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Interaction

Interaction. James Slack CPSC 533C March 3, 2003. Introduction. Visualization give us interfaces for complex computer-based systems Interaction reduces cognitive load 3 classes of interlocking feedback loops. The 3 Feedback Loops. Visual-Manual Control View Refinement and Navigation

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Interaction

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  1. Interaction James Slack CPSC 533C March 3, 2003

  2. Introduction • Visualization give us interfaces for complex computer-based systems • Interaction reduces cognitive load • 3 classes of interlocking feedback loops

  3. The 3 Feedback Loops • Visual-Manual Control • View Refinement and Navigation • Problem Solving

  4. Visual-Manual Control Loop • Low level interaction • Visual control of hand position • Selection of objects on the screen • Reaction times

  5. Choice Reaction Times • How fast can you choose something? • Visual signal: 130 msec response time • 700 msec if signals aren’t expected • Reaction time proportional to logarithm of the number of choices • Speed-accuracy trade-off

  6. 2D Positioning and Selection • How fast can you select something (from a display, including positioning)? • Selection time proportional to logarithm of distance divided by target object width (Fitts’ law) • Fitts’ law can account for other time details associated with HCI, like lag

  7. Visual-Manual Feedback Loop Human processing Detect start signal Judge distance to target no Effect hand movement In target? yes Next task Machine processing Update display Measure hand position Colin Ware, Information Visualization, Chapter 10, page 338

  8. Skill Learning • Power law of practice • Applies to repeated tasks over time • Experience is a large factor in learning • Design interfaces should minimize learning new tasks • People can tolerate small changes

  9. Vigilance • Principle: target detection, sparse targets • Is this boring? Vigilance is hard • Vigilance drops greatly over first hour • Fatigue large negative influence • Need to focus, no multitasking • Irrelevant signals reduce vigilance

  10. Reminder • Vigilance is hard • Move visual signal into optimal spatial or temporal range helps detection • Make signals different from noise • Use of colour, motion, texture to make things stand out

  11. View Refinement & Navigation Loop • Exploration of extended, detailed spaces • Locomotion • Viewpoint control • Map orientation • Focus, context, scale • Rapid interaction with data

  12. Navigation Control Loop Spatial data model Cognitive logical and spatial model Working memory Visualization of task Assess progress Computer databases Navigation control Long-term memory Colin Ware, Information Visualization, Chapter 10, page 343

  13. Locomotion • Moving gives dimensionality to space • Movement should correspond to real life • Relative movement over time is more important than smooth motion • Low frame rate (~2 fps) ok, but lag is issue

  14. Spatial Navigation Metaphors • Movement is usually constrained to avoid confusion (affordances) • 4 main classes of movement metaphors: • World-in-hand • Eyeball-in-hand • Walking • Flying

  15. World-in-hand • Perception that the environment is moving, observer is stationary • Good: for discrete, relatively compact data objects • Bad: for long distances, extended terrains • Used in: computer game “Black & White”

  16. Eyeball-in-hand • Camera (or eye) is manipulable • Not the most effective method for viewpoint control • Good: ? • Bad: occlusion, hard to get some views, limited by user’s hand positions

  17. Walking • Walk around in virtual reality • Movement in real world constrained (using treadmills) • Good: relevant to typical locomotion • Bad: restricted affordances

  18. Flying • Navigation as if in an airplane • Unconstrained movement • More flexible, usable than other interfaces • Good: relevant to typical locomotion • Bad: given real flight controls, users were confused (users had to learn a new skill)

  19. Reading Maps • How to get from here to there (Siegel) • Declare key landmarks • Develop rules for connecting key landmarks, things in between • Form cognitive spatial map for distances between landmarks and relative position

  20. Landmark rules • In virtual environments (Vinson), • Should be enough landmarks visible at all times • Landmarks should be visually distinct • Landmarks should be seen at every scale • Landmarks should be placed in areas of interest

  21. Map Orientation • Track-up display orientation • Up is always the correct way to go • ‘Right’ is always ‘right’ • North-up display orientation • North is up, use a compass • ‘Right’ becomes ‘left’ if you go ‘down’ • Common frame of reference?

  22. Visualizing with Maps • Overview maps are important if the space is large • User location and direction should be noted • Key landmark images should be provided • Instructions other than the map should be provided for navigation

  23. Focus, Context, Scale • Spatial Scale: understanding how changes in scale relate • Structural Scale: levels of detail give us an appropriate amount of information • Temporal Scale: time compression and data samples from many different time ranges

  24. Distortion • Hide information that the user doesn’t need to see by focusing attention where it’s relevant • Fish eye, table lens, hyperbolic tree browser are good examples of distortion

  25. Other Navigation Techniques • Rapid zooming • Elision techniques • Hiding information until it is needed, give appearance of data being far away, unimportant • Multiple Windows • One context each, but each window is linked

  26. Rapid Interaction with Data • Interaction should be fluid and dynamic • Users have to relate cause and effect • Users may want to customize how visualization system displays their data • Brushing: highlighting individual data elements interactively (parallel coordinates)

  27. Problem-Solving Loop • Using visual representations of data to solve problems • Interactive cycle, use a conceptualization as aid to finding solution

  28. Problem-Solving Loop Visual-spatial model Computer based model Refine and test hypotheses through visualization Working memory Visualization of task Cognitive logical verbal model Computer databases Navigation control Long-term memory network Colin Ware, Information Visualization, Chapter 10, page 366

  29. Human Memory • 3 Types • Iconic • Working • Long-term

  30. Iconic Memory • Simple visual buffer holds retinal images • Will quickly deteriorate if not read out • The interface between computer display and human processing system

  31. Working Memory • Limited in capacity • A ‘cache’ of sorts for human processor • Separate subsystems for different tasks • A general purpose working memory?

  32. Long-term Memory • Lifelong memory • Includes: episodic memory, motor skills, perceptual skills • Estimated: 109 bits (~100 megabytes) stored over 35 year period • Ideas, thoughts get lost in concept network • Misremembering events over time

  33. Chunks & Concepts • A chunk is a piece of information as a mental representation • Chunks are either specific or general; high-level concepts are a result of experience • Concepts formed from hypothesis testing process, starting from an initial idea

  34. Human Computer Similarities • Both systems share common traits: • Registers / Iconic Memory • Caches / Working Memory • Main Memory or storage / Long-term memory • How is this possible? • Known to be efficient using computers

  35. Not Really the Same • Digital information is much more detailed • Digital information can be retained indefinitely • Human visual memory tends to dissipate • Human storage isn’t thought of as atomic elements but of chunks and concepts

  36. Concept Maps, Mind Maps • Links between concepts form cognitive aid • The SPIRE system (ThemeScapes) • Trajectory maps: an extrapolation of ideas • Unified Modeling Language (UML) • Too cryptic, hard to understand relationships

  37. Conclusion • Similar structures exist in humans to interact, navigate and problem solve • Feedback loops are common structures that reinforce positive behavior • Visualization aids problem solving

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