“五重唯识”观辨析

林国良

【摘要】“五重唯识”观以有为、无为一切法为所观境，以慧心所为能观体；以三自性理论为观法之总纲，以五法理论和心识结构理论（三分或四分说）为观法之具体展开，破遍计所执性，层层遣依他起性，最终证圆成实性。唯识宗特有的观法是四寻思、四如实智。《大乘法苑义林章》对“五重唯识”及相关内容的论述，既是对以往唯识经典中的唯识观的全面总结，也丰富和发展了唯识观。

【关键词】五重唯识·四寻思·四如实智·唯识观

    窥基在《大乘法苑义林章》（以下简称《义林章》）中提出的“五重唯识”，一般被看作是唯识宗特有的观法。但在此之前，唯识经典说到的唯识宗特有的观法，是四寻思和四如实智，那么，“五重唯识”作为观法，究竟有什么特点？它与四寻思四如实智的观法，又是什么关系？

一、“五重唯识”之观法

《义林章》的“唯识义章”，首先是“出体”：“第一出体者，此有二种：一所观体，二能观体。所观唯识，以一切法而为自体，通观有无为唯识故，略有五重。”[1]

由此可见，“五重唯识”作为一种观法，其“所观”是有为、无为一切法，而一切法“唯识”；进而，“唯识”的一切法，在此观法中又可分五个层次，这就是“五重唯识”。

此五层次的观法包括：（一）遣虚存实识；（二）舍滥留纯识；（三）摄末归本识；（四）隐劣显胜识；（五）遣相证性识。值得注意的是，这五个名称，最后都是“识”字，其含义：一是突出“一切唯识”，二是强调所观是识。

此五层观法，即“五重唯识”，涉及到所观境、能观心和具体观法。

1、所观境

如上所说，“五重唯识”的所观境，就是有为、无为一切法。但有为、无为一切法为什么可纳入“五重唯识”，或者说，一切法为什么是“唯识”？《义林章》引用《成唯识论》关于“唯识”之“识”的解释：“识言总显一切有情各有八识、六位心所、所变相见、分位差别，及彼空理所显真如；识自相故，识相应故，二所变故，三分位故，四实性故。如是诸法皆不离识，总立识名。”[2]所以，一切法（可归为五位法：识法、心所法、色法、心不相应行法、无为法），或是识本身（识法），或是识的相应（心所法），或由识变现（色法），或依识等而假立（心不相应行法），或不离识（无为法），故总称为“识”。

《义林章》的“第二辨名”，又从“事”与“理”的关系分析了五位法：“五法事理皆不离识，故名唯识。不尔，真如应非唯识。亦非唯一心，更无余物。摄余归识，总立识名。非摄归真，不名如也。”[3]

此处意谓：1、五位法中，前四位称为“事”，第五无为法称为“理”。“事”中，识法和心所法可总称为“心法”，但色法肯定不是心法，这就是“事”“亦非唯一心，更无余物”。但色法由识法与心所法变现，所以也可归入识；此外，心不相应行法也可广义地包括在心法中，这就是“摄余归识，总立识名”。但这些“事”，都只能归入识，总称为识，不能归入真如（“非摄归真”）。因为这些“事”有虚妄性，不能像真如那样具有“如”实性（“不名如也”）。2、无为法（真如）是“理”，即是一切法（“事”）的本性，“理”也“不离识”，故也可称“唯识”，否则的话，“真如应非唯识”。至此，“五法事理皆不离识，故名唯识。”

2、能观体

“五重唯识”的所观境是包括境与心的一切法，那么，能观体是什么呢？《义林章》指出：“能观唯识，以别境慧而为自体”[4]，即能观的主体是别境心所中的慧心所。这也意味着，慧心所也能以识为所观境。

慧，通于善、恶、无记三性，如恶见基于恶慧，正见就是善慧。慧有与生俱来的（俱生慧），也有通过学习等过程得到的，如与佛法修学相关的慧，是闻慧、思慧、修慧。而闻思修三慧，又可分为散慧与定慧、有漏慧与无漏慧等。此外，智也以慧为体，如加行智、根本智、后得智，其体都是慧。

然而，心所不能独立生起，须依心王而生起，所以，慧心所实际上是不能独立作为能观体的，那么，能观的心王又是什么呢？《义林章》指出：“若能观识，因唯第六。”[5]“因”指凡夫位，即在凡夫位，能观之识，必定只是第六识。据理分析，第八识和第七识，“行相极微细故，难可了知”[6]，即其认识作用，太微细，说为“不可知”；前五识只能进行现量认识，认识作用太微弱，也不能是能观之主体；故而能观之主体，必定是第六识。但另一方面，识的作用，都需心所配合，共同实现，所以，能观的作用，是由第六识及其同时生起的慧心所来共同实现。这也就是说，能观的主体，是第六识及其慧心所。但识本身是无记性的，要强调修行中的能观体是善性的乃至无漏的，须依慧心所。所以，能观体也可只突出慧心所，即只说慧心所。

3、五重观法

“五重唯识”的五层观法中，第一“遣虚存实识”，或“遣虚存实”观，按窥基对其内涵的论述来看，可认为既包含了分观，也是总观。

《义林章》指出：“一、遣虚存实识。观遍计所执唯虚妄起，都无体用，应正遣空，情有理无故。观依他、圆成诸法体实，二智境界，应正存有，理有情无故……由无始来执我法为有，拨事理为空，故此观中，遣者空观，对破有执；存者有观，对遣空执。今观空有，而遣有空。有空若无，亦无空有。以彼空有相待观成。纯有纯空，谁之空有？故欲证入离言法性，皆须依此方便而入。非谓有空皆即决定，证真观位非有非空，法无分别，性离言故。说要观空方证真者，谓要观彼遍计所执空为门故，入于真性。真体非空。此唯识言，既遮所执，若执实有诸识可唯，既是所执，亦应除遣。此最初门所观唯识，于一切位思量修证。”[7]

分析以上论述，可得出如下结论。

（1）此观是基于三自性理论的观法，即观遍计所执性为无（即“虚”），而“遣”除；观依他起性与圆成实性有（即“实”），而“存”留。

但三自性是否能说是“唯识”？尤其是，遍计所执性是否能说是“唯识”？

《成唯识论》指出：“应知三性亦不离识。”又说：“谓唯识性，略有二种：一者虚妄，谓遍计所执；二者真实，谓圆成实性。为简虚妄，说实性言。复有二性：一者世俗，谓依他起；二者胜义，谓圆成实。为简世俗，故说实性。”故三自性广义地都可称“唯识性”，其中，遍计所执性是虚妄唯识性，依他起性是世俗唯识性，圆成实性是真实唯识性。遍计所执性可称为“唯识性”，实际上也不难理解。遍计所执性是在依他起性上生起的妄执，就此也可说是“唯识”，只是其本性虚妄，故为“虚妄唯识性”。

