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Attention Approximation:
From the web to multi-screen television
Caroline Jay
caroline.jay@manchester.ac.uk
Web Ergonomics...
‘Attention Approximation’
• What is it?
• Why is it useful?
• Where did it come from?
• How are we using it now?
Attention...
Attention Approximation
• Determining the ‘focus’ of attention, where ‘focus’
may vary along a number of dimensions:
– Gra...
Driving technology development with
empirical models
• Conceptual representations of interaction
built entirely on data ca...
Ecologically valid interaction models
• Task may not be predetermined.
• We want to understand what the user is
doing, and...
Translating Web content to audio
• Screen readers handled dynamic updates
badly.
• If we understood how sighted users view...
Controlled study
• Real Web pages
• View for 30 seconds
• Conditions:
– Ticker active
– Ticker stationary
• Are people mor...
Results
Stationary ticker Moving ticker
Results
Stationary ticker Moving ticker
Exploratory study
• Participants completed tasks on sites that
contained dynamic content.
– No constraints on how task was...
Data-driven analysis
• Can we predict whether people view dynamic
updates as a function of their characteristics?
• Chi-sq...
Results
• CHAID model predicts viewing behaviour with an
accuracy of ~80%
• Best predictor: action
Keystroke/Enter/Hover
4...
1.1-7.8
71%
7.8-32.9
90%
>32.9
99%
<1.1
39%
Click
77%
Area (cm2)
Click-activated updates
13Attention Approximation
All other updates
2.8-6.2
20%
>6.2
30%
<2.8
6%
>2.8
81%
1.2-2.8
59%
0.6-1.2
41%
<0.6
16%
None
20%
Duration (s) Duration (s...
Why does the model take this form?
• Area (and action) are properties of the update.
– As an update increases in size it b...
Translating dynamic updates to audio
• FireFox plugin
– Prioritize click-activated updates.
– Deliver keystroke-activated ...
A conversation with BBC R&D
• Can we predict behaviour with other types of
media?
• Can we use this to drive future media
...
18Attention Approximation
19Attention Approximation
20Attention Approximation
21Attention Approximation
Media interaction models
• Desktop, Web and social media
– Lean forward
• Newspaper, film and television
– Lean back
• Two...
Eye tracking TV viewing
C. Jay, A. Brown, M. Glancy, M. Armstrong, S.
Harper (2013). Attention approximation: from
the Web...
Attention on a single screen
24Attention Approximation
Television Second screen
Attention across two screens
• Observation of existing second
screen app use
• Unconstrained inte...
Technical issues
• Can we track eye movement over two screens?
• Is the set up ecologically valid?
26Attention Approximati...
Data validity
• Good calibration.
• Good match between eye tracking data and
video analysis.
• Good match between data col...
Results
• 5:1 split of visual attention to the TV
• Dwell times longer for the TV
Length of viewing period
> 30 seconds < ...
Television
Split of attention across two screen
Tablet
29Attention Approximation
Updates and action
30
TV:
‘There, there, there..!’
Tablet:
‘Where to see a dolphin’
Attention Approximation
31Attention Approximation
32Attention Approximation
Attention approximation in action
Attention Approximation 33
Approximating attention in the wild
• Improve the ecological validity of predictive
models.
• Detect focus to drive intera...
Touch as a proxy for visual attention
35
Web proxy logging tool:
A. Apaolaza, S. Harper & C. Jay (2013). Understanding use...
Using attention approximation in
technology development
• It’s complicated – particularly in the wild
– Influence
– Infere...
Find out more
Publications, reports and data:
http://goo.gl/1h4z4K
caroline.jay@manchester.ac.uk
The Web Ergonomics Lab
Th...
Challenge
• Model must predict future observations.
– Internal validity: reliably predicts observations in
the same settin...
Challenges
• Eye tracking is accurate, but only suitable for the
lab
– Currently investigating logging data and interactio...
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Attention Approximation: From the web to multi-screen television

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The move towards the provision of television content over two or more screens represents an enormous opportunity and a considerable challenge. A scientific understanding of what causes people to switch attention between the main screen and a 'second screen' mobile device during television viewing is key to the development of this technology. This seminar describes how ‘attention approximation’, a technique we have used to model visual attention and design screen reader presentation of Web content, can be used to investigate viewing behaviour, and ultimately drive the provision of content across multiple screens.

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Attention Approximation: From the web to multi-screen television

