The Growth of Cognitive Modeling in Human- Computer Interaction Since GOMS By Judith Reitman Olson and Gary M. Olson The University of Michigan.

Slides:



Advertisements
Similar presentations
User Modeling CIS 376 Bruce R. Maxim UM-Dearborn.
Advertisements

G063 - The Model Human Processor. Learning Objective: describe the user interface designers tool known as the ‘Model Human Processor', describe how the.
Input to the Computer * Input * Keyboard * Pointing Devices
The Growth of Cognitive modeling in Human-Computer Interaction Since GOMS Presented by: Daniel Loewus-Deitch Douglas Grimes.
Task Analysis (continued). Task analysis Observations can be done at different levels of detail fine level (primitives, e.g. therbligs, keystrokes,GOMS.
Cognition in the virtual world. Which is easiest to read? What is the time?
Instructor: Vincent Duffy, Ph.D. Associate Professor of IE Lecture 8 – Human-Computer Interaction Thurs. Feb. 8, 2007 IE 486 Work Analysis & Design II.
Fitts’ Law and the Model Human Processor
Korea Univ. Division Information Management Engineering UI Lab. Korea Univ. Division Information Management Engineering UI Lab. Human Interface PERCEPTUAL-MOTOR.
A Guide to SQL, Seventh Edition. Objectives Understand the concepts and terminology associated with relational databases Create and run SQL commands in.
Objectives Define predictive and descriptive models and explain why they are useful. Describe Fitts’ Law and explain its implications for interface design.
Technology Terminology Jeopardy A - DE - FG - MM - SS - Z $100 $200 $300 $400 $500.
Predictive Evaluation Predicting performance. Predictive Models Translate empirical evidence into theories and models that can influence design. Performance.
Chapter 4 Cognitive Engineering HCI: Designing Effective Organizational Information Systems Dov Te’eni Jane M. Carey.
I213: User Interface Design & Development Marti Hearst Tues, April 17, 2007.
Predictive Evaluation Simple models of human performance.
Statistical Natural Language Processing. What is NLP?  Natural Language Processing (NLP), or Computational Linguistics, is concerned with theoretical.
Android 4: Creating Contents Kirk Scott 1. Outline 4.1 Planning Contents 4.2 GIMP and Free Sound Recorder 4.3 Using FlashCardMaker to Create an XML File.
McGraw-Hill/Irwin The Interactive Computing Series © 2002 The McGraw-Hill Companies, Inc. All rights reserved. Microsoft Excel 2002 Exploring Formulas.
©2011 1www.id-book.com Analytical evaluation Chapter 15.
A Guide to SQL, Eighth Edition Chapter Three Creating Tables.
Chapter 5 Models and theories 1. Cognitive modeling If we can build a model of how a user works, then we can predict how s/he will interact with the interface.
XP New Perspectives on Introducing Microsoft Office XP Tutorial 1 1 Introducing Microsoft Office XP Tutorial 1.
Topics Introduction Hardware and Software How Computers Store Data
User Models Predicting a user’s behaviour. Fitts’ Law.
Input Devices What is input? Everything we tell the computer is input.
MICROSOFT WORD GETTING STARTED WITH WORD. CONTENTS 1.STARTING THE PROGRAMSTARTING THE PROGRAM 2.BASIC TEXT EDITINGBASIC TEXT EDITING 3.SAVING A DOCUMENTSAVING.
11.10 Human Computer Interface www. ICT-Teacher.com.
Input Devices. What is Input?  Everything we tell the computer is Input.
Towards supporting the user interfaces design using composition rules Sophie Lepreux, Jean Vanderdonckt {lepreux,
Numeric Processing Chapter 6, Exploring the Digital Domain.
Stanford hci group / cs October 2008 Inp ut Scott Klemmer.
User Modeling 1 Predicting thoughts and actions. Agenda Cognitive models Physical models Fall 2006PSYCH / CS
