Drew H. Abney, Alexandra Paxton, Chris T. Kello, & Rick Dale

Cognitive and Information Sciences, University of California, Merced

Complexity Matching in Dyadic Interaction

IntroductionConversation is a complex coordination of human behavior. Recent investigations on conversation and interaction have shown that various conversational behaviors match, e.g., usages of the same words, syntactic structures, accents. We create a categorization of types of matching that correspond to various patterns of matching behavior: behavioral matching, distributional matching, and complexity matching. We show that speech events of interlocutors in a conversation match at the level of complexity matching: the power law-like distributions of speech events. Furthermore, we show that the degree of complexity matching depends on the type of conversational context.

Types of MatchingBehavioral Matching

Direct correspondences between particular instances of behaviors

e.g., Eye Movements(Richardson & Dale, 2005)

e.g., Postural Sway(Shockley, Santana, & Fowler,

2003)

Does dyadic interaction lead to matching of power law-like distributions in speech events?

Supporting Empirical Questions

1. Is there scaling across linguistic structure?2. Do interlocutors’ linguistic structures match?3. If so, does this matching depend on conversational context?

28 participants (14 dyads) freely discussed topics in two ten-minute conversations (argumentative and affiliative).

56 audio files (four/dyad, two/participant) were analyzed for onset/offset of sound (intensity above – 30db). Onset/offset time series were submitted to Allan Factor analysis.

Allan Factor analysis was applied to the time series of acoustic speech events to estimate the clustering of speech events at multiple scales/levels of time.

Slope estimates with α ~ 0 are Poisson-distributed, meaning that events occur at random, independent points in time, whereas, slope estimates with α near the upper bound of α ~1 exhibit clustering that follows a power law distribution across timescales.

The α is the index of linguistic structure for each interlocutor and is what we compared across interlocutors.

α ~ 1

α ~ 0

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Signal A

Signal B

1. Scaling across linguistic structure: All t-tests for both the affiliative conversation (t(27) = 3.94, pmax

= .007) and the argumentative conversation (t(27)=4.15, pmax = .003) showed significantly higher AF estimates for empirical slopes relative to surrogate slopes.

2. Interlocutors’ linguistic structures match beyond a surrogate analysis:A main effect of function type indicated that empirical slopes (M = .32, SE = .08) were smaller than surrogate slopes (M = .49, SE = .03), F(1,13) = 10.37, p = .007.

3. More complexity matching in affiliative conversations compared to debate conversationsA main effect of Conversation Type indicated that empirical affiliative slopes (M = .26, SE = .03) were smaller than empirical argumentative slopes (M = .55, SE = .08), F(1,13) = 23.59, p < .001.

Counting Time (T)

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Counting Time (T)

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Results

Research Question

Methods and Materials

Analysis

Alla

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Distributional Matching

Behaviors match at the level of statistical, ensemble

characterizations e.g., Syntactic Priming

(Bock, 1986)e.g., Phonetic Convergence

(Pardo, 2006)

Complexity Matching

When complex systems interact and their power law

distributions are matched

Complexity Matching (West, Geneston, & Grigolini,

2008)

ConclusionsInterlocutors’ speech events in conversational contexts scale across multiple time scales.

Shows preliminary evidence for complexity matching in dyadic interaction.

Future work should address West et al.’s (2008) notion that information transfer is optimal when complex systems’ complexities match.