attention in webscience

Claudia Roda - �WebScience Montpellier Meetup 13.5.2011 Claudia Roda - �WebScience Montpellier Meetup

Human Attention in Digital Environments

Attention in Webscience

Claudia Roda American University

of Paris

Attention: is the Web!

• Social solicitations • Interruptions • Information access • Multitasking • Contextualisation

DECENTRALISED INFORMATION SYSTEMS

? Cognitive resources

Allocation • Social solicitations • Interruptions • Information access • Multitasking • Contextualisation

Attention

• Social solicitations • Interruptions • Information access • Multitasking • Contextualisation

Claudia Roda - �WebScience Montpellier Meetup

Attention regulates our interaction with

the Web … our interaction in general Attention is essential for communication

13.5.2011 Claudia Roda - �WebScience Montpellier Meetup

Given the amount of cognitive and perceptual solicitations provided by the Web, understanding human attentional processes is essential in order to both understand Web interaction and engineer the Web of the future

What attention do we study? “For most people, attention is often the main currency - we publish to get the attention of others, we cite the efforts of others so that they get attention […]” B. Huberman 2008 “�[inattentional blindness] denotes the failure to see highly visible objects we may be looking at directly when our attention is elsewhere.” Arien Mack 2003

What attention do we study? “For most people, attention is often the main currency - we publish to get the attention of others, we cite the efforts of others so that they get attention […]” B. Huberman 2008 “�[inattentional blindness], denotes the failure to see highly visible objects we may be looking at directly when our attention is elsewhere.” Mack 2003

Long term

Short term

Long term

Short term

Social

Individual

Long term

Short term

Social

Individual

Effect

Why do we study attention?

• Understand human attentional processes – Cognitive psychology, neuroscience; short term,

individual, cause and effect; automatic vs control, what is selected and when

• Define the economic value of attention • Predict behavior based on attentional traces • Support management of attention (how to

ensure effective allocation?)

• Understand human attentional processes • Define the economic value of attention

– Economics, marketing; long term, social, effect; how is attention captured and traded

• Predict behavior based on attentional traces • Support management of attention (how to

ensure effective allocation?)

• Understand human attentional processes • Define the economic value of attention • Predict behavior based on attentional traces

– Politics, marketing, communication; long term, social, effect; forecast

• Support management of attention (how to ensure effective allocation?)

• Understand human attentional processes • Define the economic value of attention • Predict behavior based on attentional traces • Support management of attention (how to

ensure effective allocation?) – HCI, management; short to long term, individual

and social, cause and effect; provide tools and recommendations

• Understand human attentional processes • Define the economic value of attention • Predict behavior based on attentional traces • Support management of attention (how to

ensure effective allocation?) – HCI, management; short to long term, individual

and social, cause and effect; provide tools and recommendations

Attentional Breakdowns: Causes

•Information Overload •Loss of context •Activity Fragmentation •… ‘In a typical day, […] people spend an average of three minutes working on any single event before switching to another event [and] somewhat more than two minutes on any use of electronic tool, application, or paper document before they switch to use another tool’ (Gonzalez and Mark 2004, fieldwork observation of information workers: analysts, software developers, and managers.)

AB: Prospective Memory Failures

• may account for up to 70% of memory failures in everyday life (Kliegel and Martin 2003; Kvavilashvili et al. 2001)

• Virtuality aggravates the problem? (loss of context, asynchronous communication, etc.)

• Task reminders (Event-based VS time-based) • High association between target and intended action, focal

targets, and single target (automaticity)

AB: Prospective Memory Failures

AB: Task Resumption Failures

O'Conaill & Frohlich 1995 (interruptions in workplace)

return to prior activity

other interrupt

other10%

not engaged4%

interrupt15%

• Task reminders - Cueing: Goal and its retrieval cue must be sampled together before goal is suspended (availability of cue).

• Context Reminders (e.g. bookmarks) • Context Restore • Task recognition is main hurdle (move

from application oriented to task oriented interfaces)

AB: Disruption of Primary Task

• Interruptions consume 28% of knowledge workers’ day (Spira, Goldes 2007)

• Negative effects of interruptions is widely reported

•Interruption management requires evaluating: –Interruption relevance –Notification content, ambient, etc. –Notification Timing (Bailey) - Interruptibilty (Fogarty) –Notification modality

•Device Coordination

AB: Disruption of Primary Task

Attentive television (Shell, Selker, & Vertegaal, 2003)

Example

eyebox2 by Xuuk, Inc. http://www.xuuk.com

AB: Missing important events and information

• There is no conscious perception of the visual world without attention to it �(Mack and Rock; Simons and Chabris) - Inattentional/Change Blindness

• Lack of context •Perceptual approach (use of pre-attentive features to

make important elements stand-out) •Adaptive visualisation: use knowledge of user’s focus, e.g. gaze information •Semantic approach (e.g.semantic web, ontlogy) •Support to Human Social Attention (follows, likes replace eye-gaze)

AB: Missing important events and information

Tag-cloud: perception + user attention + social

Example

Recommendation: user attention + social

More Attentional Breakdowns

• Retrospective Memory • Habituation related • Emotion / Motivation related • Social attention breakdown • …

MS-SenseCam

Supporting Attention on the Web

• Perception (Interface) – Filtering, emphasizing or hiding

• Reasoning (Decision support) – Assessment, Recommendation (people and

resources), .. • Operation

– Multi-tasking, automate, contextualise • Metacognition (reflect & learn)

– Analysis of attentional behavior & learn

The new web(s) perspective

• It is not about data, it’s about interaction - personal control; engaged participant; identity, …

• Streams of information are available - activity streams + sensors everywhere

• Challenges: accurate shallow models of attention, task recognition, privacy, sensor data integration, metadata

THANK YOU FOR YOUR

ATTENTION ☺

croda@aup.fr

attention in webscience

Education

introduction to python - graz university of...

attention models & current topics in neural mt•solution:...

attention in games - ala avoyan · jel classiﬁcation:...

agent-based modeling - graz university of...

measuring and visualizing attention in space with 3d...

development of attention in children.pdf

calling attention in parliament

attention in convolutional lstm for gesture...

the analysis of attention network in adhd, attention ... e...

visual attention in autism families: ‘unaffected’ sibs...

c gros-webscience-talk

attention 10 in bangkok

prediction models of social media...

attention (in the visual system)

attention deficit disorder in children

webscience montpellier

attention in deep learning

attention, attention, ...

addressing attention problems in children

attention: marketing in the attention age