Dynamics of Social Cognition

1. Dynamics of Social Cognition Drew Abney and Christopher Kello Cognitive and Information Sciences

2. Social Cognition • Social interaction and communication lie at the core of human intelligence

3. Social Coordination • Social interaction and communication are fundamentally functions of coordination

4. Dynamical Bases • What are the dynamical bases of coordination that support social cognition? • Alignment / Convergence / Priming / Synch • Behavioral vs. Distributional Matching • Interactive Alignment Model • Complexity Matching Theory • Complexity Matching Experiment

5. 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, kindapreppy 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.

6. Phonetic Convergence • Pardo (2006)

7. Syntactic Priming • Pickering and Branigan (1999)

8. Synchronization • Richardson and Dale (2005)

9. Behavioral Matching • All of these phenomena may be expressed as behavioral matching • Particular acts of behavior are (nearly) matched one-for-one

10. Distributional Matching • These phenomena also may be expressed as distributional matching • The probability distributions over kinds of behaviors are matched, not each instance P(S) Sa Sb Sc Sd Se Sa Sb Sc Sd Se Person A Person B

11. Distributional Matching • These phenomena also may be expressed as distributional matching • The probability distributions over kinds of behaviors are matched, not each instance σ σ µ µ Person A Person B

12. Theoretical Framework • InteractiveAlignment(Garrod & Pickering,2004)

13. 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?

14. 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

15. Why? • Power laws occur when processes are nested across scales of measurement phon phon phon phon phon phon phon syll syll syll word phon phon

16. Why? • Power laws occur when processes are nested across scales of measurement phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon phon syll syll syll syll syll syll syll syll syll word word word phrase

17. Complex Systems • Complex systems consist of nested processes, coupled across scales • Coupling across scales is achieved when dynamics are near critical points(Stanley, 1987)

18. Complex Systems Bertschinger &Natschläger (2004)

19. Coupled Complex Systems • What happens when two complex systems are coupled via bi-directional interactions?

20. Coupled Complex Systems CoupledDynamics

21. 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 log P(x) log P(x) log x log x Person A Person B

22. 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:

23. 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)

24. 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

25. Dyadic Interactions

26. Speech Analysis • Acoustic waveforms for each individual were processed as event series • Events were onsets and offsets of acoustic energy

27. Relation to Language Structure • Events can be analyzed on different timescales, corresponding to nested language structure discourse sentence word/phrase syllable phone

28. Inter-Event Interval Distributions IEI IEI IEI IEI α = 2

29. Allan Factor Analysis • Well-suited for dynamics of point processes

30. Allan Factor Analysis • α data > α surrogate • α debate > αaffiliative • Δ debate > Δaffiliative

31. Complexity Matching Analysis • D data <D surrogate • Daffiliative < D debate

32. 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

33. Acknowledgments DrewAbney AlexandraPaxton RickDale Jamie Faria, Adrian Barr