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Cognitive Modeling 1

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Cognitive Modeling 1. Bringing a user model into the realm of predictive evaluation ... You strive to obey human limits. If a user is an interpreter/predictor: ... – PowerPoint PPT presentation

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Title: Cognitive Modeling 1


1
Cognitive Modeling 1
  • Bringing a user model into the realm of
    predictive evaluation

2
Todays Agenda
  • What exactly is cognitive modeling?
  • Why do we care about it?
  • What are some models?
  • What are their relative strengths and
    shortcomings?
  • How do we use these models in predictive
    evaluation?

3
Cognitive Modeling Explained
4
How do we view the user?
  • Three prevalent models (not mutually exclusive!)
  • A sensory processor (literally,
    stimulus-response)
  • An interpreter or predictor (dont you ever
    think?)
  • An actor in an environment (a slice of the big
    picture)

5
System PhilosophiesShape User Models
  • Interaction with philosophy
  • Software system as tool, interface
    usability-engineered membrane
  • Human-as-processor / -interpreter
  • Interaction through philosophy
  • Software as communication medium, interface plays
    a role in social context
  • Human-as-interpreter / -actor

6
User Models Shape HCI
  • If a user is a sensory processor
  • Your model is informed by experimental
    psychology, quantitative sensory results
  • You strive to obey human limits
  • If a user is an interpreter/predictor
  • Your model is informed by cognitive psychology,
    possibly a touch of AI
  • You strive to fit a system into the users
    knowledge base

7
User Models Shape HCI, cont.
  • If a user is an actor in an environment
  • Your model is informed by ecological
    psychology, ideas from anthropology (e.g.
    ethnographic field studies)
  • You strive to fit a system into a task and a
    social context
  • Roles imply frameworks for design and evaluation

8
So why bother?
  • Idea If we can build a model of how a user
    works, then we can predict how s/he will interact
    with the interface
  • Cognitive model ? predictive evaluation
  • No mock-ups or prototypes
  • Consider, as we go What do you actually need,
    and what do gaps you fill up/bridge with
    assumptions?

9
Model Components
  • User qualities
  • Understanding
  • Knowledge
  • Intentions
  • Processing
  • Levels of detail
  • Plans (high-level)
  • Motor actions(low-level)

10
Your Turn1. Model Human Processor
  • Seminal cognitive model
  • Microprocessor-human analogue using results from
    experimental psychology
  • Card, Moran, Newell (1980s)
  • The MHP influence can be seen in some underlying
    HCI principles today
  • There are other ways of thinking
  • Actors in context vs. users of tools

11
Class DiscussionModel Human Processor
  • What are the three major subsystems and their
    functions?
  • What does it mean to say that certain
    subprocessors have variable rates?
  • What is the recognize-act cycle? Is it like
    the fetch-decode-execute of a CPU?
  • How do the authors define rationality?
  • What are some of the other assumptions underlying
    the MHP model?
  • Do you think this is a good model?

12
Discussion PointsModel Human Processor
  • Three subsystems
  • Perceptual, cognitive, motor
  • Each has own memories and processors
  • Notion of a flow of symbolically coded sensory
    information
  • Flow from perceptual to cognitive system
  • Cognitive system applies LTM to decide on an
    action, actuates motor system

13
Discussion PointsModel Human Processor
  • Variable rates
  • Cognitive cycle time decreases with increased
    task effort, practice, but increases with
    uncertainty
  • Perceptual cycle time decreases with more intense
    stimuli
  • Power law of practice (exponential decay of total
    task time with task rehearsal)
  • Fitts law (hand tracking to targets in 1D)

14
Discussion PointsModel Human Processor
  • Procedural model fundamental basis is the
    recognize-act cycle
  • WM initiates actions linked to LTM actions
    result in modified WM
  • Predicts performance, not actions
  • Assumes rationality
  • Goals Task Operators Inputs Knowledge
    Process-Limits BEHAVIOR
  • Human problem-solving finite state machine

15
2. GOMS
  • Probably the most widely known and used technique
    in the human as information processor vein
  • Heavy MHP influence
  • Same authors
  • Rationality, goal orientation assumed
  • Idea Assign times to each subtask in a linear
    task decomposition

16
Class Discussion GOMS
  • Define each of the letters in the acronym GOMS
  • What is the difference between an operator and a
    method?
  • How do you derive task times, and what good are
    they, really?
  • What are the assumptions of GOMS?
  • When is GOMS appropriate?