GOMS CS 160 Discussion Chris Long 3/5/97. What is GOMS? l A family of user interface modeling techniques l Goals, Operators, Methods, and Selection rules.
McGraw-Hill/Irwin The Interactive Computing Series © 2002 The McGraw-Hill Companies, Inc. All rights reserved. Microsoft Excel 2002 Lesson 1 Introduction.
Gary MarsdenSlide 1University of Cape Town Human-Computer Interaction - 6 User Models Gary Marsden ( ) July 2002.
Software Project Planning Defining the Project Writing the Software Specification Planning the Development Stages Testing the Software.
Key Applications Module Lesson 21 — Access Essentials
GOMs and Action Analysis and more. 1.GOMS 2.Action Analysis.
Database Systems Microsoft Access Practical #3 Queries Nos 215.
User Modeling of Assistive Technology Rich Simpson.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Prof Jim Warren with reference to sections 7.4 and 7.6 of The Resonant Interface.
Task Analysis CSCI 4800/6800 Feb 27, Goals of task analysis Elicit descriptions of what people do Represent those descriptions Predict difficulties,
Modeling Visual Search Time for Soft Keyboards Lecture #14.
User Interface Evaluation Cognitive Walkthrough Lecture #16.
ITM 734 Introduction to Human Factors in Information Systems
Evaluation Using Modeling. Testing Methods Same as Formative Surveys/questionnaires Interviews Observation Documentation Automatic data recording/tracking.
1 Cognitive Modeling GOMS, Keystroke Model Getting some details right!
Copyright © 2009 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 4: Events Programming with Alice and Java First Edition by John Lewis.
Cognitive Models Lecture # March, 2008Human Computer Intercation Spring 2008, Lecture #10 2 Agenda Cognitive models –KLM –GOMS –Fitt’s Law –Applications.
마스터 제목 스타일 편집 마스터 텍스트 스타일을 편집합니다 둘째 수준 셋째 수준 넷째 수준 다섯째 수준 The GOMS Family of User Interface Analysis Techniques : Comparison and Contrast Bonnie E. John.
Mouse Trackball Joystick Touchpad TroughputError rate T roughput (bps) Error r ate (%) Image by MIT.
Project Planning Defining the project Software specification Development stages Software testing.
Microsoft Excel Microsoft Excel 2013 is a spreadsheet application in the Microsoft Office Suite. A spreadsheet is an accounting program for the.
1 1 ITM 734 Introduction to Human Factors in Information Systems Cindy Corritore This material has been developed by Georgia Tech HCI.
Systems and User Interface Software. Types of Operating System  Single User  Multi User  Multi-tasking  Batch Processing  Interactive  Real Time.
Today We Will Review: Operating Systems (Windows) (week 3 & 4) Starting up MS Windows Desktop and its contents Functions of the desktop components Brain.
Copyright 2006 John Wiley & Sons, Inc Chapter 5 – Cognitive Engineering HCI: Developing Effective Organizational Information Systems Dov Te’eni Jane Carey.
Computer Literacy BASICS: A Comprehensive Guide to IC 3, 5 th Edition Lesson 3 Windows File Management 1 Morrison / Wells / Ruffolo.
Chapter 5 – Cognitive Engineering
Task Analysis CSCI 4800/6800 Feb 27, 2003.
CIS 376 Bruce R. Maxim UM-Dearborn
Introduction to Visual Basic 2008 Programming
Muneo Kitajima Human-Computer Interaction Group
Cognitive Modeling for HCI
GOMS as a Simulation of Cognition
Model based design NGOMSL and CPM- GOMS
Model based design keystroke level model
Human Computer Interaction Lecture 24 Cognitive Models
Presentation transcript:

The Growth of Cognitive Modeling in Human- Computer Interaction Since GOMS By Judith Reitman Olson and Gary M. Olson The University of Michigan

