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Thinking about Evidence. David Lagnado University College London. Leonard Vole accused of murdering a rich elderly lady Miss French. Romaine, Vole’s wife, was to testify that he was with her at time of murder. Vole had befriended French and visited her regularly including night of murder.

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thinking about evidence

Thinking about Evidence

David Lagnado

University College London

slide2

Leonard Vole accused of murdering a rich elderly lady Miss French

Romaine, Vole’s wife, was to testify that he was with her at time of murder

Vole had befriended French and visited her regularly including night of murder

But instead Romaine appears as witness for prosecution

Testifies that Vole was not with her, returned later with blood on his jacket, and said “I’ve killed her”

Vole needed money

French changed her will to include him; shortly after he enquired about luxury cruises

Maid testified Vole was with French at time of death

Blood on Vole’s jacket same type as French

Letters written by Romaine to lover – reveals her plan to lie and incriminate Vole

Vole is acquitted!

evidential reasoning
Evidential reasoning

How do people reason with uncertain evidence?

How do they assess and combine different items of evidence?

What representations do they use?

What inference processes?

How do these compare with normative theories?

reasoning with legal evidence
Reasoning with legal evidence

Legal domain

E.g. juror, judge, investigator, media

Complex bodies of interrelated evidence

Forensic evidence; witness testimony; alibis; confessions etc

Need to integrate wide variety of evidence to reach singular conclusion (e.g. guilt of suspect)

descriptive models of juror reasoning
Descriptive models of juror reasoning

Belief adjustment model(Hogarth & Einhorn, 1992)

Sequential weighted additive model

Over-weights later items

Ignores relations between items of evidence

Story model(Pennington & Hastie, 1992)

Evidence evaluated through story construction

Holistic judgments based on causal models

No formal, computational or process model

descriptive models of juror reasoning1
Descriptive models of juror reasoning

Coherence-based models(Simon & Holyoak, 2002)

Mind strives for coherent representations

Evidential elements cohere or compete

Judgments emerge through interactive process that maximizes coherence

Bidirectional reasoning (evidence can be re-evaluated to fit emerging conclusions)

how should people do it
How should people do it?

Bayesian networks?

Nodes represent evidence statements or hypotheses

Directed links between nodes represent causal or evidential relations

Permits inference from evidence to hypotheses (and vice-versa)

Guilt

Maid

Blood

Cut

Vole is guilty

Blood on Vole’s cuffs

Maid testifies that Vole was with Miss French

Vole cut wrist slicing ham

applicable to human reasoning
Applicable to human reasoning?

Vast number of variables

Numerous probability estimates required

Complex computations

applicable to human reasoning1
Applicable to human reasoning?

Fully-fledged BNs unsuitable as model of limited-capacity human reasoning

BUT –

a key aspect is the qualitative relations between variables (what depends on what)

Judgments of relevance & causal dependency critical in legal analyses

And people seem quite good at this!

Blood match raises probability of guilt

Alibi lowers it (not much!)

Guilt

Blood

Alibi

+

-

qualitative causal networks under construction
Qualitative causal networks(under construction!)
  • People reason using small-scale qualitative networks
  • Require comparative rather than precise probabilities
  • Guided by causal knowledge
  • More formalized & testable version of story model?
empirical studies
Empirical studies
  • Discrediting Evidence
  • Alibi Evidence
discredited evidence
Discredited evidence

How do people revise their beliefs once an item of evidence is discredited?

When testimony of one witness is shown to be fabricated, how does this affect beliefs about testimony of other witnesses, or even other forensic evidence?

E.g., Romaine’s discredited testimony

Involves a distinctive pattern of inference

explaining away
Explaining away

P(G|B) > P(B)

Finding out B raises probability of G

Guilt

Vole is guilty of murder

Blood on Vole’s cuffs

Blood

Cut

P(G|B&C) < P(G|B)

Finding out C too lowers the probability of G

Vole cut himself

Despite its simplicity and ubiquity, this pattern of inference is hard to capture on most psychological models of inference (e.g., associative models, mental models, mental logic)

discrediting vs direct evidence
Discrediting vs. direct evidence

Guilt

Guilt

Blood

Blood

Weighted additive model

Standard regression model

Cut

Cut

Bayesian network model

Causal model

CUT only becomes relevant to guilt given BLOOD

Important to distinguish ‘explaining away’ from simply adding (negative) evidence

experimental questions
Experimental questions

Do people use causal models to reason with evidence in online tasks?

Do they model discrediting evidence by ‘explaining away’?