（2）此观法的必要性，是因为众生无始来一直执著实我实法，即认为实有我法，这可称为“有执”，故须以“空观”遣此“有执”。但“空观”也非“恶取空”（即文中所说的“空执”），依他起性之“事”与圆成实性之“理”，并不空，而是“有”，故须“存”留。该空的空，该有的有，这就是唯识宗的“空有不二”之中道观。

而这样的空有观，也只是“证入离言法性”的“方便”，“证真观位，非有非空。”即真如是“离言”的，因此，说真如是有是空，都是“戏论”。而通常“说要观空方证真者”，是指“要观彼遍计所执空为门故，入于真性”。所以，所谓“观空”，纯粹是指观遍计所执性为空，以此为门径，能证真如。但相对于遍计所执性之空，“真体非空”，即真如有体，非空。

“遣虚存实”是“最初门所观唯识”。通常有种说法，将此“最初门”说成是“遣除外境”，此说法有正确的一面，即被我执和法执所执著的实我实法，人们实际上是执著其为心外实有之“外境”，故破实我实法即为“遣除外境”，这是其正确的一面。但另一方面，在窥基的论述中，“遣虚存实”所“遣”，不只是遍计所执性，进而还要“遣”依他起性。

（3）在上述引文中，窥基进一步说：“此唯识言，既遮所执，若执实有诸识可唯，既是所执，亦应除遣。”即在证真如位，“唯识”之“识”，“亦应除遣”。这是因为，“识”是依他起性，是“事”，有相，有分别；而见道位是无分别智证真如，真如是圆成实性，无相，无分别，故无分别智不见识，只见真如。此时“若执实有诸识可唯”，那无分别智就不能生起，也就无法进入见道位，无法证真如了。

但如果“唯识”也不能执，那么，是否能说：唯识也不究竟？并非。以十地菩萨为例，菩萨在见道之初，根本无分别智生起，不见一切相（即见无相）；但出根本智空观，后得智生起，又见识及其所生一切法（此即唯识）。从初地到四地，根本智与后得智不能同时生起；五地开始，两智能同时生起，无相与有相能同时见；至八地，两智能无功用无间断地同时生起。所以，实际状况是存在着识及其所生一切法，同时，也存在真如，但真如就存在于识及其所生一切法中。见道时，无分别智生起，是个根本的转折点，从此由凡入圣，进入圣位。但此时圣者的能力还有限，只能以根本无分别智证真如，见无相，此时，唯识也不能执，识也须遣。进而，当圣者能力足够强时，有相的识（及其所生一切法），还有无相的真如，都能同时见。由此可见，唯识是究竟的。见道位不见识（及其所生一切法），不是它们真的被破了，被断了，而只是它们不是根本无分别智的所缘。当圣者的根本智与后得智能同时生起时，圣者在见真如的同时，识（及其所生一切法）仍历历在目。

（4）综上所述，“遣虚存实”观，既是分观，也是总观。就分观来说，此第一观，遣遍计所执性，遣实我实法，遣实有外境；从总观来说，此观但不遣遍计所执性，进而还要遣依他起性，遣识及其所生一切法，所以，此观实际上同时包含了以下四观的基本内容。故而窥基说：“遣虚存实”观，应“于一切位思量修证”。此处的“一切位”即为“五重唯识”之五位，由此可见，“五重唯识”都要修此观。

此外，窥基在《般若波罗蜜多心经幽赞》中说：“一切唯识、二谛、三性、三无性、三解脱门、三无生忍、四悉檀、四嗢拖南、四寻思、四如实智、五忍观等，皆此观摄。”[8]这也进一步表明，“遣虚存实”观是总观，包含了唯识的一切观法。

（5）“五重唯识”中的其他四重观都是分观，具体地说，从第二观至第四观是观依他起性中的诸种关系，第五观则观依他起性与圆成实性的关系。

第二“舍滥留纯识”，其中，“滥”指境，“纯”指心，所以，此观是在心与境的关系上，舍境观心。因为，虽然第一“遣虚存实”观也舍弃了作为遍计所执性的外境，但还存有依他起性的内境，故仍有境心之别。作为唯识观，虽可观内境，但易与外境混淆，故应舍境而观心。

第三“摄末归本识”，其中，“本”指识（与心所）之自证分（若是四分说，则还包括证自证分），“末”指由自证分变现的见分和相分。在第二观中，虽已舍弃相分境，但就心来说，还有认识主体（自证分）及认识功能（见分）之别，故作为唯识观，应直观识（与心所）主体，而舍弃其变现的相分和见分。

第四“隐劣显胜识”，其中，“劣”指心所，“胜”指心王（识）。识与心所的关系是主从关系，识是主，心所是从，心所依识而起，不能独立生起，故称为“劣”。故唯识观应舍弃心所，而直观心王。

第五“遣相证性识”，其中，“相”，若狭义地看，经前四重观“除遣”后，只剩下识自体（自证分等），此“相”即指识自体；广义地说，“相”可指依他起之一切法，故识及其所生一切法，都是“事”，亦即“相”；“性”指圆成实之真如，此为一切法之本“性”。由凡入圣的关键是证真如，而初证真如，必遣依他起之一切事相，只见一切事相之本性；而不能如五地以上菩萨，能同时见“性”与“相”。

再回到总观与分观的关系来说，“遣虚存实”是总观，含摄了全部五重观。而从分观角度说，在第一观中，遍计所执的外境是“虚”，依他起性和圆成实性是“实”；第二重观中，内境是“虚”，心是“实”；第三重观中，相分和见分是“虚”，识自体（自证分等）是“实”；第四重观中，心所是“虚”，心王（识）是“实”；第五重观中，依他起性是“虚”，圆成实是“实”。如此层层“遣虚存实”，最后亲证真唯识性。

二、“五重唯识”在唯识观中地位

1、唯识观

什么是唯识观？或者说，唯识观如何修？《义林章》说：“云何名为修唯识观？谓令有漏、无漏观心种子、现行，展转增胜，生长圆满。初修习位，随所闻法，托境思惟，令此观心纯熟自在。后伏所取、能取二执，观心转明胜，境相像渐微。忽心境乃冥，观转成无漏。如是展转，下转成中，中转成上，究竟圆满，名之为修。”[9]