Introduction Published in 1990 by professors at the University of Michigan Developed a Framework for predicting how a user will interact with a design -> a useful tool for designers. Summarizes the work of Card, Moran, and Newell (1980s, 1980b, 1983) in this area

The Human Side of Human Computer Interaction Each of the three types of processes: perceptual, cognitive, and motor How GOMS could be used as a cognitive process Lots of quantitative data, which is good Modifications to designs using those numbers Many unanswered questions remain

Computer Based Tasks Illustrated

2 Parts to the Framework Presented 1 st Piece of the Framework Model Human Processor (MHP), summarizes a large body of research from cognitive psychology 2 nd Piece of the Framework: The GOMS model- actually a family of models - describes the knowledge necessary and the four cognitive components of skilled performance in tasks: goals, operators, methods, and selection rules.

Roles of Cognitive Models 1. Constrains the design space 2. Answer specific design decisions 3. Estimate the total time for task performance with sufficient accuracy 4. Provide a base to calculate training time and to guide training documentations 5. Discover which stage of activity takes the longest time or produces the most errors

GOMS Predicts user methods and operators Calculates the time needed for a task To make useful predictions, GOMS assumes that routine cognitive skills can be described as a serial sequence of cognitive operations and motor activities Consists of time parameters. Consistent across tasks -> text editors, graphics systems, and some functions from the operating system of a variety of software

Limitations of GOMS 1. Does not account for nonskilled users 2. Does not account for learning and recall 3. Does not account for errors 4. Little distinction between cognitive processes 5. Does not account for parallel processing 6. Does not address mental workload 7. Does not address functionality 8. Does not address user fatigue 9. Does not account for individual differences 10. Does not account for user ’ s acceptance 11. Does not address organizational life

Plan of the Article How quantitative results helped future work How some investigators took the work into new directions: the study of learning and transfer, the study of errors, and the analysis of parallel processes. The limitations that still remain in cognitive models of HCI

Results of Empirical Testing 1.) A keystroke, called k, for a midskilled typist is 280 msec. 2.) A mental operator, called M, often interpreted as the time to retrieve the next chuck of information from long-term memory into WM, is 1.35s. 3.) Pointing, called P, to target on a small display with a mouse takes on average 1.1 sec (though the time is variable according to Fitts’s law) 4.) Moving the hands, called H, from the keyboard to the mouse takes 400 msec

Modeling Specific Serial Components Empirical explorations Derived detailed time parameters As mentioned in the introduction, there are three general classes: Motor Movements Perception Memory and Cognition Researchers CMN = Card, Moran, and Newell, 1983 O&N = Olson and Nilsen, 1988 J&N = John and Newell, 1989 WSN = Walker, Smelcer, and Nilsen, 1988

Motor Movements Keying Time it takes to enter a keystroke Value depends on skill of typist Some parameters (CMN) Best Typist: 80 msec Good Typist: 120 msec Average Typist: 200 msec Typing random letters: 500 msec Typing complex codes: 750 msec Worst Typist: 1200 msec

Motor Movements Keying Parameters for Spreadsheets (O&N) Entering spreadsheet formulas Lotus 1 : 330 msec Multiplan 2 : 220 msec Entering column / width commands Lotus: 280 msec Multiplan: 230 msec Other Parameters (J&N) Enter command abbreviations: 230 msec Expert typing cross-hand digraphs: 170 msec Expert typing same-hand digraphs: 220 msec 1 Lotus is a spreadsheet program from Lotus Software (now part of IBM). It was the IBM PC's first killer application; its huge popularity in the mid-1980s contributed significantly to the success of IBM PC in the corporate environmentspreadsheetLotus SoftwareIBM IBM PCkiller application1980sIBM PC 2 Multiplan was an early spreadsheet program, following VisiCalc, developed by Microsoft. Introduced in 1982, initially for computers running CP/M, it was ported to a number of other operating systems including MS-DOS and Xenix.spreadsheetVisiCalcMicrosoftCP/MMS-DOSXenix

Motor Movements Moving a Mouse Time it takes to point to a target with a mouse Time varies depending on: Distance Size Value may be outdated, since the research is done on older displays.

