Figure It Out

Оглавление

Stephen P. Anderson. Figure It Out

FIGURE IT OUT. Getting from Information to Understanding

Contents

Foreword

Introduction

The Book, in Brief

PART. 1. A Focus on Understanding

CHAPTER. 1. From Information to Understanding

Living with Diabetes

Understanding Is Created

Information Is a Resource

Figuring It Out

A Distributed System of Resources

Human Understanding

CHAPTER. 2. Understanding as a Function of the Brain, Body, and Environment

Behaviorism and the Cognitive Revolution

The Computational Theory of Mind

Where the Body Meets the Mind

The Embodied Mind

From Brainbound Minds to Extended Minds

Mental Representations and the Big Divide

What Is the Truth About Embodiment?

Seeing the World Through a New Lens

The Brainbound View

The Extended View

Becoming Smarter

The Blind Man and the Stick (Redrawing the Boundaries of Cognition)

PART. 2. How We Understand by Associations

CHAPTER. 3. Understanding Is Fundamentally About Associations Between Concepts

Technology: Person, Place, or Tool?

The Effect of These Different Frames for Technology

Technology Framing and Human Rights

Associations Among Concepts Is Thinking

The Brain as a Perceptual Organ

Understanding Is Dependent on Sensory Information

Your Brain Constructs (an Experience of) Reality

Perception Is a Process of Active Construction

RECOGNIZING LETTERS

RECOGNIZING SHAPES AND COLORS

SEEING WHAT WE EXPECT TO SEE

How We’ll Explore Everyday Associations

CHAPTER. 4. Everyday Associations: Metaphors, Priming, Anchoring, and Narrative

How Metaphors Shape Associations

Metaphors and Crime: Is Crime a Virus or a Beast?

Decision Framing and Cognitive Bias

The Economic Engine, Sick Patient, or ...?

Can Merely Suggesting a Concept Frame a Decision?

Application: Choosing Our Words Carefully

How Priming and Anchoring Influence Associations

Priming

Priming and Subtle Suggestions

Anchoring

How Narratives Shape Associations

Explanation #1: We Need Stories for Survival

THE RISK WITH STORIES

WHAT’S GREAT ABOUT STORIES

Explanation #2: Stories Engage More Parts of the Brain

FACT OR FICTION, IT DOESN’T MATTER

Explanation #3: Stories Are Critical to Social Bonds

Stories and Understanding

SIMULATIONS

OTHER APPLICATIONS OF NARRATIVE

What We Hear, Read, and Say

CHAPTER. 5. Everyday Associations: Aesthetics and Explicit Visual Metaphors

How Aesthetics Trigger Associations

A “Thick” Relationship

Associations and Transference of Meaning

Heavy Suggests Value/Glass Suggests Transparency

Bringing This on Stage

Basic Shapes, Motion, and Meaning

How Explicit Visual Metaphors Force Associations

Icebergs!

Metaphors That Create Understanding

Metaphors That Communicate Understanding and Stick in Memory

Metaphors That Suggest Causality: The Three-Legged Stool

Choosing the Right Metaphor: Garden or Iceberg?

Mostly Timeless and Universally Recognized

Associations Activated by What We See

CHAPTER. 6. Closing Thoughts and Cautionary Notes About Associations

First Principles: An Antidote to Thinking by Analogy

The Scientific Method and Lateral Thinking

Associative Thinking Is Neither Good nor Bad

Associations: A Double-Edged Sword

The Benefits of Many Lenses

Down the Rabbit Hole of Associations

Final Remarks

PART. 3. How We Understand with External Representations

CHAPTER. 7. Why Our Sense of Vision Trumps All Others

A Superior Sense of Vision

When an Infographic Is Worth a 1,000 Words

“A Functional Art”4

Understanding Visual Encodings

Effective Use of Visual Encodings

Representing Precise Quantitative vs. General Qualitative Information

DEFENDING THE OFT-MALIGNED PIE CHART

TRACKING TIME WITH A PIE CHART

Showing Ordered, Ordinal, or Sequenced Data

HOW SCALABLE IS YOUR ENCODING?

