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情感工程学——理解游戏吸引力的科学方法

发布时间:2012-01-26 09:28:50 Tags:,,,

作者:Stéphane Bura

电子游戏设计原本很难为他人所理解,总觉得设计来源于天才设计师的直觉,但现在该领域已经演变成与其他领域共享技术和方法论的行为。各领域(游戏邦注:界面设计、心理学、复杂系统和物理学等)的术语开始逐渐在此领域中显现。

但是,行业似乎仍显得杂乱无章,这里没有为人们普遍接受的真理,只有某些人所共知的观点,比如哪些元素可以成就一款优秀的游戏、游戏能否算作艺术形式以及是否存在教授设计电子游戏的有效方法等。

我们缺乏比较游戏的客观标准,缺乏描述游戏的共同语言。没有恰当的描述,就不可能真正理解游戏。电子游戏的成功仍取决于传统技术的运用、营销、运气或天赋。但即便知道哪些游戏获得成功,也不能确保能够掌握其成功之道。

艺术和科学不只包含技术,还要有规则和法则。而电子游戏设计的规则又是什么呢?

我们的法则、规则和相对论是什么呢?

我们可用来更好地理解、分析和改善游戏的标准化工具是什么呢?

游戏设计空间到底有多大?我们能否找出其中还未被探索到的领域?

我们可以改变或打破哪些规则来创造出全新体验?

这篇文章阐述电子游戏设计理论,解释要如何寻找适用的规则。

也就是说,我们需要从答案明确的简单问题谈起,即“何为游戏设计?”和“何为优秀游戏?”

何为游戏设计

玩家玩游戏的目的不是为了完成游戏,就像读者看书的目的不是为了完成整部书籍的阅读一样。玩家玩游戏是为了获得情感体验。游戏设计便是以情感工程为目标的体验构建。

从本质上来说,游戏设计是件困难的事情。游戏设计师制作能够生成游戏状态的互动规则,玩家参与到互动中,导致玩家产生情感的正是这种游戏状态。

注:在情感设计方面,Donald Norman描述了3种不同层次的体验:本能(人们获得体验的方式)、行为(人们所获得的体验与游戏预定目标或功能的相符程度)和反思(体验对人们自我形象的影响)。游戏可以传达出行为层面体验。这篇文章关注的是其他的两个层面:游戏如何对玩家产生情感影响。

如果我们可以使用游戏可玩性变量来描述游戏状态,我们可以得到如下图所示的循环:

emotion cycle(from gamasutra)

emotion cycle(from gamasutra)

玩家和游戏间的互动使得游戏可玩性变量发生改变。比如,塞尔达系列游戏中找到回血道具使血量回满,这显然使游戏状态发生了改变。我们将在下文中阐述更深层次的内容。

这些变量的变动或不变触发玩家情感。比如,拥有全满的血量会让他变得更有信心。

玩家情感会影响到他与游戏的互动方式。比如,有信心可能会让他在游戏中冒更大的风险,自豪可能会使他持续获得高分,而厌倦可能会让他离开游戏。

有些情感来源于设计师精心构建的一系列事件。有些来源于玩家每个时刻与游戏间的普通互动。因为各玩家及其游戏经验间存在差异,所以无法确保玩家在游戏的特定时刻肯定能够感受到特定情感。但是,根据我们对生理学、心理学、认知或文化的理解,我们可以明白如何通过游戏情境设计来让玩家产生特定情感。

注:这篇文章探讨的不是如何通过内容、主题或故事创造情感,而是如何仅通过玩家与游戏的互动来创造情感。事实上,以上这些都是情感创造的必要组成部分。合适的场景或故事可以渲染和增强所创造的情感,但其他人已经阐述过这方面的内容,所以本文不再讨论。

游戏设计沿此循环逆向运转,尝试预测互动系统改变可能产生的玩家情感。但是,我们对于互动和情感两者间的联系了解如此贫乏,以至于需要对多数改变进行测试,才能明白其会让玩家产生何种情感。测试需要执行改变,这需要消耗时间和金钱。因而,当游戏设计需要考虑到预算问题时,创新很容易就会陷入危机。

在《The Chemistry Of Game Design》中,Daniel Cook概述了标准的准科学描述模型能够给游戏设计师带来的好处。这种模型对游戏规则设计、重复设计、体验设计甚至游戏测试都有所帮助,减少了游戏设计的成本和风险。

如果将游戏设计比作生物化学,我们的目标是像沃森和克里克那样解开游戏设计DNA的秘密,那么现阶段我们仍然还在努力取得孟德尔的成就。乔治·孟德尔是位19世纪的修道士,被称为“现代遗传学之父”。1860年左右,他花了7年的时间在修道院的花园中进行豌豆杂交试验。凭借敏锐的观察力和洞察力,他发现了遗传特性。

我认为,之所以目前还没有出现能被广泛接受的游戏设计理论,是因为沃森和克里克的发现需要以孟德尔的发现为基础,而我们现在仍然缺乏这样的基础。孟德尔的成功来源于不断地重复循环,类似于游戏开发者的做法,循环步骤就是:改变某些参数、等待、观察和衡量。

但相对我们来说,孟德尔有个优势。作为植物学家,他知道需要观察和衡量哪些元素,比如颜色、形状、质感、大小和成长速度等。而且,他无需担忧豌豆的外观或味道,但是游戏设计师必须在制作出良好游戏的同时努力从中发现游戏的成功要素。

如果“科学=衡量+洞察力”的话,我们应当衡量游戏中的哪些内容才能科学化地理解游戏设计?我们要如何将这些衡量要素同游戏质量联系起来?

