Interaction Design Models

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Interaction Design Models

  1. 1. The Resonant Interface HCI Foundations for Interaction Design First Edition by Steven Heim Chapter 7: Interaction Design Models
  2. 2. Chapter 7 Interaction Design Models <ul><li>Model Human Processor (MHP) </li></ul><ul><li>Keyboard Level Model (KLM) </li></ul><ul><li>Goals Operators Methods Selection (GOMS) </li></ul><ul><li>Modeling Structure </li></ul><ul><li>Modeling Dynamics </li></ul><ul><li>Physical Models </li></ul><ul><li>Laws: </li></ul><ul><li>Hicks Law: Choosing between alternatives </li></ul><ul><li>Fitts Law: Time to hit a target </li></ul><ul><li>Learning Law: </li></ul>1-
  3. 3. Predictive/Descriptive Models <ul><li>Predictive models such as the Model Human Processor (MHP) and the Keyboard Level Model (KLM), are a priori (pre-experience) models </li></ul><ul><ul><li>They give approximations of user actions before real users are brought into the testing environment. </li></ul></ul><ul><li>Descriptive models , such as state networks and the Three-State Model , provide a framework for thinking about user interaction </li></ul><ul><ul><li>They can help us to understand how people interact with dynamic systems. </li></ul></ul>1-
  4. 4. Model Human Processor (MHP) <ul><li>The Model Human Processor can make general predictions about human performance </li></ul><ul><li>The MHP is a predictive model and is described by a set of memories and processors that function according to a set of principles (principles of operation) </li></ul>1-
  5. 5. Model Human Processor (MHP) <ul><li>Perceptual system (sensory image stores) </li></ul><ul><ul><li>Sensors </li></ul></ul><ul><ul><ul><li>eyes, ears, touch, taste </li></ul></ul></ul><ul><ul><li>Buffers </li></ul></ul><ul><ul><ul><li>Visual memory store (VIS) </li></ul></ul></ul><ul><ul><ul><li>Auditory memory store (AIS) </li></ul></ul></ul><ul><ul><ul><li>Haptic Memory Store (HIS) </li></ul></ul></ul><ul><ul><ul><li>Taste Memory Store (TIS) </li></ul></ul></ul><ul><ul><li>Cognitive system </li></ul></ul><ul><ul><ul><li>Working memory (WM) — Short-term memory </li></ul></ul></ul><ul><ul><ul><li>Long-term memory (LTM) </li></ul></ul></ul><ul><ul><li>Motor system </li></ul></ul><ul><ul><ul><li>arm-hand-finger system </li></ul></ul></ul><ul><ul><ul><li>head-eye system </li></ul></ul></ul><ul><ul><ul><li>leg-foot-toe system </li></ul></ul></ul>1-
  6. 6. Model Human Processor (MHP) 1-
  7. 7. MHP – Working Memory <ul><li>WM consists of a subset of “activated” elements from LTM (long term memory) </li></ul><ul><li>The SISs (Sensory Image Store, VIS, AIS..) encode only the non-symbolic physical parameters of stimuli. </li></ul><ul><li>Shortly after the onset of a stimulus, a symbolic representation is registered in the WM </li></ul>1-
  8. 8. MHP – Working Memory <ul><li>The activated elements from LTM are called chunks . </li></ul><ul><ul><li>Chunks can be composed of smaller units like the letters in a word </li></ul></ul><ul><ul><li>A chunk might also consist of several words, as in a well-known phrase </li></ul></ul>1-
  9. 9. MHP – Working Memory <ul><ul><li>BCSBMICRA </li></ul></ul>1-
  10. 10. MHP – Working Memory <ul><ul><li>BCSBMICRA </li></ul></ul><ul><ul><li>CBSIBMRCA </li></ul></ul>1-
  11. 11. MHP – Working Memory <ul><li>Chunks in WM can interfere with each other due to LTM associations </li></ul><ul><li>Robert </li></ul><ul><li>Robin </li></ul><ul><li>Wing </li></ul><ul><li>Bird </li></ul><ul><li>Fly </li></ul>1-