  1. 1. Attention Approximation: From the web to multi-screen television Caroline Jay caroline.jay@manchester.ac.uk Web Ergonomics Lab, University of Manchester Research funded by EPSRC Knowledge Transfer and Impact Acceleration Accounts
  2. 2. ‘Attention Approximation’ • What is it? • Why is it useful? • Where did it come from? • How are we using it now? Attention Approximation 2
  3. 3. Attention Approximation • Determining the ‘focus’ of attention, where ‘focus’ may vary along a number of dimensions: – Granularity • Which device? • Which part of the screen? – Population • Individual • Particular group • Everyone – Time period • Seconds • Time of day 3Attention Approximation
  4. 4. Driving technology development with empirical models • Conceptual representations of interaction built entirely on data can help us – Predict technology usage – Inform interaction design • In applied research, ecological validity is important. 4Attention Approximation
  5. 5. Ecologically valid interaction models • Task may not be predetermined. • We want to understand what the user is doing, and why. – We need to know the current focus of attention. • When there are multiple parallel information streams, determining which is in focus is hard. Attention Approximation 5
  6. 6. Translating Web content to audio • Screen readers handled dynamic updates badly. • If we understood how sighted users view updates, could we translate them to audio more effectively? 6 SASWAT project, funded by EPSRC (EP/E062954/1)Attention Approximation
  7. 7. Controlled study • Real Web pages • View for 30 seconds • Conditions: – Ticker active – Ticker stationary • Are people more likely to look at the moving ticker?
  8. 8. Results Stationary ticker Moving ticker
  9. 9. Results Stationary ticker Moving ticker
  10. 10. Exploratory study • Participants completed tasks on sites that contained dynamic content. – No constraints on how task was completed. – No constraints on where task was completed. • Nine minutes of browsing. 10Attention Approximation
  11. 11. Data-driven analysis • Can we predict whether people view dynamic updates as a function of their characteristics? • Chi-squared Interaction Detector (CHAID) analysis – Action: click, hover, keystroke, enter, none – Area: cm2 – Duration: seconds – (participant) – (addition or replacement) • Validation data from later study 11Attention Approximation
  12. 12. Results • CHAID model predicts viewing behaviour with an accuracy of ~80% • Best predictor: action Keystroke/Enter/Hover 41% None 20% Click 77% Action 12Attention Approximation
  13. 13. 1.1-7.8 71% 7.8-32.9 90% >32.9 99% <1.1 39% Click 77% Area (cm2) Click-activated updates 13Attention Approximation
  14. 14. All other updates 2.8-6.2 20% >6.2 30% <2.8 6% >2.8 81% 1.2-2.8 59% 0.6-1.2 41% <0.6 16% None 20% Duration (s) Duration (s) Keystroke/Enter/Hover 41% 14Attention Approximation
  15. 15. Why does the model take this form? • Area (and action) are properties of the update. – As an update increases in size it becomes more salient. • Duration is sometimes a property of the update, and sometimes a property of user behaviour. – The longer a suggestion list appears on the screen, the more likely it is to be viewed. – People pause to view the content. 15Attention Approximation
  16. 16. Translating dynamic updates to audio • FireFox plugin – Prioritize click-activated updates. – Deliver keystroke-activated updates whenever there is a pause in typing. – Opt-in to receiving automatic updates. • Preferred by all participants in blind and double-blind evaluation when compared with FireVox baseline. 16Attention Approximation
  17. 17. A conversation with BBC R&D • Can we predict behaviour with other types of media? • Can we use this to drive future media development? 17Attention Approximation
  18. 18. 18Attention Approximation
  19. 19. 19Attention Approximation
  20. 20. 20Attention Approximation
  21. 21. 21Attention Approximation
  22. 22. Media interaction models • Desktop, Web and social media – Lean forward • Newspaper, film and television – Lean back • Two or more screens – Lean back and lean forward – Lean back and lean back – Lean forward and lean forward 22Attention Approximation
  23. 23. Eye tracking TV viewing C. Jay, A. Brown, M. Glancy, M. Armstrong, S. Harper (2013). Attention approximation: from the Web to multi-screen television. TVUX- 2013@CHI. http://goo.gl/dvAp3V Brown, M. Evans, C. Jay, M. Glancy, R. Jones, S. Harper (2014). HCI over multiple screens. CHI EA: alt.chi 2014. http://goo.gl/UJhPC5 23Attention Approximation
  24. 24. Attention on a single screen 24Attention Approximation
  25. 25. Television Second screen Attention across two screens • Observation of existing second screen app use • Unconstrained interaction • Eye tracking 25Attention Approximation
  26. 26. Technical issues • Can we track eye movement over two screens? • Is the set up ecologically valid? 26Attention Approximation
  27. 27. Data validity • Good calibration. • Good match between eye tracking data and video analysis. • Good match between data collected with and without eye tracking. 27Attention Approximation
  28. 28. Results • 5:1 split of visual attention to the TV • Dwell times longer for the TV Length of viewing period > 30 seconds < 2.5 seconds TV 27% 30% Tablet < 1% 51% 28Attention Approximation
  29. 29. Television Split of attention across two screen Tablet 29Attention Approximation
  30. 30. Updates and action 30 TV: ‘There, there, there..!’ Tablet: ‘Where to see a dolphin’ Attention Approximation
  31. 31. 31Attention Approximation
  32. 32. 32Attention Approximation
  33. 33. Attention approximation in action Attention Approximation 33
  34. 34. Approximating attention in the wild • Improve the ecological validity of predictive models. • Detect focus to drive interaction on the fly. Attention Approximation 34
  35. 35. Touch as a proxy for visual attention 35 Web proxy logging tool: A. Apaolaza, S. Harper & C. Jay (2013). Understanding users in the wild. W4A 2013. Attention Approximation
  36. 36. Using attention approximation in technology development • It’s complicated – particularly in the wild – Influence – Inference • Model according to application – Production design – Content delivery • Ultimate contribution – To advance craft-based engineering with science 36Attention Approximation
  37. 37. Find out more Publications, reports and data: http://goo.gl/1h4z4K caroline.jay@manchester.ac.uk The Web Ergonomics Lab The University of Manchester, UK http://wel.cs.manchester.ac.uk/ 37Attention Approximation
  38. 38. Challenge • Model must predict future observations. – Internal validity: reliably predicts observations in the same setting. – External validity: reliably predicts observations in other settings. 38 What is the appropriate paradigm for building this type of model? Attention Approximation
  39. 39. Challenges • Eye tracking is accurate, but only suitable for the lab – Currently investigating logging data and interaction on the device • Many factors to consider: – Interaction – Content – Environment • If we can effectively monitor these in the wild… – Privacy 39Attention Approximation

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