Motor Movements Moving a Mouse Parameters for Menu Selection (CMN): Average value, small screen, menu shaped target: 1100 msec Variation in distance and size: log 2 (D/S+0.5) sec Parameters for Nested-Menu Selection (WSN): Average value, small screen, menu shaped target: 1900 msec Variation in distance and size: log 2 (D/S+0.5) sec Fritts’ Law: T = log 2 (D/S+0.5) sec

Motor Movements Moving a Mouse Walker et al. used these results to make three adjustments to the design of menus Goal is to shorten menu selection time Three adjustments: Menu pops up to the right of the cursor instead of below Menu targets grow as the distance from the cursor’s staring position increases Virtual borders on the top, right, and bottom edges of a pop up menu

Walker et al.’s Work:

Motor Movements Hand Movements Time needed to move from the spacebar of a keyboard until the pointing control begins to move the cursor Varies depending on pointing device Parameters To Mouse: 360 msec To Joystick: 260 msec To Cursor(arrow) Keys: 310 msec To Function Keys: 320 msec

Perception Time needed to recognize or perceive an item on screen Parameters Time to respond to brief light: 100 msec Varies with intensity of light (brighter is faster): 50 – 200 msec Recognize a 6-letter word: 314 msec Saccade (Jump to a new location): 230 msec

Perception Olson and Nilsen used these parameters to derive the time needed to store a label into working memory. Calculation A saccade to the row line: 230 msec A storage of the row label: 130 msec A saccade to the column head: 230 msec A storage of the column label: 130 msec A saccade to the cell in which typing is to start: 230 msec Retrieval of the row and column labels: 1350 msec Total: 2300 msec

Memory and Cognition Memory Retrieval Time needed to retrieve information from long term memory (LTM) to working memory (WM) Varies depending on type of information Retrieval of same command is proved to be quicker

Memory and Cognition Memory Retrieval Parameters Retrieve a command name or delimiter: 1350 msec Retrieve a random command abbreviation: 1200, 1209, 1200 msec Retrieve the next part of a formula Multiplan (cursor method): 1100 msec Lotus (cursor method): 990 msec Lotus (typing method): 1350msec Retrieve command part in column width task Multiplan: 1160 msec Lotus: 1080 msec Repeated retrieval of same command Lotus: 660 msec

Memory and Cognition Executing Steps in a Task Time needed to perform a mental step Although there are different types of mental steps, the results were remarkably consistent across studies

Memory and Cognition Executing Steps in a Task Parameters Cognitive Processor (the contents of WM initiate associatively-linked actions in LTM): 70 msec Execute next rule in a formal model of skilled performance: 100 msec Execute next step in decoding abbreviations: 66, 60, 50 msec

Memory and Cognition Choosing Methods Time needed to choose a method of action Card assumes that the more choices for a response, the longer the expected response time Different studies vary significantly, which indicates that choosing methods is a complex cognitive task

Predicting Composite Performance Example 1 Typing in values then pointing to next cell with a mouse Parameters Moving the hand to the mouse: 360 msec Clicking the mouse (same as a keystroke): 230 msec Moving the hand to the keyboard: 360 msec Retrieving two digits: 1200 msec Typing two 230 each: 460 msec Retrieving the end action: 1200 msec Typing the key: 230 msec Total: 4040 msec Real results: 4.19 sec Error: 3%

Predicting Composite Performance Example 2-1 Typing in values, clicking enter to go to next cell. Use mouse only to move to next line Parameters for moving the mouse Moving hand to mouse: 360 msec Pointing to a new line with mouse: 1500 msec Clicking the mouse: 230 msec Moving hand to keyboard: 360 msec Total: 2450 msec Real results: 2.81 sec Error: 13%

