Chapter 2 human capabilities, input output systems

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Chapter 2 human capabilities, input output systems

  1. 1. HUMAN COMPUTER INTERACTION SUBJECT CODE : DCM 214 Prepared by : NURAINI MOHD GHANI p R1 Chapter 2 HUMAN CAPABILITIES : INPUT OUTPUT SYSTEMS
  2. 2. KEY POINT Human have processing constraints p g Motor limitations, e.g. Fitts’ law for pointing Prepare by : NURAI MOHD GHANI Visual range for motion, shape, colour, detail and their consequences for design decisions ed Visual attention models Alternative sensory channels Alt ti h l INI H 2
  3. 3. HUMAN CONSTRAINTSWhat do we know about human capabilities that could orshould constrain interface design? Prepare by : NURAI MOHD GH Limits on perceptual capability – e.g. contrast, resolution p p p y g , ed Limits on motor capability – e.g. reach, speed, precision Limits on attention capacity Limits on memory INI Rates of learning and forgetting Causes of error Mental M t l models & biases d l bi HANI Individual differences (the average size fits few people) Variable state (e.g. stress, fatigue) Special needs & age … 3
  4. 4. HUMAN CONSTRAINTSModel Human Processor(MHP) Prepare by : NURAI MOHD GH*One way to subdivide y edthe main constraints*Perceptual, Motor andCognitive sub-systems sub systems INIcharacterised by:– Storage capacity U– Decay time D HANI– Processor cycle time T*We will focus today onthe perceptual and 4motor processes
  5. 5. MOTOR CONSTRAINTSExample: Fi ’ law (1954)E l Fitts’ l Prepare by : NURAINI MOHD GHANI ed I H 5
  6. 6. MOTOR CONSTRAINTS Example: Fitts’ law (1954) p ( ) Prepare by : NURAI MOHD GH ed Justification? INI #By “analogy” to Shannon informationcapacity = bandwidthxlog2((signal+noise)/noise) HANI #If move fraction 1-r to target each timestep, then reach target when rnD = W/2; so n is proportional to log22D/W 6 # Empirically find good fit with log2(D/W + 0.5)
  7. 7. MOTOR CONSTRAINTS Example: Fitts’ law (1954) p ( ) Prepared by : NURAI MOHD GH e Application? INI *Time will increase with distance – can we keep everything close? HANI *Time will decrease with width – can we make width infinite? 7
  8. 8. Prepared by : NURAINI MOHD GHANI e I H 8PERCEPTION What can we see?
  9. 9. PERCEPTION Some consequences of what we can see: q #Motion – will be visible (and distracting) Prepare by : NURAI MOHD GH anywhere in visual field # Colour – main advantage is “pop-out”: edBut many disadvantages: # Sh Shape iimportant i t t recognition: SO t t in text iti INI ALL CAPS BAD # Limits on resolution – recommend HANI minimum font size; ideally individual can adjust # High resolution only in tiny area of fixation 9
  10. 10. EYE TRACKING Fixation pattern is a g p good indicator of attention Prepare by : NURAI MOHD GHANI #Where do people look, how often, for how long, in what order? ed #Recent technology is making this a standard tool for HCI INI #Also used as input device. H 10
  11. 11. PERCEPTION Importance of eye movements Prepare by : NURAI MOHD GH Must shift the tiny highresolution area around edConstantly Movements called saccades INIoccur > 2 per second all day Long How does visual system decidewhere to move next? HANI Models of attentione.g. Itti et.al. 1998 11
  12. 12. ATTENTION Simple statistical model of saliency Rosenholtz et al (2005)*Provides definition of ‘clutter’: size of Prepare by : NURAI MOHD GHlocal covariance ellipsoid ed* To measure:* Compute local feature covariance atmultiple scales* Take maximum across scales INI* Average for different features* Pool over space*P d Produces good correlation with human d l ti ith h HANIestimates of clutter* Can also use to determine whatfeature added where would best draw 12attention
  13. 13. ATTENTION#So what went wrong here?#Task: find current population of U.S. k fi d l i f S Prepared by : NURAINI MOHD GH e I HANI86% of users failed…http://www.useit.com/alertbox/fancy-formatting.html 13
  14. 14. PERCEPTUAL CONSTRAINTS Bottom up visual processing sets some constraints on optimal layouts, but must also consider top down ti l l t b t t l id t d issues: Prepare by : NURAI MOHD GH#Cultural and l#C lt l d learned f t d factors – f ili it familiarity ed#Underlying domain knowledge of user# Need to reflect logical structure, e.g., placement and grouping INIaccording to function, sequence, frequency of use# Dependence on task to be carried out, e.g. getting an overview HANIvs. seeking specific information# Note that layout and visualisation are already widely explored fields with conclusions that carry over to fields, 14 HCI
  15. 15. ALTERNATIVE SENSORY CHANNELSDifferent sensors provide parallel channel capacitySound: Prepare by : NURAI MOHD GH#Not so easy to localise but can detect from any direction y y ed# Grabs attention – warning mechanisms# Good signal of causal relation – use as confirmatory feedback INI# Monitoring state, ‘background information’# Disk, printer noise etc.# Example of user improvisation in use of ‘data’ data HANI# Interface sound design is typically arbitrary and syntheticTouchTo ch and haptics: 15# Exploit our natural ability to ‘handle’ objects
  16. 16. THANK YOUSEE YOU NEXT CLASS Prepare by : NURAI MOHD GH ed AND INIDON TDON’T FORGET TO HANI FINISH YOUR HOMEWORK 16

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