这一说法，与《成唯识论》相关说法，有相同处，也有不同处。《成唯识论》的相关说法是：“云何渐次悟入唯识？谓诸菩萨于识性相，资粮位中能深信解；在加行位能渐伏除所取、能取，引发真见；在通达位如实通达；修习位中，如所见理，数数修习，伏断余障；至究竟位，出障圆明，能尽未来化有情类，复令悟入唯识相性。”[10]

分析上述两种说法，其相同处是，都从“五位修行”来论述从凡夫到成佛的全过程。此五位即资粮位、加行位、通达位（见道位）、修习位（修道位）、究竟位。其中，前二位是凡夫位，中间二位是菩萨位，最后是佛果位。

而两种说法的不同处是：《义林章》是立足于“有漏无漏观心种子、现行”，即是从能观心出发，论述唯识观，但论述过程中也涉及到了所观境；而《成唯识论》则着眼于“识性相”，其中，“识”之“相”即依他起之事相，“识”之“性”即圆成实性，所以，该论是从所观境出发，论述唯识观。

由此可见，上述两种观法，主要是从所观与能观两个不同侧面，对唯识观修习的全过程进行了论述。两种论述有着一致性。即在第一资粮位中，“随所闻法，托境思惟，令此观心纯熟自在”；或者：“谓诸菩萨于识性相，资粮位中能深信解”。综合地说，即依能观心，对所观的“识性相”（即“托境思惟”之“境”）能有正确深入的认识，同时使能观心“纯熟自在”。第二加行位中，“伏所取、能取二执，观心转明胜，境相像渐微”；或者，“能渐伏除所取、能取，引发真见”。所以，此位任务即是伏所取和能取二执。第三通达位中，“忽心境乃冥，观转成无漏”；或者，“如实通达”（“识性相”）。所以，此位中，无漏智生起，证真如法性。第四修习位中，无漏“如是展转，下转成中，中转成上”；或者，“如所见理，数数修习，伏断余障”。所以，此位中，无漏智不断增强，剩余的俱生烦恼障和所知障逐步断除。第五究竟位中，无漏智“究竟圆满”；或者，“出障圆明，能尽未来化有情类，复令悟入唯识相性”。所以，此位中，一切障都断尽，能观的无漏智则究竟圆满。

进而再深入分析《义林章》从能观心出发，对修唯识观所作的论述。

《义林章》说：“若总言唯识，通能所观。言唯识观，唯能非所，通有无漏，通散及定。以闻、思、修，加行、根本、后得三智，而为自体。若言唯识三摩地，通有无漏，唯定非散。唯修慧，非闻、思。通三智。若言正证唯识，唯无漏，非有漏；唯定非散；唯修慧，非闻思；唯正智、后得，非加行。”[11]

此段大意是：如果是总说“唯识”，那么包括所观境和能观心（主要是慧心所）。如果是说“唯识观”，那么，只是指能观心，不包括所观境。此能观心包括有漏和无漏，也包括散位和定位。此能观心是以闻慧、思慧、修慧，以及加行智、根本智、后得智为自体。如果是说“唯识三摩地”（唯识定），那可以是有漏心或无漏心；但只是定位，不是散位；只是修慧，不是闻慧和思慧；可以是加行智、根本智和后得智。如果是“正证唯识”（即证真如），就只是无漏，不是有漏；只是定位，不是散位；只是修慧，不是闻慧和思慧；只是根本无分别智和后得智，不是加行智。

由此来理解《义林章》的“谓令有漏无漏观心种子、现行，展转增胜，生长圆满”的含义。在资粮位，能观心是“以闻思修所成之慧而为观体”[12]，所以，此位中，闻慧、思慧、修慧是“能观体”。而此位中，能观三慧的种子和现行，都是有漏的；主要处在散位（即心处于散乱状态）；但此有漏的观（现行）也能使其种子与本有的无漏种，力量不断增强。在加行位，从能观看，主要是能观心作四寻思、四如实智之观法，而“寻思、如实智，皆慧为体”[13]。另外，此位中，心已处定境，“作寻思等胜唯识观，必居定故”[14]。再从有漏与无漏看，“寻思唯有漏，如实智通无漏。”由无漏的如实智，证真如，进入通达位，这就是无漏心“生长”。进而，在修习位中，通过菩萨十地修行，无漏心“圆满”，最终证得佛果位。

以上就是从能观心出发所论述的唯识观的全貌。

2、“五重唯识”与“五位修行”

《义林章》的上述唯识观，是涵盖全部五位修行的，但“五重唯识”实际上涵盖的只是五位修行中的前三位。

仔细分析“五重唯识”，第一观是总观，既遣遍计所执性，也遣依他起性；从第二至第四观，是分观，层层遣除依他起性；第五观则最终遣识自体，证圆成实性，这相当于通达位。所以，“五重唯识”只涵盖五位修行的前三位：资粮位、加行位和通达位（见道位）。其观修的过程为，破遍计所执性，遣依他起性，证圆成实性；它并没有包括无漏心由“下转成中，中转成上，究竟圆满”的修行过程；或“如所见理，数数修习，伏断余障；至究竟位”的修行过程，即没有包括修习位和究竟位。

《义林章》讨论的另一个问题，或许可作为“五重唯识”只涵盖前三位的一个解释。那问题是：为什么经中说“唯心”，论中说“唯识”？《义林章》对此提供了一个解释：“或经义通因果，总言唯心；论说唯在因，但称唯识。”[15]其中，“因”指凡夫位，“果”指佛位。此处的意思是说：经中说“唯心”，是包括因位与果位，即包括凡夫位与佛位；而论中说“唯识”，只指因位，即凡夫位。略举一例以作说明，如凡夫位，识强智弱，故以识为主，心所（智即慧心所）为从，故称“唯识”；而佛位，智强识弱，佛的心法为四智（大圆镜智、平等性智、妙观察智、成所作智），不再以识为主，故“唯识”之名似乎也不太合适，但称“唯心”则无问题，识与心所（智即为慧心所）总称心法。如果按此解释，那么，“五重唯识”只涵盖前三位，也就不难理解了。