衡量游戏资产和游戏可玩性已不新鲜,我要寻找的是可以衡量任意题材任意游戏的抽象游戏变量。

何为优秀游戏

如果我们能够在上述问题的回答上达成一致的话,那么应该就可以找到帮助我们客观衡量游戏参数的可玩性变量。不幸的是,这个问题的答案是多种多样的。尽管随着时间推移,意见分歧的情况已经有所缓解,但是仍然存在许多不同观点。所以,这里我选择一个自己较为喜欢的观点:

“优秀游戏就是一系列有趣的选择。”——Sid Meier

以我个人经历而言,每个听过这种说法的设计师都对此表示认同,所以这种说法中或许确实包含某些真理。接下来,让我们来分析下这句话。

“选择”暗示玩家享有某种程度的自由。

Noah Falstein声称,游戏挑战中的可行选择变化可以绘制成橄榄球状的凸面模型。在典型挑战中,刚开始往往只有几个选择,随着首个选择产生的结果变成现实,可接触到的游戏次空间不断增加,从而增加可行选择的数量。

convexity(from gamasutra)

convexity(from gamasutra)

当满足成功或失败条件时,选择的数量就会减少,直到挑战结束,选择数量变为0。这种描述的有趣之处是,它认为我们可以统计选择的数量。

Falstein随即进一步阐述了游戏凸面的不规则碎片化本质。在中期任务选项间做出选择可以实现长期目标,而中期任务本身是由短期挑战组成的。

fractal_convexities(from gamasutra)

fractal_convexities(from gamasutra)

这意味着,自由并非我们正在寻找的变量,而更有可能是一系列变量的统一特征。

由于更多的自由或更多的选择并非总是好的,所以我们不应当去寻找将游戏变量值最大化的方法,而是尽量让它们呈现出我们想要设计的目标体验。

其次,有根据的、有意义的和不可更改的选择才算是真正的选择。

有根据的选择:在做出选择之前,必须向玩家提供规则系统,让他理解规则的逻辑,而且要让他相信这些规则是始终不变的。否则,由于玩家无法预测结果,选择也会变得很随意。

比如,清晰的规则可让玩家在RTS游戏中对单位或建筑物做出适当的选择。

有意义的选择:玩家必须拥有足够的数据来了解选择的背景、自己希望获得的目标、自己的可选项以及每个选项的代价。如果选择毫无代价的话,这不能算作真正的选择,因为玩家可以轮流尝试每个选项。如果既没有代价也没有背景,那么选择的出现便毫无意义。背景可以简单(游戏邦注:在《俄罗斯方块》中放置方块),也可以相当复杂(游戏邦注:《最终幻想10》中的晶球盘系统)。代价可能很小(游戏邦注:玩家用金币购买药剂),也可能很大(游戏邦注:MMOG中玩家角色职业的选择会限制他们体验到的内容)。

不可更改的选择:要使选择真正有意义,就必须设定选择能够产生持续性的影响。否则,玩家付出的代价就变得毫无意义。

比如,《星际争霸》虫族玩家可以在中途改变自己的快速攻击战略,但是这种转变需要耗费一定的时间,需要投入更多的精力,而且还会影响到胜利的机会。

顺便说下,某些游戏变量的改变可能是暂时的,也就是那些不能被视为选择的动作所产生的结果,比如由游戏系统开展的多数动作均属此类。

最后,能够做出选择意味着玩家有根据选项展开动作的方法。如果马里奥不能奔跑或跳跃,那么玩家即便选择了角色前进的方向也是毫无意义的。所以,动作也是我们需要考虑的变量参数之一。

一系列有趣的选择构成结构化体验。体验如何改变玩家或者玩家与他人的关系是衡量体验质量的标准。如果体验不会对玩家构成影响,那么只是在浪费玩家的时间和精力而已。

最后,选择的趣味性意味着它们不乏味或毫无价值,而且充满挑战。在《A Theory of Fun》中,Raph Koster猜测趣味来源于处理挑战性情境和获得解决问题技能的过程。

游戏设计变量类别

概念列表需要结构化总结才能发挥作用。我之前一直意识不到这一点,直到回想起Will Wright在GDC 2003上的讲座。他以简洁明了的多维分类系统来描述可能出现的动态系统,我的想法便受此启发而得。我决定将这些概念进行正交分解,制成表格。首个表格描述的是变量类别,如下所示:

变量分类图表(from gamasutra)

变量分类图表(from gamasutra)

动作指肢体、即时和短期反馈循环层面。

系统指思维、认知、逻辑和计划层面。

自我指精神、反思、目标、私人体验和内在改变层面。

社交指社区、分享体验、文化和关系层面。

自由衡量的是选择和选择的机遇。

精通衡量的是技能及其获取和使用。

数据衡量的是内容、信息、规则和现实的物品。

动作层面中的自由:能够帮助或妨碍玩家的所有东西,同时玩家在处理过程中还要做出短期选择。包括:动作机遇(敌人暴露出弱点,在《塞尔达传说》的地下室中找到钥匙),产生新互动的新工具(《塞尔达传说》中的回旋镖或抓钩,《马里奥兄弟》中的飞行帽),新的能力(生命值增加,力量提升)。

系统层面中的自由:能够帮助或妨碍玩家的所有东西,同时玩家在处理过程中还要做出中期到长期的选择。包括:探索路径(自由漫步的游戏玩法),明晰目标(获得任务)。创造安全的环境让玩家可以试验规则。

自我层面中的自由:能够帮助或妨碍玩家的所有东西,同时玩家在处理过程中还要做出有关体验性质的选择。包括:战略性和创造性想法(自愿接受的限制,快速奔跑),内容创造工具(关卡构建,游戏的自定义)。

社交层面中的自由:能够帮助或妨碍玩家与他人关系的所有东西。包括:便利化的多人体验和模式,内容和体验分享,活跃社区,社区支持和管理工具,玩法体验传达出的社交形象(例如,奇怪,新奇等)

动作层面中的精通:能够帮助或妨碍实体关卡中技能获取和技能使用的所有东西。包括:运动技能、情境参数的迅速增加以及恰当的反应(预示中期计划的形成),训练,即时反馈。

系统层面中的精通:能够帮助或妨碍认知技能获取和技能使用的所有东西。包括:给予玩家足够的控制让其可以开展计划,提供结构规则的相关信息(《文明》中的科技树,《模拟城市》中的图表)以及开发这些规则的能力。

自我层面中的精通:能够帮助或妨碍对游戏体验进行更好控制的技能获取和技能使用的所有东西。包括:探索元游戏数据(阅读攻略),赋予体验属于玩家个人的含义,学习过程的相关反馈(鼓励、祝贺、奖励、激励等)。

社交层面中的精通:能够帮助或妨碍社交层面技能获取和技能使用的所有东西。包括:探索元游戏,学习虚张声势,培养个人在社区中的形象,竞争地位和排名,团队玩法(公会活动),成为其他人的顾问。

