Why does explaining “why?” help learning? A subsumptive constraints account

Joseph Jay WilliamsJoseph_williams@berkeley.edu

Josephjaywilliams.com

Explanation

Explanation & Learning• Education: Biology, physics, math.

(Chi et al, 1994; 1989; Nokes et al, 2011; Siegler, 2002; Renkl, 1997)

• Cognitive Development: Conceptual change in theory of mind and number conservation.(Siegler, 1985; Amsterlaw & Wellman, 2006; Wellman & Liu, 2006)

• Cognitive Psychology: Category learning, causal reasoning, property induction.(Ahn & Kalish, 2000; Murphy, 2002; Rehder, 2006; Williams & Lombrozo, 2010)

• Artificial Intelligence(Mitchell & Cedar-Kabelli, 1986; DeJong, 2008)

• Philosophy of Science (Salmon, 1990; Woodward, 2010)

Explanation

• “Why?” Explanation: Why a fact is true

• Explaining the meaning of a text passage• Explaining one’s reasoning• Explaining how a conclusion was reached• Explaining how something works• Explaining what is anticipated• Step-focused, Gap-filling, Mental-model revising

(Nokes et al, 2011)4

Explaining explanation

• General effects:• Learning Engagement (e.g. Siegler, 2002)• Metacognitive monitoring (e.g. Chi, 2000)

• Selective effects:• Subsumptive Constraints account

(Williams & Lombrozo, 2010, Cognitive Science)

Explaining engages search for underlying generalizations

Subsumptive Constraints• Subsuming: An explanation of why a fact or

observation is true shows how it is subsumed as an instance of a generalization

• Unifying: Better explanations have broader scope

Why?

Subsumptive Constraints• Subsumption: An explanation of why a fact or

observation is true shows that it might be expected as an instance of a generalization

• Ex. 1: Observation: John is a teacher.– Generalization: Caring people tend to become teachers.– Subsumed: John is an instance of a caring person being a

teacher.• Ex. 2: Giraffes have long necks.– Generalization: Wanting a goal can make animals grow.

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Predictions

Subsumptive constraint on explanation:1. Promotes discovery of generalizations2. Favors broad generalizations: which

prior knowledge suggests apply beyond specific cases3. Impairs learning when generalizations

are unreliable4. Promotes belief revision from anomalies

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I. Discovery in Category Learning• Learn to categorize robots on the planet ZARN.

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Body Shape Generalization

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Broad Foot Shape Generalization

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Design Overview

Explain vs

Control

Differences between categories?

EXPLAIN:Explain why this might be a Glorp.

DESCRIBE: Describe this glorp.

THINK ALOUD:Say out loud

what you are thinking.

FREE STUDY

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Categorization& MemoryTest

Discovery of foot shape generalization(Williams & Lombrozo, 2010)

Describe Think Aloud Free Study0

0.1

0.2

0.3

0.4

0.5ExplainControl

Prop

ortio

n di

scov

erin

g ge

nera

lizati

on

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II. Subsumptive scope & prior knowledge

Blank Labels

Glorp

Drent

Informative Labels

Outdoor

Indoor

Antenna Generalization Foot Generalization

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Discovery of knowledge-relevant generalization

Low PK High PK0.0

0.2

0.4

0.6

0.8

1.0

ExplainFree Study

Prop

ortio

n di

scov

erin

gkn

owle

dge-

rele

vant

gen

-er

aliz

ation

N = 40715Blank labels Informative labels

Children’s sensitivity to subsumptive scope

Frequent & Sophisticated(Chouinard, 2008; Hickling & Wellman, 2001)

Drives Conceptual Change(Siegler, 2005; Wellman & Liu, 2007)

• Less developed than adults• Explaining a guide to favor subsumptive

scope?– In breadth of observations– In using prior knowledge

Learning about a novel cause

Blicket Detector

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Explanation Prompt

NO GO

GO

100% Pattern:Green = GO

Explain: “Why did this one make my machine play music?”