但关于唯识只属因位的说法，也可看成是一种狭义的说法。《义林章》另一处说有五种唯识：“然总遍详诸教所说一切唯识，不过五种。一、境唯识。《阿毘达磨经》云：鬼、傍生、人、天，各随其所应，等事心异故，许义非真实。如是等文，但说唯识所观境者，皆境唯识。二、教唯识。由自心执着等颂，《华严》、《深密》等说唯识教者，皆教唯识。三、理唯识。《三十颂》言：是诸识转变，分别所分别，由此彼皆无，故一切唯识。如是成立唯识道理，皆理唯识。四、行唯识。菩萨于定位等颂，四种寻思、如实智等，皆行唯识。五、果唯识。《佛地经》言：大圆镜智，诸处、境、识皆于中现。又《如来功德庄严经》言：如来无垢识，是净无漏界，解脱一切障，圆镜智相应。《唯识》亦言：“此即无漏界，不思议善常，安乐解脱身，大牟尼名法。如是诸说唯识得果，皆果唯识。此中所说五种唯识，总摄一切唯识皆尽。”[16]

以上五种唯识中，“教唯识”与“理唯识”，实际上是唯识经典中关于唯识的论述。另外三种，即为“境唯识”、“行唯识”和“果唯识”，其中，“境唯识”即为唯识观中的所观境；“行唯识”即为唯识观中的观法；“果唯识”即为唯识观的果位，佛果四智（大圆镜智、平等性智、妙观察智、成所作智）即为“果唯识”。由此来说，“唯识”也是可以通果位的。

但上述“唯识”与“唯心”的细微差别，实际上也帮助说明，“五重唯识”观并不是五位修行唯识观的全部。

3、“五重唯识”与四寻思、四如实智

一般说唯识宗的特有观法是四寻思和四如实智。四寻思、四如实智是加行位之观法，加行位也称四善根位，四善根是暖、顶、忍、世第一法。四寻思是观一切法之名、义、自性、差别四者，为假有实无，这是在暖、顶二位所修之法。换句话说，此二位可初步认识所取空。进而，由四寻思可引发四如实智，即在忍、世第一法二位，由四如实智确认所取空，进而理解和确认能取也空。由如实确认所取与能取二取皆空，即可进入见道位，亲证真如，从此超凡入圣。

因此，四寻思四如实智是加行位中证入见道位的特有观法。

《义林章》上述五种唯识中，“行唯识”也说是四寻思四如实智，可见在四寻思四如实智是唯识观的特有观法这一问题上，《义林章》与一般看法没有分歧。那么，“五重唯识”与四寻思四如实智的关系又是如何呢？

“五重唯识”从分观看，第一观遣外境，第二观遣内境，都是观所取空；第三观遣见分，第四观遣心所，第五观遣识自证分，都是观能取空；所以，“五重唯识”最终也是观所取和能取二执空，这是与四寻思四如实智相通之处。不同之处在于，“五重唯识”之观法，包括了资粮位、加行位、见道位的全部修行；而四寻思四如实智之观法，只是由加行位进入见道位的修行。

综上所述，“五重唯识”是唯识观之一种。此观法以有为、无为一切法为对象，观“一切唯识”；以三自性理论为观法之总纲，以五法理论和心识结构理论（三分或四分说）为观法之具体展开，破遍计所执性，层层遣依他起性，最终证圆成实性。

而《义林章》对“五重唯识”及相关内容，包括所观境、能观心和观法的论述，既有对以往唯识经典相关论述的全面总结，也以新的说法丰富和发展了唯识观。

 

[1] 《大正藏》第45册，第258页中。

[2]     同上。

[3] 《大正藏》第45册，第260页上。

[4] 《大正藏》第45册，第259页上。

[5] 《大正藏》第45册，第261页上。

[6] 《成唯识论》卷2，《大正藏》第31册，第11页中。

[7] 《大正藏》第45册，第258页中。

[8] 《大正藏》第33册，第526页下。

[9] 《大正藏》第45册，第262页上。

[10] 《大正藏》第31册，第48页中。

[11] 《大正藏》第45册，第259页下。

[12] 《大正藏》第45册，第259页中。

[13]     同上。

[14]     同上。

[15] 《大正藏》第45册，第260页上。

[16] 《大正藏》第45册，第259页下。

唯识学的主要理论

 八识、
三性三无性、
四分说、
种子说、
种姓说、
法相系统、
 唯识观行、
唯识地道。

【成唯識論】

 
【成唯識論】這是詮釋唯識三十頌的論著，凡十卷，護法等十大論師各造釋論，唐代玄奘三藏奉詔譯，翻經沙門窺基筆受，這是學習唯識學必讀的重要論書。世親晚年造《唯識三十頌》，本頌造出，未造釋文而入寂，未幾十大論師繼起，各造釋論，斯時法海波瀾，至為壯闊。唐代玄奘三藏由印度回國，攜回十家釋論百卷，奘師譯此論時，本主張十家釋論各別全譯，後以弟子窺基之請，糅集十家之義成為一部，其中異義紛紜之處，悉折中於護法之說，故《成唯識論》十卷，雖說是糅集十師之作，而實以護法一家為宗，名為傳譯，不啻新造。基師亦嘗述其傳譯參糅之績云：十家別譯之初，神昉、嘉尚、普光、窺基四人同受師命，共同翻譯。數日之後，基請退出，奘師固問其故，基對曰：「群聖製作，各馳譽於五天，雖文具傳於貝葉，而義不備於一本，情見各異，稟者無依。請錯綜群言，以為一本，楷定真謬，權衡盛則。」久之奘師乃許，故得此論行世。 
【成唯識論了義燈】七卷，唐代唯識宗二祖慧沼撰，略稱《唯識了義燈》，為唯識三疏之一。本書係顯慈恩宗正義，而破斥有關解釋《成唯識論》中的異說。事實上是祖述窺基之說，而責難圓測等的異說，門戶之見甚深，且素稱不易解讀。 
【成唯識論述記】凡十卷，唐代窺基著，又稱《成唯識論疏》，係注解《成唯識論》一書的著作。撰者為玄奘三藏上座弟子，亦為開創唯識宗的初祖，本書中以佛教論理學破斥小乘、外道，並說明萬法唯識之奧義。本書問世以後，成為研究唯識學的重要論典。 
本書內容分為五門，即：一、教時機，分為說教時會與教所被機兩種。二、論宗體，以唯識為宗而謂其體有四重。三、藏乘所攝，謂《成唯識論》為一乘之所攝，並為三藏中之菩薩藏所攝。四、說教年主，以慧愷之俱舍論序論說世親與十大論師之年代。五、本文判釋，即就本文述釋其義。注釋書有唐代道邑的《成唯識論義蘊》五卷、如理的《成唯識論疏義演》二十六卷、靈泰的《成唯識論疏鈔》十八卷等。本書另有六十卷之單行本。蓋自元代以來，本書即告失傳，清末楊仁山居士得之於日本，遂集緇素之力，鋟板刊行。 
【成唯識論掌中樞要】四卷，唐代窺基撰，略稱《成唯識論樞要》，係唯識三疏之一。本書初敘成唯識論之成立、傳入、揉譯等因緣，次釋本論之題目及所被之根機。再次釋論文，於《成唯識論述記》之未詳盡處，更詳加解釋。其中對於唯識三十頌科判、五種性、三類境等問題，均廣泛加以解釋。註釋書有唐代智周撰的《成唯識論樞要記》二卷，唐代憬興撰的《成唯識論樞要記》二卷。 
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【成唯識論義蘊】五卷（或十卷），唐代道邑撰，又稱《成唯識論述記義蘊》，這是《成唯識論述記》的註釋書，係摘出書中重要文句而加以註釋者。 
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【成唯識論觀心法要】明代僧智旭撰，凡十卷，又稱《唯識心要》。為闡釋成唯識論之著作。

