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Attention Approximation:From the web to multi-screen television
Caroline Jay
Web Ergonomics Lab, University of Manchester
Research funded by EPSRC Knowledge Transfer and Impact Acceleration Accounts
‘Attention Approximation’
• What is it?
• Why is it useful?
• Where did it come from?
• How are we using it now?
Attention Approximation 2
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
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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.
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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
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?
6SASWAT project, funded by EPSRC (EP/E062954/1)
Attention Approximation
Controlled study
• Real Web pages
• View for 30 seconds
• Conditions:
– Ticker active
– Ticker stationary
• Are people more likely to look at the moving ticker?
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 completed.
– No constraints on where task was completed.
• Nine minutes of browsing.
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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
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Results
• CHAID model predicts viewing behaviour with an accuracy of ~80%
• Best predictor: action
Keystroke/Enter/Hover
41%
None
20%
Click
77%
Action
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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)
Keystroke/Enter/Hover
41%
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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
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.
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A conversation with BBC R&D
• Can we predict behaviour with other types of media?
• Can we use this to drive future media development?
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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
Eye tracking TV viewing
C. Jay, A. Brown, M. Glancy, M. Armstrong, S. Harper (2013). Attention approximation: from the Web to multi-screen television. [email protected]://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
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Attention on a single screen
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Television Second screen
Attention across two screens
• Observation of existing second screen app use
• Unconstrained interaction• Eye tracking
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Technical issues
• Can we track eye movement over two screens?
• Is the set up ecologically valid?
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Data validity
• Good calibration.
• Good match between eye tracking data and video analysis.
• Good match between data collected with and without eye tracking.
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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%
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Television
Split of attention across two screen
Tablet29Attention Approximation
Updates and action
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TV:‘There, there, there..!’
Tablet:‘Where to see a dolphin’
Attention Approximation
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Attention approximation in action
Attention Approximation 33
Approximating attention in the wild
• Improve the ecological validity of predictive models.
• Detect focus to drive interaction on the fly.
Attention Approximation 34
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
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
Find out more
Publications, reports and data:
http://goo.gl/1h4z4K
The Web Ergonomics Lab
The University of Manchester, UK
http://wel.cs.manchester.ac.uk/
37Attention Approximation
Challenge
• Model must predict future observations.
– Internal validity: reliably predicts observations in the same setting.
– External validity: reliably predicts observations in other settings.
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What is the appropriate paradigm for building this type of model?
Attention Approximation
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
