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Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
Beyond Eye Tracking: Bringing Biometrics to Usability Research
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Beyond Eye Tracking: Bringing Biometrics to Usability Research

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User experience research has traditionally relied upon qualitative techniques that entail users telling us their feelings, wants, and needs. This creates an inherent cognitive bias – data is filtered …

User experience research has traditionally relied upon qualitative techniques that entail users telling us their feelings, wants, and needs. This creates an inherent cognitive bias – data is filtered through the participant’s cognition. That is, we may not necessarily be hearing the participants’ true feelings. They may be trying to please the moderator or may just be unable to articulate the cause of their emotions. But researchers and stakeholders alike are thirsty for quantitative data that complements the qualitative. Luckily, we live in exciting times – there are two particular technologies that are becoming more accessible that will help usability researchers break through cognitive bias and provide that ever tantalizing quantitative data: eye tracking and biometrics. Eye tracking equipment has only recently started to become affordable to most anyone who wants to use it. Researchers must now get up-to-speed on eye tracking methodology and analysis. When is it appropriate? How can we turn the data into actionable findings? What the heck do I do with all of this new data?! More importantly, we should find new research techniques that will break through cognitive bias.

This is where the second technology comes in: biometrics. Psychophysiology is the study of how emotions affect changes in the body. Changes in heart rate, breathing rate, heart rate variability, and galvanic skin response (GSR) have all been shown to be accurate indicators of a person’s emotions, among others. Just as with eye tracking, the equipment to measure these biometrics are just now starting to become accessible to usability researchers. Until very recently, the equipment to gather this data was rather obtrusive and invasive. This not only affected participant comfort, but also did not lend to conducting “discount” usability research. But new technology allows the collection of biometrics in non-invasive ways. For instance, Affectiva’s Q Sensor is worn on the wrist and wirelessly gathers a participant’s GSR. The problem with integrating psychophysiological data into usability research is that individual researchers will need to come up with not only the algorithms to interpret the biometrics but also the technology to temporally marry the biometrics to the eye tracking data. These are no small tasks. There are companies out there that will collect and interpret the data for you for a hefty fee. But this technique should be in every usability researcher’s toolkit. As such, we should come together as a research community to figure this out. We need an open dialogue. We need to share techniques and stories.

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  • Fixation = encode focal information, see periphery, plan next moveG&T = # of fixations, fixation duration, and fixation/saccade ratioEye-tracking allows us to see the unconscious decision-making process
  • FAA studies = In-dash cameras
  • Researchers don’t examine heat maps, we examine numbersHeat maps are eye candy that only frame the story that the data tellsGaze plots may be individually examined
  • RTA does not remove cognitive biasTA removes the ability to do time on taskScreened out: retinal & corneal damage, eye cancer & tumors, macular degeneration, cataracts, conjunctivitis, and nystagmusOkay to accept: amblyopia, glaucoma, and strabismus
  • Use in lie detectionSympathetic system controls the sweat glands – the more you sweat, the more conductivity
  • Use in lie detectionSympathetic system controls the sweat glands – the more you sweat, the more conductivity
  • Use in lie detection
  • Use in lie detection
  • Yes, businesses have the right to make money from intellectual propertyBut this inhibits bringing the technology to other fields that could benefit
  • Transcript

    • 1. Prepared by:Daniel Berlin – Experience Research DirectorMay 29, 2013UXPA Boston 2013 ConferencePsychophysiologyand Eye TrackingNEW AND OLD TECHNOLOGIES THATCAN COMPLEMENT USABILITYRESEARCH
    • 2. Today’s Presentation2•History of Eye Tracking and Psychophysiology•Traditional and modern Eye Tracking metrics and methodologies• Eye Tracking as data for HCI optimization, not as an input device•Available eye tracking equipment•The need to evolve neuromarketing•Psychophysiology in user experience
    • 3. Hi! I’m Dan Berlin3• BA in psychology from Brandeis University• Studies focused on visual space perception• Seven years in technical support• Sat as a participant for a usability study for a product I was working on• Realized that user experience (UX) work is the perfect combination of computers and psychology• Went to Bentley U. to earn an MBA and MS in Human Factors in Information Design• Two years at an interactive agency performing usability and neuromarketingresearch• Then did some freelance UX consulting for about a year• Two years as an Experience Research Director in Mad*Pow’s Boston office