动作层面中的数据:游戏中可以互动的信息(生命包,《最终幻想7》中的药品)。Katie Salen和Eric Zimmerman在《Rules of Play》中将我们所谓的“游戏规则”定义为操作规则,玩家需要知道这些内容才能够玩游戏(扑克牌中各种牌的排行顺序,按A键让角色跳跃,RTS或RPG游戏中无法反悔做出的动作)。

系统层面中的数据:有关游戏状态的信息,比如玩家能够做的准备(在与龙战斗前装备屠龙剑)。根据Salen和Zimmerman的理论,建构规则描述的是游戏的内在运转情况(AI,物理原理)。玩家最早不知道这些内容,但是他可以学习或猜测出部分内容。这是指MDA框架中的机制。

自我层面中的数据:与玩家与游戏之间关系以及玩法行为有关的信息。奖杯可以记录玩家的获胜经历。

社交层面中的数据:调节玩家与其他人(游戏邦注:并不一定同为游戏玩家)关系的信息,使该关系能与游戏相契合。包括:亚策略信息(战略指导,论坛中有关游戏的讨论,粉丝故事),与声誉和成就相关的徽章和荣耀。Salen和Zimmerman表示,内隐规则应该得到玩家的尊重,这种礼仪性规则的遵守无需特别提醒。该部分规则包括社交规则和游戏特有的禁忌规则(在竞争性多人游戏中不偷看其他人的屏幕)。

注:这些并非变量,而是变量的类别。游戏的不同进程或系统会对相同类别中的多种变量产生影响。比如,《侠盗猎车手》中动作层面中的自由涉及到路径和车辆的选择。这意味着,这个模型仍有待完善,每个格子都还可以细分出内容。但是,我相信这是最为细节化的一般模型。更为细节化的模型就需要每个成分再细分出具体的次类别,这已经超出了本文的讨论范围。

因为这些变量类别是抽象的,所以我们或许很难将它们与具体的互动联系起来。

以下两个表格阐述了游戏互动会导致这些类别中变量如何改变。

游戏引发的变化,点击查看大图(from gamasutra)

游戏引发的变化,点击查看大图(from gamasutra)

玩家引发的变化,点击查看大图(from gamasutra)

玩家引发的变化,点击查看大图(from gamasutra)

第1个表格展示的是来源于游戏动作的改变,第2个表格的改变来源于玩家动作。改变可能是暂时的(需要些许精力来消除),也可能是持续性的(永久性地影响变量值)。

表格相关内容

有些内容可能会出现在多个格子中。诸如“鬼鬼祟祟行动”之类的复杂互动可能同时对2或3个格子中的游戏变量产生影响。如果我们想要分析其影响力,就需要将它们分解成更为简单的部分。

如果要以多维数值变量的形式来呈现这些游戏变量,阐述它们数值的增加和减少,我不知道该数值要以何为单位。但我认为目前的实情是,在这个早期阶段,识别数值的起伏状况并大致估算出它们之间的相对大小,这样做的价值还不大。

情感和游戏设计规则

受Nicole Lazzaro编撰的有关玩家情感的作品启发,我尝试将游戏变量和情感联系起来。我逐渐开始相信,情感可以同一个或数个变量的值和变种联系起来。如果情况确实如此,那么这可以成为游戏设计规则的模板,可以通过规则来解释如何传达特定情感,如下图所示:

rule_template(from gamasutra)

rule_template(from gamasutra)

两端的箭头描述变量的可能值。矩形代表可以产生相应情感的值的变化范围。

以下是这个模板的两个实例:

如果你减少玩家认知层面上选择的数量并维持这种状态(即系统层面中的自由较低),也就是说他可能有面对挑战的工具和计划但是却没有机会使用它们,那么你或许会让玩家感到绝望。但是,如果随后你给他一个机会(增加自由),事态马上就会出现转机,希望随即出现。

hope(from gamasutra)

hope(from gamasutra)

如果你维持平衡的难度等级,提供富有挑战性的障碍,在玩家成功克服后给予适当的奖励和赞赏(即自我层面中的精通较高),你可以让他产生自豪感。现在,向他提供克服挑战的容易方式,然后给予不成比例的奖励(减少精通度),那么你可能会让他产生羞耻感,好似已不正当的手段获得奖励。

shame(from gamasutra)

shame(from gamasutra)

当然,满足条件并不确保玩家能够感受到你所选择的这种情感,这只是创造出适于表达目标情感的背景。了解这些内容后,游戏就可以向玩家提供相应的反馈或文化信号,比如播放强调希望的音乐等,就不会出现情感偏差的情况。

以下表格列举出某些与情感相关的游戏玩法变量值。和上文一样,某些内容可能出现在多个格子中。你会注意到,我在本表格中用“情感”来概述所有的精神状态、认知和感觉。

情感游戏玩法变量,点击查看大图(from gamasutra)

情感游戏玩法变量,点击查看大图(from gamasutra)

情感工程学

使用以上表格,设计师可以利用之前的互动、游戏变量和情感三者间的循环,设计能够传达出特定情感的游戏系统。可以分为以下三个步骤:

首先,设计师从情感表格中找到自己所需的情感,了解所涉及的游戏变量和值及其变种所需的范围。如果该情感出现在多个格子中,设计师可以选择自己最喜欢的那个。如果他想要避免某种情感,那么必须考虑表格中出现这种情感的所有情况。因为表格并不十分完整,所以可能还需要考虑所有设计师认为游戏中可能产生这种情感的其他情况。

其次,从游戏和玩家表格中选择能够生成这些值和变种的合适游戏系统,要么通过游戏动作做出改变,要么将做出改变的机会给予玩家。

最后,将所选择的系统具体化到目标情感的背景中。最后这个部分完全取决于设计师的个人经历、目标和限制条件,所以下文所举的例子会尽量抽象化。

让我们看看如何将这种方法运用到3种复杂情感状态中,这3种情感都包含有许多子情感,它们是本能培养、流畅和被追逐感。

本能培养

本能培养=同情心+角色代理+令人舒适的惯例+惊喜+欣慰感

同情心(系统层面中的数据持续较高):游戏实体行为一致(玩家可以理解游戏中的创造物并预测其行为),突然做出的行为(创造物足够复杂,似乎能够意识到并理解玩家与之的互动),准备(增加玩家感知创造

物行为的价值),自定义能力(创造物对自定义工具会产生不同的反应,好似它们有自己的品味和偏好),规则学习(创造物足够复杂,玩家必须花一定时间来学习他们的行为),增加得分(让创造物愉悦可以得到积极的反馈)。