  12. 12. MHP – Long-Term Memory <ul><li>The cognitive processor can add items to WM but not to LTM </li></ul><ul><li>The WM must interact with LTM over a significant length of time before an item can be stored in LTM </li></ul><ul><li>This increases the number of cues that can be used to retrieve the item later </li></ul><ul><li>Items with numerous associations have a greater probability of being retrieved </li></ul>1-
  13. 13. MHP – Processor Timing <ul><li>Perceptual —The perceptual system captures physical sensations by way of the visual and auditory receptor channels </li></ul><ul><li>Perceptual decay is shorter for the visual channel than for the auditory channel </li></ul><ul><li>Perceptual processor cycle time is variable according to the nature of the stimuli </li></ul><ul><li>Perceptual Processor cycle time = [50-200] msecs </li></ul>1-
  14. 14. MHP – Processor Timing <ul><li>Cognitive —The cognitive system bridges the perceptual and motor systems </li></ul><ul><li>It can function as a simple conduit or it can involve complex processes, such as learning, fact retrieval, and problem solving </li></ul><ul><li>Cognitive coding in the WM is predominantly visual and auditory </li></ul>1-
  15. 15. MHP – Processor Timing <ul><li>Cognitive coding in LTM is involved with associations and is considered to be predominantly semantic </li></ul><ul><li>Cognitive decay time of WM requires a large range </li></ul><ul><li>Cognitive decay is highly sensitive to the number of chunks involved in the recalled item </li></ul>1-
  16. 16. MHP – Processor Timing <ul><li>Cognitive decay of LTM is considered infinite </li></ul><ul><li>Cognitive processor cycle time is variable according to the nature of the stimuli </li></ul><ul><li>Motor —The motor system converts thought into action </li></ul><ul><li>Motor processor cycle time is calculated in units of discrete micro-movements </li></ul>1-
  17. 17. Cognition Example: Perception/LTM interaction Read out the colours and time it T1 <ul><li>Human </li></ul><ul><li>Computer </li></ul><ul><li>Interface </li></ul><ul><li>Impression </li></ul><ul><li>Memory </li></ul><ul><li>Scheme </li></ul>1-
  18. 18. Perception Speed: Time it, T2, T1 > T2 <ul><li>Green </li></ul><ul><li>Purple </li></ul><ul><li>Orange </li></ul><ul><li>Red </li></ul><ul><li>Blue </li></ul><ul><li>Yellow </li></ul>1-
  19. 19. Memory is Associative <ul><li>  </li></ul><ul><li>T E C T </li></ul><ul><li>Different interpretations, Why? </li></ul>1-
  20. 20. Model Human Processor (MHP) 1-
  21. 21. Timing values: Human Processor Model <ul><li>Perceptual processor: </li></ul><ul><li>3db decay: Visual = [90-1000] msecs, Aural= [900-3500] msecs </li></ul><ul><li>SIS (Sensory Image Stores): Visual = [7-17] letters, Aural= 5 letters, Tactile ? </li></ul><ul><li>  p Perceptual Processor cycle time = [50-200] msecs </li></ul><ul><li>Cognitive processor : </li></ul><ul><li>3db decay: Working Memory= [5-226] secs, 1 chunk = 73 s, 3 chunks = 7 secs </li></ul><ul><li>3db decay: Long Term Memory <= Lifetime </li></ul><ul><li>Storage capacity: Working memory= [2- 4] chuncks, LTM =  inf </li></ul><ul><li> c Cognitive Processor cycle time = [25-170] msecs </li></ul><ul><li>Motor processor </li></ul><ul><li>  m Motor Processor cycle time = [30-100] msecs </li></ul><ul><li>Response Time =  p +  c +  m = [105 - 470] msecs </li></ul>1-
  22. 22. Model Human Processor, MHP Principles: <ul><li>P0: Cognitive Processor, recognize-act cycle </li></ul><ul><li>Each cycle of the Cognitive processor triggers interaction from the Working Memory to the LTM. </li></ul><ul><li>WM  LTM chunk </li></ul><ul><li>P1: Perceptual Processor, cycle time </li></ul><ul><li>Cycle time varies inversely with the intensity of the stimulii, </li></ul><ul><li>e.g. Loud noise, Electric shock, </li></ul>1-
