实现黑色素合成,那么这种调控就是单向的,其多值性仅由其中部分环节失效而产生。 但多基因调控也可以拮抗的方式进行,一组基因把性状往一个方向拉,另一组往相反方向拉,最终结果取决于两者的平衡点。 很可能,我猜,以拮抗方式调控的特性,其值域会更宽,适应也更快更灵活,不过,和前面几条相比,这一点没那么显而易见,数学上的证明可能更复杂(我也没去尝试),但直觉上看起来好像是这样——为什么双手把控方向盘比单手更灵活敏捷呢?其中原理或许类似。 5)单层次调控vs多层次调控 许多复杂调控是通过一层叠一层的修饰/抑制来实现的,激素甲调控葡萄糖水平,激素乙调控激素甲的水平,激素丙调控激素乙的水平…… 产生一种新激素所需要的变异很特殊,很难发生,但与这些激素所对应的基因搭配组合却很容易改变,特别是当这些基因本身是多副本的,并且与有性繁殖所提供的特性储备机制(这个我后面还会说到)结合起来,其组合多样性将随等位体的增加而呈指数式增长,适应灵活性也随之提高,这意味着,当环境条件发生改变时,种群能够更快找出提升适应性的方案。 6)多态均衡和频率依赖选择 有些特性是否适应、适应度如何,取决于它和其他等位特性在种群中的分布频率,此即所谓频率依赖选择([[frequency-dependent selection]]),此类选择将导致该性状的若干等位特性以某种比例达成多态均衡([[polymorphic equilibrium]]),一个著名的例子是侧斑蜥蜴的雄性觅偶策略,橙喉/蓝喉/黄喉三种策略以特定比例达成多态均衡。 在多态均衡下,当环境改变时,适应性变化很可能迅速发生,因为适应所需要的变异原本就已存在,适应过程仅表现为构成均衡的多等位特性的频率变化。 在一个规模庞大、分布广、所占据生态多样性丰富的种群中,同时可以存在很多等位特性,而且多态共存的事实本身创造了很多新生态位(因为种群的特性分布本身对其中特定个体构成了一种“生态”),许多边缘特性以很低频率存在,它们事实上扮演了种群备用特性库的角色,一旦环境条件改变,原有的边缘特性便可能扩张为主流特性,这一过程,尽管没有创造新特性,但也完全有资格被称为“进化”。 7)无性vs有性 有性繁殖从两方面提高了生物的适应灵活性:首先,通过保存两份染色体,使得每个基因都个体基因组里都有至少两个副本,从而提高了系统的容错性,当一个副本失效时,系统仍能正常工作(假如这不是一个量化特性的话,而即便它是量化特性,一个副本失效也可能只是降低适应性而不是致命的),这样,一些在当前条件下适应不良的遗传特性,便可能以较低频率作为隐性基因保存下来,从而扩充种群的备用特性库,如上所述,更大的特性库可带来更高的适应灵活性。 其次,它提供了一种基因重组和交换机制,让遗传算法能够在一个世代内尝试更多特性组合,搜索更广阔的可能性空间,从而更快找到更优解,这也意味着更高的适应灵活性和更快的进化速度。 经过这番检查,可以看出,导致适应性改变的各环节中,只有阳性变异是极低概率因而需要漫长等待才能出现,而在没有阳性变异出现的时间段中,适应仍可能以许多种方式发生,考虑到这些可能性,当环境条件改变时,几个世代之内完全可能出现显著的进化过程。 实际上,上述各种适应模式,也正是生物复杂性的主要来源,当这些机制给生物带来复杂性的同时,也提升了其适应灵活性,让其在环境改变时尽快找到新的优化方案,如此带来的复杂性程度越高,其短期内发生进化的可能性也越大。 所谓适应灵活性,和动物的神经灵活性、认知灵活性、行为灵活性一样,是一种二阶适应性:它提升了生物种群在环境改变时尽快找到新适应方案的能力。 由于这种灵活性总是和前述各种复杂性联系在一起,这就可以解释,为何从较大时间跨度上看,生物复杂性总是在提升,因为只要满足一些简单的背景条件,这样的复杂性提升过程(也就是二阶适应过程)就必定会发生,不过这是另一个话题,这里暂不展开讨论。  
亲选择vs群选择

