心理生理学在游戏用户测试中的运用编辑本段回目录
我对生理游戏交互作用和心理生理学游戏评估的浓厚兴趣持续了不下五年了。从博士到博士后的那段时间，我为研究协会和游戏公司做了若干次相关的展示、发表了不少专题文章。在此期间，生理学传感器的价格已经比过去便宜多了，且现在，像 Neurosky 和 Emotiv等公司的平价生理学传感器产品也得到了推广。我在Valve的同事Mike Ambinder甚至开始研究生物反馈在商业游戏中的应用。有些内容在GDC2011上展示过了。这一是块令人兴奋的领域。在本文，我将从生理学计算方面来探讨游戏交互作用和游戏评估的运用。
为何要关注这个领域？
关键的计算系统取决于正确的算法，所以生理学传感器运用失败的后果显然不会比计算系统严重，所以，在数字游戏中运用生理传感器来探索交互作用，是一个不错的选择。这就是为什么生理计算和生理心理游戏评估在游戏用户研究群体中如此受欢迎的原因之一。那么，过去的生理传感器的应用程序有哪些，未来我们还能见识到哪些？
biofeedback_game(from mindmodulations.com)
生理交互作用和生物反馈游戏
自八十年代以来，人们已经在生物反馈游戏中，对生理输入进行过测试。如果你看看当前的研究主体和生物反馈运用程序，你会发现，这些软件应用程序其实就是生物反馈游戏。所以，你是不是要问什么是生物反馈游戏？生物反馈游戏加强我们对自身生理状态的意识到并且训练我们控制自我生理状态的一类游戏。为此，游戏开发商往往利用这类游戏中挑战、进程、主题和奖励来激发我们对自己的身体状态的兴趣。有人可能会这么反驳：生物反馈是对生理控制的游戏化（这个新词尚有争议）。
行业生物反馈游戏案例
生物反馈设备制造商Thought Technology Ltd是在竞技游戏《CalmPrix》中运用改良版GSR2传感器进行生理输入的市场调查先驱（1984年）。与此同时，雅达利开发了（但从未正式发布）心灵链接（Mindlink），这是一种基于更早的医学拓展产品的肌电描记法头带，被称为雅达利仿生学系统（Atari Bionics system）。玩家戴上头带后，因为眉毛要不断地移动，许多测试玩家都很头疼，所以该产品最终被取消。
之后，生理游戏的市场有所扩大。例如，日本版的《俄罗斯方块64》中运用了任天堂64心电图描记（ECG）生物传感器；被称为光石（Lightstone）的皮肤电反应（GSR）出现在游戏《The Journey to Wild Divine》中，尚算成功。《Wild Divine》这款生物反馈游戏旨在训练用户控制自己的生理活化功能——其根本目标是让用户感到放松；《俄罗斯方块64》具有两种不同的速度模式，可以根据用户的心率（HR）来调整游戏速度。
最近，生理游戏的交互作用中涌现了两股发展趋势。
1、生物反馈和健康游戏，如《EA Sports Active 2》利用脉搏测氧法和（或）心率传感器训练用户控制自己的生理状态。例如，任天堂Wii的活力传感器和育碧的Innergy传感器——两者都属于脉搏测氧法，都计划随放松游戏一同发售。
2、脑电描记（EEG）系统利用神经信号来充分控制或增加游戏交互作用。例如，EEG界面解决方案，它针对游戏交互作用，如OCZ（美商饥饿鲨科技开创股份有限公司）的神经脉冲促动器、Emotiv的运动氧耗（EPOC）耳机和Neurosky的Mindset。后者将随同名为《The Adventures of NeuroBoy》的演示游戏一同发行。在演示游戏中，玩家要戴上EEG Mindset耳机，然后用心灵致动能力（telekinetic power）来推、拉、提或炸游戏物品。
生物反馈游戏研究
第一个生物反馈游戏的研究原型之一是由NANS（美国宇航局）根据一个模拟器系统开发出来的（游戏邦注：该系统利用EEG控制模拟飞行的自动操作等级）。虽然没有明确地反映出来，但受测者在操作该系统时可以隐隐感觉到生物反馈改变，从而在生理电脑游戏中控制Steve 所谓的生物控制论回路。这意味着什么？在每一个游戏事件点上，游戏系统接收来自玩家的生理数据，来控制游戏世界中的反应。在理想的情况下，根据个体对游戏事件的身体反应，该系统会为玩家量身定制游戏反应。因此，游戏能自动适配不同的玩家。
Kiel讨论了在生物反馈游戏《Tokimeki Memorial Oshiete Your Heart》中潜在的作弊问题。在这款游戏中，玩家可以通过在玩游戏之前进行慢跑，以改变动态数据增加自己的胜算。Kiel进一步将情感游戏方式定义为使 用玩家心理状态操纵游戏变量的途径，这意味着游戏能区分用户在操作上的失败，或者对游戏误解而产生的不同情绪。总体上来说，Kiel和Steve过去对情 感游戏的研究是对该主题的良好概述，确实推动了领域的发展。
生物反馈游戏还包括：借助直接和间接生理传感器控制游戏的2D侧向滚动游戏、利用模糊和明确的生物反馈控制的第一人称射击游戏、将放松作为获胜条件的游戏、通过阿尔法脑波控制变形的《魔兽世界》和利用GSR控制某些游戏设置变量的《半条命2》。
生理心理游戏评估
游戏用户研究总是借鉴心理学方法。但近年来，利用生理心理传感器来测量情绪投入因素，看似已经引起了更广泛的兴趣。
