1. © Anselm Spoerri Lecture 2 Information Visualization Intro – Recap Foundation in Human Visual Perception –Sensory vs. Cultural –Attention – Searchlight Model –Stages of Visual Processing –Luminance & Color Channels –Pre-Attentive Processing –Mapping Data to Display Variables

2. © Anselm Spoerri Goal of Information Visualization Use human perceptual capabilities to gain insights into large data sets that are difficult to extract using standard query languages Support Exploration –Look for structure, patterns, trends, anomalies, relationships –Provide a qualitative overview of large, complex data sets –Assist in identifying region(s) of interest and appropriate parameters for more focussed quantitative analysis Abstract and Large Data Sets –Symbolic –Tabular –Networked –Hierarchical –Textual information

3. © Anselm Spoerri Information Visualization - Problem Statement Scientific Visualization –Show abstractions, but based on physical space Information Visualization –Information does not have any obvious spatial mapping Fundamental Problem How to map non–spatial abstractions into effective visual form? Goal Use of computer-supported, interactive, visual representations of abstract data to amplify cognition

4. © Anselm Spoerri Student Videos – Essence of Information Visualization Copy the following URL into Browser window: http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/ and Right click on hyperlink for the name below and use “Save As …” download avi file to computer Phil Bright http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/bright.avi Carlos Carrero http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/carrero.avi Daveia Thomas http://www.scils.rutgers.edu/~aspoerri/Teaching/InfoVisResources/student_videos/thomas.avi

5. © Anselm Spoerri Approach 1 Foundation in Human Visual Perception How it relates to creating effective information visualizations 2 Understand Key Design Principles for Creating Information Visualizations 3 Study Major Information Visualization Tools 4 Evaluate Information Visualizations Tools 5 Design New, Innovative Visualizations

6. © Anselm Spoerri Human Visual System – Overview Sensory vs. Cultural Attention – Searchlight Model Stages of Visual Processing Luminance & Color Channels Pre-Attentive Processing Mapping Data to Display Variables

7. © Anselm Spoerri Sources Information Visualization Perception for Design Colin Ware Academic Press, 2000 As well as: Marti Hearst (Berkeley)Marti Hearst Christopher Healey (North Carolina) Christopher Healey

8. © Anselm Spoerri Sensory vs. Cultural

9. © Anselm Spoerri Sensory vs. Cultural (cont.) Visualization = Learned Language ? –Meaning of Symbol = Created by Convention –If true, choice of visual representation arbitrary –Semiotics = Study of Symbols and how they convey Meaning Choice of Visual Representation Matters –Outlines Object outline and object itself excite similar neural processes Visual cortex designed to detect continuous contours –Similar perceptual illusions / blindness in humans and animals –Not all diagrammatic notations are equal Most visualizations are Hybrids –Learned conventions and hard-wired processing

10. © Anselm Spoerri Physical World Structured Well-Defined Surfaces Objects have mostly smooth surfaces Temporal Persistence Objects don’t randomly appear/vanish Light travels in Straight Lines reflects off surfaces in certain ways Law of Gravity

11. © Anselm Spoerri Our Premise Sensory Representations Tap into Perceptual Power of Brain Without Learning Sensory Representations Effective because well matched to early stages of neural processing –Understanding without training –Perceptual Illusions Persist Mueller-Lyon Illusion (off by 25-30%)

12. © Anselm Spoerri Attention – Searchlight Model

13. © Anselm Spoerri Attention – Searchlight Properties Searchlight Size varies with –Data density –Stress level Attention Operators work within searchlight beam Attention = Tunable Filter Eye movements 3/sec – series of saccades Popout Effects (general attention) Segmentation Effects (dividing up the visual field)  Guide Attention

14. © Anselm Spoerri Stages of Visual Processing 1 Rapid Parallel Processing –Feature Extraction: orientation, color, texture, motion –Transitory: briefly held in an iconic store –Bottom-up, data-driven processing 2 Serial Goal-Directed Processing –Object recognition: visual attention & memory important. –Slow and serial processing –Uses both short-term memory and long-term memory –More emphasis on arbitrary aspects of symbols –Different pathways for object recognition & visually guided motion –Top-down processing

15. © Anselm Spoerri Parallel Processes  Serial Processes Parallel Processing Orientation Texture Color Motion Detection Edges Regions 2D Patterns Serial Processing Object Identification Short Term Memory 5 ± 2 = 3 to 7 Objects

16. © Anselm Spoerri Visual Angle

17. © Anselm Spoerri Acuities Vernier Super Acuity (10 sec) Two Point acuity (0.5 min)

18. © Anselm Spoerri Spatial Frequency Acuity Contrast Spatial Freq. Need Sufficient Contrast for Fine Details

19. © Anselm Spoerri Acuity Distribution 103050103050 Distance from Fovea (deg.) 100 80 60 40 20

20. © Anselm Spoerri Scale Matters

21. © Anselm Spoerri Luminance “channel” Extracts Surface Information Discounts Illumination Level Discounts Color of Illumination Mechanisms 1 Adaptation 2 Simultaneous Contrast