角色代理(动作层面中的数据和系统层面中的精通持续较高):永久性的世界改变(玩家的动作能够改变创造物,错误做法会对它们造成伤害),可收集内容(自定义工具,稀有行为,稀有创造物),XP(对必要的进程和动作进行系统层面的跟踪),资源(强调互动的成本和玩家自身的奉献),自主游戏玩法模式转换(玩家可以选择创造物的行为),清晰的图标化/象征性内容(工具和行为的功能清晰明了,每种工具或行为均只有1种功能),积极和消极反馈(创造物向玩家展示互动是否让它感到愉悦),新的长期目标(创造物表达自己的需求,玩家需要经过大量努力才能满足它的需求)。

令人舒适的惯例(当系统层面中的自由较高时减弱):指导/清晰的目标(创造物的需求产生出挑战,挑战的解决方案往往是显而易见的,解决方案需要的是玩家的投资而不是技能),惯例模拟(创造物能否察觉到玩家的懒散状态,挑战能否同时存在等),清晰的精选目标(玩家可以通过努力对创造物进行特定的改变,比如创造物进化或创造物训练),部分解决(对游戏全局改变有略微影响的中期挑战,错误可以得到纠正)。

惊喜(当系统层面中的自由较低时增强,当系统层面中的精通较高时减弱):解锁内容/不稳定或随机行为(打破常规惯例的单调乏味,如稀有事件、随机事件或稀有条件的出现),意外奖励(对玩家在创造物上开展的实验性动作给予奖励)。

欣慰感(游戏邦注:即为他人的成就感到自豪)(当社交层面中的精通较高时增强):支持能力较差者(玩家给予创造物目标和进程反馈),协作(创造物需要与玩家形成紧密的反馈循环方能获取技能),对文化信号的正确回应(创造物在学习时呈现努力中的信号,创造物的技能获取过程中不时会出现愉悦的行为),教授技能/避免对手犯过的错误(创造物有不足之处,但是可以通过玩家教授来克服)。

流畅

流畅=愉快+自信+一致性+前向推动-无聊

愉快(动作层面中的自由持续较高):动作机遇(受到限制的移动,短期运动技能挑战),资源获取(平稳流畅的食物准备或积极反馈)

自信(动作层面中的精通持续较高):关联性(没有含糊不清的刺激因素),高度受训的反射/(玩家无需了解控制方式便可玩游戏),优势机会(玩家了解哪些动作可以解决部分挑战)。

一致性(系统层面中的精通持续较高):游戏实体的一致性行为(玩家可以预测下个游戏状态并做出相应反应),积极反馈(用力量提升或得分加倍来奖励局部的成功),暗示(警告玩家即将到来的危险),动作准备机会(短期战术计划),游戏玩法模式转换(玩家可以使用自己喜欢的战术),领先的机会(玩家间的竞争),开展突发行为的机会(玩家连续反应,触发对手犯错误)。

向前推动:(当系统层面中的自由较高时增强):替代性方法/共同目标(玩家可以使用最喜欢的战术),机遇产生(即便出现意外,玩家也可以灵活机动地调整会最喜欢的战术),安全领域(玩家可以从失败中恢复)。

避免无聊(动作或系统层面中的精通度不会过高):难度增加,复杂性增加,非故意设定的游戏玩法模式转换,随机行为(多拨敌人,意外的敌人类型,战术资源可用性较低的阶段)

被追逐感

被追逐感=恐惧+不可预测性+谨慎+失败预感-希望

恐惧(动作层面中的自由持续较低):线性路径(路径选择有限,隐藏地点有限),暂时性的能力丧失(受伤导致移动速度变慢),资源丧失(被抓住需要付出高昂的代价),逃生机会有限。

不可预测性(系统层面中的精通较高时减弱):消极反馈(追逐战赶上,失去逃脱可能性),不一致或随机行为(追逐者无法被跟踪或者无法预测其位置),非故意设置的游戏玩法模式转换(秘密行动/飞翔)。

谨慎(动作层面中的精通持续较低):冒一定风险(在主路径或次路径上设置一定风险,玩家很容易就会落入圈套中),误导性指引(陷阱)。

失败预感(自我层面中的精通持续较低):危险的预感(不详的音效),察觉到系统一方有作弊嫌疑(玩家就游戏对手总是能找到自己的位置提出疑问),错误重复发生(如果玩家再次发出声响,追逐者依然能够听到)。

避免希望(当动作层面中的精通较低、系统层面中的自由和系统层面中的精通较低时不增强):无法找到优势机会(追逐者没有展现出弱点),无法继续实现同期目标(逃跑妨碍了玩家使用资源或找到其他的逃脱方式),无法开展准备工作(只有幸运的逃脱才能够终止追逐)。

游戏分析

游戏变量表格可以用来概述游戏的吸引力和瑕疵。在以下的例子中,格子的颜色越深,其相关变量所创造的情感吸引力越低。有些情感在特定背景中是可取的(比如生存类恐怖游戏中出现恐惧感),在其他背景下成了不可取的情感(比如恐惧感不适合用在解谜游戏中)。

注:游戏中的灰色格子并不意味着这项内容没有价值,它代表的只是某种设计选择的结果而已。比如,《传送门》是款单关卡持续时间较短的游戏,这是设计师有意如此设计的,关卡时间的长短是其整体体验的一部分。但有些玩家确实觉得关卡可以设计得更长些。

Puzzle Quest(from gamasutra)

Puzzle Quest(from gamasutra)

马里奥银河(from gamaustra)

马里奥银河(from gamaustra)

传送门(from gamasutra)

传送门(from gamasutra)

我并非想要努力去证明《传送门》是款比《Puzzle Quest》更好的游戏。我的意思是,如果不考虑个人品味的影响,《传送门》提供的游戏体验相对更为完整。

同时,我认为《传送门》的精明之处不只在于其完整性,游戏的简明优雅也是使游戏出众的重要因素。简明优雅这种特质与诸多格子中的内容有所关联。因为这部分内容有关元设计规则,所以不在本文的讨论范围之内。

从孟德尔到门捷列夫

这是个有用的工具吗?现在我还不知道。我希望这些表格能像门捷列夫的元素周期表那样,帮助游戏设计师总结出情感设计的语言。

这种语言使我们可以更好地合作并分享理论,让我们的作品更具艺术性。正如门捷列夫和他的元素周期表那样,这种模型甚至可能引导我们预测未知元素的存在,寻找游戏设计空间中未被探索过的领域。

尝试创立这种模型的人可以借鉴乔治·孟德尔的奉献精神、耐心和科学严谨态度。孟德尔独自寻找着遗传学的规律,但是游戏设计师是个群体。因而,从某种程度上来说,我觉得游戏设计师取得与他类似的成就无需历时7年时间。

游戏邦注:本文发稿于2008年7月29日,所涉时间、事件和数据均以此为准。(本文为游戏邦/gamerboom.com编译,拒绝任何不保留版权的转载,如需转载请联系:游戏邦

Emotion Engineering: A Scientific Approach For Understanding Game Appeal

Stéphane Bura

This is a very exciting time to be a video game designer.

