Interacting with Visualization Colin Ware, Information Visualization, Chapter 10, page 335

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 Problem Solving

Visual-Manual Control Loop Low level interaction Visual control of hand position Selection of objects on the screen Reaction times Data manipulation loop, through which objects are selected and moved using the basic skill of eye-hand coordination.

Choice Reaction Times How fast can you choose something? Visual signal: 130 msec response time 700 msec if signals aren’t expected

2D Positioning and Selection How fast can you select something (from a display, including positioning)? Selection using a mouse is one of the most common interactive operations in the modern graphical user interface. Selection time = a + b log2 (D / W + 1.0) Where D is the distance to the centre of the target, W is the width of the target, and a and b are constants determined empirically. (Fitts’ law)

Visual-Manual Feedback Loop Detect start signal Judge distance to target Effect hand movement In target? Update displayMeasure hand position no yes Next task Human processing Machine processing

Skill Learning 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

Vigilance Principle: target detection Is this boring? Vigilance is hard 1.Vigilance drops greatly over first hour 2.Fatigue large negative influence 3.Need to focus, no multitasking 4.Irrelevant signals reduce vigilance

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

Navigation Control Loop Spatial data model Computer databases Visualization of task Long-term memory Cognitive logical and spatial model Working memory Assess progress Navigation control

Locomotion Moving gives dimensionality to space Movement should correspond to real life Relative movement over time is more important than smooth motion

Spatial Navigation Metaphors Movement is usually constrained to avoid confusion (affordances) 4 main classes of movement metaphors: 1.World-in-hand 2.Eyeball-in-hand 3.Walking 4.Flying

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”

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

Walking Walk around in virtual reality Movement in real world constrained (using treadmills) Good: relevant to typical locomotion Bad:?

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)

Reading Maps How to get from here to there (Siegel) 1.Declare key landmarks 2.Develop rules for connecting key landmarks, things in between 3.Form cognitive spatial map for distances between landmarks and relative position

Landmark rules In virtual environments, 1.Should be enough landmarks visible at all times 2.Landmarks should be visually distinct 3.Landmarks should be seen at every scale 4.Landmarks should be placed in areas of interest

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?

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

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

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

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

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)

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

Problem-Solving Loop Computer based model Computer databases Visualization of task Long-term memory network Visual-spatial model Working memory Cognitive logical verbal model Navigation control Refine and test hypotheses through visualization

Human Memory 3 Types 1.Iconic 2.Working 3.Long-term

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

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

Long-term Memory Lifelong memory Includes: episodic memory, motor skills, perceptual skills Estimated: 10 9 bits (~100 megabytes) stored over 35 year period Ideas, thoughts get lost in concept network Misremembering events over time

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

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

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

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