前几年威尔逊帮和道金斯帮就群选择的问题吵得撕破脸皮,起初我觉得这架吵得有点无聊,因为双方在事实问题上好像没多大分歧,分歧好像集中在如何表述这些事实。

不过后来我意识到,如何表述其实也很重要,因为不恰当的表述会将争论(以及这些争论的听众)引入歧途,我来理一理。

1)关于社会性之起源,有两派观点,一派(方便起见,姑称为道金斯派)基于内含适应性理论,认为是亲选择的结果;另一派(威尔逊派)则认为群选择即可产生同样效果。

2)对群选择理论的主流反驳意见是:那些为个体自身利益而背离群体利益的群内个体,将被自然选择偏爱,因而数量扩张、替代那些更“无私”的个体;同时,由于(more...)

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前几年威尔逊帮和道金斯帮就群选择的问题吵得撕破脸皮,起初我觉得这架吵得有点无聊,因为双方在事实问题上好像没多大分歧,分歧好像集中在如何表述这些事实。 不过后来我意识到,如何表述其实也很重要,因为不恰当的表述会将争论(以及这些争论的听众)引入歧途,我来理一理。 1)关于社会性之起源,有两派观点,一派(方便起见,姑称为道金斯派)基于内含适应性理论,认为是亲选择的结果;另一派(威尔逊派)则认为群选择即可产生同样效果。 2)对群选择理论的主流反驳意见是:那些为个体自身利益而背离群体利益的群内个体,将被自然选择偏爱,因而数量扩张、替代那些更“无私”的个体;同时,由于个体繁殖速度远超出群体分支/复制速度,因而上述替代的速度也将超出优 势群体替代劣势群体的速度,从而使得任何群选择都难以发生。 3)但这些障碍其实可以克服,假如一种群体利益由群内个体间合作所带来,那么,众多博弈论模型已经演示了,即便不存在亲缘关系,一种合作关系也可在群体内得到维持。 4)假如这些群体发展出某种组织/控制能力,使得规范能够得到强制实施,那么维持上述合作关系的可能性便进一步提升了。 5)建立合作关系、进而形成合作群体的前提,是存在共同利益,以及形成和执行合作策略所需要的认知能力;亲缘关系是共同利益的重要来源,但不是唯一来源,因而亲选择对群体合作不是必要条件; 6)当然,在合作群体的最初形成中,亲缘关系无疑可以起关键作用,除了因为它提供了重大共同利益之外,还因为个体在地理上的分布(更一般而言,在关系空间上的分布)不是随机的,因而总是更可能与其近亲发生合作博弈,从而有机会建立合作; 7)问题是(分歧点来了),成功建立并维持了上述合作关系的群体,还能不能被视为群选择理论中所谈论的那种群体?换句话说,那些因此类合作关系/规范而取得对其他群体的优势、从而得以替代其他群体的过程,能否被称为“群选择”? 8)传统上,当我们说“群选择”时,指的是:不同群体因某一特性在各群内的不同分布状态而具有不同的增殖优势,有些灭绝了,有些增殖繁荣了,于是带来优势的特性便扩散了;然而对上述合作群体,造成其增殖优势差异的,不是某一特性的分布状态,而是带有这些特性的个体之间的互动关系,一种组织/控制关系,以及它所带来的规范执行机制,这是一种在新层次上建立的新结构,而不仅仅是一种特征分布,用同一个术语来表述,至少是可疑的; 9)当一种群体合作关系紧密到让群体表现的像单一行动者,那我们就没有理由继续称其为“群体”了,此时它已经是个体了;实际上,目前被称为个体的那些东西,都是通过这样的过程进化而来的; 10)一种合作结构能否被称为“个体”,我的标准是:看合作各方是否在很大程度上共享复制通道;某些真社会性昆虫巢群已经符合这一标准,因而可以称为个体; 11)许多群体的合作紧密程度,介于无结构群体和真社会性巢群之间,作为一种结构,它们已经成为自然选择的作用对象,但因为上面所述理由,这种选择既不能被等同于作用于个体的经典自然选择,也不能被称为群选择,我更倾向于用“组织进化”来称呼它们; 12)因为我不是本质主义者,所以这种本体论上的模糊性和两可性,对我不构成困扰。  
蛋蛋与科学