    • 4. What Will NOT Be Covered In This Presentation4• The validity of current eye tracking metrics and methodologies• What Eye Tracking and Psychophysiology has already taught us abouthuman behavior• Psychophysiological traces other than skin conductance:• Heart rate variability• Heart rate• Breathing rate• Skin temperature• Neurological signals
    • 5. Why This is an Important Topic5• UX researchers should be collecting objective data• Lack of “discount” quantitative measures to complement our typically qualitativemethods• Eye tracking metrics provide objective data based on participantbehavior• But current methods are only the beginning• Pairing eye tracking with psychophysiology is the next logical step• New technology is bridging the gap to discount usability testing
    • 6. Yes, I’m Saying “Discount” Research6• GASP!• The “golden triad” of user experience dictatesthat we must collect useful, actionable dataON THE CHEAPTechnologyUserNeedsBusinessGoals• We typically don’t achieve statisticallysignificant results – it’s not worth the cost• We’re not going to bring in 12 participants forevery Agile sprint
    • 7. Eye Tracking7
    • 8. What is Eye Tracking?8• Observing and recording eye movements as a study participant traversesa website or application• Allows us to gain deeper insight into how users perform usability tasks• Allows UX researchers to collect objective behavioral data• Terminology• Fixation – when a user stops to look at something for more than 10ms• Saccade – the path between fixations (searching)• Scanpath – a set of fixations and saccades that indicate a trajectory• “Modern” eye tracking began with Goldberg & Kotval (1999)• Developed eye tracking metrics for on-screen tasks• Eye tracking is NOT: pupil dilation, blink-rate, or facial recognitionTobii 1750
    • 9. History of Eye Tracking9•Has roots in reading research andis over 100(!) years old:• Electrodes placed around the eye• Various types of contact lenses• Cameras mounted in plane cockpits• Big, heavy helmets•Became more “mainstream” inthe 1950s with FAA studies doneon pilots for cockpit design•Modern Eye Tracking equipmentis much less invasive• They typically bounce infrared light offthe retina to determine eye positionYesterdayToday
    • 10. Typical Eye Tracking Data Visualizations (the eye candy)10Heat Map Gaze Plot• # of fixations for all participants • Order of fixations for one participant
    • 11. Basic Eye Tracking Methodology11• Break the page up into separate “areasof interest” or AOIs• Compare the fixation data betweenimportant areas and less importantones• Or compare data between designs• You will always need things to compare• Eye tracking data does not tell much of astory without a comparison• There are no absolute standards for eyetracking metrics – human behaviordiffers!Areas of InterestSource:
    • 12. Basic Eye Tracking Interpretation12• Number of fixations• Is there a searching pattern?• Are fixations close together?• Are users reading the content?• Fixation duration• Are users spending a long timelooking at a single link?• Are they particularly engaged withone of the design/contentelements?• Time to 1st Fixation• How long did it take for users tolook at a call to action?12345 678Order of Gazes158521015540246810121416Area 1 Area 2 Area 3 Area 4Area of Interest#offixaons# of fixa onsDesign 1Design 2
    • 13. Eye Tracking Metrics (Fixations)13• Poole & Ball (2010) provide a great summary of Eye Tracking metrics,re-summarized here:Description What it MeasuresOverall # of fixations Increased overall fixations indicate less efficient searchFixations per AOIIncreased fixations indicate increase noticeability orimportanceFixations per AOI, adjusted fortext lengthFor text-based AOIs, divide by the number of wordsOverall fixation durationIncreased fixation duration indicates confusion orengagementGaze, dwell, orfixation cluster/cycle(Sum of fixation durations within an AOI)Compare attention between AOIs and used to measureanticipationFixation spatial density Small fixation area indicates efficient searchingRepeat fixations or post-targetfixationsIncreased off-target fixations after initial target fixationindicates low meaningfulness or visibilityTime to first fixation on-targetFaster time to first fixation on-target indicates increasednoticeabilityPercentage of participants fixatingan area of interestHigher percentages indicate increased noticeabilityOn-target (all target fixations)(On-target fixations / Total # of fixations)Lower ratio indicates lower search efficiency
