5 items a game developer should consider before they use biometrics for player experience studies

I’m co-editor at PhysiologicalComputing.net where we occasionally blog about using physiological signals (e.g. heartbeat rate) in game design and user evaluation. My particular research expertise is in the development of physiological interactive game mechanics among other things.

Recently my colleague and co-editor Stephen Fairclough, a psychophysiologist at Liverpool John Moores University, sparked a discussion on the issues involved in using psychophysiology to evaluate the player experience. A summary of this discussion can be found on Lennart Nacke’s Gamasutra blog from a few week’s back.

Below is a condensed version of a follow-up article to this discussion by Stephen that recently appeared on PhysiologicalComputing.net which I’m re-posting on Gamasutra. It presents a thought exercise on how, given the issues involved in psychophysiology, one would sell biometric based player evaluations to game developers.

For the full article click here.

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Recent posts on PhysiologicalComputing.net have concerned the utility of psychophysiology (or biometrics) in the evaluation of player experience.  Based on those posts and the comments that followed, I decided to do a thought experiment.

Imagine that I work for a big software house who want to sell as many games as possible and ensure that their product (which costs on average $3-5 million to develop per platform) is as good as it possibly can be – and one of the suits from upstairs calls and asks me “how should we be using biometrics as part of our user experience evaluation?  The equipment is expensive, it’s labour-intensive to analyse and nobody seems to understand what the data means.” (This sentiment is not exaggerated, I once presented a set of fairly ambiguous psychophysiological data to a fellow researcher who nodded purposefully and said “So the physiology stuff is voodoo.”).

Here’s a list of 5 things I would push for by way of a response to this question.

# 1

Make the point that psychophysiological data provides continuous monitoring of behaviour that delivers quantitative data without the need to interrupt the player. This form of measurement is superior to subjective methods either in real-time (no need to break from experience) or on a retrospective basis (no memory bias). 

It is more sensitive than simply monitoring gaming performance (because good performance can be achieved at low or high levels of psychophysiological activity) and besides the risk of intrusiveness due to actually wearing sensors, the approach makes no other demands on the player.

# 2

Educate the company about what psychophysiological measures do. It is not a literal quantification of an emotion or a thought or a feeling.  It is not an “inside-the-head-oscope”.  It is the electrical activity of human nervous system recorded from the heart, lungs, skin, eyes, muscles and the brain.  

To begin with, how do we expect our best games to make the player feel? Excited? Engrossed? Exhilarated? Challenged?  Let’s think about those psychological states in experiential terms – now translate them into the electrical language of the human nervous system with some physiological know-how.  For example, excitement = increased sympathetic activation = higher heart rate, blood pressure, faster/shallow breathing.  Too often people work in the opposite direction and wind up with one-to-many inferences that don’t stand up to scrutiny, e.g. increased heart rate = excitement/frustration/anxiety/happiness.

#3 

Make an argument that we focus on some particular aspect of the player experience in order to focus our selection of measures accordingly.  This means using the psychophysiological measures at our disposal in a strategic way – if we are interested in emotional experience, we focus on level of activation/positive affect experienced during game play.  If we want to measure persistence, i.e. what stops a player from tossing the handset aside due to repeated experience of failure, we may look at physiological measures of motivation.  If we want to capture the level of mental workload during the training/familiarisation phase, we would look at measures of cognition.  

I would basically argue for a hypothesis-led approach to capturing player experience.  I feel that psychophysiology is particularly vulnerable to misinterpretation when tasked with capturing ‘experience’ or ‘flow states’ or similar phenomena that are poorly defined.

#4

Carefully monitor and control player experience and skill level prior to testing. Psychophysiological reactivity is very sensitive to novelty and habituation. The physiology of gamers will probably vary according to the gaming situation and their level of experience with that situation. It makes sense to test gamers according to experience/exposure for purposes of recruitment and data analysis.

#5

Carefully control the gaming environment if we are comparing prototypes.  Use the gaming engine to systematically manipulate the environment in order to obtain an unambiguous psychophysiological response.  Do not change too many variables at the same time – this tends to make data interpretation difficult if we have lots of interacting variables in the game world.