Video game design is evolving from a barely understood activity done by genius designers driven by their gut feelings, to a craft with shared techniques and methodologies. A common vocabulary cobbled from various fields (interface design, psychology, complex systems, physics, etc.) is slowly emerging. Successes and failures are analyzed…

But it’s still a big mess, a large toolbox where any designer can find the right tool to confirm exactly what he believes in. There are no universally accepted truths, only opinions about what makes a great game, whether or not video games are an art form or whether there is an effective method to teach video game design.

We lack ways to compare games in an objective manner, ways to describe them in a shared language. Without proper description, there can be no true understanding. Success in video games still hinges on applying traditional techniques, copying, marketing, luck, or genius. And even if success is achieved, there’s no guarantee that we can know why it happened.

Arts and sciences have rules and laws, not just techniques. But what are the rules of video game design?

Where is our redox law? Our perspective rule? Our theory of relativity?

Where are the formal tools we can use to better understand, analyze, and improve games?

How big is the game design space and can we identify its virgin territories?

What are the rules we can bend or break to create totally new experiences?

This article presents a theory of what video game design is and explains how to find some of these rules.

Caveat: If I sound pedantic or over-confident in this article, please prefix any affirmation I make with “I modestly believe without being able to prove that…” This work has generated many hours of doubt and self-doubt. I do not pretend to teach anyone the definitive meaning of trust, catching-up, fear, or of collecting a power-up. This is an ongoing work, as the article’s version number – 1.0.4 – attests. If you disagree with me, please tell me why and I’m sure you’ll be convincing enough to change my mind.

That said, let’s start with easy questions that have clear answers: “What is game design?” and “What is a good game?”

What is Game Design?

Players don’t play to complete games, just as readers don’t read to finish books. Players play to feel emotions. Game design is experience crafting for the purpose of emotion engineering.

Game design is intrinsically hard because its output is an interactive system that is twice removed from its goal. The game designer produces rules for interaction that, with the participation of the player, generate game states that themselves induce emotions in the player.

Note: In Emotional Design, Donald Norman describes three different levels of experience processing: visceral (how it makes the person having the experience feel), behavioral (how well it suits its purpose or function), and reflective (how it affects the person’s self-image). Games can have a behavioral aspect, from being a learning tool to a systemic demonstration of a concept. This article focuses on the other two aspects: how games can have an emotional impact on their players.

If we can describe a given game state using a set of gameplay variables, we get the following cycle:

Interactions between the player and the game produce changes in the gameplay variables. For instance, finding a heart container in Zelda and getting a bigger full health bar obviously changes something in the game state. We’ll explore below what this could be.

Variations or stability of these variables induce emotions in the player. For instance, having a bigger full health bar could make him more confident.

Player’s emotions influence how he interacts with the game. For instance, being confident might make him take more risks; pride might keep him chasing a high score; or boredom might make him stop playing altogether.

Some of these emotions are the result of a carefully crafted sequence of events. Others stem from the normal moment-to-moment interaction with the game. Since the players and their playing experiences are so different from one another, one cannot guarantee that a given player will feel a given emotion at a given point in a game. However, from our understanding of physiology, psychology, cognition, or culture, we can identify situations that create the proper context for the expression of such an emotion.

Note: This article is not about creating emotions with the content, the subject matter, or the story, but through interactions with the game alone. Indeed, these are integral parts of the whole – emotions are enhanced by the appropriate setting or story – but the subject has been talked about at length by better qualified people elsewhere. So I’ll skip this for now.

Game design works backwards around this cycle, trying to predict player emotions from changes in the interactive system. But our knowledge of the dependencies between interaction and emotion is so sparse that most changes require testing. Testing in part requires implementing the changes, which costs time and money. Thus, in a professional setting where budget is an issue, game design innovation can quickly become a risk.

In The Chemistry Of Game Design, Daniel Cook outlines the benefits game designers would draw from a standardized quasi-scientific descriptive model. Such a model would help game rules design, iterative design, experience design, and even game testing, thus reducing the cost and risk of game design.

If we extend his metaphor to biochemistry, we – like Watson and Crick – aim to unlock the secrets of the DNA of game design, but we’re still struggling to become Mendels. Gregor Mendel was a 19th century monk who is known today as the father of modern genetics. Around 1860, he spent seven years experimenting with pea strain hybridization in his monastery’s garden. His observations, combined with some amazing insights, led him to the discovery of the characters of heredity.

I think that if there are no widely accepted grand theories of game design, it’s because Watson and Crick’s discovery was built on Mendel’s, and we’re still lacking such a base. Mendel’s success stems from the tedious repetition of a loop familiar to game developers: tweak some parameters, wait, observe, and measure.

But Mendel had an advantage over us: being a botanist, he had a fairly good idea of what to observe and measure: colors, shapes, textures, size, growth rates, etc. (Furthermore, he didn’t care if his peas looked or tasted great, while game designers are trying to understand what make games good while making good games.)

If Science = Measures + Insight, what should we measure in our games to move toward a scientific understanding of game design, and how can we equate these measures with quality?

Measurement of game assets and gameplay is nothing new (be it Ben Cousins’ systematic studies within a genre or Microsoft’s usability labs), but I’m looking for abstract game variables that could measure any game in any genre.

What is a Good Game?

If we could agree on what a good game is, the description would be a starting point for finding the gameplay variables that, like botanic for Mendel, would help us objectively measure game characteristics. Unfortunately, there are as many definitions of what a game is that you care to look for. There are fewer of what a good game is but it’s still staggering. So I’ll just pick one I like:

“A [good] game is a series of interesting choices” – Sid Meier

In my experience, this koan sticks to the memory of every designer who hears it, so there may be some truth in there. Let’s parse it.

