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A Broken Duet: Multistable Dynamics in Dyadic Interactions.

Johan Medrano1, Noor Sajid2

  • 1Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK.

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Summary
This summary is machine-generated.

Dyadic interactions can lead to persistent misunderstandings, especially between native and non-native speakers. This study uses a computational model to show how differing prior beliefs create divergent interpretations, impacting communication.

Keywords:
Bayesian filteringbistable dynamicsbroken Lorenz systemdyadic interactionslinguistic misunderstandingspeech perception

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Area of Science:

  • Computational linguistics
  • Dynamical systems theory
  • Cognitive science

Background:

  • Interpersonal communication frequently suffers from misunderstandings, particularly in cross-linguistic contexts.
  • Persistent communication breakdowns can be modeled using dynamical systems, reflecting stable yet divergent states.

Purpose of the Study:

  • To investigate the computational mechanisms underlying persistent misunderstandings in dyadic interactions.
  • To model how differing prior beliefs influence generative models and lead to divergent interpretations.

Main Methods:

  • Utilized a modified Lorenz system, a continuous dynamical model, to simulate interactions.
  • Induced bistability in the Lorenz equations to represent divergent interpretations based on initial conditions.
  • Simulated interactions with varying prior belief precision (native vs. non-native speakers).

Main Results:

  • Differing prior beliefs lead to misaligned generative models and stable, divergent states of understanding.
  • Native speakers with precise priors struggled with unexpected input, while non-native speakers with imprecise priors showed greater adaptability.
  • The precision of prior beliefs significantly impacts the ability to accommodate new information.

Conclusions:

  • Generative models play a crucial role in establishing shared understanding and mutual narratives.
  • Multistable dynamics are essential for understanding the complexities of dyadic interactions and communication breakdowns.
  • Computational models offer insights into the cognitive underpinnings of linguistic misunderstandings.