    • 14. Eye Tracking Metrics (Saccades and Scanpaths)14• Poole & Ball (2010) provide a great summary of Eye Tracking metrics,re-summarized here:Description What it MeasuresOverall # of saccades Increased saccades indicate more searchingSaccade amplitudeLarger saccades indicate meaningful cues – attention isdrawn from a distanceRegressive saccades Indicate less meaningful cuesMarked directional shiftsSaccades greater than 90 degrees may indicate a change inuser goals or a breaking of user expectationsScanpath duration Increased time indicates more searchingScanpath length Increased length indicates more searchingSpatial density Smaller density indicates directed searchingFixation/saccade ratio Higher ratio indicates less searching (more processing)
    • 15. Eye Tracking Research15• Bojko (2006) shows how a combination of eye tracking and click datacan highlight differences in search behavior• Increased time on task for the “old” website was caused by an increasednumber of fixations before an on-target click• Scanpaths showed that targets were more noticeable in the “new” design(clicked upon 1st fixation)• Some have looked into correlating eye-movement patterns with usabilityproblems (Ehmke & Wilson, 2007)• Multiple, quick fixations may indicate missing information• Promising patterns, but nothing concrete – more research is needed• Journey mapping with head-mounted eye tracker (Alves, et al, 2012)• “Real-world” tasks and scenarios
    • 16. Using Eye Tracking in a Usability Study16• Use a within-subjects study design (all participants see all stimuli)• People have different viewing behavior and the data needs to be comparable• Expose participants to the stimuli in the course of performing a task• Keeps the data relevant and contextual• Think-aloud protocol may be distracting for the participant• Some research has been done into “Retrospective Think-Aloud” (RTA)• Studies that make use of Eye Tracking have special recruiting needs• Over-recruit – you won’t be able to use the data from every participant• Screen-out respondents with cornea or retina damage/disease
    • 17. Eye Tracking Equipment17Tobii T60/120Tobii GlassesSMI REDSMI Glasses• Tobii and SMI are the majorplayers• Both offer:• Remote (monitor based)• Head-mounted (glasses)• Flexible (use your own monitor/laptop)• There are other, cheaper options• But you get what you pay for
    • 18. Going Beyond Eye Tracking Metrics18• Eye tracking metrics are just the tip of the iceberg• We need to take a step back and remember what eye tracking does best:It tells us where participants are looking at any given time• So what other temporal, objective data can we use in conjunction witheye tracking?
    • 19. Psychophysiology19
    • 20. What is Psychophysiology?20• In the late 1800s, it was discovered that Electro Dermal Activity (EDA)will change based on a person’s feelings (Vigouroux, 1888)• That is, the skin’s electrical conductance (or resistance) changes with positive ornegative arousal• This allows us to observe a person’s psychological reaction without asking anyquestions• Galvanic skin response (GSR) is the typical metric used to measure EDA• GSR measures the electrical conductivity of the skin• Sweat glands are controlled by the sympathetic system and you sweat whenaroused• More sweat = more skin conductivity• Psychophysiology is the process of analyzing physiological metrics todetermine a person’s psychological state
    • 21. What is Psychophysiology?21• Other physiological traces can tell us what is happening in the mind, butare beyond the scope of today’s presentation (Dirican & Göktürk, 2011):Trace UseEvent Related Brain Potentials(ERP)Mental workloadElectroencephalography (EEG) Task engagement and cognitive processesHeart Rate (HR) & Heart RateVariability (HRV)Arousal, mental workload, and valenceBlood Pressure (BP) StressElectromyogram (EMG) Motor preparation and emotional valenceRespiration Task demands and arousal
    • 22. Wait, isn’t that Neuromarketing?22• Neuromarketing is a newer field whereby companies (typically) useEEG/EMG data in marketing studiesfMRI EEG/EMGBloodoxygenation Brain waves
    • 23. But neuromarketing is NOT helping the UX community23• “Discount” usability testing dictates that we should beable to run 12-16 participants in 3-4 days• fMRI is expensive• EEG is time consuming and commodity equipment isunreliable• Emotiv headset has potential, but is not ready for our worldquite yet• Neuromarketing companies rely on their “special sauce”algorithm, which is not shared with the researchcommunity
    • 24. Bringing Psychophysiology to UX24• So let’s do it ourselves!• Biophysical signals can indicate usability problems• Ward & Marsden (2002) built a “good” and “bad” interface and comparedsubjects’ biometrics• They found that the “bad” interface caused higher skin conductivity, lower blood volume,and increased pulse rate• Lin and Hu (2005) had subjects play a game and do increasingly frustrating tasks– with similar results• Understanding participants’ biometrics gives us insight into trends• Stickel (2009) found that participants who did not do well on tasks maintainedhigh stress levels and continued to perform poorly on subsequent tasks
    • 25. Bringing Psychophysiology to UX25• There are, of course, some caveats• We want to mimic real-world experiences during a usability study• A person sitting at a computer with wires protruding from various body partsisn’t exactly real-world• Participant comfort is paramount• Think-aloud vs. Retrospective Think-aloud• Employing psychophysical methods during a usability study has the same problem as with eyetracking: a talking participant is a distracted participant• We want to minimize cost (time and money)