生理心理评估在游戏用户研究中的运用
利用生理传感器来评估游戏投入程度需要大量的工作和游戏用户研究学者的专门知识。正如Steve最近在他的博客中所说的，利用敏感的生理测量法来评估游戏玩法，存在着许多误区。在这类研究中，游戏用户研究人员通常遵循实验心理学方法的4条特征化了的功能：
1、比较受控条件。
2、统计能力的重要性（对测量法的正确测试）。
3、控制参与样本。
4、补偿设计（排除顺序影响）。
没有高级的实验控制，敏感多变的生理数据将非常难解读。因此，我们可能会过分简化某些生理数据的解释和忘记一对多关系的心理效应。例如，即使电皮肤反应的测量结果太过模糊，人们还是频繁地借助它推断情绪质量。
通常来说，考虑到生理测量特定的实验法和理解，这些困难意味着设备和时间代价（如研究者和参与者的时间）往往超过了这些方法的不确定效益，也盖过传统的用户经验法（游戏邦注：如问卷调查和访谈或眼球追踪法）。研究人员需要广泛地训练解读（相关的）生理参数（使用心理学测量法）的能力。然而，某些第三方承包商实验室（如Immersyve、Bunnyfoot和Vertical Slice ）广泛宣传各种生理参数能给正确研究问题带来的效益。不幸的是，当前并不存在测量学、分析或可视化工具的黄金指标。除非解决了这个“易理解”的问题，这些测量法才有可能广泛运用到游戏用户研究中。对于不熟悉生理测量法的游戏用户研究人员，可以参阅Ben写的关于近年来的心理生理学研究法的文章，他的文章中解析了几种生理测量法及其在游戏测试中的运用。
心理生理学研究与游戏
情绪性游戏体验和投入式游戏体验的研究已经风行了十年之久，普遍的生理测量法是评估玩家情绪的面部肌电描记术（EMG）、心血管测量法（如内动间隔）、皮肤电反应（GSR）和脑电图描记法（EEG运用较少，因为它的分析过程太复杂）。 Matias Kivikangas 和他的同事最近编写了一个关于生理心理游戏研究现状的综述文。这些文章提出了按时间维度辨识生理分析，即按游戏事件（如时间点或小关卡）研究阶段性的心理生理反应和行为反应，和研究对游戏变量（时间跨度或大关卡）变化的紧张反应。
最后，游戏研究要攻克的难题之一是，解读数据的意义以服务于开发者的设计决策。在这个方向上，Regan的《利用模糊逻辑进行EMG和GSR的情绪解读》迈出了第一步。为了促进对这些大数据集合的理解，我们有必要更加努力地大步向前。也许通过发明某种视觉化工具，可以加快导航和简化生理游戏的解读——在此，我看到了今后的研究和行业合作机会。（本文为游戏邦/gamerboom.com编译，作者：Lennart Nacke）
Physiological Game Interaction and Psychophysiological Evaluation in Research and Industry
by Lennart Nacke
I have been deeply interested in physiological game interaction and psychophysiological game evaluation for at least the past five years, spanning my PhD and postdoc time, several presentations for research institutions and game companies, a growing list of publications, and other articles. In the meantime, physiological sensors have become much cheaper and today we are seeing companies like Neurosky and Emotiv with low-cost physiological sensor products reaching a large amount of customers. My colleague Mike Ambinder at Valve is now even looking into applications of biofeedback input for commercial game titles (PDF) some of this was demonstrated at GDC 2011. So, this is definitely an exciting field to work in. For the rest of this article (which reproduces parts of my workshop paper), I will recapture my CHI workshop talk and discuss some of the applications for game interaction and game evaluation from a Physiological Computing side.