  23. 23. Model Human Processor, MHP Principles: <ul><li>P2: Encoding of Perceived information </li></ul><ul><li>The perceived information (visual, aural, tactile) is first encoded prior to its storage to LTM . The way it is encoded determine how it will be mapped to the access cues. </li></ul><ul><li>P3: Discrimination Principle </li></ul><ul><li>Number of cycles to complete the cognition is context dependent. </li></ul><ul><li>Difficulty of retrieval (of the correct item) from LTM is dependent on their relationship to the input cues that are presented. </li></ul>1-
  24. 24. Model Human Processor, MHP Principles: <ul><li>P4: Cognitive Processor Rate </li></ul><ul><li>Cognitive processing becomes faster </li></ul><ul><li>Under increased task demands (e.g. panic situations) </li></ul><ul><li>After practice </li></ul><ul><li>Performance Gain: due to repeated practice </li></ul><ul><li>T n = T 1 n –  ,  = [0.2 – 0.6] </li></ul><ul><li>where n is the number of attempts, </li></ul><ul><li>T1 the initial time, </li></ul><ul><li>Tn the time at the n th repetition. </li></ul><ul><li>a.k.a. Experience !! </li></ul>1-
  25. 25. Model Human Processor, MHP Principles: <ul><li>P7: Uncertainty Principle </li></ul><ul><li>Decision time increases with uncertainty. </li></ul><ul><li>T = Ic * H where Ic = [0 – 157] msecs </li></ul><ul><li>For n equi-probable alternatives: </li></ul><ul><li>H = log 2 (n + 1)  Hicks Law </li></ul><ul><li>For n alternatives each with a different probability p j : </li></ul><ul><li>H =  p j log 2 (1/p j + 1) </li></ul>1-
  26. 26. Keyboard Level Model (KLM) <ul><li>The KLM is a practical design tool that can capture and calculate the physical actions a user will have to carry out to complete specific tasks </li></ul><ul><li>The KLM can be used to determine the most efficient method and its suitability for specific contexts. </li></ul>1-
  27. 27. Keyboard Level Model (KLM) <ul><li>Given: </li></ul><ul><ul><li>A task (possibly involving several subtasks) </li></ul></ul><ul><ul><li>The command language of a system </li></ul></ul><ul><ul><li>The motor skill parameter of the user </li></ul></ul><ul><ul><li>The response time parameters </li></ul></ul><ul><li>Predict: The time an expert user will take to execute the task using the system </li></ul><ul><ul><li>Provided that he or she uses the method without error </li></ul></ul>1-
  28. 28. Keyboard Level Model (KLM) <ul><li>The KLM is comprised of: </li></ul><ul><ul><li>Operators </li></ul></ul><ul><ul><li>Encoding methods </li></ul></ul><ul><ul><li>Heuristics for the placement of mental (M) operators </li></ul></ul>1-
  29. 29. KLM - Operators <ul><li>Operators </li></ul><ul><ul><li>K Press a K ey or button, (0.08 to 1.20) secs </li></ul></ul><ul><ul><li>P P oint with mouse, (1.1) secs  Fitts’ Law </li></ul></ul><ul><ul><li>H H ome hands to keyboard or peripheral device (0.4 ) </li></ul></ul><ul><ul><li>D D raw line segments (0.9*n + 0.16*L) secs </li></ul></ul><ul><ul><li>M M ental preparation (1.35) secs </li></ul></ul><ul><ul><li>R System R esponse (system/activity dependent) </li></ul></ul>1-
  30. 30. KLM – Encoding Methods <ul><li>Encoding methods define how the operators involved in a task are to be written, e.g., ‘ipconfig’ With CLI in a command window: </li></ul><ul><li>MK[ i ] K[ p ] K[ c ] K[ o ] K[ n ] K[ f ] K[ i ] K[ g ] K[ RETURN ] </li></ul><ul><li>It would be encoded in the short-hand version as </li></ul><ul><ul><li>M 8K [ ipconfig RETURN ] </li></ul></ul><ul><ul><li>This results in a timing of 1.35 + 8* 0.20 = 2.95 seconds for an average skilled typist. </li></ul></ul><ul><ul><li>Using Windows: Network  Repair option (Home wireless) </li></ul></ul><ul><ul><li>H[mouse] MP[Network Icon] K[Right click] P[Prepare] K[Left Click] </li></ul></ul><ul><ul><li>Time = 4.35 secs </li></ul></ul>1-