Representing Categories, Types, or Classifications

Working with Visual Properties

A Couple of Issues to Consider

Issue #1: Color and Scalability

Issue #2: Mixing and Matching Encodings

Using Gestalt Psychology to Show Relationships

The Application of Gestalt Principles

Gestalt Anti-Pattern #1

Gestalt Anti-Pattern #2

Good Examples of Gestalt Principles

Visual Doesn’t Mean Pictures

What We Know About How We See Things

CHAPTER. 8. An Intelligent Understanding of Color

How Many Colors Are in the Rainbow?

When Colors Are Constant

The Subjectivity of Color Identification

An Argument for Relative Linguistics

An Argument for the Universal

Why All the Fuss over Color?

Color, Cultures, and Universal Associations

The Color Purple

“Pink Is for Girls, Blue Is for Boys”

Not All Colors Are Created Equal

How Is This Knowledge Useful?

Color Blindness

A Few Solutions

Never Rely Solely on Color

Color Is Contextual

A Challenge to Be Color Conscious

CHAPTER. 9. Ways We Use Space to Hold and Convey Meaning

Thinking Extended into Our External Environment

A Natural Orientation

Cooking in the Kitchen

A Shared Grammar

An Exploration of Time

A CRITICAL LOOK AT THE COMMON CLOCK

WHEN TIME STOPS AND STARTS

THE REDESIGNED CALENDAR

Up-Leveling Our Use of Space (A Return to the Kitchen)

Three Barriers That Hold Back Visual Thinking

Barrier #1: Transferring Skills at Sorting Things into Sorting Abstractions

VISUAL THINKING FOR PERSONAL MATTERS

Barrier #2: An Over-Reliance on a Few, Existing Models

Barrier #3: Recognizing How Visual Models Reveal Information

A Universal Pattern Behind All Visual Models

From Substrate to Placement and Territories

Distinguishing Between Objects, Placement, and Territory

TIC-TAC-TOE

A DIRTY EXAMPLE

ORGANIZING PEOPLE

ORGANIZING PROJECTS

VISUALIZING DATA

PATTERNS IN GAMES

What the Objects, Placement, Territories Model Unlocks

OBJECTS

PLACEMENT

TERRITORY

“But Wait, What About ...”

CLARIFICATION #1: CONCEPT MODELS

CLARIFICATION #2: SMALL MULTIPLES AND CHARTS

CLARIFICATION #3: RECURSION—OBJECTS CAN BE SUBSTRATES FOR OTHER OBJECTS

CLARIFICATION #4: PRETTY PICTURES ARE NOT THE SAME AS INFORMATIVE VISUALS

Place These Thoughts into Memory

PART. 4. How We Understand Through Interactions

CHAPTER. 10. Interacting with Information

More Than Visual Information

Interactions Are Pervasive

Doing, Not Just Seeing

Everyday Interactions

How People Play Tetris

The Light Bulb Moment

Epistemic Interactions Are Universal Across Mediums

What Is Interaction?

Modeling Interaction

Two Kinds of Action

Why Epistemic Actions Matter

CHAPTER. 11. A Pattern Language for Talking About Interactions

Four Interaction Themes

Foraging for Resources

Searching

Probing

Animating

Collecting

Foraging Interactions, Summarized

Tuning the World

Cutting

Cloning

Collecting as a Tuning Action

Think of Interactions Like Atoms

Filtering

Tuning Interactions, Summarized

Externalizing Thought

Annotating

Linking

Generating

Externalizing Interactions, Summarized

Constructing Knowledge

Chunking

Composing

Fragmenting

Rearranging

Repicturing

Constructing Interactions, Summarized

Putting It All Together: How Tony Stark Understands

PART. 5. Coordinating for Understanding

CHAPTER. 12. Seeing the System of Cognitive Resources

Seeing a System of Understanding

The Locus of Understanding

Everyday Understanding with Everyday Things

Cognitive Resources as Material Objects

Expanding the Locus of Understanding

Can a Horse Do Math?

