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Exploring Human-Computer Interaction: Fitts' Law, Steering Law, and GOMS Model, Study notes of Reasoning

Royal Academy of Music, University of LondonReasoning

An overview of key concepts in Human-Computer Interaction (HCI), focusing on Fitts' Law, Steering Law, and the GOMS Model. Fitts' Law predicts movement time for pointing tasks, Steering Law models pointer movement through 2D tunnels, and GOMS Model offers predictive models for interaction. The document also discusses the importance of understanding user diversity and the limitations of these models.

Typology: Study notes

2021/2022

Uploaded on 09/12/2022

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LMU München – Medieninformatik – Andreas Butz + Paul Holleis – Mensch-Maschine-Interaktion 1 – SS2010
Looking Back: Fitts’ Law
• Predicts movement time for rapid, aimed pointing tasks
• One of the few stable observations in HCI
• Index of Difficulty:
• How to get a and b for a specific device / interaction technique
– vary D and W and measure MT; fit a line by linear regression
• Various implications for HCI
– Consider button sizes
– Use edges and corners
– Use current location of the cursor
– Use average location of the cursor(?)
– Possibility to compare different input devices
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Partial preview of the text

Download Exploring Human-Computer Interaction: Fitts' Law, Steering Law, and GOMS Model and more Study notes Reasoning in PDF only on Docsity!

Looking Back: Fitts’ Law

  • Predicts movement time for rapid, aimed pointing tasks
  • One of the few stable observations in HCI
  • Index of Difficulty:
  • How to get a and b for a specific device / interaction technique
    • vary D and W and measure MT; fit a line by linear regression
  • Various implications for HCI
    • Consider button sizes
    • Use edges and corners
    • Use current location of the cursor
    • Use average location of the cursor(?) - Possibility to compare different input devices

Looking Back: Steering Law

  • Models the movement time of a pointer through a 2D tunnel
  • Extension of Fitts’ Law
  • Tunnels with constant width: Index of Difficulty: D / W
  • Extension for arbitrary tunnel shapes:
  • Implications for HCI
    • Nested menus
    • Navigation tasks
    • Extensions for virtual reality / 3D movements possible

To Recap: Predictive Models

  • Model:
    • Simplification of a complex situation / action, e.g. human interaction
  • Predictive:
    • Make educated guesses about the future

» relying on knowledge about past actions / states

» relying on a model of interaction

  • Examples:
    • Fitts’ Law (directed aimed movement)
    • Law of Steering (navigation through a tunnel)
    • Hick’s Law / Hick-Hyman Law (choose an item within a menu)
    • ...

Descriptive Models

- (The categorisation is not sharp, for more insights, see [MacKenzie 2003])

  • Descriptive models
    • provide a basis for understanding, reflecting, and reasoning about certain facts and

interactions

  • provide a conceptual framework that simplifies a, potentially real, system
  • are used to inspect an idea or a system and make statements about their probable

characteristics

  • used to reflect on a certain subject
  • can reveal flaws in the design and style of interaction
  • Examples:
  • Descriptions, statistics, performance measurements
  • Taxonomies, user categories, interaction categories MacKenzie, I. S., 2003, Motor Behaviour Models for Human-computer Interaction In HCI Models, Theories, and Frameworks: Toward a Multidisciplinary Science (Book), 27-

Example: Guiard’s Model of Bimanual Skill (1 / 2)

  • Many tasks are asymmetric with regard to left / right hand
  • Guiard’s model identifies the roles and actions of the non-preferred and preferred hands Non-preferred hand - leads the preferred hand - sets the spatial frame of reference for the preferred hand - performs coarse movements Preferred hand - follows the non- preferred hand - works within established frame of reference set by the non-preferred hand - performs fine movements

Example: Guiard’s Model of Bimanual Skill (2 / 2)

Microsoft Office Keyboard

Method 1

Method 2

Method 3

Sub-

goal

Main goal

with

methods

GOMS Example: Move Word (1 / 2)

Goal: move the word starting at the cursor position to the end of the text [select use-keyboard delete-and-write use-mouse ] verify move Goal: use-keyboard Goal: select word [select use and n * use and and ] verify selection ... Goal: delete-and-write ... Goal: use-mouse Goal: select word [select click at beginning and drag till the end of the word double-click on the word] verify selection Goal: move word [select click on word and drag till end of text Goal: copy-paste-with-mouse ...]

GOMS Example: Move Word (2 / 2)

  • Selection rules:
    • Rule 1: use method use-keyboard if no mouse attached
    • Rule 2: use method delete-and-write if length of word < 4
    • Rule 3: use method use-mouse if hand at mouse before action
    • ...
  • Selection rules depend on the user (→^ remember user diversity?)
  • GOMS models can be derived in various levels of abstraction
    • e.g. goal: write a paper about X
    • e.g. goal: open the print dialog

GOMS Example: ATM Machine

GOAL: GET-MONEY

. GOAL: USE-CASH-MACHINE

. INSERT-CARD

. ENTER-PIN

. SELECT-GET-CASH

. ENTER-AMOUNT

. COLLECT-MONEY

. COLLECT-CARD

GOAL: GET-MONEY

. GOAL: USE-CASH-MACHINE

. INSERT-CARD

. ENTER-PIN

. SELECT-GET-CASH

. ENTER-AMOUNT

. COLLECT-CARD

. COLLECT-MONEY

(outer goal satisfied!) (outer goal satisfied!)

  • GOMS gives an early understanding of interactions
  • “How to not loose you card”

GOMS Example: ATM Machine

GOAL: GET-MONEY

. GOAL: USE-CASH-MACHINE

. INSERT-CARD

. ENTER-PIN

. SELECT-GET-CASH

. ENTER-AMOUNT

. COLLECT-MONEY

. COLLECT-CARD

GOAL: GET-MONEY

. GOAL: USE-CASH-MACHINE

. INSERT-CARD

. ENTER-PIN

. SELECT-GET-CASH

. ENTER-AMOUNT

. COLLECT-CARD

(outer goal satisfied!). COLLECT-MONEY (outer goal satisfied!)

GOMS – Characteristics

• Usually one high-level goal

• Measurement of performance: high depth of goal structure

 high short term-memory requirements

• Predict task completion time (see KLM in the following)

 compare different design alternatives

Keystroke-Level Model

  • Simplified version of GOMS
    • only operators on keystroke-level
    • no sub-goals
    • no methods
    • no selection rules
  • KLM predicts how much time it takes to execute a task
  • Execution of a task is decomposed into primitive operators
    • Physical motor operators » pressing a button, pointing, drawing a line, …
    • Mental operator » preparing for a physical action
    • System response operator » user waits for the system to do something

KLM Operators

  • Each operator is assigned a duration (amount of time a user would take to perform it):

Predicting the Task Execution Time

  • Execution Time
    • OP: set of operators
    • nop: number of occurrences of operator op
  • Example task on Keystroke-Level: Sequence:

1. hold-shift K (Key)

2. n·cursor-right n·K

3. recall-word M (Mental Thinking)

4. del-key K

5. n·letter-key n·K

  • Operator Time Values: K = 0.28 sec. and M = 1.35 sec 2n·K + 2·K + M = 2n·0.28 + 1.91 sec
  •  time it takes to replace a n=7 letter word: T = 5.83 sec