Predicting Composite Performance Example 2-2 Typing in values, clicking enter to go to next cell. Use mouse only to move to next line Parameters for typing each number into the cell Retrieving (or looking for) two digits: : 1200 msec Typing two 230 msec each: 460 msec Retrieving the end action: 1200 msec Typing the : 230 msec Total: 3090 msec Real results: 2.46 sec Error: 26%

Predicting Composite Performance Summary The performance could be challenged, especially the mental operations Average error is within 14% of the observed value, means it’s still useful in design

Example Based on the Summary of Findings

Example – Time Prediction for ing Yourself ActionTime (msec) Saccade to Browser "To" section + perceive + point with mouse1830( ) Click on Browser "To" section230 Move hand to keyboard360 Type in 16 characters * 16) Move hand to mouse360 Saccade to subject section + perceive + point with mouse1830( ) Click on subject section230 Move hand to keyboard360 Type in 11 characters "Hello World"2530(230 * 11) Move hand to mouse360

Calculations (continued) Saccade to message body section + perceive + point with mouse1830( ) Click on the message body section230 Move hand to keyboard360 Type in 11 characters "Hello World"2530(230 * 11) Move hand to mouse360 Saccade to send button + perceive + point with mouse1830( ) Click on stopwatch230 Saccade to stopwatch + perceive + point with mouse1830( ) Click on stopwatch230 Total19370(19 seconds)

Extensions of the Basic Framework Classes of extension Grammars (TAG) Production Systems Learning and Transfer Analysis of Errors Parallel Processes Critical Path Analysis

Classes of extension Grammars Task-Action Grammar Consist of goals, rules, and action Goals are translated into action by rules Production Systems Consist of rules Similar to grammar, makes things more explicit Can determine the number of loads needed to be stored in WM to perform an action

Example of TAG

Example of Production Systems

Learning and Transfer Time to Learn Cognitive Complexity Theory Time needed to learn a production system step Kieras and Polson: 30 s Ziegler, Vossen, and Hoppe: 17 s Card: 20 s Current “Best Guess”:25 s Time needed to learn a TAG rule No quantified results Shown that 28 well-known rules was learned nearly 3 times faster than 12 complicated rules Varies depending on learning situation (e.g. amount of given explanation)

Learning and Transfer Transfer of Training from One System to Another Learning times same order of magnitude over many situations and experiments. Consistency in design is key -> number of rules not as important as experience carryover.

Analysis of Errors Multiple causes of error WM overflow Length of time item remains in WM Research shows that errors increases as WM load increases Still a lot of room for research, but a good start People forget the crucial “join” statement at the end of an SQL query when lots of items are in WM.

Parallel Processes Previous analysis (GOMS) assumes actions are performed in sequence People type faster two successive letters on different hands than different letters with the same hand - indicates the presence of parallel process Situations for parallel process User experiences multiple external signals in parallel Mental events that occur in parallel External actions that occur in parallel GOMS calculates a clerk need 2 s to type in 1 item, but in reality, they need less than.5 s

Critical Path Analysis Finds the path of events that a user takes Predicts time for parallel processes Harder to examine than serial process Example: Critical path of a world-class typist: 30 msec Critical path of a regular typist: 200 msec Need to identify critical paths that take the most time – can ignore tasks that take shorter time than others if they are performed in parallel.

Future Research Directions (1990) Nonskilled or Casual User [GOMS only considers experienced users] Learning [GOMS only considers experienced users] Errors and Mental Workload [GOMS does not account for potential errors in time calculations] Cognitive Process [GOMS does not account complex mental operations] Parallel Processes [GOMS does not account for this] Individual Differences [Not in GOMS]

Cognitive Modeling in Human- Computer Interaction Unanswered issues: Fatigue Acceptance of system Functions Still useful for many applications, especially in systems that require repetitive actions

Conclusion Cognitive models can screen out certain classes of poor designs that involve highly repetitive and stylized tasks Based on simple case study we did, principles appear to be sound, and these principles are useful especially in the early design stages