17
Discussion Points GOMS
  • Goals desired endstates
  • Subdivide into lower-level operations
  • Operators lowest-level task-oriented actions
    (move mouse, read dialog box)
  • Methods sequence of operators for accomplishing
    a goal
  • Selection rules to choose between multiple
    methods
  • GOMS attempts to predict method choice

18
Discussion Points GOMS
  • Goal photocopying a piece of paper
  • GO-TO-COPIER
  • if (user is Bob)
  • KEY-CARD-ACTIVATE-COPIER
  • else
  • COIN-ACTIVATE-COPIER
  • PLACE-ORIGINAL-ON-GLASS
  • MAKE-COPY
  • Is this decomposition detailed enough?

19
Discussion Points GOMS
  • Determine times for each operator, and for the
    task sequence, just add up the times to get total
    time sequence
  • Assumes expert users behaving as rational
    problem-solvers why?
  • Assumes you know a good sequence of tasks and can
    estimate times decently well
  • GOMS power degrades when one of these
    assumptions does not hold

20
One More GOMS Question
  • Book talks about case study with NYNEX telephone
    system
  • Specialized GOMS analysis (equipped for parallel
    tasks) used to determine critical path, task
    timings
  • Analysis concluded new system would be slower
    system was abandoned, saving millions of
    dollars
  • Anything wrong with that conclusion?

21
Variant GOMS-KLM
  • GOMS keystroke-level model (KLM)
  • Analyze only observable interactions with
    standard input elements (mouse, kbd.)
  • GOMS w/ operators K, B, P, H, M, D, R
  • Keypress, mouse Button press, Point, Homing (or
    Hand move to input device), Mental prep, Draw
    (line segments) with mouse, Response by system
  • Each operator takes a prescribed time

22
A Fun GOMS-KLM Problem
  • Consider a GOMS-KLM decomposition of selecting
    File / Print from a pulldown menu
  • Now consider the same task using only the
    keyboard, with the ALT-F accelerator to open the
    File menu and then the P key to select the Print
    option
  • Use texts operator timings for these scenarios
    assume hands start on keyboard

23
GOMS-KLM ProblemMouse for menu selection?
  • What is the right operator sequence?
  • HmousePBleft-clickMPBleft-click
  • Complicated rules for placing Ms but boils
    down to chunking (one M before each chunk of a
    task)
  • Candidate Ms before each B, K, and P involved in
    specification or selection of a command
    eliminate the Ms that are fully anticipated or
    in a cognitive unit
  • Textbook timings (all in seconds)
  • H 0.40, P 1.10, B 0.20, M 1.35
  • Total predicted time 4.35 s

24
GOMS-KLM ProblemKeyboard for Menu Selection?
  • Recall mouse operator sequence over two chunks
    (open File menu, select Print option) HPBMPB
  • Assuming same two chunks, you have
    MKALTKFMKP
  • Book times for K based on typing speed
  • Good typist, K 0.12 s, total time 3.06 s
  • Poor typist, K 0.28 s, total time 3.54 s
  • Non-typist, K 1.20 s, total time 6.30 s
  • Possible moral Shortcut keys not necessarily
    faster than using the mouse

25
Other models/variantsto know about
  • know about know they exist
  • NGOMSL similar to GOMS, but expresses goals as
    noun-action pair
  • more sophisticated, handles expert-novice better
  • Cognitive Complexity Theory uses a hierarchical
    goal decomposition with production rules (if a,
    then b) for a generalized transition network
  • better predictive power, size of production rule
    set a good measure of task complexity

26
SummaryIssues in Cognitive Modeling
  • Terminology
  • Whats expert vs. novice?
  • Granularity problems (see GOMS)
  • Still no user, per se
  • No notion of what the real users want
  • Time-consuming and lengthy
  • One user, one computer
  • No social context

27
SHW2 Heuristic Evaluation
  • Well discuss in detail next time (once Ive had
    a chance to read your papers)
  • Problems you encountered?
  • Questions you have about heuristic evaluation?

28
Next on the Menu
  • Some loose ends
  • SHW2 post-mortem
  • Motor behaviors Fitts law
  • Interpretive evaluation
  • Ethnography
  • Cognitive modeling with context
  • Situated action, activity theory, distributed
    cognition
  • Design, DOET
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