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

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

4. Social Coordination Social interaction and communication are fundamentally functions of coordination

5. 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

6. 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.

7. Phonetic Convergence Pardo (2006)

8. Syntactic Priming Pickering and Branigan (1999)

9. Synchronization Richardson and Dale (2005)

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

11. Distributional Matching These phenomena also may be expressed as distributional matching – The probability distributions over kinds of behaviors are matched, not each instance Person A P(S) S a S b S c S d S e Person B S a S b S c S d S e

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

13. Theoretical Framework Interactive Alignment (Garrod & Pickering, 2004)

14. 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?

15. 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

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

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

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

19. Complex Systems Bertschinger & Natschläger (2004)

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

21. Coupled Complex Systems Coupled Dynamics

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

23. Complexity Matching

24. 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)

25. 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

26. Dyadic Interactions

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

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

29. Inter-Event Interval Distributions α = 2 IEI

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

31. Allan Factor Analysis α debate > α affiliative α data > α surrogate Δ debate > Δ affiliative

32. Complexity Matching Analysis D affiliative < D debate D data < D surrogate

33. 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

34. Acknowledgments Jamie Faria, Adrian Barr Drew Abney Rick Dale Alexandra Paxton