【2015-06-07】

@吴昊老是重名很无奈 @whigzhou 辉总,知乎看到这个问题,觉得高票答案扯蛋,却自己提不出最合适解释,您怎么看?【为什么人类的睾丸长在体腔外?】刘哈哈:转自豆瓣–南度的日记:《蛋疼三部曲》之一:

@whigzhou: 大概看了下,对问题的描述和介绍的各种假说挺有意思,但他自己的分析不行,比如他老是用“这个解释虽然漂亮,但却不能解释为什么其他动物不把睾丸放在外面,难道它们的精子就不需要磨练么?”这种说辞来反驳,他显然没意识到:这个逻辑可以秒杀任何进化生物学解释。

@吴昊老是重名很无奈: 是的,我也是这(more...)

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【2015-06-07】 @吴昊老是重名很无奈 @whigzhou 辉总,知乎看到这个问题,觉得高票答案扯蛋,却自己提不出最合适解释,您怎么看?【为什么人类的睾丸长在体腔外?】刘哈哈:转自豆瓣--南度的日记:《蛋疼三部曲》之一: @whigzhou: 大概看了下,对问题的描述和介绍的各种假说挺有意思,但他自己的分析不行,比如他老是用“这个解释虽然漂亮,但却不能解释为什么其他动物不把睾丸放在外面,难道它们的精子就不需要磨练么?”这种说辞来反驳,他显然没意识到:这个逻辑可以秒杀任何进化生物学解释。 @吴昊老是重名很无奈: 是的,我也是这个感觉,他对很多假说的反驳还是有道理的,虽然经不起深究。我想过是不是性选择造成的,但是似乎不像男性对女性的乳房一样,女性对男性的睾丸外挂却没有表现出相应的心理机制。 @whigzhou: 既然外挂在哺乳动物中那么普遍,这事情肯定不能从人类的条件去想 @姚广孝_wayne:然而进化生物学现在走进了一个误区,即喜欢用“这样有什么好处”来替代本来想论证的“为什么会这样”,而前者往往只需要首先脑补,然后寻找证据 @whigzhou: 找出“这样有什么好处”是论证“为什么会这样”的重要步骤,先构造假说,再找数据验证,这难道不是科学研究的常规方法吗? @whigzhou: 这和破案中考虑作案动机是一个道理,可供探索的可能性空间几乎是无限的,不借助某些线索的启发,就只能瞎蒙乱撞,瞎蒙乱撞不是科学方法 @whigzhou: 进化生物学家研究性状起源时,和通过反向工程破解电路板的人一样,采取的是丹内特所称的设计立场,也就是功能主义立场,即,首先假定它是具有某种功能的,然后猜测它可能具有什么功能,然后做一系列测试去验证猜测,几番努力还是找不到,再考虑其他可能,比如副产品、退化残余、漂变之类 @real_whisper:科学研究的唯一方法是分析归纳。科学必须基于事实判断,上来就定义“好处”这种价值判断不是科学方法。 @whigzhou: 那你说说啥叫“分析”? @慕容飞宇gg:辉总,从进化论的角度来说,“这样有什么好处”和“为什么会这样”有区别吗? @whigzhou: 有。你还得构造并验证它如何带来此等好处的完整因果链,就好比你光有作案动机不能定罪,还得构造因果链并加以证明  
[微言]寡妇再婚与睾酮水平

【2013-10-25】

@whigzhou: 不考虑其它因素,一个社会寡妇再婚越容易,男人的雄性特征会越发达,对吧?