  31. 31. KLM Example 2 <ul><li>Consider the scenario of correcting a mirtake  mistake </li></ul><ul><li>Move hand to mouse H[mouse] </li></ul><ul><li>Position mouse after the bad char PK[Left] </li></ul><ul><li>Return to keyboard H[keyboard] </li></ul><ul><li>Delete bad char MK[backspace] </li></ul><ul><li>Type in new char K[char] </li></ul><ul><li>Reposition insertion point H[mouse]MPK[Left] </li></ul><ul><li>Total time = 3*h+ 2*p+ 2*k + 2*k + 2*m </li></ul><ul><li>= 3*0.4+ 2(1.1+.1+.08+ 1.35) </li></ul><ul><li>= 6.46 secs </li></ul>1-
  32. 32. Calculation of P: Pointing and Positioning Time <ul><li>Based on Experimentation, average P = 1.1 secs </li></ul><ul><li>Based on Fitts’ Law: </li></ul><ul><li>P = a + b log 2 ( A / W + 1), </li></ul><ul><li>A = distance from the target </li></ul><ul><li>W = Width (cross-section) of the target </li></ul><ul><li>a= 0, b = 0.1 </li></ul><ul><li>Example: Suppose the mouse is in the middle of a 19 cm screen and we have to move to a 2 cm wide tool bar at the top of the screen then the time taken will be </li></ul><ul><li>0.1* log 2 (19/2 + 1) = 0.1* log 2 (10.5) = 0.4 secs </li></ul>1-
  33. 33. Menu selection: Macintosh OS vs. Windows <ul><li>Menu placement: </li></ul><ul><li>Macintosh: Aligned with the edge (No offset)  </li></ul><ul><li>Users need not be very accurate when hitting the target. Typically, they stop 50 mm from the edge </li></ul><ul><li>Windows: Offset by the Title bar (pull down)  </li></ul><ul><li>Need more accuracy as the Width of target is 5 mm. </li></ul><ul><li>Performance using Fitts’ Law: </li></ul><ul><li>P = a + b log 2 ( A / W + 1), </li></ul><ul><li>A = distance from the target , typically 80 mm </li></ul><ul><li>W = Width (cross-section), Mac = 50mm, Windows = 5mm </li></ul><ul><li>Mac: 50 + 150 * log 2 (80/50 + 1) = 256 msecs </li></ul><ul><li>Windows: 50 + 150 * log 2 (80/5 + 1) = 663 msecs </li></ul><ul><li>Further Details : Slides 60-75 </li></ul>1-
  34. 34. KLM – Heuristics for M Operator Placement <ul><li>The KLM operators can be placed into one of two groups— physical or cognitive . </li></ul><ul><li>The physical operators are defined by the chosen method of operation, such as clicking an icon or entering a command string. </li></ul><ul><li>The cognitive operators are governed by the set of heuristics </li></ul>1-
  35. 35. Rules for keeping/deleting the M operator <ul><li>Rule 0: </li></ul><ul><li>Place M in front of all K where you want to enter new text and all P (mouse) that selects commands. </li></ul><ul><li>Rule 1: </li></ul><ul><li>If M is also associated with the previous operator, then delete it, e.g. </li></ul><ul><li>PMK  PK </li></ul><ul><li>Rule 2: </li></ul><ul><li>If there is a string of M then delete all except the first. </li></ul>1-
  36. 36. Rules for keeping/deleting the M operator <ul><li>Rule 3 : </li></ul><ul><li>If K is a redundant terminator then delete the M in front of it, e.g. </li></ul><ul><li> M<command> K M<argument> K </li></ul><ul><li> M<command> K <argument> K </li></ul><ul><li>Rule 4: </li></ul><ul><li>Delete M, if K terminates a command string. Keep M, if K terminates an argument string, e.g. </li></ul><ul><li>M<command> K  <command> K </li></ul><ul><li>M<argument> K  M <argument> K </li></ul>1-
  37. 37. What the KLM Does Not Do <ul><li>The KLM was not designed to consider the following : </li></ul><ul><ul><li>Errors </li></ul></ul><ul><ul><li>Learning </li></ul></ul><ul><ul><li>Functionality </li></ul></ul><ul><ul><li>Recall </li></ul></ul><ul><ul><li>Concentration </li></ul></ul><ul><ul><li>Fatigue </li></ul></ul><ul><ul><li>Acceptability </li></ul></ul>1-