    • 26. Bringing Psychophysiology to UX26• Focus the conversation on GSR• Less invasive to measure• Less subject to noise• Fast response time to view event related changes• Can run multiple sessions per day with minimal incremental cost• Process is still tricky, but is promising• GSR is one of the most promising biometric measures of arousal(Henriques, et al, 2011)• Though, there is the problem of valence: did the participant experience positive or negativearousal?• This can probably be alleviated by simply looking at what the user was doing – determine thecontext of the GSR spike• Heart Rate Variability has also been shown to measure emotional valence
    • 27. Available GSR Capture Equipment27• Affectiva Q Sensor• Had great promise, but is going end of life in 2014• Thought Technology Procomp Infiniti• A workhorse for physiological data capture in academia• Neulog• Seems like a promising alternative to Procomp• For now, I think we’re stuck with a wired sensorAffectiva Q SensorNeuLog GSR LoggerTT Procomp Infiniti
    • 28. What Can We Expect From this Effort?28• We can expect to break through the participants’ cognitive bias that isinherent in traditional usability studies• Ever have a participant struggle through a task and rate it as easy?• We can expect to get objective, quantitative data to which stakeholderscan more easily relate• Explaining that people sweat when aroused is easier than explaining scanpaths• We can expect to have a better understanding of what our participantsare feeling• If a design is causing participants undue stress, it would be best if we knewabout it
    • 29. In Conclusion29• Embrace “traditional” eye tracking• Marry GSR and eye tracking data• This is a VERY manual process right now – more tools are needed• Scorn “secret sauce” – share your techniques and findings (both goodand bad) with the UX community• This may be the quantitative measure for which we’ve been waiting!• Join the conversation! Search for the “Psychophysiology in Usability”group on LinkedIn
    • 30. References30• Alves, R., Lim, V., Niforatos, E., Chen, M., Karapanos, E., & Nunes, NJ. (2012) AugmentingCustomer Journey Maps with quantitative empirical data: a case on EEG and eye tracking.Retrieved from: http://arxiv.org/abs/1209.3155• Bojko, A. (2006) Using Eye Tracking to Compare Web Page Designs: A Case Study. Journal ofUsability Studies, 3(1). Retrieved from:http://www.upassoc.org/upa_publications/jus/2006_may/bojko_eye_tracking.html• Dirican, AC., & Göktürk, M. (2011) Psychophysiological Measures of Human Cognitive StatesApplied in Human Computer Interaction. Procedia Computer Science, 3, 1361-1367.• Ehmke, C. & Wilson, S. (2007) Identifying Web Usability Problems from Eye-Tracking Data.Proceedings of HCI 2007. Retrieved from:http://www.bcs.org/upload/pdf/ewic_hc07_lppaper12.pdf• Goldberg, J. & Kotval, X. (1999) Computer interface evaluation using eye movements: methodsand constructs. International Journal of Industrial Ergonomics, 24, 631-645.• Henriques, R., Paiva, A., & Antunes, C. (2012) On the need of new methods to mineelectrodermal activity in emotion-centered studies. Retrieved from:http://web.ist.utl.pt/claudia.antunes/artigos/henriques2012admi.aamas.pdf• Lin, T. & Hu, W. (2005) Do Physiological Data Relate to Traditional Usability Indexes? Proceedingsof OZCHI 2005, Canberra, Australia.
    • 31. References31• Poole, A. & Ball, L. (2005) Eye tracking in human-computer interaction and usability research. InC. Ghaoui (ed.), Encyclopedia of human computer interaction. Idea Group, Pennsylvania, 211-219. Retrieved from: http://www.alexpoole.info/blog/wp-content/uploads/2010/02/PooleBall-EyeTracking.pdf• Russell, Mark. (2005) Using Eye-Tracking Data to Understand First Impressions of a Website. InB. Chaparro (ed.), Usability News. February 2005, 7(1). Wichita State University. Retrieved from:http://psychology.wichita.edu/surl/usabilitynews/71/eye_tracking.asp• Stickel, C., Ebner, M., Steinbach-Nordmann, S., Searle, G., & Holzinger, A. (2009) EmotionDetection: Application of the Valence Arousal Space for Rapid Biological Usability Testing toenhance Universal Access. HCII Conference San Diego, Springer Lecture Notes in ComputerScience. Retrieved from:http://elearningblog.tugraz.at/scms/data/alt/publication/09_hci_emotion.pdf• Vigouroux, R. (1888) The electrical resistance considered as a clinical sign. Progres Medicale, 3,87-89.• Ward, R., Marsden, P., Cahill, B., & Johnson, C. (2002) Physiological Responses to Well-Designedand Poorly-Designed Interfaces. Proceedings of CHI 2002 Workshop on Physiological Computing.Minneapolis, MN. Retrieved from:http://physiologicalcomputing.net/chi2002/chi_papers/ward_physiological_responses_to_well_designed_and_poorly_designed_interfaces.pdf
    • 32. Thank you! Any questions?32Dan BerlinExperience Research Director, Mad*Powdberlin@madpow.net@banderlin

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