唯識學探源－第五項 有分識 Yinshun

第五項 有分識

有，是欲有、色有、無色有──三有；分，是成分，也就是構成的條件與原因。在以分別說部自居的赤銅鍱部，把細心看成三有輪迴的主因，所以叫有分識。赤銅鍱部，在阿育王時代，移植到錫蘭，又發展到緬甸、暹羅一帶，一般人稱他為南傳佛教。現在的南傳佛教，以覺音三藏的注釋作中心。覺音是後起的，是西元四世紀的人物，思想上有相當的演變，所以有人叫他做新上座部。有分識，在漢譯的論典裡，一致的說是上座分別說部，即赤銅鍱部的主張。關於有分識，無性《攝大乘論釋》卷二，有簡單的敘述，也就是奘門所傳九心輪的根據。「無性釋」說：

「上座部中，以有分聲亦說此識，阿賴耶識是有因故。如說：六識不死不生，或由有分，或由反緣而死，由異熟意識界而生。如是等能引發者，唯是意識。故作是說：五識於法無所了知，唯所引發；意界亦爾。唯等尋求。見唯照囑。等貫徹者，得決定智。安立是能起語分別。六識唯能隨起威儀，不能受善不善業道，不能入定，不能出定；勢用，一切皆能起作。由能引發，從睡而覺。由勢用故，觀所夢事。如是等分別說部，亦說此識名有分識」。

無性《攝論釋》關於有分識的說明，奘門師弟，給以九心輪的解說。依窺基說：實際上只有八心，因為從有分出發，末了又歸結到有分；把有分數了兩次，成一個輪形，所以名為九心輪。《成唯識論樞要》卷下，就是這樣說的：

「上座部師立九心輪：一有分，二能引發，三見，四等尋求，五等貫徹，六安立，七勢用，八返緣，九有分。然實但有八心，以周匝而言，總說有九，故成九心輪」。

西藏所傳無著論師的《攝大乘論》，也有有分識的記載，但只有七心：

「聖上座部教中，亦說名曰：有分及見，分別及行，動及尋求，第七能轉」。

此七心或九心的見解，在漢譯《解脫道論》（卷一〇）中，有較為詳確的說明：

「於眼門成三種，除夾上中下。於是上事，以夾成七心，無間生阿毘地獄。從有分心、（生）轉（心）、見心、所受心、分別心、令起心、速心、彼事心，……從彼更度有分心」。

九心的次第演進（除有分唯有七心），是依五識最完滿的知意活動而說。五識的中、下，意識的上、下，雖不出此九心的範圍，但都是不完具的。《解脫道論》是上座銅鍱者的論典，依銅鍱者的看法：意識是一切心理作用的根本，一切心識作用，不外意識的不同作用，所以他是一意識師，一心論者。照他說：「有分心」，是「有根心如牽縷」，是三有的根本心，即是生命內在的心體，從過去一直到未來。在從五識而作業受果的過程中，從有分生起七種心：第一「轉心」，是根境相接時，有分心為了要見外境而引起內在的覺用。奘譯作「能引發」（即藏譯之動），近於警心令起的作意。因轉心的活動，現起眼等（根）識，直見外境，是第二「見心」。五識雖剎那間直取對象，但還不能有所了知。接著生起第三「受心」，承受見心所攝取的資料，加以體察。一譯作「尋求心」，是屬於攝取對象與了解對象間的心用。等到明確的了解，知道是什麼，即是第四「分別心」。奘譯稱之為能起語言分別的「安立」。在此分別心以前，奘譯多一「等貫徹」（藏譯也沒有），所以玄奘所傳的九心輪，有影射瑜伽五心輪的痕跡：如見是率爾心，尋求是尋求心，等貫徹與安立是決定心，勢用以下是染淨心、等流心。而《解脫道論》的次第，是接近無色四蘊的。如見心是識，受心是受，分別心是想，令起、速行心是思。這點，是值得注意的。奘譯在安立以下，起勢用心；而《解脫道論》卻多一第五「令起心」。這是說，從見到分別，是對外境的認識過程；因認識而引起推動內心作業的意志作用。因此，接著就是動作行為的第六「速行心」。令起是引起作業的，速行是作業的。作業而得果，也即是作業終了時心識的休息，是第七「彼事心」。奘譯作「反緣」。但這還是從行動而返歸內心的過程；到了內心澄靜的本來，那就是復歸於有分心了。從上面所說，可知有分是心識的內在者，深潛而貫通者。見、受、分別，是向外的認識作用；速行是向外的意志作用；轉、令起、彼事，是介於中間的。轉是內心要求認識的發動，已不是內在的深細心，也還不是認識。令起心是認識的牽動內心，引起內心意行的反應；但還不是行為者。彼事是行業的反歸內心過程。認識與行為的作用一止，那又是有分了。【圖片】

　　　　　　　 ┌─┐　　┌─┐　　┌─┐　　　┌─┐
　　　　　 ┌─┤見├──┤受├──┤分│　　　│速│
　　　　　 │　└─┘　　└─┘　　│別├─┬─┤行├┐
　　　　　 │　　　　　　　　　　　└─┘　│　└─┘│
　　　　 ┌┴┐　　　　　　　　　　　　　┌┴┐　　┌┴┐
　　　　 │意│　　　　　　　　　　　　　│令│　　│彼│
　　 ┌─┤轉│　　　　　　　　　　　　　│起│　　│事├─┐
　　 │　└─┘　　　　　　　　　　　　　└─┘　　└─┘　│
　 ┌┴┐　　　　　　　　　　　　　　　　　　　　　　　　┌┴┐
　 │有│　　　　　　　　　　　　　　　　　　　　　　　　│有│
　 │分│────────────────────────│分│
　 └─┘　　　　　　　　　　　　　　　　　　　　　　　　└─┘