  38. 38. Applications for the KLM <ul><li>Case 1 (Mouse-Driven Text Editor) </li></ul><ul><ul><li>During the development of the Xerox Star KLMs served as expert proxies </li></ul></ul><ul><li>Case 2 (Directory Assistance Workstation) </li></ul><ul><ul><li>The KLM clarified the tradeoffs between the number of keystrokes entered in the query and the number of returned fields </li></ul></ul>1-
  39. 39. GOMS- Goals, Operators, Methods, Selection <ul><li>Goal/task models can be used to explore the methods people use to accomplish their goals </li></ul><ul><li>Card et al. suggested that user interaction could be described by defining the sequential actions a person undertakes to accomplish a task. </li></ul><ul><li>Example: Main Goal: Cut and Paste a paragraph </li></ul><ul><li>Subgoals: </li></ul><ul><ul><li>Locate e.g. move pointing device, line feed </li></ul></ul><ul><ul><li>Select e.g. click and drag, arrow keys </li></ul></ul><ul><ul><li>Cut e.g. Ctrl X, pop up menu </li></ul></ul><ul><ul><li>Drag </li></ul></ul><ul><ul><li>Paste e.g. pop up menu, Ctrl V </li></ul></ul>1-
  40. 40. GOMS Components <ul><li>Goals - Tasks are deconstructed as a set of goals and subgoals. </li></ul><ul><li>Operators - Tasks can only be carried out by undertaking specific actions. </li></ul><ul><li>Methods - Represent ways of achieving a goal </li></ul><ul><ul><li>Comprised of operators that facilitate method completion </li></ul></ul><ul><li>Selection Rules - The method that the user chooses is determined by selection rules </li></ul>1-
  41. 41. GOMS – CMN-GOMS <ul><li>CMN-GOMS can predict behavior and assess memory requirements </li></ul><ul><li>CMN-GOMS (named after Card, Moran, & Newell ) -a detailed expansion of the general GOMS model </li></ul><ul><ul><li>Includes specific analysis procedures and notation descriptions </li></ul></ul><ul><li>Can judge memory requirements (the depth of the nested goal structures) </li></ul><ul><li>Provides insight into user performance measures </li></ul>1-
  42. 42. GOMS – Other GOMS Models <ul><li>NGOMSL (Natural GOMS Language), developed by Kieras, provides a structured natural-language notation for GOMS analysis and describes the procedures for accomplishing that analysis (Kieras, 1997) </li></ul><ul><ul><li>NGOMSL Provides: </li></ul></ul><ul><ul><ul><li>A method for measuring the time it will take to learn specific method of operation </li></ul></ul></ul><ul><ul><ul><li>A way to determine the consistency of a design’s methods of operation </li></ul></ul></ul>1-
  43. 43. GOMS – Other GOMS Models <ul><li>CPM-GOMS represents </li></ul><ul><ul><li>Cognitive </li></ul></ul><ul><ul><li>Perceptual </li></ul></ul><ul><ul><li>Motor operators </li></ul></ul><ul><li>CPM-GOMS uses Program Evaluation Review Technique (PERT) charts </li></ul><ul><ul><li>Maps task durations using the critical path method (CPM). </li></ul></ul><ul><li>CPM-GOMS is based directly on the Model Human Processor </li></ul><ul><ul><li>Assumes that perceptual, cognitive, and motor processors function in parallel </li></ul></ul>1-
  44. 44. GOMS – Other GOMS Models <ul><li>Program Evaluation Review Technique (PERT) chart Resource Flows </li></ul>1-
  45. 45. Modeling Structure <ul><li>Structural models can help us to see the relationship between the conceptual components and the physical components of the system, allowing us to judge the design’s relative effectiveness. </li></ul>1-
  46. 46. Modeling Structure – Hicks Law <ul><li>Hick’s law can be used to create menu structures </li></ul><ul><li>Hick’s law states that the time it takes to choose one item from n alternatives is proportional to the logarithm (base 2) of the number of choices, plus 1. </li></ul><ul><li>This equation is predicated on all items having an equal probability of being chosen </li></ul>1-