How does the discredit of one item of evidence affect other items?

slide18

EVIDENCE 1

Neighbour says that suspect has stolen previously

EVIDENCE 2

Neighbour says he saw suspect outside house on night of crime

EVIDENCE 1

Footprints outside house match suspect’s

?

Does the discredit of item 2 affect item 1?

Neighbour is lying because he dislikes suspect

NO when different source

Scenario: House burglary, local man arrested

HYPOTHESIS: Local man did it

YES when same source

extension of discredit
Extension of discredit

When do people extend the discredit of one item to other items?

SAME

E.g. two statements from same neighbour

SIMILAR

E.g. two statements from two different neighbours

DIFFERENT

E.g., one statement and one blood test

Causal model approach would expect people to distinguish SAME from DIFFERENT cases

bn models
BN models

Witness A

Blood test

Witness B

Witness

GUILT

GUILT

Discredit

Discredit

Same/Similar

Different

experiment 1
Experiment 1

Mock jurors given simplified criminal cases

Four probability judgments (of guilt)

Baseline

Stage 1 (Evidence 1) Footprint match

Stage 2 (Evidence 2) Neighbour sees suspect

Final (Discredit 2) Neighbour is lying

Compare judgments at Final stage and Stage 1

Does discredit return judgments to Stage 1?

Vary relations between items of evidence

SAME, SIMILAR, DIFFERENT source

slide22

Witness1

Witness2

Discredit2

Both items undermined

Forensic1

Witness2

Discredit2

Results

  • Final judgments significantly lower than at Stage 1 for all conditions
  • Discredit does not simply remove item 2; also affects belief in item 1
  • When discredit presented LAST, it is extended regardless of relations between items
summary
Summary

Discrediting information extended regardless of relation to other evidence

This pattern is consistent with Belief Adjustment model

Recency effect leads to over-weighting of discrediting information

Neglect relations between items

Further test of BAM: manipulate order of evidence presentation

experiment 2
Experiment 2

Vary order of presentation of evidence

LATE……E1 E2 D2

EARLY….E2 D2 E1

Both orders ‘ought’ to lead to same conclusions

Relatedness

SAME, DIFFERENT

slide25

Witness1

Witness2

Discredit2

Both items undermined

Forensic1

Witness2

Discredit2

Results: Late condition

  • Final judgments lower than at Stage 1 for both conditions
  • Discredit does not simply remove item 2
  • Replicates EXP 1
  • When discredit presented LAST, it is extended regardless of relations between items
slide26

Witness1

Discredit1

Witness2

Both items undermined

Discredit1

Only 1st item undermined

Witness1

Forensic1

Results: Early condition

  • Pattern of judgments differ for SAME and DIFF
  • SAME
    • Final = Stage 2
  • DIFF
    • Final > Stage 2
  • Appropriate sensitivity to relation between items
  • When discredit presented EARLY, only extended to related items
problematic for current models
Problematic for current models

Why are people ‘rational’ in early but not late condition?

Belief Adjustment model

Cannot explain early condition because does not consider relations between evidence

Story model

Cannot explain bias in late condition (and needs to be adapted to online processing)

coherence based grouping account
Coherence-based/grouping account

Mind strives for most coherent representation

Evidence grouped as +ve or -ve relative to guilt

+ve and -ve groups compete, but within-group items mutually cohere (irrespective of exact causal relations)

When an item of one group is discredited, this affects other items that cohere with it

late condition
LATE condition

Incriminating evidence grouped together (regardless of source)

Discredit affects the group (not just individual item)

GUILT

A

B

+

+

D

+

+

early condition
EARLY condition

First item of evidence discredited

Second item only discredited if from related source

No grouping effect

+

GUILT

B

A

+

D

+

+

study 3
Study 3

Grouping hypothesis predicts that coherent groupings only emerge with elements that share the same direction (cf. Heider, 1946)

Therefore discredit extended when evidence items both +ve or both -ve, but not with mixed items

design
Design

Four evidence conditions

A+, B+, discredit B+

A-, B-, discredit B-

A+, B-, discredit B-

A-, B+, discredit B+

Two levels of relatedness: similar and different

Predictions

1&2 non-mixed -> discredit affects both items

3&4 mixed -> discredit affects only second item

examples condition 2 different
Examples: Condition 2 - - different

Lab tests reveal no footprint match

Neighbour says she was with suspect at time of crime

Neighbour lying because in love with suspect

Evidence 1 Evidence 2 Discredit

examples condition 3 different
Examples: Condition 3 + - different

Neighbour lying because in love with suspect

Lab tests reveal footprint match

Neighbour says she was with suspect at time of crime

Evidence 1 Evidence 2 Discredit

summary1
Summary

Grouping hypothesis supported

Discredit extended when items share common direction, not when mixed

Mutually coherent elements stand or fall together (even when no clear causal relation between them)