從唯識學上本識的見地去看，應注意它的初生心、命終心。無性《攝論釋》所說的「六識不死不生」；「或由有分，或由反緣而死，由異熟意識界而生」：《解脫道論》也有類似的文證。此不死不生的六識，不是眼、耳、鼻、舌、身、意六識，是眼等五種根識與意界。銅鍱者是一意識師，以意識為精神的根本；五識僅是認識外境的見用，意界是「五識若前後次第生識」。所以，五識與意界──六識，是不配作初生命終心的。三有根本的有分心，是意識中內在的貫通者，《解脫道論》用國王來比喻它。它在業感成熟（彼事心）以後，最初托生，即是異熟主體的意識；有分也可說是三有之因。但死時，自有在一般意識活動完全休止，歸到有分心而死的；也有在反緣而流返有分的過程中──彼事心，就死了的。以彼事心而死，或是卒然而死的。在人類卒死時，常有一生的經歷，突然浮現在心上而後死的。銅鍱者的以意識為本，與意界是常論者的以意界為本，雖有多少不同；但它在一般知意的內在，指出一「如牽縷」的貫通三世心，那就可見它與唯識的本識是如何的接近了。

Hierarchical control system

Artificial Intelligence Demystified

MACHINE LEARNING

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 GUEST BLOG , DECEMBER 23, 2016 / 0

Introduction

Artificial Intelligence has become a very popular term today. There is sure to be at least one article in the newspaper daily on the revolutionary advancements made in the field. But, there seems to be some confusion about what AI really is.

Is it Robotics? Will the Terminator movie actually come true? Or is it something that has crept into our daily lives without us even realizing it?

This article will give you a broad understanding on the buzzwords associated with AI, its applications, the careers & opportunities it has and its future.

What is Artificial Intelligence?

Artificial Intelligence is simply the ability of a computer to exhibit “intelligence”. This intelligence can either mimic human intelligence or observe real world problems and intelligently find solutions for it.

10 Major Milestones in the History of AI

Did you know? Chef Watson- a part of IBM’s Watson program-can now cook for you! This AI cooking app uses algorithms to choose a quirky set of ingredients (this can be done by the user too) and comes up with the perfect recipe. So, Bon Appétit!

Buzzwords associated with AI

1. Machine Learning

Machine Learning is a field in Data Science, where machines can “learn” themselves, without being explicitly programmed by humans. By analyzing past data called “training data”, the Machine Learning model forms patterns and uses these patterns to learn and make future predictions. The precision of predictions made using ML models has been increasing every day.

The 5 steps in the Machine Learning Process

Machine Learning Techniques

Machine Learning is used in practically every field these days, even though some of the uses may not always be very obvious. The main techniques of Machine Learning are:

• Classification: Based on training data having observations with known categories, classification predicts the category to which a new observation belongs. Eg: Predicting whether the price of a house would fall under which class- very costly, costly, affordable, cheap or very cheap.
• Regression: Predicting a value from a continuous data set. Eg: Predicting the price of a house based on various factors such as location, size, time of buying, etc.
• Clustering: Assigning a set of observations into subsets (i.e.clusters) so that the observations in the same cluster are similar in some sense. Eg: Netflix (An online movie company) having different clusters of viewers, where people with similar viewing habits fall in the same cluster.
• Recommendation Systems: Uses ML algorithms to help users find new products / services based on data of the user or product / service. Eg: Netflix recommending you a certain movie based on watching patterns of the people in your cluster or Amazon suggesting you products based on popularity

• Anomaly Detection: Identifying observations that do not conform to an expected pattern or other items in a dataset. Eg: An outlier (i.e. an anomaly) in credit card transactions could be a potential banking fraud.
• Dimensionality Reduction: The process of reducing the number of random variables under consideration to obtain a set of variables that are significant.

Types of Machine Learning

Popular Machine Learning Algorithms

Today, Machine Learning is probably the most important field in AI. Hence, several Machine Learning algorithms have been devised each solving a particular type of problem. Each algorithm falls into one of the 3 types of learning. The most popular Machine Learning algorithms are :

1. Linear Regression
2. Logistic Regression
3. Support Vector Machines
4. Decision Trees
5. Random Forest
6. Artificial Neural Networks
7. K-Means Clustering
8. K-Nearest Neighbour
9. Naive Bayes Classifier
10. Ensemble Learning

Did you know? A Scottish cartoonist has taken Machine Learning to an all new level by creating an intelligent program that can write scripts for “Friends”! Through gathering Big Data (dialogues from all the 10 seasons) and using recurrent neural networks, he could create all-new episodes for this popular sitcom series!

2. Deep Learning

What is Deep Learning?

Deep Learning is a branch of Artificial Intelligence that is producing life-changing results. Deep Learning means neural networks with a large number of hidden layers. It is an attempt to replicate the functioning of a human brain. Just like the exact functioning of a human brain is unknown, not much is known about the exact working of Deep Learning too. It is like a black box, i.e. where the input and output can be seen and are known, but the internal working is a mystery! Interestingly, Data Scientists believe that if we crack the working of Deep Nets, we will be closer to understanding how a human brain works!

Where & How is Deep Learning used?

Today, Deep Learning has applications in Natural Language Processing, Image Recognition (explained in the later section), Spam Filtering, Fraud Detection, etc. This is just a fraction of what Deep Learning can do! Google’s search engine, Facebook’s photo tagging feature, Baidu’s speech recognition – all involve Deep Learning behind the scenes. As these companies invest more and more in this area, the advancements in the field are mind-boggling!

1. Google: Apart from optimizing search results, Google uses Deep Learning in a variety of immensely vital but slightly lesser-known fields. Google Brain and Google DeepMind, the two brainchildren of Google are working quite furiously to achieve greater heights in AI. Google has been actively researching and exploring virtually all aspects of machine learning, including deep learning and more classical algorithms.

AlphaGo, a project of Google’s DeepMind, is perhaps one of the most popular breakthroughs in Deep Learning. Go is a game of stones on-board where you try to make points of territory. It’s a game of intense complexity- it is 10100 times more complex than Chess! The algorithm in Alpha-Go combines Monte-Carlo Tree Search with Deep Neural Networks and uses Reinforcement Learning approach to better its result.

How AlphaGo works: AlphaGo is built using two different neural-network “brains” that cooperate to choose its moves. These brains are multi-layer neural networks which are almost identical in structure to the ones used for classifying pictures for image search engines like Google Image Search. They start with several hierarchical layers of 2D filters that process a Go board position just like the way an image-classifying network processes an image. Roughly speaking, these filters identify patterns and shapes. After this filtering, 13 fully-connected neural network layers produce judgments about the position they see. Broadly, these layers perform classification or logical reasoning.