  47. 47. Modeling Structure – Hicks Law <ul><li>T = a + b log 2 ( n + 1) </li></ul><ul><li>The coefficients are empirically determined from experimental design </li></ul><ul><li>Raskin (2000) suggests that </li></ul><ul><li>a = 50 and b= 150 </li></ul><ul><li>are sufficient place holders for “back-of-the-envelope” approximations </li></ul>1-
  48. 48. Modeling Structure – Other Forms of Hicks Law <ul><li>For n equi-probable alternatives: </li></ul><ul><li>H = log 2 (n + 1)  a + b log 2 (n + 1) </li></ul><ul><li>For n alternatives each with a different probability p j : </li></ul><ul><li>H =  p j log 2 (1/p j + 1) </li></ul><ul><li>generalizes to c + d *  p j log 2 (1/p j + 1) </li></ul>1-
  49. 49. Modeling Structure – Hicks Law <ul><li>Menu listing order must be logical and relevant </li></ul><ul><li>Menus are lists grouped according to some predetermined system </li></ul><ul><li>If the rules are not understood or if they are not relevant to a particular task, their arrangement may seem arbitrary and random, requiring users to search in a linear, sequential manner. </li></ul>1-
  50. 50. Modeling Dynamics <ul><li>Understanding the temporal aspects of interaction design is essential to the design of usable and useful systems </li></ul><ul><li>Interaction designs involve dynamic feedback loops between the user and the system </li></ul><ul><ul><li>User actions alter the state of the system, which in turn influences the user’s subsequent actions </li></ul></ul><ul><li>Interaction designers need tools to explore how a system undergoes transitions from one state to the next </li></ul>1-
  51. 51. Modeling Dynamics – State Transition Networks <ul><li>State Transition Networks can be used to explore: </li></ul><ul><ul><li>Menus </li></ul></ul><ul><ul><li>Icons </li></ul></ul><ul><ul><li>Tools </li></ul></ul><ul><li>State Transition Networks can show the operation of peripheral devices </li></ul>1-
  52. 52. Modeling Dynamics – State Transition Networks <ul><li>STNs are appropriate for showing sequential operations that may involve choice on the part of the user, as well as for expressing iteration. </li></ul>1-
  53. 53. Modeling Dynamics – Three-State Model <ul><li>The Three-State Model can help designers to determine appropriate I/O devices for specific interaction designs </li></ul><ul><li>The TSM can reveal intrinsic device states and their subsequent transitions </li></ul><ul><ul><li>The interaction designer can use these to make determinations about the correlation between task and device </li></ul></ul><ul><ul><li>Certain devices can be ruled out early in the design process if they do not possess the appropriate states for the specified task </li></ul></ul>1-
  54. 54. Modeling Dynamics – Three-State Model <ul><li>The Three-State Model (TSM) is capable of describing three different types of pointer movements </li></ul><ul><ul><li>Tracked : A mouse device is tracked by the system and represented by the cursor position </li></ul></ul><ul><ul><li>Dragged : A mouse also can be used to manipulate screen elements using drag-and-drop operations </li></ul></ul><ul><ul><li>Disengaged movement : Some pointing devices can be moved without being tracked by the system, such as light pens or fingers on a touch-screen , and then reengage the system at random screen locations. Also lift mouse, move and put it down. </li></ul></ul>1-
  55. 55. Modeling Dynamics – Three-State Model 1- Mouse 3-State Model . Trackpad 3-State Model . Alternate mouse 3-State Model. Multibutton pointing device 3-State Model .
