Eye Tracking in UX

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Eye Tracking in UX by Mind Map: Eye Tracking in UX

1. Types of Eye Trackers

1.1. Headgear painful, cocain drops

1.2. Gazepoint

1.2.1. $500-2800

1.2.2. need middle-higher version for UX use cases

1.2.3. License isn't transferable between computers

1.2.4. requires higher end computer to run it

1.3. The Eye Tribe

1.3.1. $200

1.3.2. cons software isn't great

1.3.3. open API

1.4. Cons to cheaper

1.4.1. more difficult to calibrate

1.4.2. can be hard to use with people with glasses shiny points get misinterpreted

1.4.3. Less forgiving of head movement

1.5. Tobii Glasses

1.5.1. $30,000

1.5.2. $3,000/month lease

1.5.3. Goggles like glasses

1.5.4. Can be used for things not just a computer screen mobile plane cockpits

1.6. Tobii X300 eye tracker

1.6.1. $45,000

1.6.2. Kinect looking device

1.6.3. high resolution, high gaze resolution 60 gazes a second from gazepoint, 300 gazes a second for this one

1.6.4. more freedom of head movement

2. How to use it in UX

2.1. When

2.1.1. Offers information on foveal, not peripheral vision

2.1.2. Captures behavior not otherwise easily observable

2.1.3. Used during usability testing

2.2. Do A/B with usability tests

2.2.1. ask people to do think aloud

2.2.2. ask those to not do think aloud

2.2.3. see how it effects them

2.3. Schedule extra usability participants

2.3.1. allow to skip the eye trackers for those who feel uncomfortable

2.4. Doesn't Tell you

2.4.1. peripheral vision

2.5. Can tell you

2.5.1. where user focused potentially tells you weather there is something hard to comprehend and understand

2.5.2. The flow in which they were identifying information did it follow the intended flow or are other things distracting that flow

2.5.3. Difference between the types of people (novice, expert) parse the screens

3. Outputs

3.1. Heat Map

3.1.1. With Talk Aloud vs with Retrospective Quite a bit different gaze patterns

3.2. Gazeplot

3.2.1. Plot/dot map

3.2.2. Audi Case Study Conscious Driving Subconscious Driving

4. Results and Design Decisions

4.1. Examples

4.1.1. 1 Research finding Even when users deliberately trigger the popup window, they are checking the header in order to confirm that their action was executed correctly Result - use clear headers in pop-up windows

4.1.2. 2 Comparing / Contrasting Information trying to compare imagery not right next to each other caused a lot more back and forth

4.1.3. Netflix Big screen UI

4.1.4. Cleveland Indians Stadium Where fans are looking during the games 50 participants Used to help price advertising by who looks where

5. We have Data, Now What

5.1. More Thorough Studies

5.1.1. Not a replacement

5.1.2. Creates more targeted solutions

5.2. Selling Usability Services

5.2.1. Wow your audience

5.2.2. Add more buy in from your users

6. Should You Do It?

6.1. 3 questions

6.1.1. 1. Actionable Insight?

6.1.2. 2. Simplest Method?

6.1.3. 3. Buy-In Boost?

7. Other BioMetrics

7.1. Mionix Labs Quantified Gaming

7.1.1. How sweaty your hands get

7.2. Lego Mindstorms self made

7.3. EEG

7.3.1. Electroencephalography

7.3.2. Measures emotional

8. Presenter

8.1. Jeanne Petty & Hilary Davis

9. What is Eye Tracking

9.1. Shows where users are looking

9.2. What order

9.3. Length of fixation

9.4. Number of fixations

9.5. What users Don't look at

10. Eye movements

10.1. Saccades

10.1.1. way your eye moves around to interpret what it's seeing

10.1.2. Smooth pursuit

10.1.3. Jerky comprehension

10.2. Fovea

10.2.1. the area in which we can really comprehend high resolution imagery

10.2.2. (two thumbs held out in front of you is about the size)

10.3. Fixations

11. How Eye Tracking is Used

11.1. Psychological research

11.2. Market Research

11.3. Product Evaluation & Design

11.4. User Experience

11.5. Accessibility Application Design

11.6. Game Design

12. How Does it Work

12.1. 1. Eye tracker consists of cameras and projectors and algorithms

12.2. 2. The projectors create a pattern of near-infrared light on the eyes

12.3. 3. Cameeras take high-frame rate images of the user's eyes and the patterns

12.4. 4. The image processing algorithms find specific details in the user's eyes and reflections patterns

12.5. 5. Based on these details, mathematical algorithms