@whigzhou: 寡妇有望继承的亡夫遗产越多,这个效应会越强烈

@whigzhou: 因为较高的睾酮水平通常对应着较高的死亡风险(当然也有着相应的收益机会),寡妇再嫁和分得亡夫遗产的机会越小,女性在择偶时会越偏好较低的睾酮水平

@柳贾:请问脸部俊俏算雄性特征吗?

@whigzhou: 那要看什么算(more...)

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【2013-10-25】 @whigzhou: 不考虑其它因素,一个社会寡妇再婚越容易,男人的雄性特征会越发达,对吧? @whigzhou: 寡妇有望继承的亡夫遗产越多,这个效应会越强烈 @whigzhou: 因为较高的睾酮水平通常对应着较高的死亡风险(当然也有着相应的收益机会),寡妇再嫁和分得亡夫遗产的机会越小,女性在择偶时会越偏好较低的睾酮水平 @柳贾:请问脸部俊俏算雄性特征吗? @whigzhou: 那要看什么算俊俏了,Ed Stark还是贝克汉姆还是蔡国庆?比较客观的指标是睾酮水平,不过这不容易获得  
[微言]被虐的有袋类

【2013-10-06】

@Ent_evo #没想明白的问题#有袋类比有胎盘类“弱”吗?习惯上我们觉得自从人类带着有胎盘类进了澳洲,有袋类就是被虐的份儿。真的是这样吗?有袋类到底哪里弱了呢?又是为什么弱呢?澳洲好歹也是个大陆,进化能区分“澳洲大小的大陆”和“欧亚大小的大陆”吗?是什么机制导致了这个区分呢?

@whigzhou: “弱”不是因为它们是有袋类,而是它们经历的环境/对手史不如旧大陆的丰富,因而留下了更多未经考验和强化的软肋吧?

@Ent_evo: 如(more...)

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4923
【2013-10-06】 @Ent_evo #没想明白的问题#有袋类比有胎盘类“弱”吗?习惯上我们觉得自从人类带着有胎盘类进了澳洲,有袋类就是被虐的份儿。真的是这样吗?有袋类到底哪里弱了呢?又是为什么弱呢?澳洲好歹也是个大陆,进化能区分“澳洲大小的大陆”和“欧亚大小的大陆”吗?是什么机制导致了这个区分呢? @whigzhou: “弱”不是因为它们是有袋类,而是它们经历的环境/对手史不如旧大陆的丰富,因而留下了更多未经考验和强化的软肋吧? @Ent_evo: 如果只说理化环境的话讲不通,澳洲可是它们的主场,怎么说也应该比远道而来的别的生物更加适应。如果讨论生物环境的话也略奇怪,你说一个小岛上对手单调这可以理解,澳洲自己也是个大陆,动物物种又不像人类那样遍布全球、跨文化交流,三千公里和一万公里就差这么多吗? @whigzhou: 物种的进化遗产的来历并不局限于物种历史,而是该物种所在世系(上溯至生物起源)的整个进化史,对这里的问题而言,对遗产差异负责的环境/对手历史差异至少可上溯至澳洲与旧大陆形成生殖隔离的时候 @whigzhou: 另外,戴蒙德在《崩溃》里说澳洲土地比较贫瘠(因为缺少火山活动、造山运动和冰川),可能也有关系 @游灵--鎏琳:同意钻石老爷的观点,澳洲土地贫瘠,气候不佳,初级生产力不够所以大型动物被土著人类灭绝了,小型动物干不过有胎盘类