  56. 56. Light Pen – Three-State Model 1- State 0 State 1 State 2 Touch screen Button down Button Up Remove Pen
  57. 57. Modeling Dynamics – Glimpse Model <ul><li>Forlines et al. (2005): </li></ul><ul><ul><li>Because the pen and finger give clear feedback about their location when they touch the screen and enter state 2, it is redundant for the cursor to track this movement </li></ul></ul><ul><ul><li>Pressure-sensitive devices can take advantage of the s1 redundancy and map pressure to other features, e.g. </li></ul></ul><ul><ul><li>Light Pressure </li></ul></ul><ul><ul><li>Strong pressure </li></ul></ul><ul><ul><li>Undo commands coupled with a preview function (Glimpse) can be mapped to a pressure-sensitive direct input device </li></ul></ul>1-
  58. 58. Modeling Dynamics – Glimpse Model 1-
  59. 59. <ul><li>Some applications </li></ul><ul><ul><li>Pan and zoom interfaces —Preview different magnification levels </li></ul></ul><ul><ul><li>Navigation in a 3D world —Quick inspection of an object from different perspectives </li></ul></ul><ul><ul><li>Color selection in a paint program —Preview the effects of color manipulation </li></ul></ul><ul><ul><li>Volume control —Preview different volume levels </li></ul></ul><ul><ul><li>Window control —Moving or resizing windows to view occluded objects </li></ul></ul><ul><ul><li>Scrollbar manipulation —Preview other sections of a document </li></ul></ul>Modeling Dynamics – Glimpse Model 1-
  60. 60. Physical Models <ul><li>Physical models can predict efficiency based on the physical aspects of a design </li></ul><ul><li>They calculate the time it takes to perform actions such as targeting a screen object and clicking on it </li></ul>1-
  61. 61. Physical Models – Fitts’ Law <ul><li>Fitts’ law states that the time it takes to hit a target is a function of the size of the target and the distance to that target </li></ul><ul><li>Fitts’ law can be used to determine the size and location of a screen object </li></ul>1-
  62. 62. Physical Models – Fitts’ Law <ul><li>There are essentially three parts to Fitts’ law: </li></ul><ul><ul><li>Index of Difficulty (ID)— Quantifies the difficulty of a task based on width and distance </li></ul></ul><ul><ul><li>Movement Time (MT)— Quantifies the time it takes to complete a task based on the difficulty of the task (ID) and two empirically derived coefficients that are sensitive to the specific experimental conditions </li></ul></ul><ul><ul><li>Index of Performance (IP) [also called throughput (TP)] Based on the relationship between the time it takes to perform a task and the relative difficulty of the task </li></ul></ul>1-
  63. 63. Physical Models – Fitts’ Law <ul><li>Fitts described “reciprocal tapping” (xylophone) </li></ul><ul><ul><li>Subjects were asked to tap back and forth on two 6-inch-tall (H) plates with width W of 2, 1, 0.5, and 0.25 inches </li></ul></ul>1-
  64. 64. Physical Models – Fitts’ Law <ul><li>Fitts proposed that ID, the difficulty of the movement task, could be quantified by the equation </li></ul><ul><li>ID = log 2 (2 A / W ) </li></ul><ul><li>Where: </li></ul><ul><li>A is the amplitude ( distance to the target) </li></ul><ul><li>W is the width of the target </li></ul><ul><li>This equation was later refined by MacKenzie to align more closely with Shannon’s law: </li></ul><ul><li>ID = log 2 ( A / W + 1) </li></ul>1-
  65. 65. Physical Models – Fitts’ Law <ul><li>The average time for the completion of any given movement task can be calculated by the following equation: </li></ul><ul><li>MT = a + b log 2 ( A / W + 1) </li></ul><ul><li>Where: </li></ul><ul><li>MT is the movement time </li></ul><ul><li>Constants a and b are arrived at by linear regression </li></ul>1-
  66. 66. Physical Models – Fitts’ Law <ul><li>Values of the constants a and b in msecs, [Ref: McKenzie, Buxton, ACM 1991] </li></ul><ul><li>Pointing MT = -107 + 223 log 2 (A/W + 1) </li></ul><ul><li>Dragging MT = 135 + 249 log 2 (A/W + 1) </li></ul><ul><li>Application: Where to place Icons, text areas, buttons etc. on the screen. </li></ul>1- Mouse a b Pointing -107 223 Dragging 135 249