Control: “What happened to my machine when I put this one on?”

75% Pattern:RED = GO

100% Pattern:Yellow = NO GO

75% Pattern:WHITE = GO

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100% vs. 75% Pattern

*

PK matched PK favors 75%0

0.2

0.4

0.6

0.8

1

Verbalize

Explain

Prop

ortio

n Ch

oice

s fa

vorin

g 10

0% P

atter

n

20

PK favors 75% pattern

NO GO

GO

100% Pattern:Green = GO

Explain: “Why did this one make my machine play music?”

Control: “What happened to my machine when I put this one on?”

75% Pattern: =

GO

100% Pattern:Yellow = NO GO

100% Pattern: = NO GO

PK favors 75% pattern: Big blocks make it go

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Effect of prior knowledge

*

PK matched PK favors 75%0

0.2

0.4

0.6

0.8

1

Verbalize Explain

Prop

ortio

n Ch

oice

s fa

vorin

g 10

0% P

atter

n

Hazards of explanation

Malle, 2011

Explanation’s benefits and hazards

Subsumptive Constraints:

Driven to find generalizations

Enhancement

Impairment

Reliable patterns Spurious or misleading patterns

Learning Engagement:Motivation, Attention, Extended Processing

Enhancement

Enhancement

Explain

Williams, Lombrozo & Rehder, CogSci 2011; Kuhn & Katz, 2009

Predicting people’s behavior

Person

Rarely or frequently donates?Predict

Explain

N = 182

10 people: 5 rarely donated, 5 frequently

Anna isliving on East Coast

dominating28

A science major

Anna isliving on East Coast

dominating28

A science major

Explain why Anna rarely donates

to charities.Vs.

Control (Study)

Feedback Anna rarely donates to charities.

Instances vs. Generalizations

Reliable Patterns:10/10 predictions

Misleading Patterns:Mistakes in predictions

Unique features Pattern related features

Irrelevant features

Picture Name Age Personality "living on the" "a graduate of a"

Rarely donates to charities

  Anna 28 dominating East coast science major

  Joseph 32 friendly West coast humanities major

  Sarah 24 boastful West coast science major

  Jessica 26 self-assured East coast science major

  Kevin 30 energetic West coast humanities major

Frequently donates to charities

  Steven 42 cautious East coast science major

  Josh 38 discreet West coast humanities major

  Laura 37 studious West coast science major

  Janet 45 self-conscious West coast humanities major

  Karen 39 quiet East coast science major

energetic

quiet

Unique features:10/10 predictions

Effect of explaining?

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45

Explanation Impairment Effect

Reliable Unreliable0

0 .2 5

0 .5

Explain

Free Study

Lear

ning

Err

or28

On East CoastDominating

science major

Anna donates to charities.All 10 descriptions shown

five times.

Category Learning

Reliable Misleading5

10

ExplainThink AloudN

umbe

r of

bloc

ks to

lear

n

Control

*

N = 240

Anomalous Evidence

• Anomalies contradict current or prior beliefs• Often ignored (Chinn & Brewer, 1993 )• When does explaining lead to their use?• Manipulated number of anomalies• Learning about importance of deviation in

ranking scores

Anomalous Evidence

Strength of Anomalous Evidence

Interaction with strength of anomalous evidence

Conclusions• Explaining “Why?” guided by a subsumptive constraint:

1. Promotes discovery of generalizations2. Favors generalizations that prior knowledge suggests apply broadly beyond specific cases3. Impairs learning when generalizations are unreliable4. Promotes belief revision from anomalies

• Implications for predicting explanation’s effects• Future directions

– More complex contexts– How does explaining recruit other cognitive processes?

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Acknowledgements

• Bob Rehder• Norielle Adricula, Dhruba Banerjee, Adam

Krause, Sam Maldonaldo, Kelly Whiteford• Joe Austerweil, Randi Engle, Nick Gwynne,

Luke Rinne, and Karen Schloss.• Concepts and Cognition Lab.

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