Why Should You Care?
Digital games are an excellent field of application to explore the pros and cons of interacting with physiological sensors, because the consequence of failure are less severe than in critical computing systems, where life or death depends on the right algorithm. This is one of the reasons why physiological computing and psychophysiological game evaluation have become so popular in the game user research community. So, what applications of physiological sensors have we seen in the past and which ones are we likely to see in the future?
Physiological Interaction and Biofeedback Games
Since the 80s, people have tested physiological input in biofeedback games. If you look at the current research body and the current biofeedback applications that you can buy on Amazon and the like, you will find that many of those software applications are actually biofeedback games. So, what is a biofeedback game you ask (or you might not if you read this blog regularly). Those are games developed to make us more aware of our physiological state and train us to control it. For doing this, manufacturers usually use games, as the challenges, progression, theme, and rewards in a game are usually enough to get us excited about our bodily states. One could even argue (using a controversial neologism) that biofeedback is the gamification of physiological control.
Some Industry Biofeedback Gaming Applications
The biofeedback equipment manufacturer Thought Technology Ltd. was one of the first players on the market to investigate physiological input with a modified GSR2 sensor and the racing game CalmPrix in 1984 (see Kiel’s post related to this). Around the same time, Atari developed—but never officially released—the Mindlink, an electromyography headband based on an earlier medical plugin product called the Atari Bionics system. Since many test players got headaches from moving their eyebrows, the product was cancelled.
Later physiological games that were developed for a larger market were, for example, a Nintendo 64 electrocardiographic (ECG) biosensor included with the Japanese version of Tetris 64 and a galvanic skin response (GSR) called Lightstone developed for the game “The Journey to Wild Divine,” which was moderately successful. While the Wild Divine biofeedback game trained users to control their physiological activation—ultimately with the goal to help them relax—Tetris 64 would adapt the game speed based on the user’s heart rate (HR) with two different speed modes.
Recently, two development trends are visible for physiological game interaction.
Biofeedback and health games such as EA Sports Active 2 tend to use pulse oximetry and/or HR sensors training users to control their physiological state. Examples are the Nintendo Wii Vitality sensor and the Ubisoft Innergy sensor—both pulse oximeters—which are planned to be shipped with relaxation games.
Electroencephalographic (EEG) systems use neural signals to fully control or augment game interaction. Examples are affordable EEG interface solutions that are specifically targeting game interaction, such as OCZ Neural Impulse Actuator (NIA), Emotiv EPOC, or the Neurosky Mindset. The latter ships with a demo game called “The Adventures of NeuroBoy”, where the player has to use telekinetic powers to push, pull, lift, or burn objects using the EEG MindSet.
Biofeedback Game Research
One of the first research prototypes for a biofeedback game was developed at NASA based on a simulator system that used EEG to control the automation level of a simulated flight. Biofeedback changes were not explicitly shown, but implicitly felt by subjects operating the system, thus exerting control over what Steve calls the biocybernetic loop in physiological computer games. What does this mean? At each game event point, the game system receives physiological data from the player to control how the game world responds. Ideally, this response is tailor-made for an individual player based on testing how players respond bodily to game events. Therefore, the game becomes adaptive to the player.
