Dynamics of Social Cognition
Drew Abney and Christopher Kello
Cognitive and Information Sciences
Social Cognition
• Social interaction and communication
lie at the core of human intelligence
Social Coordination
• Social interaction and communication
are fundamentally functions of coordination
Dynamical Bases
• What are the dynamical bases of coordination
that support social cognition?
1. Alignment / Convergence / Priming / Synch
2. Behavioral vs. Distributional Matching
3. Interactive Alignment Model
4. Complexity Matching Theory
5. Complexity Matching Experiment
Lexical Alignment
• Brennan and Clark (1996)
Director: a docksider
Matcher: a what?
Director: um
Matcher: is that a kind of dog?
Director: no, it's a kind of um leather shoe, kinda preppy penny loafer
Matcher: okay, okay, got it
Thereafter, the director referred to this object as the penny loafer.
Director: another fish, the most realistic looking one with the pink stripes, green & pink
Matcher: a rainbow trout?
Director: yeah, yeah
From then on, the director referred to the fish as the rainbow trout.
Phonetic Convergence
• Pardo (2006)
Syntactic Priming
• Pickering and Branigan (1999)
Synchronization
• Richardson and Dale (2005)
Behavioral Matching
• All of these phenomena may be expressed
as behavioral matching
– Particular acts of behavior are (nearly)
matched one-for-one
Distributional Matching
• These phenomena also may be expressed
as distributional matching
P(S)
– The probability distributions over kinds of
behaviors are matched, not each instance
Sa Sb Sc Sd Se
Sa Sb Sc Sd Se
Person A
Person B
Distributional Matching
• These phenomena also may be expressed
as distributional matching
– The probability distributions over kinds of
behaviors are matched, not each instance
σ
µ
Person A
σ
µ
Person B
Theoretical Framework
• Interactive
Alignment
(Garrod &
Pickering,
2004)
Interactive Alignment Model
• Matching processes serve to:
– Align levels of representation
– Establish common ground
– Couple dynamics across scales
• What kind of dynamics should we expect from
human language, cognition, and behavior?
Power Law Dynamics
• Fluctuations and distributions of behavioral
measurements often follow power laws
– Probability of observing a measured quantity is a
power law function of the quantity itself
𝑃(𝑥) ≈ 𝑥 −𝛼
Why?
• Power laws occur when processes are nested
across scales of measurement
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syll
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Why?
• Power laws occur when processes are nested
across scales of measurement
phon
phon
phon
syll
phon
phon
phon
syll
word
phon
phon
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phon
phon
syll syll
phon
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syll syll
word
phrase
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word
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syll
Complex Systems
• Complex systems consist of nested processes,
coupled across scales
• Coupling across scales is achieved when
dynamics are near critical points (Stanley, 1987)
Complex Systems
Bertschinger &
Natschläger (2004)
Coupled Complex Systems
• What happens when two complex systems are
coupled via bi-directional interactions?
Coupled Complex Systems
Coupled
Dynamics
Complexity Matching
• A formal prediction using fractional methods
recently developed in statistical mechanics by
West, Geneston, and Grigolini (2008):
– Coupled complex systems should exhibit matching
exponents in their power law dynamics
𝜶
log P(x)
log P(x)
𝑃(𝑥) ≈ 𝑥 −𝛼
𝜶
log x
log x
Person A
Person B
Complexity Matching
• A formal prediction using fractional methods
recently developed in statistical mechanics by
West, Geneston, and Grigolini (2008):
– Coupled complex systems should exhibit matching
exponents in their power law dynamics
– Distributional matching for complex systems
– Specifically worked out for event renewal
processes with Inter-Event Interval distributions:
1
𝑃 𝐼𝐸𝐼 ≈
,𝛼 ≈ 2
𝛼
𝐼𝐸𝐼
Complexity Matching Experiment
• Social interaction should yield complexity
matching, but how can we measure it?
– Need to express behavior as a point process
• We analyzed speech signals from a dyadic
interaction experiment by Paxton & Dale (2013)
Dyadic Interactions
• Pairs of individuals discussed “hot button” issues
– Same-sex marriage
– Progressive taxation
– Abortion
• Based on prior questionnaires, each dyad
discussed one issue they agreed on, and one
issue they disagreed on
– “Affiliative” versus “Debate” conditions
Dyadic Interactions
Speech Analysis
• Acoustic waveforms for each individual were
processed as event series
– Events were onsets and offsets of acoustic energy
Relation to Language Structure
• Events can be analyzed on different timescales,
corresponding to nested language structure
discourse
sentence
word/phrase
syllable
phone
Inter-Event Interval Distributions
IEI IEI
-2
10
IEI IEI
-4
P(IEI)
10
-6
α=2
10
-8
Debate
10
Affiliative
-10
10
1
10
2
10
3
10
4
10
Inter-Event Interval (msec)
5
10
Allan Factor Analysis
• Well-suited for dynamics of point processes
N i T  N i 1 T 
N i T   N i 1 T 
2 N i T 
2
N i T 
N i 1 T 
AT  
AT   T

Allan Factor Analysis
2
10
A(T)
Debate
Affiliative
Shuffled
1
10
• α data > α surrogate
• α debate > α affiliative
• Δ debate > Δ affiliative
0
10 -1
10
0
1
10
10
T
2
10
Complexity Matching Analysis
30
Surrogate:Debate
25
Empirical:Debate
|A(Ta)-A(Tb)|
Surrogate:Affiliative
20
Empirical:Affiliative
15
10
• D data < D surrogate
5
0
0
• D affiliative < D debate
5
10
15
20
25
T
30
35
40
45
Conclusions
• Speech readily exhibits power law dynamics
– Not a byproduct of acoustic analysis
• Speech dynamics exhibit complexity matching
– Extension of behavioral matching
– Driven by complex systems theory from
statistical mechanics
• Complexity matching is a dynamical basis
of social cognition
– Predicted to maximize information transmission
Acknowledgments
Drew
Abney
Alexandra
Paxton
Jamie Faria, Adrian Barr
Rick
Dale