Choices imply that the player has a certain degree of freedom.

Noah Falstein (referenced here) professes that one can map the availability of choices during a given challenge to a convexity. A typical challenge starts with few choices since the starting conditions are set. As consequences from the first choices materialize, the sub-space of the attainable game space grows, thus increasing the number of available choices.

When success or failure conditions are met, the number of choices decreases until the challenge is completed and no choices are left. This is for instance how the game can lead the player toward a climactic ending. What is interesting in this description is that it shows that choices are something we can count.

Falstein goes on to note the fractal nature of convexities in a game. Long-term goals can be attained by choosing between options of medium-term missions, themselves composed of short-term challenges.

This means that freedom is not one of the variables we’re looking for, but more probably a defining characteristic for a series of variables.

Since more freedom or more choices is not always better, we shouldn’t be looking for ways of maximizing the values of our game variables, but for ways of attaining the ranges in which they procure the experience we want to design.

Next, a choice is only real if it is informed, meaningful and irreversible.

Informed choice: To be able to make a choice, the player must be provided with a system of rules which logic he can understand and that he can trust to be consistent. Otherwise, his choice is random since he cannot predict its consequences.

This is how, for instance, he can choose which unit or building to produce in a RTS.

Meaningful choice: The player must have sufficient data to describe the context of his choice, the objects of his desire, his options, and the costs associated which each of them. If there are no costs, it’s not really a choice since each option can be tried in turn. If there are neither costs nor contexts, choices don’t matter. Context can be simple (Placing a block in Tetris) to extremely complex (Final Fantasy X’s sphere grid experience system). Cost can vary from small (Buying a potion when the player has plenty of gold) to big (Choosing one’s character class in a MMOG, limiting the content one can experience).

Irreversible choice: A choice, to be truly significant, must create a set of conditions that have a high degree of persistence. Otherwise, this means that the cost paid is meaningless.

For instance, a player can change his mind after committing to a Zerg rush strategy in StarCraft, but it will cost him time, require some effort, and impede his chances of winning.

Incidentally, this means that some game variable changes can be temporary – those that are the consequences of actions that are not considered choices, like most actions performed by the game’s systems.

Finally, making a choice implies that one has the means to act upon it. It would be of no use to the player to decide where to go if Mario couldn’t run or jump. So action is also one of our variable characteristics.

A series of interesting choices implies a structured experience, an overarching context. Such experience can be qualified by how it changes the player (self) or his relation to others (social). If the experience didn’t affect the player, it would just be wasted time and energy.

Lastly, if the choices are interesting, they’re neither boring nor trivial. They’re challenging. In A Theory of Fun, Raph Koster posits that fun stems from dealing with challenging situations and acquiring skills to solve them. Similarly, Daniel Cook tracks the player’s mastery with skill trees.

Game Design Variable Categories

This list of concepts needed some structure to become useful. It eluded me until I remembered Will Wright’s amazing Dynamics for Designers lecture at GDC 2003. His ability to describe the possible dynamic systems in a neat multi-dimensional taxonomy was eye-opening. I decided to organize these concepts into orthogonal families that would provide the axes for various tables.

The first one describes the categories of variables:

Action is the level of the body, the visceral, immediacy and short feedback loops.

System is the level of the mind, the cognitive, logic and plans.

Self is the level of the soul, reflexive thoughts, goals, private experiences and inner changes.

Social is the level of the community, shared experiences, rituals, culture and relationships.

Freedom deals with measuring choices and opportunities for choices.

Mastery deals with measuring skills, their acquisitions and their uses.

Data deals with measuring content, information, rules and real-life objects.

Freedom at the Action level: Everything that empowers or hinders the player while making short-term choices. Action opportunities (An enemy presenting its weak spot, Finding a key in a Zelda dungeon). New tools allowing new interactions (Zelda’s boomerang or grappling hook, Mario’s flying cap). New abilities (Increased health, Increased strength).

Freedom at the System level: Everything that empowers or hinders the player while making medium to long-term choices. Avenues of exploration (Free-roaming gameplay), clear goals (Getting a quest), letting the player experiment with the rules and creating safe environments where to do so.

Freedom at the Self level: Everything that empowers or hinders the player while making choices about the nature of his experience. Strategic and creative thinking (Specialization, Self-imposed limitations, Speed runs). Content creating tools (Level building, Customization, Machinima).

Freedom at the Social level: Everything that empowers or hinders the player in his relationship with other people. Facilitated multiplayer experience and modes. Sharing content and experiences. Active community. Community support and community management tools. Social image conveyed by the playing experience (Coolness, Geekiness, Weirdness, Novelty, etc.).

Mastery at the Action level: Everything that empowers or hinders skill acquisition and skill use at the immediate or physical level. Athletic skills. Rapid appreciation of the parameters of a situation and appropriate response (which can imply the forming of a medium-term plan). Training. Immediate feedback. Affordance.

Mastery at the System level: Everything that empowers or hinders cognitive skill acquisition and skill use. Giving the player the level of control he needs to act on his plans (agency).

Providing information about the constitutive rules (see below) (Tech tree in Civilization, Graphs in SimCity). The ability to exploit these rules. It’s the dynamics in Robin Hunicke, Marc LeBlanc and Robert Zubek’s MDA framework.

Mastery at the Self level: Everything that empowers or hinders skill acquisition and skill use that allow for better control over the game experience. Exploiting metagame data (Reading a walkthrough). Ascribing own meaning to the experience. Feedback about the learning process (Being encouraged, congratulated, rewarded, mocked, stirred up, etc.).

Mastery at the Social level: Everything that empowers or hinders skill acquisition and skill use at the social level. Exploiting the metagame. Learning to bluff. Shaping one’s image in the community. Being invested with and performing a role. Competing for ranking. Group play (Guild raids). Being a mentor.

Data at the Action level: Information that takes form in the game, that can be interacted with (Health pack, FFVII’s materias). Persistent information at this level can take the form of a collection (Pokémon’s Pokédex). Katie Salen and Eric Zimmerman in Rules of Play define the operational rules as what we usually call the “rules of the game”, the ones you have to know to be able to play (The ranking of hands in poker, Press A to jump, No credit in RTSs or RPGs).