The networks are trained by repeatedly checking their results and feeding back corrections that adjust the numbers to make the network perform better. This process has a large element of randomness, so it’s impossible to know exactly how the network does its “thinking”, only that it tends to improve after more training.

Did you know? In March 2016, AlphaGo beat the legendary Go player-Lee Sedol-with a score of 4-1, a feat previously believed to be at least a decade away.

2. Facebook: Facebook AI Research (FAIR) focuses on using Deep Learning to improve the social networking experience. FB is trying to build more than 1.5 billion AI agents, one agent for every Facebook user. The social media giant formed the Applied Machine Learning team called FBLearner Flow. It combines several machine learning models to process several billion data points drawn from the activities of its 1.5 billion users to make predictions about user behaviour and keep them glued to Facebook for hours!

For example: the algorithms created from FBLearner Flow’s models help to define your news feed, the advertisements you see, the people you may know and many more!

Therefore, in the AI war between Facebook and Google, there isn’t a winner, as the research concentrations and applications are quite different in nature.

3. Natural Language Processing

Natural Language Processing is the process by which computers translate human language into a language that the computer can understand. Siri, Cortana and Alexa are all examples of NLP that we use every day. So how does Artificial Intelligence fit into NLP? Here’s how. Consider this. You want to learn a new language. How do you go about doing so? You start by learning new words in the language and understanding the usage. But, you will not really understand what works and what doesn’t, unless you are exposed to the language and learn from the usage. This is exactly how Deep Learning is used in NLP. The computer “learns” by using a technique called “embeddings”, which Deep Learning implements. In this technique, words and phrases are mapped to vectors of real numbers. This mapping is carried out by Neural Networks.

How does Siri use Natural Language Processing?

NLP forms the heart and soul of Siri. When a user asks Siri something, the sequence of actions taking place is as follows. Through voice recognition, Siri first uses a discretization algorithm to turn your voice into digital data. Next, your question is routed through Apple servers, and a flowchart is run on it to find a possible solution. This step is easy enough for simple sentences like “What is the weather like today?”. But it becomes difficult when sentences like “Will Larry be attending the meeting today?” are asked because it is quite difficult for a machine to understand such a complex thought process. This is where NLP comes into play. NLP breaks the command down into tokens and uses syntactic analyzers to parse through and understand the sentence. In addition to this, Machine Learning algorithms are used to optimize the results and learn from the past results. Finally, the results are produced to the user.

Did you know? Robots can now socialize! Kismet, an emotionally intelligent robot from MIT’s AI Lab affective computing experiment, can interact by recognizing human body language and voice tone.

4. Pattern Recognition

As the name suggests, Pattern Recognition is a part of Artificial Intelligence which deals with recognizing patterns in data. It’s used for quality and process control. Applications includes self-driving cars, neuroscience, cancer treatment and energy physics.

How do self-driving cars use pattern recognition?

The much talked about “Self-Driving Cars” collects and analyzes Big Data from sensors and maps to identify pedestrians, vehicles and other objects based on their shape, size and pattern. After predicting what all the objects around it might do next, it is then designed to safely drive around them. The technologies used are radar, lidar, GPS, odometry, and computer vision.

How energy physics uses pattern recognition?

It is used to associate the energy depositions in a multi-component, non-magnetic high-energy particle detector. Higgs detection is a great example of pattern recognition in particle-physics.

Did you know? Self-driving cars have many versions. Google has removed steering wheels and pedals and is improving on the different levels of autonomy that can be achieved. Whereas, Tesla and Baidu are making advancements in this technology by slowly adding autonomous features that enable efficient driving in different environments. Tesla has come up with a conventional car having Autopilot (i.e. self-driving) capabilities at a safety level which is much greater than that of a human driver.

4. Image Analysis

Image Analysis involves extracting meaningful information from images. The idea is to imitate the human visual cortex using Machine Learning algorithms like neural networks. Handwriting recognition, automatic image recognition and geomorphologic (form or surface features of the earth or another celestial body) terrain feature classification are some popular forms.

The ImageNet challenge is a competition started in 2010. Here research teams submit programs that classify and detect objects and scenes. Since then, there has been excellent progress in image processing. In 2010, a good visual recognition program had around 40% classification error rate. In 2015, a deep convolutional neural net program for image recognition had about a 3.5% classification error rate!

How Facebook uses Image Analysis?

Image Analysis forms a big part of Facebook’s auto-tagging feature. A facial recognition software is used to detect the categories of users’ friends to match the newly uploaded pictures with the ones that have been tagged elsewhere. This software uses Machine Learning algorithms like neural nets. The algorithm is fed with large amounts of training data and the machine then learns to classify and recognize people in the uploaded images and suggests to you friends who could be there with you in the photo. So Facebook is heavily investing in AI. They recently acquired FacioMetrics, a facial image analysis start-up-to delve deeper into AI research.

These are the just some of the main advantages and applications of AI. The field is huge and has a lot more to this!

Careers and Opportunities in AI

Until a few years ago, Artificial Intelligence was mainly used by the military and the government with the help of a select few professionals in the field. But now more and more people are educating themselves and are becoming proficient in the field. They are now realizing the improvement that AI can make in a business. Today, AI is used practically in every field. Some of the possible career opportunities in AI include-

• Artificial General Intelligence : In the hierarchy, this profession would be placed right on top. Companies like DeepMind are working on this field. Generally, impeccable PhD candidates that have a stellar research background are chosen.
• Data Scientist : Data Science is probably the most sought-after profession in Artificial Intelligence. The plus point here is that it doesn’t have a learning curve that is too steep. Machine Learning forms the heart of Data Science. People wanting to join the field must learn Statistics, some Programming and obtain Domain Knowledge.
• Data Mining and Analysis : After a Data Scientist, a Data Analyst is probably the most popular job. Similar to a Data Scientist, an analyst however places lesser emphasis on Statistics. Hence, most people with diverse backgrounds, with a strong desire and ability to learn could apply for these jobs.
• Machine Learning Researcher : A field that probably not many outside the Computer Science and Electrical background could handle. In fact, without a PhD, even a Computer Scientist would be quite handicapped here! It involves discovering new areas in Machine Learning to deal with an uncharted territory of complex problems. The job primarily involves a lot of research.
• Machine Learning Application : This involves applying Machine Learning effectively to areas where it is already being used. A graduate or a master’s student could apply for these jobs.