Romaine & Agatha Christie knew this!

alibi evidence
Alibi evidence

Often crucial evidence (if true, absolves suspect)

Treated with suspicion

Hard to generate (even if innocent)

Very little formal or empirical work

Ongoing psychological studies – what makes a good alibi? (e.g., how much detail is best)

Also interesting from normative viewpoint

witness vs alibi models
Witness vs. Alibi models

Suspect committed crime

H

E

E*

A

E

H

Suspect motivated to lie

Suspect at crime scene

D

Witness report of suspect at crime scene

Suspect claims he was not at crime scene

+

+

+

+

-

+

With impartial witness – knowing that suspect was at crime scene ‘screens off’ witness report from guilt judgment

In alibi case – if suspect says he wasn’t there, but he was, this raises likelihood of guilt (beyond that if you just find out he was there)

Even though P(H|A)<P(H)

P(H|E&E*)=P(H|E)

P(H|E&A)>P(H|E)

To understand alibi evidence – need to represent potential deception

pilot study
Pilot study

Compare discredit of witness vs. alibi evidence

Manipulate reason for discredit

Deception (X was lying in his statement)

Error (X was mistaken in his statement)

Mock jurors given crime scenarios

3 judgments of guilt

Baseline

After statement (alibi/witness)

After discredit of statement

results1
Results

Witness – discredit returns belief to baseline (j1 = j3) irrespective of reason

Alibi – discredit returns belief to baseline in error condition, but greatly enhances guilt in deception condition

Fits with causal network predictions

general alibi model
General alibi model

H

E

A

Suspect motivated to lie

D

Suspect claims he was not at crime scene

Case 1: Suspect provides alibi

Higher motivation to lie if guilty than if innocent

(hence link from H to D)

Given alibi, discovery of E incriminates via two routes

E raises likelihood of H directly

E raises likelihood of H indirectly

(via its effect on D)

+

+

+

-

No screening-off ie P(H|E&A) > P(H|E)

general alibi model1
General alibi model

H

E

A

Friend motivated to lie

D

Friend claims suspect was not at crime scene

Case 2: Close relative/friend provides alibi

AND they know whether or not suspect is guilty

Higher motivation to lie if guilty than if innocent

(hence link from H to D)

Given alibi, discovery of E incriminates via two routes

+

+

-

+

No screening-off ie P(H|E&A) > P(H|E)

general alibi model2
General alibi model

H

E

A

Case 3: Close relative/friend provides alibi

BUT they do NOT know whether suspect is guilty

Motivation to lie irrespective of actual guilt or innocence of suspect

(effectively no link from H to D)

Given alibi, discovery of E incriminates only via direct route

+

Friend motivated to lie

D

-

+

Friend claims suspect was not at crime scene

Screening-off ie P(H|E&A) = P(H|E)

general alibi model3
General alibi model

H

E

A

Case 4: Impartial stranger provides alibi

AND they do NOT know whether suspect is guilty

Low Motivation to lie AND this is unrelated to actual guilt or innocence of suspect

(effectively no link from H to D)

Given alibi, discovery of E incriminates only via direct route

+

Stranger motivated to lie

D

-

+

Stranger claims that suspect was not at crime scene

Screening-off ie P(H|E&A) = P(H|E)

experimental study
Experimental study

Do people conform to these models?

Background info:

eg Victim is attacked on her way home … suspect is arrested

Alibi: ‘suspect was elsewhere at time of crime’

Manipulate who provides the alibi

Discredit Alibi

e.g., suspect seen on CCTV near crime scene at time of crime

slide46

Results so far

>

=

=

=

  • Scenarios don’t clarify that close friend knows H (as shown by subjects’ judgments about this)
  • Strong order effects ---
  • ALIBI, CCTV >> CCTV, ALIBI
conclusions so far
Conclusions so far
  • People construct and use causal models
  • ‘Explaining-away’ inferences
  • Grouping of variables can lead to biases
  • Sensitive to Alibi model
  • Puzzling order effect with Alibis
  • Judgment involves both causality and coherence?
thank you
Thank you!

Leverhulme/ESRC Evidence project

Nigel Harvey

Phil Dawid

Amanda Hepler

Gianluca Baio

Students

Miral Patel

Nusrat Uddin

Charlotte Forrest