Data at the System level: Information about the game state. Player preparation (Setting-up the Tetris board so as to clear four lines with an I piece, Equipping a dragon-slaying sword before fighting a dragon). The constitutive rules that, according to Salem & Zimmerman, describe the inner workings of the game (AI, Physics, Catch-up behaviors). The player doesn’t know them at first but he can learn or guess some of them. It’s the mechanics in the MDA framework.

Data at the Self level: Information relative to the relationship between the player and the game, to the act of playing. Trophies, traces left by the experience or created by the player.

Data at the Social level: Information governing the relationships between the player and other people (not necessarily players themselves), as it pertains to the game. Metagame information (Strategy guides, Forum discussions about the game, Fan fiction). Badges and honors linked to reputation and achievements. Salen & Zimmerman’s implicit rules, the rules of etiquette, of the magic circle, that should be respected without having to mention them. They include social rules (Don’t be a jerk) and game-specific taboos (Don’t spy on someone else’s monitor in a competitive multiplayer game).

Second caveat: These are not our variables but the categories they belong to. A given game can thus have an influence on several variables in the same category, linked to different processes or systems. For instance, Freedom at the Action level in GTA handles both the choice of paths and of vehicles. This means that this model is still incomplete, each cell being its own dimension. However, I believe this is the most detailed generic model using these variables. A more detailed model would require specific sub-categories for each cell, a work that is well beyond the scope of this article.

Although this table went through many revisions, I cannot affirm that it spans the whole of game design space. After all, I have only experienced a limited subset of the currently known games. However, I seem to be able to describe the effect of any game interaction I can think of as variations in one or several variables belonging to these categories. Whether I’m on to something or have thoroughly blinded myself is for you to judge.

Since these variable categories are abstract, it may be difficult to understand how they’re linked to concrete interactions.

The two following tables give examples of how game interactions cause changes in variables in each of these categories.

The first table shows changes emanating from the game’s actions, the second from the player’s actions. Changes can be either temporary (requiring little effort to cancel) or persistent (affecting the value of the variable durably).

About these tables

Some items appear in several cells. Complex interactions, like for instance “Being stealthy”, have too many concurrent effects on the game variables to fit into two or three cells. They must be broken down into simpler parts if we want to analyze their influence.

If I have chosen to represent these game variables as mono-dimensional numeric variables, speaking of increase and decrease in their values, I have no idea which units I should use for them. Is freedom quantified by counting available choices weighted by their importance or is it a succession of fitness functions rewarding more and more states in the game space? At this early stage, it matters little if we can recognize fluctuations in values and roughly evaluate their relative sizes, which I believe is the case.

Emotions and Game Design Rules

Spurred by Nicole Lazzaro’s work on players’ emotions, I tried to link game variables and emotions. I came to believe that a given emotion could be associated with the values and variations of one or several variables. If this were the case, this would be a template for a game design rule, a rule explaining how a given emotion can be achieved:

The two-headed arrow describes the possible values for the variable. The rectangle indicates the range in which a decrease, a persistent value or an increase generates conditions for the associated emotion.

And here are two instantiations of this template:

If you reduce the number of choices a player has at the cognitive level – which means that he may have the tools and plans to face a challenge but is overmatched or doesn’t have the opportunity to use them – and maintain him in this state (Low Freedom at the System level), you may drive him to despair. But if you then give him an opportunity (Increase), suddenly there’s a way out. Suddenly, there’s hope.

If you maintain a balanced difficulty that provides challenging obstacles with appropriate rewards and acknowledgements of the player’s successes (High Mastery at the Self level), you can induce a sense of pride in him. Now, offer him an easy way out of a challenge and reward him disproportion ally for taking it (Decrease), and you induce the shame that comes with ill-gotten gains.

Of course, such conditions don’t guarantee that the player will feel the chosen emotion. They just create a context that is favorable to the expression of this emotion. Knowing that, the game can provide the player with corresponding feedback or cultural cues, like booming music to underline hope, without causing an emotional dissonance.

Here’s a table listing some emotions associated with the values and variations of the gameplay variables. As previously, some items may appear in several cells. You’ll notice that I use

the widest possible definition of “emotions” in order to also cover other mental states, perceptions and feelings.

* Mimicry requires focus, implicit rules and a somewhat applicable knowledge of the mimicked process.

** Agon can practically occur anywhere, even in Too Low Mastery if there is randomness (Parcheesi) or metagame factors (Rock Paper Scissors).

Emotion Engineering

Using the previous tables, a designer can move backwards in the (interactions/game variables/emotions) loop and design game systems that can induce chosen emotions. This requires three steps.

First, the designer picks the emotions he’s looking for in the emotions table and finds out which game variables are involved, as well as the required ranges of values and their variations, if any. If the emotion appears in several cells, he can pick the one(s) he prefers. If he wants to avoid an emotion, he must make sure to consider all its instances in the table (and, since the table is by no means complete, where else he thinks the emotion could occur while playing his game).

Second, he chooses in the game and player tables which appropriate game systems can generate these values and variations, either from the game’s actions or by giving the player opportunities to make these changes.

Lastly, he instantiates the chosen systems within the context of the desired emotion. Since this last part is of course totally dependent on the designer’s personal experience, goals and constraints, the examples below try to be as abstract as possible.

Let’s look at how to apply this method to three complex emotional states, each composed of several “atomic” emotions: inducing nurturing instinct, flow, or the sensation of being hunted.

Each of the instantiations of game systems shown in italics is only one of the possible choices a designer could make.

Nurturing Instinct

Nurturing instinct = Empathy + Agency + Comfortable routine + Surprise + Naches

Empathy (Persistent High Data at the System level): Consistent behaviors from game entities (Player can understand the creatures and anticipate their behaviors), Emergent behavior(Creatures are complex enough to seem intelligent and aware of the interactions), Preparation (Player’s actions take some effort which increases their perceived value), Abilitycustomization (Creatures respond differently to customized tools, as is they have tastes and preferences), Learning a rule (Creatures are complex enough that the player must spend time to learn their behaviors), Score increase (Creatures give positive feedback when pleased).