22. © Anselm Spoerri Luminance is not Brightness Luminance = physical measure Brightness = perceived amount of light Eye sensitive over 9 orders or magnitude –5 orders of magnitude (room – sunlight) –Receptors bleach and less sensitive with more light –Takes up to half an hour to recover sensitivity Eye is NOT a light meter Designed to detect CHANGES Not good for detecting Absolute Values Extremely sensitive to Differences & Changes

23. © Anselm Spoerri Simultaneous Contrast

24. © Anselm Spoerri Edge Detection

25. © Anselm Spoerri Luminance for Shape-from-Shading

26. © Anselm Spoerri Color Trichromacy Three cones types in retina

27. © Anselm Spoerri Cone Sensitivity Functions – Blue / Green / Red a 400500600700 Wavelength (nm) 20 100 80 60 40

28. © Anselm Spoerri Color Implications Color Perception is Relative Sensitive to Small Differences –hence need sixteen million colors Not Sensitive to Absolute Values –hence we can only use < 10 colors for coding

29. © Anselm Spoerri Color = Classification Rapid Visual Segmentation Color helps us determine type Only about six categories

30. © Anselm Spoerri Color Coding Large areas = low saturation Small areas = high saturation 12 Colors for labeling

31. © Anselm Spoerri Channel Properties – Take Home Messages Luminance Channel Detail Form Shading Motion Stereo Chromatic Channels Surfaces of Things Labels Categories (about 6-10) Red, green, yellow and blue are special (unique hues)  More Important

32. © Anselm Spoerri Color - Take Home Messages Use Luminance for Detail, Shape and Form Use Color for Categorization - few colors Minimize Contrast Effects Strong colors for small areas Contrast in luminance with background Subtle colors for large areas

33. © Anselm Spoerri Pre-Attentive Processing Some Visual Properties Processed Pre-Attentively –No need to focus attention Pre-Attentive Properties Important for Design of Visualizations –Can be perceived immediately –Can mislead viewer < 200 - 250ms –Eye movements = at least 200ms –Some processing can be done very quickly  Implies low-level processing in parallel

34. © Anselm Spoerri Segmentation by Primitive Features How many areas ?

35. © Anselm Spoerri Pre-Attentive Processing How many 3s ?

36. © Anselm Spoerri Color  Pre-Attentive (Pops out) How many 3s ?

37. © Anselm Spoerri Orientation and Size - Gabor Primitives

38. © Anselm Spoerri Pre-Attentive Experiment a 3612 Number of distractors 500 700 900 Number of irrelevant items varies Pre-attentive 10 msec per item or better. Decision = Fixed Time regardless of the number of distractors  Preattentive

39. © Anselm Spoerri Pre-Attentive Processing - Color

40. © Anselm Spoerri Pre-Attentive Processing - Orientation

41. © Anselm Spoerri Pre-Attentive Processing - Motion

42. © Anselm Spoerri Pre-Attentive Processing - Size

43. © Anselm Spoerri Pre-Attentive Processing - Simple shading

44. © Anselm Spoerri Pre-Attentive – Summary

45. © Anselm Spoerri Conjunction (does not pop out)

46. © Anselm Spoerri Compound features (do not pop out)

47. © Anselm Spoerri Example: Conjunction of Features Viewer cannot rapidly and accurately determine if target (red circle) is present or absent when target has two or more features, each of which are present in the distractors. Viewer must search sequentially.

48. © Anselm Spoerri Laws of Pre-Attentive Display Must Stand Out in Simple Dimension –Color –Simple Shape = orientation, size –Motion –Depth

49. © Anselm Spoerri Pre-Attentive Channels Form orientation/size Color Simple Motion/Blinking Spatial, Stereo Depth, Shading, Position

50. © Anselm Spoerri Pre-Attentive Demo Pre-Attentive Demo by Christopher Healey Target = Red Circle Distractors –blue circles (colour search) –red squares (shape search) –blue circles and red squares (conjunction search)

51. © Anselm Spoerri Pre-Attentive Conjunctions Position + Color Position + Shape Stereo + Color Color + Motion  Spatial location + some aspect of form

52. © Anselm Spoerri Pre-Attentive Lessons Design Symbols Based on simple visual attributes Make symbols distinct Support Rapid Visual Search (10 msec/item) Use different channels for different types of information Do not use large areas of strong color Faces, etc are not pre-attentive

53. © Anselm Spoerri Example

54. © Anselm Spoerri Mapping Data to Display Variables Position (2) Orientation (1) Size (spatial frequency) Motion (2)++ Blinking? Color (3)

55. Accuracy Ranking for Quantitative Perceptual Tasks Angle Slope Length Position Area Volume ColorDensity More Accurate Less Accurate (Mackinlay 88 from Cleveland & McGill)

56. © Anselm Spoerri Ranking of Visual Properties for Different Data Types QUANTITATIVE Position Length Angle Slope Area Volume Density Color Saturation Color Hue ORDINAL Position Density Color Saturation Color Hue Texture Connection Containment Length Angle NOMINAL Position Color Hue Texture Connection Containment Density Color Saturation Shape Length