  Misunderstandings in dyadic interactions often persist despite our best
efforts, particularly between native and non-native speakers, resembling a
broken duet that refuses to harmonise. This paper delves into the computational
mechanisms underpinning these misunderstandings through the lens of the broken
Lorenz system -- a continuous dynamical model. By manipulating a specific
parameter regime, we induce bistability within the Lorenz equations, thereby
confining trajectories to distinct attractors based on initial conditions. This
mirrors the persistence of divergent interpretations that often result in
misunderstandings. Our simulations reveal that differing prior beliefs between
interlocutors result in misaligned generative models, leading to stable yet
divergent states of understanding when exposed to the same percept.
Specifically, native speakers equipped with precise (i.e., overconfident)
priors expect inputs to align closely with their internal models, thus
struggling with unexpected variations. Conversely, non-native speakers with
imprecise (i.e., less confident) priors exhibit a greater capacity to adjust
and accommodate unforeseen inputs. Our results underscore the important role of
generative models in facilitating mutual understanding (i.e., establishing a
shared narrative) and highlight the necessity of accounting for multistable
dynamics in dyadic interactions.