While there are many other AI jobs, these are the most talked about ones under a broad umbrella. This is a wonderful time for anyone to start working in AI. The field is just getting started. Even if you are a beginner, learning new things every day and scaling up is the key.

The Future of AI

Artificial Intelligence is undoubtedly changing the world. It is making lives easier. But, as the efficiency of AI increases, so does the growing concern that it is changing the world too much, with the fear that machine intelligence would soon surpass human intelligence. The fear that the Terminator and Matrix (movies on AI) will become a reality is increasing too. So, to what extent are these fears warranted? Is there any truth to them at all?

Ominously, the answer is yes. Don’t get us wrong. We don’t mean that there will be a machine uprising in the near future making humans obsolete, an inferior species. As of now, Artificial General Intelligence is a myth. It does not exist. AI still does not have the human cognitive abilities and may not so in the near future. But, we cannot entirely write off the possibility of this happening. It is certainly possible, even if it is very unlikely. Maybe in a few decades, or by the end of this century or many centuries later. Artificial General Intelligence and Superintelligence could become a reality.

Superintelligence is the ability of a machine to seamlessly perform every task that a human can perform, and better! Thanks to their perfect recall (computers have an eidetic memory as opposed to humans), and ability to multitask, they will fare far better than humans at practically everything. The book “Superintelligence: Paths, Dangers, Strategies” by Nick Bostrom talks about exactly this- Superintelligence as a possible concept.

Does AI have the power to automate you?

Recently, there has been a lot of talk around AI automating humans and disrupting millions of jobs. As of now, machines are good at tasks that involve Big Data and a great amount of iteration. Machines don’t have intuition and can’t match humans’ ability to take decisions in tricky situations.

Example- machines can analyse huge amounts of data far more accurately and quickly than a human can, but the final decision that a Data Scientist is always a mix of data and intuition, which comes with experience.

End Notes

AI has been surrounded by quite a lot of controversy. On one hand, companies (not only limited to tech giants) are investing millions in AI research and development. On the other hand, Stephen Hawking has voiced his concern that AI could be the end of mankind. Elon Musk & Bill Gates have also agreed with this.

However, in the debate of AI being a boon or a bane, we believe boon will always win. This isn’t because we are being ignorant about the catastrophic situations that will unfold if superintelligence is achieved. It is because steps are already being taken to prevent the potential hazards AI may bring along with it. AI’s progress will continue only if it is in alignment with general human interest. So, don’t fear it! Go ahead and just enjoy the revolution.

So which camp do you belong to? The pro-AI camp or the AI-against one? Do you think AI will disrupt more jobs than it actually creates? We would love to hear your opinion.

Meditation and Neuroplasticity: Five key articles

By Peter Malinowski | March 4, 2014

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Meditation not only changes our mind but also our brain – this is what more and more neuroscientific research suggests.

Neuroplasticity – the change of brain structures as a result of experience – is considered to be one of the most important discoveries of neuroscience. Over the last 10 years evidence has been growing that not only the acquisition of navigational knowledge by London Taxi drivers (see video) or learning a new motor task like juggling (see article), but also meditation practice can lead to significant changes to brain structures. Here I respond to a recent request and list five key articles on that topic.

Article 1: Meditation experience is associated with increased cortical thickness

To my knowledge this is the first study showing differences in brain structure between meditators and non-meditators. Magnetic Resonance Imaging (MRI) revealed that experienced meditators had a thicker cortex than non-meditators. This was particularly true for brain areas associated with attention, interoception and sensory processing.

Lazar, S. W., Kerr, C. E., Wasserman, R. H., Gray, J. R., Greve, D. N., Treadway, M. T., … & Fischl, B. (2005). Meditation experience is associated with increased cortical thickness. Neuroreport, 16(17), 1893-1897. [pdf]

doi: 10.1097/01.wnr.0000186598.66243.19

Article 2: Long-term meditation is associated with increased gray matter density in the brain stem

This study compared long-term meditators with age-matched controls with Magnetic Resonance Imaging and found structural differences in regions of the brainstem that are known to be concerned with mechanisms of cardiorespiratory control.

Vestergaard-Poulsen, P., van Beek, M., Skewes, J., Bjarkam, C. R., Stubberup, M., Bertelsen, J., & Roepstorff, A. (2009). Long-term meditation is associated with increased gray matter density in the brain stem. Neuroreport, 20(2), 170-174.    [pdf]

doi: 10.1097/WNR.0b013e328320012a

Article 3: The underlying anatomical correlates of long-term meditation: larger hippocampal and frontal volumes of gray matter

Another study that compared long-term meditators with matched control participants. The main findings were that meditators had larger gray matter volumes than non-meditators in brain areas that are associated with emotional regulation and response control (the right orbito-frontal cortex and the right hippocampus).

Luders, E., Toga, A. W., Lepore, N., & Gaser, C. (2009). The underlying anatomical correlates of long-term meditation: larger hippocampal and frontal volumes of gray matter. Neuroimage, 45(3), 672-678. [pdf]

doi: 10.1016/j.neuroimage.2008.12.061

While the studies listed so far merely compared existing differences between meditators and non-meditators and thus do not provide information of causality (a possible explanation would be that these people were drawn to meditation because their brains are different – rather than the difference being a result of meditation), below are two studies demonstrating actual impact of meditation practice by means of longitudinal designs (comparing pre- and post-meditation brain scans).

Article 4: Mindfulness practice leads to increases in regional brain gray matter density

Compared to a control group participation in an 8-week Mindfulness-Based Stress Reduction (MBSR) programme resulted in increased grey matter in the left hippocampus, a brain area strongly involved in learning and memory.

Hölzel, B. K., Carmody, J., Vangel, M., Congleton, C., Yerramsetti, S. M., Gard, T., & Lazar, S. W. (2011). Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Research: Neuroimaging, 191(1), 36-43. [pdf]

doi: 10.1016/j.pscychresns.2010.08.006

Article 5: Mechanisms of white matter changes induced by meditation

Here we have a very exciting study showing the impact of meditation practice on the connections between brain areas using Diffusion Tensor Imaging (DTI). After only four weeks of meditation changes in white matter – which is strongly involved in interconnecting brain areas [see myelin] – were present in those participants who meditated but not in the control participants who engaged in relaxation exercises. Interestingly, these changes involved the anterior cingulate cortex, a part of the brain that contributes to self-regulation, an important aspect when people start engaging with meditation practice. (read more about this article in a previous post )

Tang, Y. Y., Lu, Q., Fan, M., Yang, Y., & Posner, M. I. (2012). Mechanisms of white matter changes induced by meditation. Proceedings of the National Academy of Sciences, 109(26), 10570-10574. [pdf]