  67. 67. Physical Models – Fitts’ Law <ul><li>By calculating the MT and ID, we have the ability to construct a model that can determine the information capacity of the human motor system for a given task. </li></ul><ul><ul><li>Fitts referred to this as the index of performance (throughput) </li></ul></ul><ul><li>Throughput TP is the rate of human information processing </li></ul><ul><li>TP = (ID, Degree of Difficulty ) / (MT, Movement Time ) </li></ul>1-
  68. 68. Physical Models – Fitts’ Law <ul><li>Implications of Fitts’ Law </li></ul><ul><ul><li>Large targets and small distances between targets are advantageous (obviously)!! </li></ul></ul><ul><ul><li>Screen elements should occupy as much of the available screen space as possible </li></ul></ul><ul><ul><li>The largest Fitts-based pixel is the one under the cursor </li></ul></ul><ul><ul><li>Screen elements should take advantage of the screen edge whenever possible </li></ul></ul><ul><ul><li>Large menus like pie menus are easier to uses than other types of menus. </li></ul></ul>1-
  69. 69. Physical Models – Fitts’ Law <ul><li>Limitations of Fitts’ Law </li></ul><ul><ul><li>There is no consistent way to deal with errors </li></ul></ul><ul><ul><li>It only models continuous movements </li></ul></ul><ul><ul><li>It is not suitable for all input devices, for example, isometric joysticks </li></ul></ul><ul><ul><li>It does not address two-handed operation </li></ul></ul><ul><ul><li>It does not address the difference between flexor and extensor movements </li></ul></ul><ul><ul><li>It does not address cognitive functions such as the mental operators in the KLM model </li></ul></ul>1-
  70. 70. Physical Models – Fitts’ Law <ul><li>W is computed on the same axis as A </li></ul><ul><li>Horizontal and vertical trajectories Targeting a circular object. </li></ul>1-
  71. 71. Physical Models – Fitts’ Law <ul><li>Bivariate data </li></ul><ul><ul><li>The smaller of the width and height: </li></ul></ul><ul><li>ID min ( W , H ) = log 2 [ D /min ( W , H ) + 1] </li></ul><ul><li>Targeting a rectangular object . </li></ul>1-
  72. 72. Physical Models – Fitts’ Law <ul><li>W —The “apparent width” calculated along the approach vector </li></ul><ul><li>ID W = log 2 ( D / W’ + 1) </li></ul>1-
  73. 73. Physical Models – Fitts’ Law, Target Orientation 1-
  74. 74. Physical Models – Fitts’ Law <ul><li>Amplitude Pointing : One-dimensional tasks </li></ul><ul><ul><li>Only the target width (whether horizontal or vertical) is considered </li></ul></ul><ul><ul><li>The constraint is based on Horizontal width W , and target height ( H ) is infinite or equal to W </li></ul></ul><ul><ul><li>AP errors are controlled at “the final landing” </li></ul></ul><ul><li>Directional Pointing: If W is set at ∞ then H becomes significant </li></ul><ul><ul><li>The constraint is based on H </li></ul></ul><ul><ul><li>DP errors are corrected incrementally during the pointing movement (Accot & Zhai, 2003) </li></ul></ul>1-
  75. 75. Physical Models – Fitts’ Law Implications <ul><ul><li>Overly elongated objects hold no advantage ( W / H ratios of 3 and higher). </li></ul></ul><ul><ul><li>Objects should be elongated along the most common trajectory path (widgets normally approached from the side should use W , those approached from the bottom or top should use H ). </li></ul></ul><ul><ul><li>Objects should not be offset from the screen edge (consistent with the Macintosh OS). </li></ul></ul><ul><ul><li>Objects that are defined by English words generally have W H and should be placed on the sides of the screen. (However, greater amplitude measurements may be significant on the normal “landscape”-oriented screens.) (Accot & Zhai, 2003) </li></ul></ul>1-

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