Kiel discussed the potential cheating issues in a biofeedback game called Tokimeki Memorial Oshiete Your Heart in his first DiGRA paper on affective gaming. In the game, players could cheat by jogging before play to increase their success with a virtual animated date. Kiel went on to define affective gaming as using the player’s emotional state to manipulate gameplay variables. This means distinguishing user emotions, such as at-game (e.g., physical failures) or in-game frustration (e.g., misunderstanding) . In general, Kiel’s and Steve’s past papers on affective gaming give you a good overview of the ideas that were pushing the field.
Games developed in biofeedback research include among others: a 2D side-scrolling game with many direct and indirect physiological sensor controls augmenting game controls, a first-person shooter game using implicit and explicit biofeedback, games that use relaxation as a winning condition, modifications of World of Warcraft to control shapeshifting via alpha brainwaves and Half-Life 2 mods where GSR controlled some gameplay variables.
Psychophysiological Game Evaluation
Game user research has always borrowed from psychological methodology and more recently we have seen an increased interest especially in research in using psychophysiological sensors for measuring factors of emotional engagement.
Psychophysiological Evaluation in Game User Research
Using physiological sensors to evaluate game engagement requires much work and expertise from the game user researcher. As Steve recently outlined on this blog, there are many pitfalls for researchers when using sensitive physiological measures to evaluate gameplay. In this type of research, the game user researcher usually follows an experimental psychology approach that is characterized by 4 features:
Comparing controlled conditions
Importance of statistical power (right tests for measures)
Controlled participant sample
Counterbalanced design (to remove order effects)
Physiological data is sensitive, variable and difficult to interpret without a high level of experimental control.Therefore, we are at risk to oversimplify some interpretations of physiological data and do not keep in mind its one-to-many relationship to psychological effects. For example, galvanic skin response is used too often to infer emotional qualities although it is a highly ambiguous measure regarding emotional labels.
In general, these difficulties regarding proper experimentation with and interpretation of physiological measures mean that equipment and time costs (such as researcher and participant time) often outweigh the non-established benefits of these methods over traditional user experience methodology (such as questionnaires and interviews or sometimes eye tracking). Research personnel needs to be trained extensively in interpreting (and correlating) physiological metrics (with psychometric measures). However, some third-party contractor labs, such as Immersyve, Bunnyfoot, and Vertical Slice are vocal in advertising the benefits of various physiological metrics for the right research questions. Unfortunately, there is currently no gold standard of methodologies, analysis or visualization tools. Unless, the problem of “easy interpretability” is solved, these measures will likely not become common to game user research. A good starting point for game user researchers unfamiliar with physiological measures is for example Ben’s recent Gamasutra article based on psychophysiological research methods of recent years, explaining some physiological measures and how they can be applied for testing games. Another starting point is this panel presentation I organized a few years back for GDC and Future Play.
Psychophysiological Research with Games
The study of emotional and engaging experiences in video games has become more popular over the last decade. Common physiological measures are facial electromyography (EMG) for assessing player emotions, cardiovascular measures such as interbeat intervals, GSR, and only sparsely EEG because of its complex analysis procedure. Matias Kivikangas and colleagues have recently provided a good overview of the current state of psychophysiological game research. Common approaches have emerged from this previous work, distinguishing physiological analysis on a temporal dimension: Studying phasic psychophysiological and behavioral responses at game events (points in time, or the micro level as Steve calls it) and studying tonic responses to variations in game variables (time span or the macro level) .
One problem game research has to solve is making the interpretation of this data meaningful in terms of facilitating design decisions for developers. A first step in this direction has been taken in Regan’s emotional interpretation of EMG and GSR data using fuzzy logic. More steps in this direction are necessary to facilitate the interpretation of these large datasets, possibly creating visual aids for faster navigation and easier interpretation of physiological game engagement. This is where I see much research and industry collaboration potential in the next years.（source:gamasutra）