A Shifting Coalition of Resources

CHAPTER. 13. Coordinating a System of Resources

Local Coordination

Everyday Coordination Practices

Facilitation as a Form of Coordination

Situation 1: Kicking Off a New Project

Situation 2: The Retail Shopping Experience

Touchpoints and Integrations

Tips for Coordination at the Local Level

Macro-Level Coordination

A Puzzling Problem

A Framework to Coordinate People for Understanding

1. SHARED STANDARDS—WAYS WE COMMUNICATE

2. INVISIBLE ENVIRONMENTS—WAYS WE ALIGN, CONCEPTUALLY

3. VISIBLE ENVIRONMENTS—WAYS WE COLLABORATE

4. PSYCHOLOGICAL SAFETY—WAYS WE BEHAVE

5. PERSPECTIVES—WAYS WE SEE (AND SEE DIFFERENTLY)

Macro-Level Coordination and 21st Century Knowledge Work

PART. 6. Tools and Technologies for Understanding

CHAPTER. 14. A Critical Look at Tools and Technologies for Understanding

Tools for Understanding and Their Limitations

Bret Victor and Dynamic Drawing Tools

Critiquing Spreadsheets, Tables, and Similar Representations

USING A SPREADSHEET TO TRACK RECRUITING APPLICANTS

TABLES IN SOFTWARE

When Easy Access to Information Keeps Us in the Dark

Deciding to Work at Understanding

SOLUTION 1

SOLUTION 2

WHY THIS MATTERS

When Technology Keeps Us in the Dark

MACHINE LEARNING AS A TOOL FOR UNDERSTANDING

SPOTTING COMPUTATIONAL ERRORS WILL BECOME MORE DIFFICULT

WORKING WITH MACHINES

Tools for Taming Complexity

Tools for Increasingly Complex Challenges

Model Thinking

Social Challenges

Liberating Structures

Pathline

From Marks on Cave Walls to ...?

CHAPTER. 15. A Perspective on Future Tools and Technologies for Understanding

Tools for Group Cognition

“What’s Next?”

Origins of the Internet

Theme #1: Increasing Computational Abilities

Machines Working for Us (the Replacement Narrative)

Humans Working with Machines

GENERATIVE DESIGN

COMPUTER MODELING

VIDEO GAMES AND EXPLORABLE EXPLANATIONS

Theme #2: A Shift from Tools to Spaces

Not About the Tech!

Smart Dumb Things

Theme #3: The Merging of Digital and Physical Realities

Dumb Objects Become Smart

When We Can’t Tell Atoms from Bits

What’s Next?

What Doesn’t Change?

Connecting the Dots

Index. A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

Y

Z

Acknowledgments. STEPHEN WOULD LIKE TO THANK:

KARL WOULD LIKE TO THANK:

About the Authors

Footnotes. Foreword

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

Отрывок из книги

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STEPHEN P. ANDERSON and KARL FAST

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FIGURE 1.7 Polisis uses machine learning to translate policy documents into a visual form.

Polisis produces an interactive, visual summary of a specific contract. Notice the difference between these two approaches: ToS;DR shifts the cost of understanding by doing work for us, then asking us to trust their conclusion. Polisis shifts the cost of understanding by making the document easier for us to figure out—there’s still work to be done. With Polisis, there is no easy recommendation, but rather clarity. From their project’s web page: “You don’t have to read the full privacy with all the legal jargon to understand what you are signing up for.” Polisis makes the information understandable, empowering you to make a more informed choice; it’s a tool that facilitates understanding. It’s probably not as easy to understand as the lists provided by ToS;DR, but it provides more detailed information about what information is collected and why.

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