Agency (Persistent High Data at the Action level, Persistent High Mastery at the System level): Permanent world change (Player’s actions change the creatures, Mistakes can hurt them),Collectibles (Customized tools, Rare behaviors, Rare creatures), XP (System level track of progress and effort needed), Resources (Put the emphasis on the cost of interaction and the player’s commitment) (Treats, Meds), Voluntary gameplay mode switch (Player can choose the creatures’ activities), Lowering difficulty of current challenge (Slow rhythm, No pressure, No insurmountable challenges), Iconic/Symbolic content with clear affordance (Tools’ and activities’ functions are clear, one per tool or activity), Positive & negative feedbacks (Creatures show the player whether an interaction is pleasing or not), New long-term goal (Creatures express needs that take a lot of effort to satisfy).

Comfortable routine (Decrease when High Freedom at the System level): Guidance/Clear goal (Creatures’ needs generate challenges, Challenge solution is often obvious; it requires investment, not skill), Simulation conventions (Whether inactivity is perceived by the creatures, Whether challenges can be concurrent, etc.), Clear chosen goal (Player can work toward a specific change in the creature) (Creature evolution, Creature training), Local resolutions (Medium-term challenges with little impact on global change, Mistakes can be corrected).

Surprise (Increase when Low Freedom at the System Level, Decrease when High Mastery at the System level): Unlocking content/Inconsistent or random behaviors (Breaks the routine’s monotony)

(Rare events, random or with rare conditions), Easter eggs (Reward experimentation with creatures).

Naches (Pride for one’s child’s or mentee’s accomplishments) (Increase when High Mastery at the Social level): Mentoring (Player can give goals and progress feedback to the creatures),Cooperation (Creature skill acquisition requires a tight feedback loop with the player), Correct response to cultural cue (Creatures display signs of effort when learning, Creatures’ skill acquisition is punctuated by joyful behavior on their part), Teaching a skill/Recurring opponent’s mistake (Creatures have disabilities that they can overcome through being taught).

Flow

Flow = Exhilaration + Confidence + Coherence + Forward thrust – Boredom

Exhilaration (Persistent High Freedom at the Action level): Action opportunities (Limited constraints on move set, Short-term athletic skill challenges), Resource gain (Steady flow to feed preparation or positive feedback – see below).

Confidence (Persistent High Mastery at the Action level): Affordance (No ambiguous stimuli), Highly trained reflexes/Kinesthetic isomorphism (Player can play without thinking about the controls), Advantage opportunities (Player can identify which sequences of moves can solve local challenge).

Coherence (Persistent High Mastery at the System level): Consistent behaviors from game entities (Player can predict next game state and react accordingly), Positive feedback (Reward local successes with easy challenges, power increase or score multipliers), Hints (Warn the player of imminent danger), Opportunities for exploiting preparation (Short-term tactical combos),Voluntary gameplay mode switch (Player can use favorite tactics), Opportunities for taking the lead (Player catch-up), Opportunities for exploiting emergent behavior (Chain reactions, Player can provoke opponent’s mistakes).

Forward thrust (Increase when High Freedom at the System level): Alternative methods/Concurrent goals (Player can use favorite tactics), Generating opportunities (Even if caught off-guard,player can maneuver so he can fall back to favorite tactics), Safeguards (Player can recover from near defeat).

Avoid Boredom (No Excess Mastery at the Action or System levels): Difficulty increase, Complexity increase, Involuntary gameplay mode switch, Random behaviors (Sustained level of perceived difficulty at the Action level, Use semi-random temporary increases in difficulty to keep the player on his toes) (Waves of enemies, One unpredictable enemy type, Periods of low availability of tactical resources).

Being Hunted

Being Hunted = Dread + Unpredictability + Caution + Anticipation of failure – Hope

Dread (Persistent Low Freedom at the Action level): Linear path (Limited number of significant path choices, Limited number of hiding places), Temporary loss of ability (Being slowed down by a wound), Resource loss (High cost for being caught), Limited opportunities to escape when seen.

Unpredictability (Decrease when High in Mastery at the System level): Negative feedback (Hunters catch-up, No possible escape), Inconsistent or random behaviors (Hunters cannot be tracked or their positions predicted), Involuntary gameplay mode switch (Stealth/Flight).

Caution (Persistent Low Mastery at the Action level): Taking a risk (Risky portions in main path or secondary path, where the player can be spotted more easily), Misdirecting affordance(Traps).

Anticipation of failure (Persistent Low Mastery at the Self level): Pretend danger (Ominous noises), Perceived cheating on the part of the game (How can they always find me?), Opportunities for recurring mistake (Hunters can hear the player when he makes noises).

Avoid Hope (No Increase when Low in Mastery at the Action level, Freedom at the System level and Mastery at the System level): Can’t exploit advantage opportunity (Hunters show no weaknesses), Can’t pursue concurrent goals (Fleeing prevents access to resources or means of escape), Can’t exploit preparation (Hunt can only end by a narrow escape that feels lucky).

Game Analysis

One can use the game variables table to sum-up the appeal and flaws of a game as they pertain to its systems and the emotions they induce. In the following examples, the darker a cell is, the lower the emotional appeal created by its associated variables. I tried to use common sense when judging this appeal, since some emotions might be desirable in a given context (Fear in a survival horror game) and not in another (Fear in puzzle game).

Note: That a game has a grayed-out cell doesn’t mean that this makes it bad, it is just a consequence of the design choices. For instance, Portal is a short game because it’s been purposefully designed as one: its length is part of the overall experience it offers. Some players might still find it too short.

I’m not trying to prove that Portal is objectively a better game than Puzzle Quest. I’m saying that, personal taste aside, Portal provides a more complete experience.

By the way, I believe that the brilliance of Portal doesn’t stem solely from its completeness, but also from its elegance. Elegance is a quality of the relations between the cells of the table. Since it’s about meta-design rules, it’ll be the subject of another article.

From Mendel to Mendeleev

Is this a useful tool? I don’t know yet. I hope that like Mendeleev’s periodic table of elements, this or a different model will help game designers establish a common language devoid of fuzziness and interpretations.

A language that would allow us to better work together, share theories and turn our craft into an art. And like Mendeleev and his table, such a model might even lead us to predict the existence of yet unknown elements, unexplored territories in the game design space.

Gregor Mendel’s sheer dedication and patience as well as his scientific rigor are an inspiration to anyone attempting such an endeavor. Mendel worked alone, when game designers are part of a vibrant and well connected community. Somehow, I don’t think we’ll have to wait seven years to produce a work worthy of his efforts. (Source: Gamasutra)

 


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